2022 Seminars
Tuesday, January 12, 2021
CCIB Student Seminar, Cody Stevens
Stevens Title: Out of the shadows: Co-acting cis-regulatory elements control T-box transcription factors midline and H15 during development
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Tuesday, January 7, 2021
Dr. June M Kwak, Professor of New Biology, Director and Dean in DGIST, Korea
Title: Cellular precision in Plant Development
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2021 Seminars
Tuesday, December 7, 2021
Dr. Daniel Kraut, Associate Professor of Chemistry & Graduate Program Director, Villanova University (Host: Klein)
Title: The 26S Proteasome Switches Between ATP-Dependent and Independent Mechanisms in Response to Substrate Ubiquitination
Abstract: The Ubiquitin-Proteasome System is responsible for the bulk of protein degradation in eukaryotic cells. Proteins are generally targeted to the 26S proteasome by the attachment of polyubiquitin chains. A number of proteins also contain ubiquitin-independent degrons (UbID) that allow for proteasomal targeting without the need for ubiquitination. UbIDsubstrates are degraded less processively than ubiquitinated substrates, but the mechanism underlying this difference remains unclear. We therefore designed two model substrates containing both a ubiquitination site and a UbID for a more direct comparison. We found UbID degradation to be overall less robust with complete degradation only occurring with relatively unstable substrates (those that were at least transiently unfolded as determined by protease sensitivity). Surprisingly, UbIDdegradation was unaffected by the non-hydrolyzable ATP analog ATPγS, which halts ATP-dependent engagement and translocation of substrates. Furthermore, ubiquitin-dependent degradation proceeded in a strikingly similar fashion to UbID degradation in the presence of ATPγS, suggesting the 26S proteasome may have a “default” ATP- and ubiquitin-independent mechanism that is capable of unfolding and degrading less-stable proteins.
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Tuesday, November 30, 2021
CCIB Student Seminar, Tess Konnovitch and Truman Dunkley
Konnovitch Title: The Effects of Warming and Nutrients on Rates of Ecological Change
Dunkley Title: The Effect of Mechanical Alterations to the Host Cell in E. Coli UTI89 Pathogenicity
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Tuesday, November 23, 2021
CCIB Faculty Spotlight: Dr. Lisa Payne, Assistant Professor and New CCIB Member, Department of Psychology, Rutgers-Camden
Title: The Upside of Inhibition: How the brain protects working memory
Abstract: Many investigations into selective attention seek to characterize enhanced processing of some task-relevant stimulus or attribute. A complementary perspective is that withdrawal from irrelevant, distracting information plays an active role in information processing. Inhibiting the processing of irrelevant input makes responses faster and more accurate, helps protect material held in short-term memory against disruption, and keeps irrelevant information from distorting memories. Change in cortical alpha band activity (8-14 Hz) has been used as a marker of attentional selectivity and is believed to reflect sensory inhibition. Specifically, cueing subjects to ignore task-irrelevant information gives rise to increased electroencephalogram (EEG) alpha-band power. In order to maximize the likelihood that subjects follow the cues to ignore items, the test on each trial is to recall the attended stimuli. However, an unexpected recognition test after cued trials were completed revealed chance performance for task-irrelevant stimuli and successful recognition for those they had attended. Furthermore, a sensitive analog measure of short-term memory’s fidelity revealed that alpha activity predicted the degree to which task-irrelevant stimulus distorted recall of the to-be-remembered stimulus. These results demonstrate that deployment of suppression-related processes can aid short-term memory by filtering out task-irrelevant information.
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Tuesday, November 16, 2021
CCIB Student Seminar, Heather Ciallella and Siddharth Bhadra-Lobo
Ciallella Title: Predicting Prenatal Developmental Toxicity Based on the Combination of Chemical Structures and Biological Data
Bhadra-Lobo Title: Dock2D: Toy datasets for the molecular recognition problem
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Tuesday, November 9, 2021
CCIB Student Seminar. Ezry St.Iago-McRae and Connor Pitman
St.Iago-McRae Title: Improving Upon Cholesterol-Tetra(Ethylene Glycol) as an Anchor for Synthetic DNA to a POPC Membrane: A Novel Application of Free Energy Perturbations
Pitman Title: The Role of Methionine-Methionine Interactions in the Conformations of Intrinsically Disordered Proteins
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Tuesday, November 2, 2021
Dr. Henry John-Adler, Professor, Department of Ecology, Evolution and Natural Resources, School of Environmental & Biological Sciences, Rutgers-New Brunswick (Host: Savage)
Title: Scratch the Itch: Coexistence of Eastern Fence Lizards with Chigger Mites
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Tuesday, October 26, 2021
CCIB Student Seminar, Vedangi Hambardikar and Josh Chamberlain
Hambardikar Title: Role Of Mitochondrial Inorganic Polyphosphate (PolyP) In Maintaining Mammalian Bioenergetics And Redox Balance, By The Regulation Of The Pentose Phosphate Pathway (PPP).
Chamberlain Title: Bacterial Ceramides: the Impact of Ceramide on Membrane Formation, Structure, and Stability
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Tuesday, October 19, 2021
Dr. Andrew Dobson, Professor, Ecology and Evolutionary Biology, Princeton University (Host: Gonzalez)
Title: The Ecology, Economics and Evolution of Emerging Pathogens
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Tuesday, October 12, 2021
Dr. Mark Alber, Director of the Interdisciplinary Center for Quantitative Modeling in Biology, University of California – Riverside (Host: Klein)
Title: Combined computational and experimental study of the interplay
between patterned actomyosin contractility and cell-ECM interactions during
formation of Drosophila wing pouch
Abstract: The regulation and maintenance of an organ’s shape is a major outstanding question in developmental biology. The Drosophila wing imaginal disc serves as a powerful system for elucidating design principles of the shape formation in epithelial morphogenesis. Yet, even simple epithelial systems such as the aforementioned wing disc are extremely complex. A tissue’s shape emerges from the integration of many biochemical and biophysical interactions between proteins, subcellular components, and cell-cell and cell-ECM interactions. How cellular mechanical properties affect tissue size and patterning of cell identities on the apical surface of the wing disc pouch has been intensively investigated. However, less effort has focused on studying the mechanisms governing the shape of the wing disc in the cross-section. Both the significance and difficulty of such studies are due in part to the need to consider the composite nature of the material consisting of multiple cell layers and cell-ECM interactions as well as the elongated shape of columnar cells. Results obtained using iterative approach combining multiscale computational modelling and quantitative experimental approach will be used in this talk to discuss direct and indirect roles of subcellular mechanical forces, nuclear positioning, and extracellular matrix in shaping the major axis of the wing pouch during the larval stage in fruit flies, which serves as a prototypical system for investigating epithelial morphogenesis. The research findings demonstrate that subcellular mechanical forces can effectively generate the curved tissue profile. while extracellular matrix is necessary for preserving the bent shape even in the absence of subcellular mechanical forces once the shape is generated. The developed integrated multiscale modeling environment can be readily extended to generate and test – hypothesized novel mechanisms of developmental dynamics of other systems, including organoids that consist of several cellular and extracellular matrix layers.
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Tuesday, October 5, 2021
CCIB Student Seminar: Xuelian Jia and Elena Chung
Jia Title: Mechanism-driven Modeling of Drug-Induced Liver Toxicity Using Structure Alerts and Oxidative Stress
Chung Title: A computational approach for identifying carcinogens, mutagens, and reproductive toxicants using public bioactivity databases
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Tuesday, September 28, 2021
Dr. Tali Reiner-Brodetzki, Post Doc Savage Lab, CCIB, Rutgers-Camden
Title: From Species to Molecular Basis of Behavior
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Tuesday, September 21, 2021
Dr. Daniel Russo, Post Doc Zhu Lab, CCIB, Rutgers-Camden
Title: Developing new approaches for screening chemical bioactivity using big data.
Abstract: Determining the effect chemicals are having on biological systems is critical for human health and safety. Traditionally, these effects have been determined experimentally using animals as model organisms. In addition to ethical concerns for animal well-being, this testing framework is time consuming, costly, and often does not extrapolate well to human biology. As such, developing non-animal alternatives is a major research effort spanning a variety of industries. Advancements in automation and robotics are allowing large-scale high-throughput screening programs to rapidly test thousands of chemicals for biological activity using in vitro cellular assays, resulting in enormous amounts of robust data being publicly available. Additionally, machine learning advancements are being generated to use these data to model in vivo biological activity. Despite these advances, non-animal alternatives for complex in vivo biological endpoints are severely lacking. In this presentation, a variety of new approaches and methods for screening chemicals for bioactivity that take advantage of the current ’big data’ explosion will be described.
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Tuesday, September 14, 2021
Dr. Lauren Hinkel, Post Doc Klein Lab, CCIB, Rutgers-Camden
Title: Investigating Pseudomonas aeruginosa‘s response to antimicrobial sphingoid bases
Abstract: Pseudomonas aeruginosa is a Gram-negative opportunistic pathogen that causes difficult to treat nosocomial infections in individuals with conditions such as chronic obstructive pulmonary disorder (COPD) and cystic fibrosis (CF). Multidrug resistant P. aeruginosa is a serious threat to public health and novel approaches focused on the treatment and control of these infections are needed. P. aeruginosa is not only resistant to exogenous antibacterial compounds, but it is also highly resistant to many innate-immune mechanisms. We have characterized, through genetic and biochemical approaches, the interaction between the host-derived antimicrobial sphingosine and P. aeruginosa. We identify a putative metabolic operon, sphBCD, which encodes a putative cytochrome c (SphB), oxidoreductase enzyme (SphC), and aldolase enzyme (SphD), that act on sphingosine, and evaluate the role of sphBCD in P. aeruginosa’s ability to resistant high levels of sphingosine. This study expands on our knowledge of how P. aeruginosa evades a component of host innate immunity and provides a foundation for future work focused on identifying ways to disrupt Pseudomonas’s sphingosine-resistance machinery.
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Tuesday, September 7, 2021
Dr. Jeremiah Zarthman, Associate Professor, Department of Chemical and Biomolecular Engineering, University of Notre Dame (Host: Yakoby) – Virtual Seminar
TITLE: From spikes to intercellular waves: decoding how Ca2+ signaling dynamics mediate organ size control
ABSTRACT: A key goal for developmental biology is to identify the principles that govern how cells robustly coordinate their behavior to form functional organs. However, this goal requires deciphering the mechanisms of signal integration at the cell and organ scale. As a key example, calcium (Ca2+) signaling dynamics mediate a significant fraction of information flow between cells. However, the biophysical mechanisms that govern the emergent multi-scale patterns of Ca2+ signaling dynamics remain elusive. Recent experimental studies in Drosophila wing imaginal discs, a standard biophysical model of organ growth control, demonstrate the emergence of several distinct patterns of Ca2+ activity: Ca2+ spikes, intercellular Ca2+ transients, tissue-level Ca2+ waves, and a global “fluttering” state. Here, we used a combination of computational modeling and experiments to identify two different cell populations. These two subpopulations of cells include “initiator cells,” defined by elevated levels of Phospholipase C activity, and “standby cells,” which exhibit baseline activity. We found that the strength of hormonal stimulation and extent of gap junctional communication jointly determine the predominate class of Ca2+ signaling activity. Further, single-cell Ca2+ spikes are stimulated by insulin, while intercellular Ca2+ waves depend on Gαq activity. Our computational model successfully recapitulates how the dynamics of Ca2+ transients varies during organ development. Phenotypic analysis of perturbations to the calcium signaling network support a role in mediating organ size control. This work provides insight into how organ size regulation emerges through feedback between biochemical growth signals and heterogeneous cell signaling states.
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Tuesday, April 27, 2021
Dr. Denise Kirschner, Professor, Microbiology and Immunology, University of Michigan Medical School, Co-Director, Center for Systems Biology (Host: Dr. Benedetto Piccoli)
Title: A systems biology approach to understanding the immunobiology of tuberculosis infection and treatment
Abstract: Tuberculosis (TB) is one of the world’s deadliest infectious diseases. Caused by the pathogen Mycobacterium tuberculosis (Mtb), the standard regimen for treating TB consists of treatment with multiple antibiotics for at least six months. There are a number of complicating factors that contribute to the need for this long treatment duration and increase the risk of treatment failure. The structure of granulomas, lesions forming in lungs in response to Mtb infection, create heterogeneous antibiotic distributions that limit antibiotic exposure to Mtb. We can use a systems biology approach pairing experimental data from non-human primates with computational modeling to represent and predict how factors impact antibiotic regimen efficacy and granuloma bacterial sterilization. We utilize an agent-based, computational model that simulates granuloma formation, function and treatment, called GranSim. A goal in improving antibiotic treatment for TB is to find regimens that can shorten the time it takes to sterilize granulomas while minimizing the amount of antibiotic required. We also created a whole host model, called HOSTSIM, to study Mtb dynamics within a human host. Overall, we use these models to help better understand TB treatment and strengthen our ability to predict regimens that can improve clinical treatment of TB.
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Tuesday, April 20, 2021
Dr. Ying Gu, Associate Professor of Biochemistry and Molecular Biology; Institutes of Energy and the Environment (IEE); Center for Lignocellulose Structure and Formation, Penn State University (Host: Xingyun Qi)
Title: The deceptively simple glucan chain and a complicated cellulose synthase complex
Abstract: Cellulose is a deceptively simple b-1,4 linked glucose polymer. It is the most abundant biopolymer on earth and is an economically important source of food, paper, textiles, and biofuel. As a critical component of plant cell wall, cellulose organization is important for anisotropic cell growth. Despite its economical and biological significance, the regulation of cellulose biosynthesis is far from being fully understood. Cellulose is synthesized at the plasma membrane by cellulose synthase complexes (CSCs). Our lab has revealed new elements of regulation of the cellulose synthesis machinery, including proteins that control cellulose synthase delivery, guidance, and removal from the cell surface. By a combination of proteomic, live-cell imaging, and genetic approaches, we revealed that the de novo secretion of CSCs is mediated by cooperation among cellulose synthase interacting protein 1 (CSI1), exocyst complex, and a plant-specific protein PATROL1 in Arabidopsis thaliana. Upon delivery of CSCs to the plasma membrane, they synthesize cellulose microfibrils in a direction mirroring the underlying cortical microtubule in a CSI1-dependent manner. The retrieval of CSCs from the PM depends on clathrin-mediated endocytosis by two separate complexes: Adaptor complex (AP2) and TPLATE/TSET complex. Cellulose synthase complexes represent cargo proteins that are not present in yeast and mammalian cells. Therefore, plants offer unique opportunities to characterize the function of endocytosis and exocytosis that may provide insights into the evolution of protein trafficking in eukaryotes.
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Tuesday, April 13, 2021
Dr. Thomas Werner, Associate Professor, Biological Sciences, Michigan Technological University (Host: Dr. Nir Yakoby)
Dr. Werner will be speaking on the evo-devo of wing and body pigmentation patterns in fruit flies, with special emphasis on the role of the Wingless morphogen. Other ongoing fruit fly projects in the lab, e.g., the evolution of mushroom toxin resistance and my new book series “The Encyclopedia of North American Drosophilids”, will also be showcased.
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Tuesday, April 6, 2021
Dr. Gloria Garrabou Tornos, Faculty of Medicine, University of Barcelona, Muscle Research and Mitochondrial Function Laboratory, University Hospital Clinic of Barcelona (Host: Dr. Marien Solesio)
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Tuesday, March 30, 2021
CCIB Faculty Spotlight Seminar: Dr. Anthony Geneva, Assistant Professor, Department of Biology, Rutgers-Camden
Title: Enabling Anolis lizard evolutionary genomics with high quality de novo genomes
Abstract: Adaptation and speciation are largely responsible for the origin and maintenance of biological diversity but despite this central role in evolution, many fundamental questions about these interrelated processes remain. The adaptive radiation of Anolis lizards (anoles) is ideally suited for testing hypotheses about speciation and adaptation because they represent a replicated natural experiment. Anole species on the Greater Antilles that occupy similar ecological niches have independently evolved strikingly similar morphologies and behaviors. These ecomorphs have been the focus of decades of detailed analyses which strongly support the adaptive nature of this convergence. Nevertheless, we lack a clear understanding of how morphological adaptation contributes to speciation and we know virtually nothing about the genetic basis of these convergent traits.
In this seminar, I present ongoing research in my lab using anole genomes to investigate the processes of adaptation and speciation. I will detail the development of genomic resources for anoles including nine highly complete and contiguous reference genome assemblies and discuss NSF-funded plans to understand the genetic basis of morphological convergence in this group.
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Tuesday, March 23, 2021
Dr. Adam Engler, Professor of Bioengineering; Resident Scientist, Sanford Consortium for Regenerative Medicine, University of California – San Diego (Host: Dr. Eric Klein)
Title: Understanding and Exploiting Cancer Mechanobiology
Abstract: Mammary epithelial cells (MECs) are classically known to respond to differences in extracellular matrix (ECM) stiffness by transitioning to a malignant, non-polarized state on stiffer ECM, i.e. Epithelial-Mesenchymal Transition (EMT). While this is akin to stiff mammary tumors that one can detect with manual palpation, breast cancer fibrosis is dynamic and stiffening occurs over months to years. I will describe our efforts to more accurately mimic the onset of tumor-associated fibrosis using dynamic methacrylated-hyaluronic acid (MeHA) hydrogels, whose stiffness that can be modulated from normal 100 Pa to malignant 5000 Pa, utilizing a two step polymerization process. Contrary to previous observations, we find that collective decisions by MECs in 3D aggregates–called acini–indicate partial protection from the stiffened niche (PNAS 2019). To interpret MEC mechano-signaling that result in this protection, I will also present our new understanding of the molecular mechanisms used by MECs to interpret stiffness, i.e. Hippo/YAP/TAZ/LETS (Nature 2018) and Twist signaling (Nature Cell Bio 2015). After cells leave this niche, however, mechanical changes can be exploited to improve metastatic detection. I will conclude my presentation with new data showing that we can use differences in cell-ECM adhesion strength to mark metastatic cells even in mixed or lineage committed populations (Cancer Res 2020), and that these cells undergo adurotactic migration down stiffness gradients as shown in computational and experimental models (Cell Reports 2021). These data suggest potential improvements to our prognostic capacity when diagnosing and treating epithelial tumors.
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Tuesday, March 9, 2021
CCIB Student Seminar: Heather Ciallella and Mark Nessel
CIALLELLA TITLE: Predicting Developmental Toxicants Based on Chemical Structures and Biological Data
NESSEL TITLE: Nitrogen and phosphorus enrichment cause declines in invertebrate populations: A global meta-analysis
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Tuesday, March 2, 2021
CCIB Student Choice Seminar: Dr. Alejandro Soto-Gutierrez, MD, PhD, Associate Professor, Department of Pathology, University of Pittsburgh, School of Medicine (Host: Student Organizing Committee)
Title: Biofabrication of Human Livers
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Tuesday, February 23, 2021
CCIB Student Seminar Christopher Sottolano and Cody Stevens
Stevens Title: Two independent CRMs regulate midline transcription in Drosophila oogenesis
Sottolano Title: Study of the evolution of GRK protein among Drosophila and Sophophora subgenre
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Tuesday, February 16, 2021
Dr. John Paul DeLong, Associate Professor, School of Biological Sciences; Director of Cedar Point Biological Station, University of Nebraska – Lincoln (Host: Dr. Angelica Gonzalez)
TITLE: “Modeling the tangled bank: eco-evolutionary dynamics in multi-species, multi-trait, and non-equilibrium systems.”
ABSTRACT: Nature is complex, a tangled bank. Yet we hypothesize that a relatively simple process – evolution by natural selection – can account for the tangle. Research on focal traits in simplified systems clearly shows how selection leads to evolution, but how clear is that process when embedded in a tangled bank? In nature, the number of individuals, diversity of species and species interactions, stochasticity, physical heterogeneities, and abiotic influences may influence selection, but even beginning to understand this seems a challenge. Here I introduce a new modeling framework – Gillespie eco-evolutionary models, or GEMs – that allow us to take steps toward understanding evolution in complex ecological scenarios. I give a brief overview of how the models work and show examples of how key features of a tangled bank, such as stochasticity, non-equilibrium dynamics, and multiple interacting traits and species may influence selection.
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Tuesday, February 9, 2021
Dr. Matthew D. Green, Associate Professor, Chemical Engineering, School for Engineering of Matter, Transport, and Energy; Affiliate Faculty, Center for Negative Carbon Emissions; Affiliate Faculty, Materials Science and Engineering; Affiliate Faculty, School of Molecular Sciences, Arizona State University (Host: Dr. David Salas de la Cruz)
TITLE: Engineering macromolecular systems for water treatment, carbon capture, and advanced materials
Abstract: Polymeric materials, due to their adaptability and array of functionality, touch almost every aspect of our daily lives. This seminar will focus on two areas in which new behavior was engineered into “old” materials. In the first part, I will discuss my lab’s efforts to design new polymers for water treatment membranes. Membrane-based water purification techniques are the current state of the art, but face limitations including thermodynamically limited transport, high material and operation costs, the perm-selectivity tradeoff, and fouling-prone or chlorine-sensitive membrane materials. Through the addition of charged sites, specifically zwitterions, to poly(arylene ether sulfone)s, the hydrophilicity, water permeability, and fouling resistance were all improved while maintaining low salt passage. Additionally, advanced manufacturing techniques can be used to create and tune pretreatment membranes that extend the lifespan of RO membranes.
In the second part, I will discuss a series of nanocomposites we have developed to study molecular-level behavior. Currently, mechanoresponsive polymers use a limited subset of active backbone chemistries to yield changes in optical properties. My lab has developed a strategy to yield mechanoresponsive fluorescence by adding quantum dots and fluorescently labeled carbon nanotubes. Pronounced changes in fluorescence emerge following plastic deformation, indicating a transduction of mechanical force into fluorescence. We have also begun exploring photo-mediated nanocomposite fabrication techniques in order to extend additive and advanced manufacturing techniques to nanocomposite production. I will report on the effects reactive nanofillers have on the thermomechanical and morphological properties of the materials.
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Tuesday, February 2, 2021
Dr. Pablo Peixoto, Associate Professor, Department of Natural Sciences The City University of New York (Host: Dr. Marien Solesio)
TITLE: Retraction or rewiring: role of mitochondrial ROS in synaptic function
ABSTRACT: Neuronal function requires the reshaping and rewiring of neural connections through a
fundamental process named synaptic plasticity. It is well known that the strength of
neurotransmission is regulated by feedback signaling between the pre- and postsynaptic
neuron, which leads to short- and/or long-term structural adaptations. However, little is
known about how this regulation occurs. The current objective is to elucidate this missing
mechanistic link by exploring emerging signaling roles of mitochondria, which are
organelles that sustain the local energy demand of synaptic function and plasticity. Based
on the rationale that neuronal activity sets the pace of aerobic energy conversion and of
emission of chemically reactive oxygen species (ROS) from mitochondria, I hypothesize
that controlled and localized mitochondrial emission of ROS regulates synaptic function
and plasticity. In this talk, I will present preliminary data using optogenetics to modulate
synaptic function and motor behavior in drosophila. Our results will impact therapies aimed
improving neuronal communication in age-related neurodegeneration.
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Tuesday, January 26, 2021
CCIB Student Seminar: Lingyu Guan
Title: Large-scale computational discovery of functions and binding mechanisms of tRNA fragments
2020 Seminars
Tuesday, December 8, 2020
CCIB Student Seminar: Jesse Sandberg (Brannigan) & Anushriya Subedy (Klein Lab)
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Tuesday, December 1, 2020
Dr. Pedro Urquiza (Solesio Lab)
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Tuesday, November 24
CCIB Student Seminar Nidhi Sheth (Grgicak Lab) & Zheming An (Piccoli Lab)
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Tuesday, November 17, 2020
Dr. Albert Siryaporn, Assistant Professor, Department of Physics & Astronomy, Department of Molecular Biology & Biochemistry, University of California, Irvine (Host: Dr. Eric Klein)
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Tuesday, November 10, 2020
Dr. Mia Levine, Assistant Professor, Department of Biology, University of Pennsylvania (Host: Dr. Nir Yakoby)
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Tuesday, November 3, 2020
CCIB Student Seminar Vedangi Hambardikar (Solesio Lab), Xuelian Jia (Zhu Lab) & Siddharth Bhadra-Lobo
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Tuesday, October 27, 2020
CCIB Student Seminar Ezry St.Iago-McRae (Fu Lab) & Eliza Yost (Klein Lab)
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Tuesday, October 20, 2020
Dr. Evgeny Pavlov, Associate Professor, Molecular Pathobiology, New York University (Host: Dr. Maria E. Solesio)
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Tuesday, October 13, 2020
Dr. Henry Hess, Professor, Department of Biomedical Engineering, Columbia University (Host: Dr. Jinglin Fu)
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Tuesday, October 6, 2020
Dr. Brooke Flammang, Assistant Professor, Federated Department of Biological Sciences, New Jersey Institute of Technology/Rutgers University – Newark (Host: Dr. Angelica Gonzalez)
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Tuesday, September 29, 2020
Dr. Talant Ruzmetov (Lamoureux Lab)
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Tuesday, September 22, 2020
Dr. Devlina Chakravarty (Lamoureaux Lab)
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Tuesday, September 15, 2020
CCIB Student Seminar James Kelley (Grigoriev Lab) & Stacy Love (Salas Lab)
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Tuesday, March 3, 2020
CCIB Student Seminar
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Tuesday, February 25, 2020
Dr. Warren Booth, Associate Professor, Department of Biological Science, University of Tulsa (Host: Dr. Amy M. Savage)
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Tuesday, February 18, 2020
Dr. Sagar Khare, Associate Professor, Department of Chemistry and Chemical Biology, Rutgers University (Host: Dr. Jinglin Fu)
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Tuesday, February 11, 2020
Dr. Akhilesh B. Reddy, Associate Professor, Department of Pharmacology, University of Pennsylvania (Host: Dr. Kwangwon Lee)
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Tuesday, February 4, 2020
CCIB Student Seminar
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Tuesday, January 28, 2020
Dr. Andy Leifer, Assistant Professor, Department of Physics and the Princeton Neuroscience Institute, Princeton University (Host: Dr. Grace Brannigan)
2019 Seminars
Tuesday, December 10, 2019
CCIB Students Chris Sottolano (Yakoby Lab) & Nidhi Sheth (Grgicak Lab)
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Tuesday, December 3, 2019
CCIB Faculty Spotlight – Dr. Eric Klein
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Tuesday, November 19, 2019
Dr. Yi-Wei Chang, Assistant Professor, Department of Biochemistry & Biophysics, Perelman School of Medicine, University of Pennsylvania (Host: Dr. Eric Klein)
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Tuesday, November 12, 2019
Dr. Zemer Gitai, Professor, Director of Graduate Studies, Department of Molecular Biology, Princeton University (Host: Dr. Eric Klein)
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Tuesday, November 5, 2019
CCIB Students Heather Ciallella (Zhu Lab) & Gabriele Stankeviciute (Klein Lab) & Swati Sharma (Zhu Lab)
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Tuesday, October 29, 2019
Dr. Fidelma Boyd, Professor, Department of Biological Sciences, University of Delaware Biology
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Tuesday, October 22, 2019
Dr. Seth Rudman, Postdoc, Paul Schmidt Lab, University of Pennsylvania (Host: Dr. Angélica González)
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Tuesday, October 8, 2019
CCIB Students Stacy Love (Salas Lab) & Zheming An (Piccoli Lab)
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Tuesday, October 1, 2019
Dr. Gustavo Rohde, Assosciate Professor, Department of Biomedical Engineering, Department of Electrical and Computer Engineering, University of Virginia (Host: Dr. Hao Zhu)
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Tuesday, September 24, 2019
Dr. Alistair Boettiger, Assistant Professor, Department of Developmental Biology, Stanford University (Host: Dr. Jongmin Nam)
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Tuesday, September 24, 2019
Dr. Alistair Boettiger, Assistant Professor, Department of Developmental Biology, Stanford University (Host: Dr. Jongmin Nam)
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Tuesday, September 17, 2019
Dr. Matt Hansen, Senior Research Scientist, Tishkoff Lab, Perelman School of Medicine, University of Pennsylvania (Host: Dr. Grace Brannigan)
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Tuesday, September 10, 2019
CCIB Student: Jim Kelley (Grigoriev Lab)
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Tuesday, April 30, 2019
Dr. Nathan Fried, Biology, Rutgers-Camden
Title: Developing an undergraduate-driven Rutgers-Camden Computer-Augmented Behavioral Research Core to explore the intersection between sleep, pain, and opioid abuse.
Abstract: More than 1.5 billion people suffer from chronic pain worldwide, yet our current understanding of it is surprisingly incomplete. This has led to an over-reliance on opioids to treat pain, and has contributed to the opioid epidemic. A major complication of studying chronic pain is the subjective nature of it; each patient experiences pain in vastly different ways. While this makes it challenging to assess pain in humans, it makes it nearly impossible to assess it in rodents, the primary animal model for pain research. Consequently, the reliability of rodent behavior has become controversial, with some arguing that the low percentage of pain therapeutics entering Phase 1 clinical trials is due to the misinterpretations of behavior (i.e., only 11% of drugs that work in rodents work in humans). To address this, we developed a new “behavior-centric” technology that removes assumptions about the animal’s internal state by integrating high-speed imaging of mouse kinematics, statistical modeling, and machine learning to objectively assess whether the animal is in pain. I am currently adapting this strategy to drosophila for the study of sleep and pain in an effort to harness their vast genetic toolkit and behavioral dynamics. To this end, I am adopting a newly developed markerless tracking deep-learning software to create the first-ever Rutgers-Camden Computer-Augmented Behavioral Research Core that will be available to all faculty. Further, I am integrating these research questions into Bite-Sized Authentic-Research-Experiences (B-SARE) to create an undergraduate-driven research program.
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Tuesday, April 23, 2019
Dan Russo & Sean McQuade, Rutgers-Camden CCIB PhD Students
Russo Title: Convolutional neural networks and virtual molecular projections for end-to-end modeling of nanoparticle bioactivity.
McQuade Title: Using Opinion Formation Models to Describe Large Scale Configurations of Intelligent Agents
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Tuesday, April 16, 2019
Dr. Robert Marmion, Post-doc Researcher, Lewis-Sigler Institute for Integrative Genomics, Princeton University (Host: Dr. Nir Yakoby)
Title: Using CRISPR to develop heterozygous gene dosage models of RASopathies
Abstract: A large class of human developmental abnormalities, known as the RASopathies, is caused by deregulated activation of the RAS signaling pathway. Most of the structural and functional phenotypes observed in affected individuals can be successfully mimicked in model organisms, but the underlying mechanisms remain unclear. To date, many studies rely on overexpression, failing to employ the gene dosage present in disease. Furthermore, these studies utilize fixed timepoints to measure signaling. We are utilizing CRISPR to develop in locus genome modifications to establish disease alleles and monitor signaling dynamics in the early Drosophila embryo.
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Tuesday, April 9, 2019
Dr. Juan R. Perilla, Principal Investigator, Department of Chemistry & Biochemistry, University of Delaware (Host: Dr. Grace Brannigan)
Title: Discovery through the lens of the computational microscope.
Abstract: The essential conundrum of modern biology, namely the question of how life emerges from myriad molecules whose behavior is governed by physical law alone, is embodied within a single cell—the quantum of life. The rise of scientific supercomputing has allowed for the study of the living cell in unparalleled detail, from the scale of the atom to a whole organism and at all levels in between. In particular, the past three decades have witnessed the evolution of molecular dynamics (MD) simulations as a “computational microscope”, which has provided a unique framework for the study of the phenomena of cell biology in atomic (or near-atomic) detail. Here I present an overview of our efforts to determine the molecular details during the life-cycle of multiple infectious diseases using the computational microscope .
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Tuesday, April 2, 2019
Dr. Yuzhen Ye, Associate Professor, School of Informatics and Computing, Indiana University (Host: Dr. Hao Zhu)
Title: Hypothetical proteins, and more hypothetical proteins from microbiome studies
Abstract: Recent studies have shown that microbiota play an important role in human health and diseases, and the efficacy of therapeutic treatments such as cancer immunotherapy and chemotherapy. Microbiome studies have resulted in the accumulation of microbial genomic sequences at an unprecedented pace. Numerous hypothetical proteins can be computationally predicted from every metagenome, with many of the proteins having no predicted functions, even when the metagenome represents relatively well-studied microbial communities, such as those associated with human gut. Metaproteomics allows more direct characterization of proteins and their quantities in microbial communities, however, interpretation of metaproteomics data is computationally challenging. In this seminar, I will mainly talk about the new algorithms (Graph2Pro and Graph2Pro-Var) that we have developed for improved peptide/protein identification from metaproteomic MS/MS data, by optimizing the use of matching metagenomic and metatranscriptomic data. I will also talk about a new approach (community profiling) that we developed for functional association prediction, taking advantage of the accumulation of metagenomic data derived from different environments.
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Tuesday, March 26, 2019
Linlin Zhao & Ruchi Lohia, Rutgers-Camden CCIB PhD Students
Zhao Title: Hybrid read-across prediction of hepatotoxicity: linking chemical structure, in vitro bioassay and gene expression information to adverse effects
Lohia Title: Conformational effects of a disease-associated hydrophobic-to-hydrophobic substitution located at the midpoint of the intrinsically disordered region of proBDNF
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Tuesday, March 12, 2019
Dr. Paul Janmey, Professor, Physiology, University of Pennsylvania (Host: Dr. Eric Klein)
TITLE: Squeezing cells and their nuclei through narrow spaces: the importance of intermediate filaments
ABSTRACT: The mechanical properties of intermediate filaments (IFs) are different from those of the other cytoskeletal filaments. Even though F-actin and microtubules (MTs) are much stiffer than IFs, networks of crosslinked F-actin and MTs cannot resist either large shear strains nor compressive loads as well as networks of IFs. These physical differences of the three cytoskeletal filaments, as well as their different intracellular distributions, make IF networks, especially the perinuclear vimentin IF network, important for cells that move in constricted 3-D environments. The vimentin cage around the nucleus strongly regulates cell motility though confined 3-D channels and protects the nucleus from damage that occurs as cells move or are subjected to compressive stresses.
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Tuesday, March 5, 2019
Dr. Xiaoyang Wu, Assistant Professor,The Ben May Department for Cancer Research, University of Chicago (Student Choice)
TITLE: Skin tissue engineering with epidermal stem cells
ABSTRACT: Gene therapy with adult stem cells isolated from skin can be used for treatment of a variety of otherwise terminal or severely disabling diseases. However, development of clinically relevant skin stem cell therapy remains challenging to the field due to lack of a good animal model to test the efficacy and safety of potential therapy in vivo. We have now resolved the technical hurdles and developed a new mouse model for skin stem cell therapy. With this novel platform, we have explored the feasibility and clinical potential of skin gene therapy. Our results will provide an important proof-of-concept and serve as the basis for development of effective therapeutic strategies with skin stem cells targeting various diseases in the future.
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Tuesday, February 26, 2019
Sung Won Oh & Mark Nessel, Rutgers-Camden CCIB PhD Students
OH TITLE: DNA-Mediated Proximity Assembly Circuit for Regulating Biochemical Reactions.
NESSEL TITLE: The importance of body size: scaling of body elemental content in vertebrates and invertebrates
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Tuesday, February 19, 2019
Dr. Matthew Rockman, Associate Professor, Biology, New York University (Host: Dr. Nir Yakoby)
TITLE: Developmental evolution is a population-genetics problem
ABSTRACT: The phenotype of an animal depends both on its own genotype and that of its mother, who contributes the egg, with its complex store of cytoplasmic determinants of development. Life-history evolution therefore relies jointly on heritable variation among mothers in one generation and among their offspring in the following generation. We are genetically dissecting this distinctive evolutionary regime in a marine annelid, Streblospio benedicti. These animals vary heritably in their development: some females make large eggs that develop directly into benthic juveniles, and others make small eggs that develop into planktonic larvae. This dichotomy is one of the most characteristic patterns in marine macroevolution, and our studies provides the first insight into its quantitative- and population-genetic and genomic basis.
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Tuesday, February 12, 2019
Dr. Julie Biteen, Associate Professor,Chemistry, University of Michigan (Host: Dr. Grace Brannigan)
TITLE: Single-molecule and single-cell imaging for bacterial cell biology
ABSTRACT: Because of the small size of bacterial cells, the mysteries of their subcellular structure, dynamics and cooperativity are well-suited to single-molecule and super-resolution investigations. Our lab has been developing new methods to locate, track, and analyze single molecules to answer fundamental, unanswered questions in live bacterial cells. I will discuss these methods, as well as their applications to measuring and understanding the dynamical interactions essential for DNA replication and mismatch repair in living Bacillus subtilis cells. Finally, I will present our ongoing work to extend our targets from single cells to microbial communities. Overall, our results provide fundamental insight of relevance to human health and disease.
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Tuesday, February 5, 2019
Dr. Lei Xie, Professor, Computer Science, Hunter College (Host: Dr. Hao Zhu)
TITLE: Harnessing Data Science for Precision Drug Discovery.
ABSTRACT: Genome-Wide Association Studies, whole genome sequencing, and high-throughput techniques have generated vast amounts of diverse omics and phenotypic data. However, these sets of data have not yet been fully explored to improve the effectiveness and efficiency of drug discovery, which continues along the one-drug-one-gene-one-disease paradigm. In order to tackling complex diseases such as cancer and Alzheimer’s disease, we need to target complex biological system under individual genetic and environmental background. In this talk, I will present our recent work in developing a data-driven framework for the multi-scale modeling of chemical modulation of biological networks, and applying it to personalized side effect prediction, rational discovery of dual-action multi-targeted precision anti-cancer therapy, and modeling binding/unbinding kinetics to link in vitro chemical potency to in vivo activity. I will discuss the challenges and opportunities in applying data science to precision drug discovery, with the focus on omics data integration, interpretable machine learning, and integration of data-driven modeling and mechanism-based modeling.
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Tuesday, January 29, 2019
Nicole Revaitis, Rutgers-Camden CCIB PhD Students
TITLE: The dynamics of EGFR signaling activation in the follicular epithelium.
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2018 Seminars
Tuesday, December 11, 2018
Heather Ciallella & Nathaniel Merrill, Rutgers-Camden CCIB PhD Students
Ciallella Title: Predictive Multitask Deep Learning Modeling of Estrogen Receptor Activities
Ciallella Abstract: Endocrine disruption is an important toxicity mechanism for many chemicals, especially those of environmental interest, such as pesticides. Traditional experimental testing, both in vitro and in vivo, to identify toxicants that can induce endocrine disruptions are expensive and time-consuming. Computational modelling is a promising alternative method for chemical toxicity evaluations. The rapid generation of data obtained from high-throughput screening (HTS) assays and computational power increase advanced the computational modelling into a big data era. New modelling approaches, such as deep learning, are being used for model development. This work features an extensive modeling study using classic machine learning algorithms, normal deep neural networks, and multitask deep neural networks for 18 ToxCast estrogen receptor (ER) binding assays. A random selection of 20% of all the compounds was used as a test set, with the remaining 80% compounds for model development. Chemical descriptors consisted of two binary fingerprints (FCFP6 and MACCS keys). Each descriptor set was combined with six traditional machine learning algorithms (AdaBoost Decision Tree, Bernoulli Naïve Bayes, k-Nearest Neighbors, Naïve Bayes, Random Forest, and Support Vector Machines), two deep learning approaches (normal deep neural networks and multitask deep neural networks) to develop models for all 18 ER endpoints. The resulting model performance was evaluated using the correct classification ratio (CCR), sensitivity, and specificity for the prediction of the test set. A consensus model was obtained by averaging predictions from all individual models. The results showed that individual models have predictivity accuracy (CCR) ranging from 0.5000 to 0.7716 and the consensus model has similar performance compared to the best individual model (multitask deep neural network) with an accuracy of 0.7565. We concluded that multitask deep neural network is a better algorithm to deal with large databases with mechanism-related toxicity endpoints (i.e., ER bindings). It is expected that this modelling strategy can be used for other computational toxicology modelling studies, especially for toxicity pathway modelling.
Merrill Title: Stability using Linear-In-Flux-Expressions
Merrill Abstract: The Linear-in-Flux-Expressions (LIFE) methodology was developed for analyzing metabolic systems, particularly in simulating variability in patient populations. With LIFE, the relationships between parameters are used to reduce the overall complexity of the system. When LIFE methodology is used, simulated system perturbations more closely match empirical data. These systems can also be associated to generalized graphs, and the graph properties provide insight into the dynamics of the metabolic system. This work addresses two main problems: 1. for fixed metabolite levels, find all fluxes for which the system is at equilibrium, and 2. for fixed fluxes, find all metabolite levels which are equilibria for the system. General results relating stability of systems to the structure of the associated graph are shown. It is also shown that a certain structure of the graph necessary for stable dynamics.
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Tuesday, December 4, 2018
Dr. Nidhal Carla Bouaynaya, Rowan University, Professor, Electrical & Computer Engineering
TITLE: Deep Learning – Is it the Answer to Artificial Intelligence? A Healthcare Perspective
ABSTRACT: Within the field of machine learning, deep learning approaches have resulted in state-of-the-art accuracy in visual object detection, speech recognition, and many other domains including genomics. Deep learning techniques hold the promise of emerging technologies, such as autonomous unmanned vehicles, smart cities infrastructure, personalized treatment in medicine, and cybersecurity. However, deep learning models are deterministic, and as a result are unable to understand or assess their uncertainty, a critical part of any predictive system’s output. This can have disastrous consequences, especially when the output of such models is fed into higher-level decision making procedures, such as medical diagnosis or treatment. This talk is divided into two parts. First, we provide intuitive insights into deep learning models and show their applications in healthcare, specifically medical image segmentation for tumour surveillance and radiation therapy. We then introduce Bayesian deep learning to assess the model’s confidence in its prediction and show preliminary results on robustness to noise and artifacts in the data as well as resilience to adversarial attacks.
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Tuesday, November 27, 2018
Dr. Jinglin Fu, Chemistry, Rutgers-Camden
TITLE: Develop biochemical reaction compartment with controlled spatial confinement and regulatory function
ABSTRACT: Micro-compartment plays a key role in cellular metabolic functions, which increases the overall activity and specificity of encapsulated enzyme pathways. Proteins that are spatially-confined on a membrane or within a vesicle experience a unique microenvironment that can alter water dynamics, local electrostatic interactions, variation in the solvent solubility and molecular crowding. The ability to mimic and exert control over spatial confinement could translate biochemical pathways into a variety of noncellular applications, ranging from diagnostics and drug delivery to the production of high-value chemicals and smart materials. Here, we described a simple and robust strategy for the DNA nanocage-templated encapsulation of metabolic enzymes with high assembly yield and controlled packaging stoichiometry.
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Tuesday, November 13, 2018
Gabriele Stankeviciute & Liam Sharp, Rutgers-Camden CCIB PhD Students
Stankeviciute Title: Caulobacter Crescentus Adapts to Phosphate-Starvation by Synthesizing Anionic Glycolipids
Sharp Title: Boundary Lipids of the Nicotinic Acetylcholine Receptor in Quasi-Native Membranes
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Tuesday, November 6, 2018
Dr. Yuzhen Ye, School of Informatics and Computing, Indiana University
TITLE: Hypothetical proteins, and more hypothetical proteins from microbiome studies
ABSTRACT: Recent studies have shown that microbiota play an important role in human health and diseases, and the efficacy of therapeutic treatments such as cancer immunotherapy and chemotherapy. Microbiome studies have resulted in the accumulation of microbial genomic sequences at an unprecedented pace. Numerous hypothetical proteins can be computationally predicted from every metagenome, with many of the proteins having no predicted functions, even when the metagenome represents relatively well-studied microbial communities, such as those associated with human gut. Metaproteomics allows more direct characterization of proteins and their quantities in microbial communities, however, interpretation of metaproteomics data is computationally challenging. In this seminar, I will mainly talk about the new algorithms (Graph2Pro and Graph2Pro-Var) that we have developed for improved peptide/protein identification from metaproteomic MS/MS data, by optimizing the use of matching metagenomic and metatranscriptomic data. I will also talk about a new approach (community profiling) that we developed for functional association prediction, taking advantage of the accumulation of metagenomic data derived from different environments.
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Tuesday, October 30, 2018
Dr. Lawrence C. Edelman, Rutgers-New Brunswick, Assistant Director Intellectual Property, Office of Research Commercialization
Title: Basics of IP (Intellectual Property), including Patents, Trademarks, Copyrights and Trade Secrets, and aspects unique to their development in the University Setting, as well as some information about our office, the Rutgers Office of Research Commercialization.
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Tuesday, October 23, 2018
Dr. Peter Galie, Rowan University, College of Engineering
TITLE: Shear stress regulates blood-brain barrier integrity through the CD44 receptor
ABSTRACT: The “no-reflow” period that follows ischemia-reperfusion injury during stroke is characterized by reduced blood flow and an increase in blood-brain barrier (BBB) permeability, which exacerbates injury to the surrounding tissue. Here, we evaluate the hypothesis that reduced shear stress on the endothelium contributes to BBB breakdown through attenuation of a cd44-mediated signaling mechanism. Our studies rely on a three-dimensional in vitro model of a cerebral arteriole that mimics specific aspects of the in vivo BBB, including an endothelium featuring tight junctions, a robust basement membrane, and interaction with astrocytic endfeet. The in vitro vessels are patterned inside a collagen/hyaluronan (HA) composite scaffold within a microfluidic device that allows for the application of controlled levels of shear stress to the endothelium. Perfusion with culture medium exerting a shear stress of at least 0.07 Pa for a period of four days establishes vessel permeability on par with in vivo vasculature, as determined by dextran exclusion experiments and transendothelial electrical resistance (TEER) using impedance spectroscopy. Ceasing the fluid flow results in a significant increase in the permeability of the in vitro vessels within two hours. Additionally, removal of HA from the scaffold prevents the formation of tight junctions, suggesting that a cell surface receptor specific for HA is involved in the mechanotransduction of fluid shear stress. Knockout of the cd44 receptor using CRISPR-Cas9 negated flow-mediated barrier formation, as shown by reduced tight junction formation and barrier integrity. Overall, our in vitro model demonstrates the importance of shear stress for maintaining the BBB. Moreover, our findings indicate that the cd44 receptor is a crucial component of the mechanosensing complex that mediates the endothelial response to shear stress in the central nervous system.
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Tuesday, October 16, 2018
Dr. Martin Yarmush, Rutgers-New Brunswick, Department of Biomedical Engineering
Title: Emerging Technologies & Biomedical Engineering Innovation
Abstract: This presentation will briefly describe several topics that fall within the province of translational biomedical engineering. The topics include: dynamic cell and tissue microsystems; cellular therapeutics; organ engineering and storage; novel wound healing approaches; and the development of an automated robotic venipuncture device integrated with downstream point-of-care analysis capabilities. Emphasis will be placed on the significance of the work including the intended scientific and technological gaps to be filled, and the opportunities for translating the work to the clinical and industrial realms.
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Tuesday, October 9, 2018
Lingyu Guan & Christopher Sottolano, Rutgers-Camden CCIB PhD Students
Guan Title: Analysis of rRNA-derived fragments in human HEK293 cells
Sottolano Title: Study of how the coevolution of Gurken and EGFR regulates Drosophila eggshell diversity
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Tuesday, October 2, 2018
Dr. Bomyi Lim
UPENN, Department of Chemical and Biomolecular Engineering
Title: Dynamic control of gene expression in developing embryos.
Abstract: Traditional studies of gene regulation in the Drosophila embryo centered primarily on the analysis of fixed tissues. These methods provided considerable insight into the spatial control of gene activity, such as the borders of eve stripe 2, but yielded only limited information about temporal dynamics. The advent of quantitative live-imaging and genome-editing methods permits the detailed examination of the temporal control of endogenous gene activity. I’ll present evidence that the pair-rule genes fushi tarazu (ftz) and even-skipped (eve) undergo dynamic shifts in gene expression. We observe sequential anterior shifting of the stripes along the anterior to posterior axis, with stripe 1 exhibiting movement before stripe 2 and the more posterior stripes. The precision of pair-rule temporal dynamics might depend on enhancer–enhancer interactions within the eve locus, since removal of the endogenous eve stripe 1 enhancer via CRISPR/Cas9 genome editing led to precocious and expanded expression of eve stripe 2. These observations raise the possibility of an added layer of complexity in the positional information encoded by the segmentation gene regulatory network.
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Tuesday, September 25, 2018
Dr. Yong Chen
Department of Chemistry, Rutgers-Camden
TITLE: Sensing and transducing ECM and tissue stiffness in vitro and in vivo
ABSTRACT: With the increasing generation of medical and genomic data, it is critically important to effectively combine evidence, signals, or information from multiple data sources to enable efficient scientific discovery. In this talk, I will share several stories on my experience in integrating biomedical data. First I will talk about strategies in “pooling” data from distinct populations in the context of identifying genetic interactions, and present a meta-analytic framework to detect genetic interactions by leveraging heterogeneity from population structure in estimated low-order effect sizes. Second, I will describe my ongoing projects on (i) quantifying effect sizes of risk factors for pediatric type II diabetes using EHR data, (ii) risk prediction using high dimensional post-vaccination events in VAERS data, and (iii) challenges and ideas on combining efficacy and safety results from different clinical trials.
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Tuesday, September 18, 2018
Dr. Guillaume Lamoureux
Department of Chemistry, Rutgers-Camden
TITLE: Machine learning of protein structure and function.
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Tuesday September 11, 2018
Cody Stevens & Josh Waters
CCIB PhD Students, Rutgers-Camden
STEVENS TITLE: Mechanisms underlying posterior fate determination in Drosophila oogenesis
WATERS TITLE: Fungal Adaptation of Carbon-Utilization Through Transcriptional Rewiring
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Tuesday, April 24, 2018
Nicole Revaitis & Daniel Russo
CCIB PhD Students, Rutgers-Camden
REVAITIS TITLE: Dynamics of the EGFR during Drosophila oogenesis
RUSSO TITLE: Multitask deep neural networks for prediction of estrogen receptor activity.
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Tuesday, April 17, 2018
Dr. Richard Assoian
Assistant Professor of Ecology, Evolution and Natural Resources at Rutgers University
Title: Sensing and transducing ECM and tissue stiffness in vitro and in vivo
Abstract: Do cells understand physics? Can they feel and respond to changing forces in their environment like they can respond to changes in their chemical environment? In this talk, I will discuss work in our lab that tries to understand how cells sense mechanical changes in their environment, convert that information into intracellular chemistry, and then use that information to regulate proliferation.
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Tuesday, April 10, 2018
Dr. Malin Pinsky
Assistant Professor of Ecology, Evolution and Natural Resources at Rutgers University
Title: Life in a giant water bath: consequences for ecological dynamics in the ocean
Abstract: The same ecological and evolutionary processes operate in marine and terrestrial environments, and yet ocean life survives in a dramatically different fluid environment. The ocean is, in effect, a 1.3 sextillion liter water bath with muted thermal variation through time and space, limited oxygen, and intense convective and conductive processes. In this talk, I will trace some of the consequences for evolution, physiology, population dynamics, community assembly, and conservation at sea, including striking contrasts and similarities to patterns on land. I will present evidence that marine animals have evolved narrower thermal tolerances and live closer to their upper thermal maxima than species on land. I will also show that marine species have responded faster and often more predictably to temperature change and temperature trends, across time-scales from seasons to decades. Finally, I will discuss the surprisingly similar histories of extinction in the ocean and on land, and what we can learn from the longer history of human impacts on land. These differences imply the need for distinctly new conservation and climate adaptation approaches in the ocean.
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Tuesday, April 3, 2018
Dr. Ricardo Mallarino
Assistant Professor of Molecular Biology at Princeton University
Title: The developmental basis of pigment pattern evolution in rodents
Abstract: Mammalian color patterns are among the most conspicuous characters found in nature and can have a profound impact on fitness. However, little is known about the mechanisms underlying their formation and subsequent evolution. We capitalized on the naturally occurring color pattern of the African striped mouse, Rhabdomys pumilio, to investigate the formation of periodic stripes, a common pattern in mammals. In striped mice, stripes result from underlying differences in melanocyte maturation, which give rise to spatial variation in hair color. Through transcriptomic analyses of the developing skin, we identified the transcription factor Alx3 as a major hierarchical regulator. During striped mouse embryogenesis, patterned expression of Alx3 precedes pigment stripes and acts to directly repress Mitf, a master regulator of melanocyte differentiation. Moreover, Alx3 is also differentially expressed in the dorsal stripes of chipmunks, which have independently evolved a similar color pattern. Thus, differences in the spatial control of Alx3 lead to striped patterns in rodents, revealing both a new factor regulating pigment cells and a previously unappreciated mechanism for modulating hair color. I’ll end by discussing how my lab, through a variety of multidisciplinary approaches, uses the mammalian skin as a model to uncover molecular mechanisms of phenotypic diversity.
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Tuesday, March 27, 2018
Gabriele Stankeviciute, Spyros Karaiskos & Daniel Pinolini
CCIB PhD Students, Rutgers-Camden
Stankeviciute Title: Characterizing Chemical Composition of Stalk Peptidoglycan
Stankeviciute Abstract: Cell shape diversity among prokaryotes is most studied in rod-shaped bacteria such as Bacillus subtilis and Escherichia coli, but unusual shape characterization and its physiological regulation of its coordinated synthesis is lacking. Caulobacter crescentus serves as a model organism when studying cell differentiation in odd-shaped bacteria; The matured cells all maintain a protruding cellular domain called a stalk, its presence is directly coupled to its life cycle. Interestingly, the stalk elongates dramatically when growing in phosphate-limiting conditions, forcing the cell wall and lipid membranes to continue extending the cell envelope. From various microscopy and HPLC analyses of the muropeptides that comprise the peptidoglycan, (PG), it is evident that the PG incorporated into the stalk is somehow chemically distinct than what is in the cell body. This project focuses on figuring out the stalk chemical composition as well as the genetic mechanisms that control these changes.
Karaiskos Title: tRNA fragments and their targeting interactome in human cells
Karaiskos Abstract: Transfer RNA fragments (tRFs) are a class of small RNA molecules derived from mature or precursor tRNAs. Although tRFs have been characterized very recently, gradually they have been attracting more attention. Similar to microRNAs, there is evidence that tRFs are found across a wide range of organisms and tissues in cytoplasmic compartments or loaded to RISC complexes, often in numbers comparable to microRNAs. Despite clear differences between tRFs and microRNAs,there is accumulating evidence that tRFs may play a vital role in post-transcriptional gene regulation and RNA silencing.
Here we describe potential interactions of human tRF with their putative target RNAs associated with human Argonaute complexes as elucidated from CLASH (Crosslinking, Ligation, And Sequencing of Hybrids) sequencing results. We found that Argonaute-loaded tRFs target a wide range of transcripts corresponding to various gene types, from rRNA to lincRNA to protein-coding transcripts. In the latter, 3′ UTR regions are the likely primary target of tRFs, although there is a significant number of interactions of tRFs with coding and 5′ UTR regions. Furthermore, we observed a novel phenomenon – a large number of putative interactions between tRFs and intronic sequences. We provide ample evidence supporting the notion that tRF“seed” sequences can be located on either end of a tRF molecule in agreement with experimentally validated results from earlier studies.
We performed extensive computational analysis, including k-mer search in the UTR regions conserved across multiple species and testing for motif enrichment in tRF-targeted sequences derived from the CLASH experiments. Contrary to the traditional view of microRNAs “seed” sequences, located on the 5′ end of the microRNAs molecule, we found that tRF “seeds” can be located both on the 5′ and 3′ end of the tRF molecule as well as in the middle of the fragment.
Pinolini Title: Characterization of the Melatonin Signaling Pathway
Pinolini Abstract: Melatonin is a hormone substance ubiquitous to all walks of life, from bacteria to human beings. It is known to play a vital role in the synchronization of certain circadian regulated biological functions, including blood pressure regulation, core body temperature regulation, brain wave activity, and hormone production. Perhaps the most well-known of these functions, however, is the role that melatonin has in regulating the 24-hour human sleep-wake cycle, and its ability to influence sleep onset. Disruptions in this rhythmic regulation have also been linked to a variety of health concerns, including metabolic and various mental health disorders. Despite the extensive characterization of the effects of melatonin and its frequent use as an over the counter medication, little is known about the mechanisms by which melatonin exerts these effects at the cellular level.
To this end, we chose to use the model eukaryotic organism Neurospora crassa to investigate the mechanisms by which melatonin exerts its effects at the cellular level. Our data suggests that melatonin is rhythmically produced in N. crassa in a fashion very similar to human melatonin production, and that exposure to melatonin effects the clock in the same way that the human 24-hour rhythm is affected. Our data also suggest that there is a structurally similar receptor protein present in N. crassa that is responsible for these effects, much like the known melatonin receptor in human cells. The successful outcome of the project will provide new insights into this important biological process, and also provide insight into possible treatment methods for rhythmic sleep disorders.
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Tuesday, March 20, 2018
Dr. Eric Fortune
Associate Professor of Biological Sciences at New Jersey Institute of Technology
TITLE: Neurophysiological mechanisms for the control of cooperative behavior
ABSTRACT: Cooperative behaviors are found across taxa and can be critical for survival and reproduction. Discovering the mechanisms by which animals coordinate their behavior is critical for understanding social behavior in animals including humans, and for the development of sophisticated algorithms for the coordination of interactions between humans and machines. We study a species of songbird known as the plain-tailed wren (Pheugopedius euophrys) that have an adaptation that makes them well suited for the study of cooperation: these wrens sing an extraordinary duet in which the male and female rapidly alternate the production of song syllables, often with less than a tenth of a second in between syllables. This behavior requires that females and males coordinate their own singing patterns with sensory cues from their partner.
We have used a combination of behavioral and neurophysiological studies to reveal the mechanisms that underlie some of the mechanisms for feedback control of these cooperative performances. We have demonstrated that both female and male birds have a neurophysiological memory of their combined, cooperative output, a behavior that neither bird can produce alone. Further, we have demonstrated that sensory encoding differs between the sexes in a way that suggests that females provide leading cues for the control of duet performances. Finally, we have observed the replay of motor codes by neurons involved in singing control when the birds are not singing.
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Tuesday, March 6, 2018
Dr. Mihai Pop
Professor in the Center for Bioinformatics and Computational Biology at the University of Maryland
TITLE: Challenges and opportunities in computational microbiome-ology
ABSTRACT: The use of high-throughput sequencing technologies to interrogate the genomes of the many microbes that inhabit our world has revolutionized modern microbiology research. Initial studies have recapitulated our prior knowledge about the roles microbes play in the health of the environment and that of virtually all other living organisms on the planet, and have also dramatically expanded our knowledge about the diversity of microbes populating our planet. These pioneering studies have created substantial “buzz” around microbiome science and its promise, however the field remains in its infancy. It is now time for robust and reproducible research tools to be developed that will allow microbiome science to fulfill its promise. In my talk I will provide a broad overview of the field and describe research in my lab aimed at developing validated computational pipelines for the analysis of microbiome data. I will conclude with a discussion of challenges that remain unaddressed in the field.
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Tuesday, February 27, 2018
Dr. Jianlin Cheng
Director of Bioinformatics, Data Mining, and Machine Learning Lab Computer Science Department at the College of Engineering at the University of Missouri
TITLE: Deep Learning Methods for Protein Bioinformatics
ABSTRACT: Deep learning has emerged as the most powerful machine learning method for big data analysis in many areas, such as computer vision, image processing, speech recognition, and natural language processing. Since deep learning was introduced into the field of bioinformatics in 2012, it has significantly advanced analysis and modeling of protein sequence, structure and function. In this talk, I will present our recent deep learning methods for classifying protein sequences to folds and predicting residue-residue contacts. Particularly, I will focus on the novel deep convolutional neural network architectures that can automatically learn multi-level features from unstructured raw sequence data of any length to recognize structural patterns in proteins, which can be widely applied to all kinds of classification and clustering problems in bioinformatics, computational biology and chemoinformatics.
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Tuesday, February 20, 2018
Dr. Amish Patel
Reliance Industries Term Assistant Professor in the Department of Chemical and Biomolecular Engineering at the University of Pennsylvania
TITLE: Characterizing Protein Hydration to Predict its Interactions and to Design of Ligands that Bind to it with High Affinity and Specificity
ABSTRACT: By uncovering the molecular underpinnings of solvation (of water and other liquids) adjacent to complex nanostructured surfaces, i.e., surfaces with chemical patterns and/or texture at the nanoscale, our group strives (i) to understand, predict, and eventually control biomolecular interactions and assembly, and (ii) to inform the design of the next generation of advanced materials. I will share examples of our recent successes in both of these areas.
I will describe our efforts in uncovering the role that water plays in mediating the interactions and self-assembly of complex molecules, including proteins, peptides, and surfactants. The extent to which the inherent structure of water is perturbed by these complex molecules, determines the thermodynamics and the kinetics of their assembly. However, accurately characterizing this perturbation is challenging, because proteins have incredibly complex surfaces that disrupt the inherent structure of water in countless different ways, depending not only on the chemistry of the underlying protein surface, but also on the precise topography and chemical pattern of amino acids. I will discuss our recent efforts on quantitatively characterizing this disruption of water structure, with the goal of using this information to efficiently predict the interfaces through which these complex monomers interact with one another and self-assemble. Our approach also informs strategies for optimally modulating protein interactions, and facilitates the design of ligands that will optimally bind to proteins of interest.
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Tuesday, February 13, 2018
Dr. Christopher Voigt
Professor of Biological Engineering, Massachusetts Institute of Technology
TITLE: How to program a cell: Genetic circuit design automation
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Tuesday, February 6, 2017
Dr. Yi Lu
Professor in the Department of Chemistry from Illinois University
TITLE: Functional DNA nanotechnology: precise spatial and dynamic controls of nanomaterials assembly and its applications in sensing, imaging and targeted drug delivery
ABSTRACT: Genetic control of the assembly of complex biological structures in response to chemical or biological stimuli is one of the hallmarks of biology. DNA plays a key role in the assembly process, as it has been shown to be highly programmable, resulting in a number of 2D and 3D nanostructures. Despite the promise, functionalizing these structures has been challenging. We have developed a novel method of using phosphorothioate DNA as anchors, and a bifunctional linker as a rigid molecular fastener that can connect nanoparticles to specific locations on the DNA backbone. Precise distance controls between two nanoparticles or proteins with nanometer resolution have been demonstrated. Furthermore, discovery of the genetic code is one of the most important achievements in biology. Inspired by this pioneering work, we have reported discovery of DNA codes for fine control of the shape and morphology of nanomaterials. These DNA codes will allow synthesis of novel nanomaterials with predictive shapes.
In addition to spatial control, dynamic control of nanomaterials assembly in response to internal stimuli under ambient conditions is also important. To meet this challenge, we took advantage of functional DNAs that can either bind to a target molecule (known as aptamers) or perform catalytic reactions (known as DNAzyme) for dynamic control of assembly of gold nanoparticles, iron oxide nanoparticles, quantum dots, and nanotubes, in response to chemical and biological stimuli from small metal ions to large biomolecules, including cancer cell markers. Since the nanomaterials possess unique optical, electrical, and magnetic properties, these systems have been converted into colorimetric, fluorescent, electrochemical sensors, and magnetic resonance imaging agents for detection of a broad range of analytes with high sensitivity and selectivity. The application has also been expanded to targeted drug delivery.
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Tuesday, January 30, 2017
Sean McQuade & Ruchi Lohia
CCIB PhD Students, Rutgers-Camden
McQUADE TITLE: Virtual Patient Generation for Quantitative Systems Pharmacology Simulators
McQUADE ABSTRACT: Quantitative Systems Pharmacology(QSP) models simulate an individual’s response to treatment with a Virtual Patient(VP). A VP’s usefulness in describing a clinical patient lies in the details of constructing this simulated metabolic network. Equilibrated Virtual patients provide a natural way to find correlations among fluxes in the QSP model, yielding responses to treatment that are more similar to clinical patients.
LOHIA TITLE: Conformational effects of various hydrophobic-to-hydrophobic substitution located at the midpoint of the intrinsically disordered region of proBDNF
LOHIA ABSTRACT: Although the role of electrostatic interactions and mutations that change charge states in intrinsically disordered proteins (IDPs) is well-established, many disease-associated mutations in IDPs are charge-neutral. Earlier, we studied the effects of the disease-associated Val66Met substitution at the midpoint of the prodomain of precursor brain-derived neurotrophic factor (proBDNF) using fully atomistic molecular dynamics simulations. Val66Met substitution is found in 25% of the American population, which has been widely studied for its association with aging-related and stress-related disorders, reduced volume of the hippocampus, and variations in episodic memory. We found that the local secondary structure, transient tertiary contacts, and compactness of the protein are correlated to backbone configuration around residue 66. The midpoint location and the substitution at the most highly charged region of the protein played a critical role in causing the conformational changes of Val66Met substitution. To gain further insight into the generalizability of the found mechanism with which a hydrophobic-to-hydrophobic substitution can effect the IDP’s conformational ensemble and, to further establish the significance of substitution location, we studied 5 more hydrophobic substitutions at residue 66. We report on fully-atomistic temperature replica exchange molecular dynamics simulations of the 90 residue proBDNF for Ala66, Ile66, Leu66, Phe66 and Tyr66 sequence. Analyzing and comparing the residue level insight from all 5 simulations helped us in further establishing the significance of charge-neutral mutations in IDPs.
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Tuesday, January 23, 2018
Dr. James Gillooly
Associate Professor, University of Florida
TITLE: Metabolic Scaling of Stress: Implications for Endotherm Life History
ABSTRACT: The impact of physiological stress on species’ life history is well-established, yet cross-species comparisons of stress levels remains a challenge. Physiological stress may impact many feature of a species’ life history, yet quantifying stress levels across species. Here I will consider the relationships of stress hormone levels in birds and mammals (cortisol and corticosterone) to whole organism energetics. I will then speculate about how these relationships may influence lifespan in these groups.
2017 Seminars
Tuesday, December 12, 2017
Dr. Ryan Petrie
Assistant Professor, Department of Biology, Drexel University
TITLE: Pressure-driven cell motility in three-dimensional extracellular matrix
ABSTRACT: Even when we are standing still, the cells in our bodies are going places. It is now clear that an individual cell can change how it moves in response to the material surrounding it. My lab is interested in understanding how the structure of the three-dimensional (3D) extracellular matrix dictates the molecular and physical mechanisms driving cell motility. For example, we recently discovered human fibroblasts moving through a cross-linked 3D matrix pull their nucleus forward like a piston to increase intracellular pressure and drive protrusion of the leading edge. Using a variety of biochemical, biophysical, and live cell imaging approaches, the Petrie lab aims to understand how intracellular pressure is controlled by actomyosin contractility in migrating cells in response to matrix structure. Further, we will seek to establish if the intracellular pressure generation machinery in metastatic cells is abnormal compared to primary fibroblasts and test the hypothesis that defective pressure regulation promotes cancer cell invasion into 3D extracellular matrix.
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Tuesday, December 5, 2017
Dr. Troy Shinbrot
Professor, Biomedical Engineering, Rutgers-New Brunswick
TITLE: Cellular Morphogenesis in silico
ABSTRACT: Recent advances in the in silico modeling of cellular dynamics now permit explicit analysis of cellular structure formation. This provides the scientific community with the capability of testing hypotheses in precisely controlled environments for the first time. We describe an agent-based approach that simulates cells that reproduce, migrate and change shape. We find that both expected morphologies and previously unreported patterns spontaneously self-assemble. Most of the states found computationally have been observed in vitro, and it remains to be established what role these self-assembled states may play in in vivo morphogenesis.
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Tuesday, November 28, 2017
Linlin Zhao & Sung Won Oh
CCIB PhD Students, Rutgers-Camden
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Tuesday, November 14, 2017
Dr. Yong Kim
Assistant Professor, Department of Chemistry & Environmental Science, NJIT
TITLE: Magnesium regulates the circadian oscillator in cyanobacteria
ABSTRACT: The circadian clock controls 24-hour biological rhythms in our body, influencing many time-related activities such as sleep and wake. The simplest circadian clock is found in cyanobacteria, with the proteins KaiA, KaiB, and KaiC generating a self-sustained circadian oscillation of KaiC phosphorylation and dephosphorylation. KaiA activates KaiC phosphorylation by binding the A-loop in KaiC, while KaiB attenuates it by sequestering KaiA from the A-loop. Structural analysis revealed that magnesium regulates the phosphorylation and dephosphorylation of KaiC by association or dissociation with catalytic Glu residues that activate phosphorylation. In the absence of KaiA, high magnesium concentration made KaiC dephosphorylate, whereas low magnesium concentration made KaiC phosphorylate. KaiC alone behaved as an hourglass-type timekeeper when magnesium concentration was alternated between low and high levels in vitro. The magnesium concentration in cyanobacteria in vivo was lower in light than in darkness. Our findings suggest how a circadian oscillator evolved from an hourglass timer to a self-sustained oscillator at a mechanistic level.
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Tuesday, November 7, 2017
Sreelekha Revur & Lingyu Guan
CCIB PhD Students, Rutgers-Camden
REVUR TITLE: Identification of a novel regulator of stalk synthesis in Caulobacter crescentus
GUAN TITLE: Analysis on human rRNA-derived fragments
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Tuesday, October 31, 2017
Dr. Matthew Niepielko
Gavis Lab, Molecular Biology, Princeton University, CCIB’s 1st PhD Graduate
TITLE: Drosophila germ granule mRNAs self-organize through a nucleation and recruitment mechanism
ABSTRACT: The co-packaging of different mRNA types into macromolecular structures called ribonucleoprotein particles (RNPs) is a conserved strategy for the regulation of mRNA metabolism. In many animals, the formation of complex RNPs called germ granules is essential for the post-transcriptional regulation of mRNAs that are required for germline maintenance. In Drosophila, the germ granules are assembled at the posterior of the egg and are inherited by the primordial germ cells during embryogenesis. We and others have shown that the mRNAs in these granules are organized as distinct clusters, called homotypic clusters, which contain multiple copies of any one particular mRNA. It remains unclear, however, how mRNAs become incorporated into these granules and self-organize. Here, we have used quantitative image analyses to investigate how two mRNAs, nos and pgc, are packaged into germ granules. We find that RNPs containing a single copy of nos or pgc can populate the same granule and our data suggest that these initial RNPs seed homotypic clusters that grow by self-recruitment. Homotypic clusters of different mRNAs grow independently of each other and alongside the development of the granule protein scaffold. Finally, our results indicate that homotypic cluster growth is primarily responsible for the accumulation of mRNAs within germ granules.
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Tuesday, October 24, 2017
Dr. Karim Azer
Senior Director, Sanofi
TITLE: End to end Quantitative Systems Pharmacology (QSP) Modeling: Strategy & Evolution
ABSTRACT: Significant advances in data measurement technologies and capabilities, and advancements in computing, are gradually transforming the biological and clinical sciences into data and computational sciences. These opportunities in leveraging computational and data sciences can be harnessed, as biological and clinical knowledge on disease and drug perturbations to a healthy homeostatic biome can be translated into mathematical equations and computer models. With the appropriate investment in the computational sciences, these computer models can be subsequently used for in-silico drug discovery (e.g. identification of novel targets) or drug development (e.g. virtual human trials).
Quantitative Systems Pharmacology (QSP) modeling is used in both research and development applications at Sanofi, in a wide array of therapeutic areas, including rare diseases, immunology and inflammation, cardiovascular, and diabetes. Existing QSP project-facing applications at Sanofi include the quantitative assessment of combination strategies, advancing mechanistic hypotheses for differential treatment response in disease sub-populations, predicting pharmacological responses in new populations based on limited data, and many more. The QSP simulation and prediction frameworks are based on our ability to appropriately capture existing knowledge and data about a population of interest and effect of standard of care therapy into the models.
In this Talk, we will present on overview of the strategy and application of QSP as a computational modeling approach at Sanofi. We will provide a high level representation of the platforms we have built. Finally, we will close with an in-depth look at one of our platforms, how it was developed, and how it’s being applied in a real life setting.
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Tuesday, October 17, 2017
Dr. Charles L. Epstein
Professor of Mathematics, University of Penn
TITLE: The Ins and Outs of the Diffusion Approximation
ABSTRACT: Population Genetics is the study of how the distribution of different types of individuals evolves in a reproducing population. The standard models incorporate effects of random mating, mutation, selection, and migration. In their simplest form these models are discrete Markov chains that model a finite population. These discrete models are very difficult to analyze and compute with, and so methods were developed to replace the discrete models with continuum models allowing for the usage of calculus. We describe both the discrete and continuous models and how they are related, as well as recent work on the analysis and numerics of the continuous models.
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Tuesday, October 10, 2017
Catherine Guay & Nathaniel Merrill
CCIB PhD Students, Rutgers-Camden
GUAY’S TITLE: Uncovering regulatory regions at genome scale
MERRILL’S TITLE: Relating Circadian Period and Phase of Entrainment & Ancient Food Web Analysis
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Tuesday, October 3, 2017
Dr. Xiao Hu
Assistant Professor, Department of Physics & Anatomy, Biomedical & Translational Sciences and Biomedical Engineering, Rowan University
TITLE: Protein-Based Composite Biomaterials
ABSTRACT: Proteins are important biological macromolecules that have been used as materials for centuries, not only because they are environmentally friendly, renewable, and non-toxic, but also due to their excellent strength, elongation, toughness, slow degradability and great biocompatibility. Combining proteins with other polymers can generate novel composite materials with diverse properties, such as suitable mechanical and chemical properties, favorable electrical and optical features or other excellent characteristics. In this talk, we will use several protein based composite materials (e.g. silk-zein composites, silk-wild silk composites, silk-poly(lactic acid), silk-graphene composites) as examples to demonstrate our recent studies on this topic. By identifying molecular interactions between two polymers, we are able to create new composite materials with controllable thermal stability and mechanical resiliency. These composite materials were then fabricated into films, fibers, sponges, gels, and particles, as well as thermal, optical and electrical devices. Such material systems also provide advantages in comparison to traditional polymers due to the materials biodegradability, biocompatibility, and tenability in the body, and can be widely used in many biomedical fields such as biosensors, nano medicine, tissue regeneration and drug delivery.
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Tuesday, September 26, 2017
Liam Sharp
CCIB PhD Student, Rutgers-Camden
TITLE: A Coarse Grained Study of Nicotinic Acetylcholine Receptor-Lipid Interactions
ABSTRACT: Historically, the nicotinic acetylcholine (nAChR) receptor is suggested to reside within cholesterol rich domains. We performed coarse-grained molecular dynamics on neuro-muscular nAChR in membrane of ternary compositions. Using cholesterol, lipids with saturated and polyunsaturated acyl chains, we show nAChR residing in cholesterol depleted domains, remaining close to cholesterol enriched domains. The beta subunit appears to prefer the cholesterol depleted domain, while the alpha subunits appear to prefer cholesterol enriched domains. Both cholesterol and long chained unsaturated lipids are further observed to embed between and within individual subunits of nAChR.
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Tuesday, September 19, 2017
Dr. Alexander Tropsha
Associate Dean for Pharmacoinformatics and Data Science, University of North Carolina
TITLE: Application of text mining and data mining to drug discovery and repurposing
ABSTRACT: Modern sources of information about drug activities include multiple and diverse in nature big data streams such as unstructured texts (social media networks, published biomedical literature, and electronic health records), electronic databases of chemical-biological interactions and pathways, and laboratory data (results of biological screening of chemical libraries). Concurrent exploration of these sources affords repurposing of observational textual data for research projects and can lead to new actionable hypotheses about drug-target-disease relationships. I will discuss selected text mining and machine learning tools and their application to mining social media, scientific literature, and chemical genomics databases. I will present case studies on using such data to build models forecasting novel uses or predicting side effects of medications. I will also discuss a new reinforcement-learning based approach to chemical library design that has its roots in text mining methods.
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Tuesday, September 12, 2017
Dr. Jinglin Fu
Chemistry, Rutgers-Camden
TITLE: Assembly of Bio-mimetic Multienzyme Complex on DNA Nanoscaffolds
ABSTRACT: Biology has evolved complex, multi-step enzyme pathways to produce molecules and harvest energy processes that are vital to the metabolism and propagation of all living systems. The function of these pathways is critically dependent on the relative position, orientation, and number of the participating enzymes1,2. The ability to exert control over such systems on the nanoscale will not only allow us to study the effects of spatial organization on the functions of biochemistry pathways, but also increase our ability to translate biochemical pathways into a variety of noncellular applications. DNA nanostructures have recently emerged as promising assembly scaffolds to organize molecules on the nanoscale based on their sequence-driven, programmable self-assembly5,20-24. For example, multi-enzyme cascades can be positioned on DNA nanostructures with precise control over the spatial distances with the goals to enhance the mass transport of substrates25-27, the engineering of substrate channeling mechanisms28, and the regulation of spatial interactions between enzyme pairs29,30. Here, we present a methodology for exploiting DNA nanostructures as assembly scaffolds that organize the spatial arrangements of multi-enzyme cascades with control over their relative distance, compartmentalization, and substrate diffusion paths.
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Tuesday, April 25, 2017
Dr. Ekta Khurana
Weill Medical College, Cornell University
TITLE: What about the other 98%: Mutations in non-protein-coding DNA in cancer
ABSTRACT: The genomes of cancer patients show thousands of mutations in DNA. However, only a few of them play a role in driving tumor growth (called cancer drivers) while most of them do not have any impact on cancer growth (called passengers). Drivers in protein-coding cancer genes have been identified by extensive efforts over the past decades – yet many patients do not show any mutations in known drivers. ~99% somatic mutations obtained from whole-genome sequencing of tumors localize to regions that do not code for proteins but rather regulate the RNA expression of protein-coding genes. Although variants in protein-coding regions have received the majority of attention, numerous studies have now noted the importance of non-coding variants in cancer – for example, mutations in the promoter of the TERT gene. Identification of functional non-coding variants that drive tumor growth remains a challenge and a bottleneck for the use of whole-genome sequencing in the clinic.We have developed a novel computational approach to predict non-coding cancer drivers. This method first identifies the mutations predicted to have high functional impact (for example, by disrupting binding of transcription factors to DNA). It then integrates the signals of high functional impact with the recurrence of mutations across multiple tumor samples to identify the elements that show more and higher functional impact mutations than expected randomly – thus identifying the non-coding drivers of cancer. I will present the details of this method and discuss our ongoing efforts to analyze ~2,900 tumor whole-genomes in the International Cancer Genome Consortium.
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Tuesday, April 18, 2017
Dr. Robert Tycko
National Institute of Health
TITLE: Molecular Structure of Amyloid Fibrils: Insights from Solid State NMR
ABSTRACT: Amyloid fibrils are of critical importance in neurodegenerative diseases and in certain biological processes. When my lab began working on amyloid fibrils in 1998, their molecular structures were largely mysterious. By now, we know a great deal about these structures, primarily through solid state NMR experiments by my lab and by other labs. This lecture will describe our main findings regarding the structures of fibrils formed by the amyloid-? peptide associated with Alzheimer’s disease (including recent studies of correlations between variations in fibril structure and variations in disease characteristics) and by the low-complexity domain of the transcriptional activator protein FUS, whose propensity to form amyloid fibrils is puzzling and possibly biologically relevant. I will also introduce various aspects of solid state NMR methods and their application to biological systems in general.
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Tuesday, April 11, 2017
Dr. William Saidel
Professor of Biology at Rutgers-Camden
TITLE: Does the Jamming Avoidance Reflex of Weakly Electric Fish Display Stochastic Resonance? A phenomenological analysis
ABSTRACT: Different species of weakly electric fish (wef) generate a continuous pulsatile electric discharge (EOD or electric organ discharge) at frequencies ranging from ~100 to ~1000 Hz with which to probe their environment. Such a fish detects the distortion of its resultant field when a 2nd field from a conspecific (generating a field with an EOD at an equal frequency) intersects its field. In response, one of the fish raises its frequency and the other lowers its frequency. This behavior is called the Jamming Avoidance Reflex (JAR). We developed a procedure by which we record and play back to the fish its own EOD at various amplitudes including those that do not elicit the JAR. The addition of Gaussian white noise may recover the JAR. This is called stochastic resonance. Gaussian white noise does not contain frequency specific information. Thus, the JAR in this case is elicited in the absence of a frequency-specific signal.
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Tuesday, April 4, 2017
Dr. Mark Goulian (Klein)
Professor of Biology at University of Pennsylvania
TITLE: Regulated stochasticity in bacterial signal transduction
ABSTRACT: Cells sense and respond to physical and chemical cues in the environment through signal transduction systems—networks of interacting proteins that detect specific input signals and control appropriate cellular responses. In bacteria, such signaling systems are frequently composed of circuits containing two key components: a sensor kinase and a response regulator. These two-component systems have been found in remarkable numbers within individual organisms and across different bacterial species. They play a central role in regulating basic aspects of microbial physiology and enable adaptation to diverse environmental conditions. I will describe recent work in which we have explored the origin and consequences of cell-to-cell variability in the output of a two-component system in E. coli.
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Tuesday, March 28, 2017
Nastassia Pourdier Duteil & Spyros Karaiskos
Rutgers-Camden, CCIB Students
Pourdier Duteil’s TITLE: The role of cell movement and growth in the spatiotemporal dynamics of EGFR activation
Pourdier Duteil’s ABSTRACT: Tissue patterning and cell fates determination are regulated by cell signaling pathways. A highly-conserved signaling pathway across animals, the epidermal growth factor receptor (EGFR), controls both the posterior-anterior and the dorsal-ventral axes during Drosophila melanogaster oogenesis. The TGF-alpha-like ligand Gurken (GRK) is secreted from around the oocyte nucleus to the perivitelline space and activates EGFR in the overlaying follicle cells. Complexity is found in the dynamic localization of the oocyte nucleus, and hence the source of GRK. Early, the nucleus is present at the posterior end. Later, the nucleus is situated at the dorsal anterior side of the oocyte. Thus, EGFR activation is dynamic. Furthermore, the oocyte within the egg chamber continuously grows during oogenesis. Lastly, the overlaying follicle cells gradually shift from anterior to posterior. Current models consider solely GRK diffusion from a static location. Here, based on experimental data, we built a mathematical model using reaction-diffusion PDEs to recapitulate the spatiotemporal dynamic activation of EGFR. Our model reveals the crucial role of growth and cell movement in shaping the distributions of GRK and signaling.
Karaiskos’ TITLE: Dynamics of tRNA fragments and their targets in aging mammalian brain
Karaiskos’ ABSTRACT: Background: The progress of next-generation sequencing technologies has unveiled various non-coding RNAs that have previously been considered products of random degradation and attracted only minimal interest. Among small RNA families, microRNA (miRNAs) have traditionally been considered key post-transcriptional regulators. However, recent studies have reported evidence for widespread presence of fragments of tRNA molecules (tRFs) across a range of organisms and tissues, and of tRF involvement in Argonaute complexes.
Methods: To elucidate potential tRF functionality, we compared available RNA sequencing datasets derived from the brains of young, mid-aged and old rats. Using sliding 7-mer windows along a tRF, we searched for putative seed sequences with high numbers of conserved complementary sites within 3′ UTRs of 23 vertebrate genomes. We analyzed Gene Ontology term enrichment of predicted tRF targets and compared their transcript levels with targets of miRNAs in the context of age.
Results and Discussion: We detected tRFs originating from 3’- and 5’-ends of tRNAs in rat brains at significant levels. These fragments showed dynamic changes: 3’ tRFs monotonously increased with age, while 5’ tRFs displayed less consistent patterns. Furthermore, 3’ tRFs showed a narrow size range compared to 5’ tRFs, suggesting a difference in their biogenesis mechanisms. Similar to our earlier results in Drosophila and compatible with other experimental findings, we found “seed” sequence locations on both ends of different tRFs. Putative targets of these fragments were found to be enriched in neuronal and developmental functions. Comparison of tRFs and miRNAs increasing in abundance with age revealed small, but distinct changes in brain target transcript levels for these two types of small RNA, with the higher proportion of tRF targets decreasing with age. We also illustrated the utility of tRF analysis for annotating tRNA genes in sequenced genomes.
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Tuesday, March 21, 2017
Dr. Milan Stojanovic (Fu)
Associate Professor of Biomedical Engineering and the Associate Director of the Division of Clinical and Pharmacology & Experimental Therapeutics at Columbia University
TITLE: Progress in Oligonucleotide Technologies
ABSTRACT: Dr. Stojanovic will discuss advances that our group recently made in two applications of oligonucleotides. First, I will describe a system made of antibodies and oligonucleotides that can perform molecular computing on cell surfaces, evaluating individual cells, labeling them for separation and elimination, all based on presence and absence of cell surface markers. Second, I will update everyone on several new types of assays that can be done only with oligonucleotide-based receptors (so called aptamers), allowing sensitive detection of small molecules.
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Tuesday, March 7, 2017
Dr. Veronica Hinman (Nam)
Associate Professor of Biological Sciences at Carnegie Mellon University
TITLE: The Evolution of GRNs for Developmental Change
ABSTRACT: In this talk Dr. Hinman will discuss the ways that developmental Gene Regulatory Networks (GRNs) can evolve. GRNs describe the regulatory interactions between genes and are increasingly used to provide systems-level explanations of how cells are specified during development. Sea urchins (Ph. Echinodermata), in particular have become a model system for understanding how GRNs control development. We therefore use other species of echinoderms (including sea stars and sea cucumbers) to establish GRNs and then to compare these to the well-known sea urchin networks. This has allowed to determine how GRNs might be robust to evolutionary change as well as how these networks can evolve to make novel structures. I will also present some recent work that explains how transcription factors might themselves evolve. It is well documented that GRNs can evolve extensively through mutations to cis-regulatory modules, but our work also explains how these networks can evolve following changes in transcription factor binding specificities. This provides a new layer of investigation as to how development can evolve.
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Tuesday, February 28, 2017
Min Kyung Kim & Joseph Kawash
Rutgers-Camden, CCIB Students
KIM’S TITLE: Development and biological applications of computational methods for inferring metabolic flux distributions from transcriptomic data
KIM’S ABSTRACT: Studying changes in the cellular metabolism is important to understand what a living cell does for survival in response to environmental or genetic perturbations. Although metabolic flux (i.e. reaction rate) distributions are desirable information to this end, it is challenging to directly quantify them. Several computational methods thus have been developed to predict system-wide and condition-specific intracellular metabolic fluxes by integrating transcriptomic data with genome-scale metabolic models. While powerful in many settings, existing methods have several shortcomings, and it is unclear which method has the best accuracy in general because of limited validation against experimentally measured fluxes. We developed two different methods called E-Flux2 (E-Flux method combined with minimization of l2 norm) and SPOT (Simplified Pearson cOrrelation with Transcriptomic data), which can be chosen depending on the availability of knowledge on objective function. The predictive accuracy of algorithm was then validated by calculating the Pearson correlation between predicted fluxes and measured fluxes. To this end, we compiled 20 experimental conditions (11 in E. coli and 9 in S. cerevisiae), of transcriptome measurements coupled with corresponding central carbon metabolism intracellular flux measurements, which is the largest dataset assembled to date. In both organisms, our method outperformed a representative sample of competing methods. In collaborative publications, our method has been used to study metabolism of Phaeodactylum tricornutum, Synechococcus sp. PCC 7002, and Staphylococcus aureus. Easy-to-use implementations of E-Flux2 and SPOT are available as part of the open-source package MOST (https://most.ccib.rutgers.edu/)
KAWASH’S TITLE: Comparative genome analysis provides insight to adaptation in woolly mammoth
KAWASH’S ABSTRACT: The woolly mammoth (Mammuthus primigenius) is an ancient species of megafauna that inhabited the upper Arctic regions of the globe until the demise of the most recent population about 3500 years ago. Though the woolly mammoth did not persist, its sister species, the Asian elephant, has successfully carried on to the present date. Despite being studied extensively anatomically, little is known about the genetic divergence and evolution between woolly mammoth and Asian elephant. Until recently, whole genome sequence availability of woolly mammoth specimens has been sparse and typically of low quality, a common challenge for ancient genomes; therefore few comparative genetic studies have been performed. These studies have been limited in sample size as well as breadth of mutation type. We expand upon current findings through the systematic identification of single nucleotide, as well as, indel, and structural variant fixed mutations for woolly mammoth. Our sample included individuals spanning a wide geographical (encompassing the range or 2 separate clades) and chronological (60,000 – 4,000 years ago) range. Woolly mammoth unique changes were identified affecting exon regions of several genes associated with metabolism, circadian rhythms, immunity, cold adaptation, and anatomy. Through the analysis of 4 woolly mammoth and 4 Asian elephant samples, we identified variation affecting large portions of the genome not previously reported, suggesting signs of evolutionary adaptation to the Arctic environment.
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Tuesday, February 21, 2017
Dr. Simon Garnier (Savage)
Assistant Professor, New Jersey Institute of Technology
TITLE: Living Architectures in New World Army Ants
ABSTRACT: One of the most spectacular examples of construction by social insects are the self-assembling structures formed by New World army ants. In order to conquer the complex terrain of the tropical forests of Central and South America, these nomadic ants create temporary support structures with their own bodies – bridges, pothole covers, and buttresses – forming the backbone of dynamical ant “superhighways”. In particular, bridges formed by the army ants can self-assemble across a wide variety of environments and spanning conditions, and have been shown to recover from damage, adapt their size according to traffic, and even spontaneously disassemble when under-used. The army ants’ living architectures are an existence proof of how complex and dynamical biological structures can be achieved from the cooperation of large numbers of limited individuals.
Over the last few years, such natural systems have inspired the development of a new kind of robotics, where simple, independent agents act together to build large-scale structures as needed, guided only by their reactions to the local situations they encounter. Large robotic swarms that could self-assemble could accomplish remarkable tasks, such as creating bridges to navigate complex terrain, plugs to repair structural breaches, or supports to stabilize a failing structure. Nevertheless, how to achieve complex artificial self-assemblages remains poorly understood. During this talk I will review our latest discoveries on – and current investigations in – the mechanisms of construction in New World army ants, with the goal to provide insight into achieving successful self-assembly in artificial systems.
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Tuesday, February 14, 2017
Min Kyung Kim & Joseph Kawash
Rutgers-Camden, CCIB Students
KIM’S TITLE: Development and biological applications of computational methods for inferring metabolic flux distributions from transcriptomic data
KIM’S ABSTRACT: Studying changes in the cellular metabolism is important to understand what a living cell does for survival in response to environmental or genetic perturbations. Although metabolic flux (i.e. reaction rate) distributions are desirable information to this end, it is challenging to directly quantify them. Several computational methods thus have been developed to predict system-wide and condition-specific intracellular metabolic fluxes by integrating transcriptomic data with genome-scale metabolic models. While powerful in many settings, existing methods have several shortcomings, and it is unclear which method has the best accuracy in general because of limited validation against experimentally measured fluxes. We developed two different methods called E-Flux2 (E-Flux method combined with minimization of l2 norm) and SPOT (Simplified Pearson cOrrelation with Transcriptomic data), which can be chosen depending on the availability of knowledge on objective function. The predictive accuracy of algorithm was then validated by calculating the Pearson correlation between predicted fluxes and measured fluxes. To this end, we compiled 20 experimental conditions (11 in E. coli and 9 in S. cerevisiae), of transcriptome measurements coupled with corresponding central carbon metabolism intracellular flux measurements, which is the largest dataset assembled to date. In both organisms, our method outperformed a representative sample of competing methods. In collaborative publications, our method has been used to study metabolism of Phaeodactylum tricornutum, Synechococcus sp. PCC 7002, and Staphylococcus aureus. Easy-to-use implementations of E-Flux2 and SPOT are available as part of the open-source package MOST (https://most.ccib.rutgers.edu/)
KAWASH’S TITLE: Comparative genome analysis provides insight to adaptation in woolly mammoth
KAWASH’S ABSTRACT: The woolly mammoth (Mammuthus primigenius) is an ancient species of megafauna that inhabited the upper Arctic regions of the globe until the demise of the most recent population about 3500 years ago. Though the woolly mammoth did not persist, its sister species, the Asian elephant, has successfully carried on to the present date. Despite being studied extensively anatomically, little is known about the genetic divergence and evolution between woolly mammoth and Asian elephant. Until recently, whole genome sequence availability of woolly mammoth specimens has been sparse and typically of low quality, a common challenge for ancient genomes; therefore few comparative genetic studies have been performed. These studies have been limited in sample size as well as breadth of mutation type. We expand upon current findings through the systematic identification of single nucleotide, as well as, indel, and structural variant fixed mutations for woolly mammoth. Our sample included individuals spanning a wide geographical (encompassing the range or 2 separate clades) and chronological (60,000 – 4,000 years ago) range. Woolly mammoth unique changes were identified affecting exon regions of several genes associated with metabolism, circadian rhythms, immunity, cold adaptation, and anatomy. Through the analysis of 4 woolly mammoth and 4 Asian elephant samples, we identified variation affecting large portions of the genome not previously reported, suggesting signs of evolutionary adaptation to the Arctic environment.
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Tuesday, February 7, 2017
Dr. Lauren Aleksunes (Zhu)
Deputy Director of NTP Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM) from the National Institute of Environmental Health Sciences
TITLE: Alternatives to Animal Testing: Novel Methods and Applications
ABSTRACT: Toxicology in the 21st century (Tox21) has ushered in a scientific revolution focusing on novel approaches to understanding chemical hazard and safety, with an emphasis on non-animal high-throughput screening (HTS) methods, adverse outcome pathways, and computational systems models to predict effects on human biology. The NTP Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM) is an office within the Division of the National Toxicology Program, National Institute of Environmental Health Sciences, and serves as a partner in the Tox21 high-throughput screening (HTS) program. NICEATM activities support the development and evaluation of new, revised, and alternative methods to identify potential hazards to human health and the environment, with a focus on replacing, reducing, or refining animal use. The NICEATM group is composed of toxicologists, chem- and bio-informaticists, computational biologists, validation specialists, literature review experts, and communications and administrative staff. NICEATM works with sixteen U.S. federal agencies with an interest in non-animal testing, via the Interagency Coordinating Committee on Validation of Alternative Methods (ICCVAM), and focuses on research efforts driven by agency priorities, both regulatory and scientific in nature. As a Tox21 partner, NICEATM applies computational toxicology techniques to analyze high-throughput screening data sets and build predictive models of chemical impacts on human health and disease pathways. Activities include the design and oversight of validation studies of novel in vitro assays for endpoints like skin sensitization, projects to implement alternative tests in regulatory policy such as the Endocrine Disruptor Screening Program, curation of high-quality data from stakeholders and the public literature, organization of scientific workshops and education, communication, and training, with national and international partners. Recent efforts have focused on the development of online tools in an “Integrated Chemical Environment” (ICE) to make NICEATM analysis workflows publically available and provide datasets that are transparent, computable and searchable for model-building and evaluation.
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Tuesday, January 31, 2017
Liam Sharp & Catherine Guay
Rutgers-Camden, CCIB Students
GUAY’S TITLE: High-throughput spatial cis-regulatory analysis by turning chaos into order
GUAY’S ABSTRACT: Cis-regulatory modules (CRMs) are essential for cell-type specific gene expression patterns and contain abundant gene regulatory information in their DNA sequences. The biggest roadblock for efficient utilization of gene regulatory information contained within CRMs is the lack of a high-throughput method for cis-regulatory analysis. To address this critical challenge, we have developed a novel high-throughput, quantitative method for spatial cis-regulatory analysis using sea urchin embryos as a test bed. The new method takes advantage of i) stochastic and mosaic incorporation of reporter constructs in early embryos upon transgenesis and ii) a novel method for high-throughput, single embryo-resolution measurement of the copy numbers of expressed and incorporated reporter constructs in many mosaic embryos. Because the level of reporter expression in an embryo is determined by a combination of the intrinsic activity of a given CRM and the cells that harbor the reporter construct at the time of measurement, we hypothesized that the profile of reporter expressions measured at single-embryo resolution in a sufficiently large number of mosaic embryos is determined solely by spatial activity of a given CRM. Our proof-of-principle experiment showed that the new method can rapidly classify ?100 CRM::reporter constructs based on their spatial activities without relying on imaging tools. The new method has the potential to increase the throughput of spatial cis-regulatory analysis at least three orders of magnitude compared to traditional imaging-based analyses. We anticipate that the new method can vastly accelerate discovery of cell-type specific CRMs, identification of gene regulatory networks, and evolutionary comparison of CRM functions
SHARP’S TITLE: A Coarse Grained Study of Nicotinic Acetylcholine Receptor-Lipid Interactions
SHARP’S ABSTRACT: 3D printing has a significant potential to respond to the need for structural complexity in biomaterial design. However, there are limited number 3D printable polymers available for fabrication of medical devices. In addition, currently printed devices only serve as a structural support, they permit but don’t promote biological function, due to lack of the bioactivity of the available polymers (including poly(lactic acid) (PLA), and polycaprolactone (PCL). In this seminar, the 3D printing technologies and ink materials will be summarized, with strong emphasis on the design criteria for ink development to create bioactive scaffolds and medical devices.
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Tuesday, January 24, 2016
Dr. Lauren Aleksunes (Zhu)
Associate Professor, Ernest Mario School of Pharmacy, Environmental and Occupational Health Sciences Institute, Rutgers-New Brunswick
TITLE: Improving the Prediction and Detection of Drug-Induced Kidney Injury
ABSTRACT: Increases in urinary concentrations in the absence of changes in traditional markers. Relationships between urinary biomarkers and cisplatin excretion as well as the influence of chemotherapy cycle number on biomarker responses will be discussed. Supported by an AFPE Fellowship, NIH DK080744, NIH DK093903, and NIH CA072720.
2016 Seminars
Tuesday, December 13, 2016
Dr. Murat Guvendiren (Salas)
Assistant Professor, Department of Chemical, Biological and Pharmaceutical Engineering, NJIT
TITLE: Designing Polymeric Biomaterial Inks for 3D Printing of Medical Devices
ABSTRACT: 3D printing has a significant potential to respond to the need for structural complexity in biomaterial design. However, there are limited number 3D printable polymers available for fabrication of medical devices. In addition, currently printed devices only serve as a structural support, they permit but don’t promote biological function, due to lack of the bioactivity of the available polymers (including poly(lactic acid) (PLA), and polycaprolactone (PCL). In this seminar, the 3D printing technologies and ink materials will be summarized, with strong emphasis on the design criteria for ink development to create bioactive scaffolds and medical devices.
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Tuesday, December 6, 2016
Linlin Zhao & Sung Won Oh
CCIB Students
ZHAO’S TITLE: Experimental errors in QSAR modeling sets: what we can do and what we cannot do
ZHAO’S ABSTRACT: Numerous chemical datasets have become available for quantitative structure–activity relationship (QSAR) modeling studies. However, the quality of various data sources may be different based on the nature of experimental protocols. Therefore, the potential experimental errors in the modeling sets may induce the development of poor QSAR models and further affect the predictions of new compounds. In this study, we explored the relationship between the ratio of questionable data, which was obtained by simulating experimental errors, in the modeling sets and the QSAR modeling performance. To this end, we used eight datasets (four continuous endpoints and four categorical endpoints) that has been extensively curated in-house and by our collaborators to create over 1,800 various QSAR models. Each dataset was duplicated to create several new modeling sets with different ratios of simulated experimental errors (i.e. randomizing the activities of part of the compounds) in the modeling process. Five-fold cross-validation process was used to evaluate the modeling performance, which deteriorates when the ratio of experimental errors increases. All the resulting models were also used to predict external sets of new compounds which were excluded at the beginning of modeling process. The modeling results showed that the compounds with relatively large prediction errors in cross-validation process are likely to be those with simulated experimental errors. However, after removing certain number of compounds with large prediction errors in cross-validation process, the external predictions of new compounds did not gain improvement. Our conclusion is that the QSAR predictions, especially consensus predictions, are able to identify compounds with potential experimental errors. But removing those compounds will not result in better modeling performance due to overfitting. Apparently extra experimental testing is necessary for those compounds found to be questionable by QSAR predictions.
OH’S TITLE: Logic-Gated Catalytic Circuits for Molecular Sensing
OH’S ABSTRACT: In biochemical pathways, many enzyme functions are regulated by inhibition byproducts, or product feedback inhibition. In this project, artificial swinging arms are designed to channel the transfer of intermediates in multienzyme reactions. Swinging arms play important role in multi-step, catalytic transformations in multienzyme complexes. DNA logic-AND-gated circuits will be implemented to swinging arms to control the release and activation of swinging arms to control the transport of intermediates in enzyme reactions. Logic gate circuits are composed of single-stranded DNA molecules to regulate the pathway activities and specificities. In order to regulate the multienzyme pathway, logic gate circuits will be implemented on DNA nanostructures for controlling and switching pathway activities to produce different final products depending on specific inputs. An ‘AND’ logic-gated swinging arm is designed then native polyacrylamide gel electrophoresis (PAGE) is used to characterize the opening and closing of logic-gate circuits for release of swinging arm. Currently, DNA-based logic gated circuits for controlling swinging arms has been created with toehold design for DNA strand displacement, resulting in releasing of swinging arm. After logic-gated circuits are controlled, it will be characterized using FRET (fluorescence resonance energy transfer microscopy) to evaluate the release of the swinging arm and the activation of enzyme pathway by input of two key strands. Eventually, logic-gated NAD-swinging arm will be applied to detecting biotargets with signal amplification in a small test tube and visible color change.
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Tuesday, November 29, 2016
Ruchi Lohia & Sruthi Murlidaran
CCIB Students
RUCHI’S TITLE: Mechanism underlying conformational effects of a disease-associated hydrophobic-to-hydrophobic substitution on an intrinsically disordered region
RUCHI’S ABSTRACT: Disease-associated Single Nucleotide Polymorphisms (SNPs) are common in the disordered regions of proteins. Most mutational studies of IDP’s consider loss or gain of a charged residue. In this study, we explore the local and global effect of a charge-neutral mutation between two hydrophobic residues, with known effects on function: the Val66Met SNP in the 100-residue disordered prodomain of Brain Derived Neurotropic Factor (BDNF). Val66Met is the most frequently found SNP in the BDNF disordered domain, and is associated with various neurological and psychiatric disorders such as bipolar disorder and Alzheimer’s disease. Previously, NMR studies demonstrated that the prodomain is disordered with different secondary structure preferences for Val and Met at 275K (Anastasia et al 2013). We used large-scale, fully atomistic temperature replica exchange molecular dynamics simulations of both the Val and Met forms of the BDNF prodomain. MD simulations identify similar regions of residual secondary structure compared to NMR studies. Interestingly, we observe reversed temperature dependence of the secondary structure around the SNP for Val and Met. With increasing temperature, Val66 is less likely to assume helical secondary structure, while Met66 is more likely, consistent with established reduction of the valine side-chain entropy upon helix formation. At room temperature, we also observe an increase in the radius of gyration of the Met66 prodomain relative to the Val66 prodomain, which can be reliably attributed to differential hydrogen bonding preferences of SNP-adjacent residues, affecting their likelihood of hydrogen bonding with distant residues. These results indicate the neutral substitution may exert its effects by critically adjusting entropic cost of local secondary-structure elements, which, in turn, affects the conformational ensemble via differential long-range beta bridging patterns. Furthermore, although the SNP is neutral, it alters the exposure of charged residues around the SNP, which can potentially affect the binding characteristics of the prodomain.
SRUTHI’S TITLE: Relative affinities of general anesthetics for pseudo-symmetric intersubunit binding sites of heteromeric GABA(A) receptors
SRUTHI’S ABSTRACT: GABA(A), a pentameric ligand gated ion channel is critical for regulating neuronal excitability.These inhibitory receptors, gated by ?-amino butyric acid(GABA), can be potentiated and also directly activated by intravenous and inhalational anesthetics. Although the receptor is considered to be an important target for general anesthetics, the mechanism of receptor modulation remains unclear. These receptors are predominantly found in 2?:2?:1? stoichiometry, with four unique inter-subunit interfaces. This pseudosymmetric nature of the receptor has led experimental studies to show ambiguous predictions in identifying the binding site. Here we use thermodynamically rigorous free energy perturbation (AFEP) techniques and Molecular Dynamic simulations to rank the different inter-subunit site by affinity. AFEP calculations predicted selective propofol binding to interfacial sites, with higher affinities for ?/? than ?-containing interfaces. The simulations revealed the key interactions leading to propofol selective binding within GABAA receptor subunit interfaces, with stable hydrogen bonds observed between propofol and ?/? cavity residues, lower tendency to form hydrogen bonds with the more hydrophilic +?/?- interfacial cavity. Sevoflurane, a comparatively lesser potent anesthetic, though shows preference towards ?/? interface as binding sites, does not display micromolar affinity to any particular site. Flooding simulations furthers the AFEP predictions, with identifying sevoflurane bindings sites in ?+/?- and +?/?-interfacial cavity and providing insights into the sevoflurane-binding pathway, ligand-protein interactions and receptor occupancy.
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Tuesday, November 15, 2016
Dr. Neal Zondlo
Professor, Chemistry & Biochemistry, Delaware University
TITLE: Insights into the nature of aromatic interactions and structural effects of protein post-translational modifications
ABSTRACT: Aromatic residues, proline residues, and post-translational modifications have important roles in protein structure and function. We investigated the control protein structure via aromatic interactions and via post-translational modifications. Aromatic interactions with C–H bonds (C–H/p interactions) are commonly observed in protein structure, protein-protein interactions, carbohydrate recognition, drug binding, and biomaterials. In proteins, C–H/p interactions are particularly observed between aromatic residues and proline or glycine, including at biomolecular interfaces and within membranes. C–H/p interactions are strongest with polarized C–H bonds, leading to their description as analogous to cation/p interactions, with additional contributions in water from the hydrophobic effect. We have examined the fundamental nature of C–H/p and S–H/p interactions using designed peptides and analysis by NMR spectroscopy, x-ray crystallography, IR spectroscopy, DFT calculations, and bioinformatics analysis. Across 50 peptides, the strength of the aromatic-proline C–H/p interaction was observed to be dependent on the electronics of the aromatic ring, with the strongest interactions with the most electron-rich aromatic rings. Stronger C–H/p interactions were associated with more favorable enthalpy and more unfavorable entropy. In contrast to either a primarily electrostatic basis for the C–H/p interaction or the hydrophobic effect, these interactions exhibited similar enthalpies and similar dependence on aromatic electronic properties in water and in protic or aprotic organic solvents. The crystal structure of Boc-Trp-flp-NHMe revealed a type VIa1 b-turn conformation with a short C–H…Caromatic distance of 2.51 Å, well below the 2.9 Å sum of the van der Waals radii of H and C. The C–H bond in this close C–H/p interaction was directed toward the carbons of the aromatic ring, in contrast to the preference for the centroid of the aromatic ring observed in cation/p interactions. PDB and CSD analysis revealed similar trends, with short H…C through-space distances (as close as 2.3 Å) associated with interactions at the carbons, rather than the centroid of the aromatic ring. S–H/p interactions involving cysteine have been implicated in stabilization of protein structures, but are not well described due to a lack of structural data on thiol geometries with aromatic rings. The crystal structure of Boc-4-SH-Phe-OtBu exhibited a short intermolecular S–H/p interaction of 2.63 Å, with similar geometries as observed in C–H/p interactions. DFT calculations revealed substantial stabilizing molecular orbital overlap between the C–H or S–H s* LUMO and the aromatic p HOMO. These results suggest a primarily molecular orbital overlap (stereoelectronic) basis for the strongest C–H/p and S–H/p interactions. These results have substantial implications for the modeling of interactions with aromatic groups. In addition, serine/threonine phosphorylation and O-GlcNAcylation are central to signal transduction. We examined the structural effects of serine versus threonine phosphorylation and O-GlcNAcylation in two general peptide contexts, at Ser/Thr-Pro motifs in natively disordered peptides and within alanine-rich a-helical model peptides. Using CD and NMR spectroscopy, we found that the structural effects of threonine phosphorylation were greater than those of serine phosphorylation in all peptide contexts examined, with phosphorylation inducing polyproline helix in a proline-rich context or functioning as an inducible start or stop signal in a-helices, with phosphothreonine at the N-terminus the most helix-stabilizing residue and phosphothreonine in the interior of an a-helix inducing a random coil. Phosphorylation of the tau protein results in a significant disorder-to-order transition, with implications in mechanisms of Alzheimer’s disease and CTE.
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Tuesday, November 8, 2016
Dr. Cristiano Dias
Associate Professor, Department of Physics, NJIT
TITLE: Computational studies of amyloid fibrils and their interactions with micelles
ABSTRACT: The aggregation of proteins into amyloid-like fibrils is a ubiquitous process that arises for many amino acid sequences given the right template and conditions. While some naturally occurring fibrils contribute to the survival of living organisms, e.g., silk fibrils that are used by spiders to capture their prey, amyloid fibrils are responsible for plaque formation in tissues which is a hallmark of Alzheimer’s disease. Thus, a detailed understanding of how proteins aggregate forming fibrils has important implications in biology and medicine.
Here, I will present a toy model to show that mature fibrils exist in equilibrium with dissolved proteins in a solution. The existence of equilibrium enables thermodynamic quantities to be determined from temperature dependent studies. I will then present results from all-atom molecular dynamics simulations in which thermodynamic quantities related to the dissociation of a protein from a fibril have been computed. This study is enabling an understanding of the molecular mechanisms accounting for the stability of amyloid fibrils. Thermodynamic quantities are also being incorporated into a coarse-grained model of fibril growth. If time permits, I will discuss how this model is being used to study micelle deformation by amyloid fibrils.
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Tuesday, November 1, 2016
Dr. Yugang Sun
Chemistry Professor, Temple University
TITLE: Multifunction of Pt Nanocrystals in Photocatalytic HER
ABSTRACT: Platinum (Pt) nanocrystals are usually used in chemical reactions because of their excellent catalytic performance, for example, photocatalytic water splitting of water. In a typical design, Pt nanocrystals can accept photo-excited electrons from light absorbers such as semiconductor quantum dots (QDs) to catalyze hydrogen evolution reaction (HER) [1]. Charge transfer from QDs to Pt nanocrystals is very inefficient, and shuttle molecules (e.g., methylviologen) or other shuttle species are necessary to facilitate the charge transfer. In this presentation, a binary superparticles made of Pt nanocrystals and AgCl nanocrystals [2] will be discussed as a new class of Pt-based nanostructures that can directly accept photoexcited electrons from QDs without assistance of mediate molecules to efficiently catalyze HER with internal quantum yield of 8.6%. In addition to receiving electrons from semiconductor QDs, Pt nanocrystal can also absorb visible light to generate energetic electrons, which can inject to conduction band of a semiconductor to drive chemical reactions including HER. Depositing Pt nanocrystals on spherical SiO2 particles can significantly enhance visible absorption coefficient of the Pt nanocrystals due to the unique scattering modes near the SiO2 particles. In SiO2@Pt nanocrystals@TiO2 core-shell nanostructures, the enhancement in visible absorption enables the efficient generation of energetic electrons in photoexcited Pt nanocrystals, which can easily transfer to the TiO2 surface layer to drive HER and many other chemical reactions [3].
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Tuesday, October 25, 2016
Dr. Tony Hu
Director of the Data Mining and Bioinformatics Lab, Drexel University
TITLE: Big Data Analysis for Microbiome Data
ABSTRACT: We know little about the microbial world. Microbiome sequencing (i.e. metagenome, 16s rRNA) extracts DNA directly from a microbial environment without culturing any species. Recently, huge amount of data are generated from many micorbiome projects such as Human Microbiome Project (HMP), Metagenomics of the Human Intestinal Tract (MetaHIT), et al. Analyzing these data will help us to better understand the function and structure of microbial community of human body, earth and other environmental eco-systems. However, the huge data volume, the complexity of microbial community and the intricate data properties have created a lot of opportunities and challenges for data analysis and mining. For example, it is estimate that in the microbial eco-system of human gut, there are about 1000 kinds of bacteria with 10 billion bacteria and more than 4 million genes in more than 6000 orthologous gene family. The challenges are due to the complex properties of microbiome: large-scale, complicated, diversity, correlation, composition, hierarchy, incompleteness etc. Current microbiomes data analysis methods seldom consider these data properties and often make some assumptions such as linear, Euclidean space, metric-space, continue data type, which conflict with the true data properties. For example, some similarities are non-metric because the prevalent existence of some species; and the interactions among species and environment are complex in high order. Thus it is urgent to develop novel computational methods to overcome these assumptions and consider the microbiome data properties in the analysis procedure. In this talk, we will some computational methods to analyze and visualize microbiome big data. Our studies are focusing on the following 4 tasks: 1) novel machine learning and computational technologies for dimension reduction and visualization of microbiome data based on non-Euclidean spaces (manifold learning) to discover nonlinear intrinsic features and patterns in these data to overcome the linear assumptions, 2) new probabilistic models and non-metric visualization methods to discover signatures and components in microbiome to overcome the difficulties of analyzing compositional data, 3) novel statistical methods for variable selection in microbiome data by integrating group information among variables, 4) novel nonlinear methods for network reconstruction for microbial co-occurrence data and time series data to complicate microbial interactions.
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Tuesday, October 18, 2016
Dr. David Zimmerman
Office of Research Commercialization, Rutgers University
TITLE: SoCrates A Software and Creative Works Licensing Program at Rutgers
ABSTRACT: A growing number of schools are providing support for licensing and distribution of university-based software and copyrighted materials. In addition to traditional success stories such as Stanford and MIT, schools such as Minnesota, New Hampshire, BYU, McMaster, and others have generated millions of dollars for their faculty, departments and schools through such licensing.
The SoCrates program is a new initiative at Rutgers Office of Research Commercialization (“ORC”) that will support the licensing and commercialization of other, non-patent-based types of IP such as content (creative works) and software. In its early stages SoCrates is currently supporting the development, distribution and licensing of assets such as: apps, diagnostic questionnaires, enterprise software, videos, classroom teaching modules and as well as other assets. Departments served by the SoCrates program include: GSAPP, School of Criminal Justice, Chemistry, SHRP, Food Science, NJ Medical School, and others.
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Tuesday, October 11, 2016
Dan Russo and Nicole Revaitis
CCIB Students
RUSSO TITLE: CIIProCluster: Developing Predictive Toxicity Models Using Big Data
RUSSO ABSTRACT: Accurate predictive computational models for complex toxicity endpoints, e.g. oral acute toxicity and hepatotoxicity, have remained elusive. The difficulties in model development for these endpoints can be attributed to the complex mechanisms relevant to the toxicity phenomena. Recent work has shown that incorporating biological data into model development has resulted in better predictivity and allowed for intuition on mechanisms of action for toxicants. However, in the current big data era, finding and characterizing relevant biological data to evaluate the chemical toxicity of interest is a major challenge. The Chemical In-vitro, In-vivo Profiling portal was created to use big data sources for the prediction of new compounds (ciipro.rutgers.edu). As a major advancement of the CIIPro project, we present CIIProCluster, a new approach for creating predictive toxicity models based on the available bioassay data for chemicals of interest. Briefly, all available biological data for the target compounds are automatically extracted. Chemical features relevant to each bioassay testing are identified by using Fisher’s Exact Test (p < 0.5) to rank chemical fragments existing in the target compounds of each bioassay dataset. The available bioassays can be clustered by the chemical fragment features that contribute to their activation. In this study, PubChem bioassays were prioritized and clustered based upon the chemical-in vitro relationships described above for rat oral acute toxicity. Clusters of PubChem bioassays not only are able to predict acute toxicity for new compounds but also show toxicity mechanisms for toxicants containing the relevant chemical features. This new approach can be easily applied to generate predictive models for other animal toxicity endpoints.
REVAITIS TITLE: The TGF-? like ligand, Gurken, controls the dynamics of EGFR activation during Drosophila oogenesis.
REVAITIS ABSTRACT: The epidermal growth factor receptor (EGFR) signaling pathway is an essential regulator of tissue development across animals. During Drosophila melanogaster oogenesis, GRK is restricted around the oocyte nucleus, and activates a uniformly expressed EGFR in the overlaying follicle cells. In several other Drosophila species, GRK distribution extends towards the posterior of the egg chamber, the precursor of the mature egg. The differences in GRK distribution among species is the mechanism underlying different activation patterns of EGFR, which are monitored by the diphosphorylated ERK (dpERK) in the follicle cells. These changes are responsible for the evolution of eggshell structures, including the respiratory tubes called dorsal appendages, and the lumen-like structure called the dorsal ridge. Combining experimental work and computational modeling, we aim to understand the mechanism controlling different distributions of GRK among species. This model encompasses several parameters of the EGFR signaling pathway. The most novel of which is the dynamic positioning of the oocyte nucleus, which generates a transient signal across the overlaying follicle cells. Other parameters include the rate of GRK diffusion, the internalization of the EGFR, and the effects of pathway’s inhibitors. Using these parameters, we were able to model the distribution of GRK and EGFR activation. This model will be used to predict changes in the signaling network that can account for GRKs’ distributions.
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Tuesday, October 4, 2016
Ashley Forsythe
Career Services, Rutgers-Camden
TITLE: The Who, What, Where, When and How of Career Services.
ABSTRACT: The Career Center is here is help you with all things related to your career goals; including the job/internship search, networking, and/or assistance continuing your education. An overview of Career Center services will help you to see what is available to you and what you may utilize in your journey. Job searching techniques and resources will also be discussed to help all students, especially those graduating or currently seeking a position.
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Tuesday, September 27, 2016
Dr. Erol Akay
University of Penn
TITLE: Social inheritance in animal social networks and its consequences
ABSTRACT: The social network structure of animal populations has major implications for survival, reproductive success, sexual selection, and pathogen transmission of individuals. But as of yet, no process-based theory of social network structure exists that can explain the diversity of social networks observed in nature, and serve as a null model for detecting species and population-specific factors. In this talk, I will present a simple and generally applicable model of social network structure. In our model, network structure emerges from social inheritance, in which newborns are likely to bond with maternal contacts, and via forming bonds randomly. We compare model output to data from several species, showing that it can generate networks with properties such as those observed in real social systems. Our model demonstrates that important observed properties of social networks, including heritability of network position or assortative associations, can be understood as consequences of social inheritance. I will also present data from a long-term study on spotted hyenas in which we can quantify social inheritance directly.
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Tuesday, September 20, 2016
Wenyi Wang
CCIB Student
TITLE: Virtual Gold Nanoparticle Library: Simulation, Modeling and Experimental Validation
ABSTRACT: The use of nanomaterials in cosmetics, food and medicine has been sky-rocketing over the past decade. Experimentally designing biocompatible nanomaterials (e.g. featuring the desired bioactivities) is expensive and time-consuming. Computational modeling methods are expected to be able to predict biological activities of nanomaterials before chemical synthesis accurately. However, the current available approaches are either not applicable for nanomaterials or strongly unsatisfactory in terms of predictive performance due to the limitation of the existing modeling approaches and the complexity of nanomaterial structures. Here we designed and constructed a novel computational approach that can develop large virtual nanomaterial (i.e. nanoparticle) libraries to guide the future experimental studies. To this end, we first experimentally synthesized 34 Gold Nanoparticles (GNPs), which have different sizes and surface ligands, as the modeling set. Then we tested their hydrophilicity, HO-1 level in A549 cell line and cellular uptake against A549 cell line and HEK293 cell line. For these 34 GNPs, we virtually simulated their structures, calculated the chemical descriptors and developed Quantitative Nanostructure Activity Relationship (QNAR) models. The resulting QNAR models were firstly cross-validated by using the modeling set GNPs themselves, then used to predict and design new GNPs. Based on the prediction results, we synthesized seven new GNPs with new structures and tested them against the same four bioassays in the two cell lines. The high predictivity of the QNAR models, indicated by both cross-validation and external prediction, showed its capability for designing new GNPs. Therefore, with the virtual GNP library and experimental validation, we proved the feasibility and applicability of using computational modeling to greatly reduce the experimental cost of nanoscience. The development of virtual GNP library and the resulting QNAR approach paved the path to the new generation of nano-modeling and can be easily applied for designing biocompatible nanomaterials.
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Tuesday, September 13, 2016
Catherine Rosenberg
Coriell Institute
TITLE: Approaches to Calculating Limb Volume for Lymphedema Patients
ABSTRACT: Lymphedema is a condition of localized fluid retention and tissue swelling caused by a compromised lymphatic system. This is a condition that can sometimes be associated with post cancer treatments and has a greater possibility in occurring in patients that have been treated for breast cancer, gynecological cancers, or sarcomas. However; lymphedema can develop with other types of cancer or patients can be born with the condition. Currently, there are a few surgical interventions available to assist patients in maintaining control of their edematous limb. Therefore, it is imperative to be able to monitor each patient’s individual post-surgical progress. To monitor the overall improvement from one of the surgical interventions a patient undergoes is extremely important and be done in various ways. However, not all facilities have the equipment necessary to calculate the volume of a limb in identical ways. Therefore, we looked at some possibilities that strictly use circumferential measurements to numerically calculate the volume of a limb. These methods include using formulas associated with different geometric shapes. A mathematical model for the estimation of a true volume was generated using cubic splines for an initial comparison of volume calculations. The ultimate goal is to find the most accurate numerical formula with the least amount of error that computes the volume closely to both water displacement and the perometer, an optical-electronic device that is the new “gold-standard” for limb volume calculation. Our numerical method of choice cannot rely on a specific number of intervals since every patient could potentially have a different number of cross sectional volume segments.
2015-2016 Seminars
Tuesday, April 26, 2016
Dr. Grace Brannigan
CCIB, Rutgers-Camden
TITLE: Mechanisms of small molecule action on membrane proteins
ABSTRACT: Membrane proteins constitute about 60% of all drug targets, but the presence of a complex lipid bilayer around the native state of the protein dramatically increases the difficulty of determining structure and quantifying function, resulting in numerous obstacles to elucidating mechanisms of action of small molecules. For many membrane proteins, both structure and function have a complex dependence on membrane composition that is difficult to characterize experimentally, originates in both direct interactions with lipids as well as indirect effects via the membrane, and is neglected in most drug-discovery tools. The family of pentameric Ligand-gated Ion Channels, including the GABA(A) receptor and nicotinic acetylcholine receptor (nAChR), are especially promising central nervous system targets that are also highly sensitive to lipid composition. Here I present results from a multi-pronged computational approach that aims to fully incorporate the unique properties of lipid bilayers into our understanding of the mechanisms underlying action of molecules such as anesthetics, neurosteroids, and thyroid hormone on the GABA(A) receptor and nAChR.
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Tuesday, April 19, 2016
Dr. Laura Scheinfeldt
Coriell Institute
TITLE: Challenges in translating GWAS results to clinical care
ABSTRACT: Several factors contribute to health-related quality of life, including: healthcare quality and access, individual behavior and lifestyle choices, environment, and genetics. The way in which genetic information can contribute to health-related quality of life can be further subdivided into the research component that focuses on understanding the underlying biology and inherited component of disease, and the implementation component that applies research knowledge in a clinical setting to improve health outcomes. Clinical genetic testing for Mendelian disorders is standard of care in many cases; however, it is less clear to what extent and in which situations clinical genetic testing will improve preventive efforts, diagnosis and/or prognosis of complex disease. One of the challenges in implementing clinical genetic testing in the management of complex disease is that much of the research reported to date relies on tag single nucleotide polymorphisms (SNPs) to act as proxies for assumed underlying functional variants that are not yet known. This assumption is especially problematic when reported studies do not include representative population samples, and the results are generalized across diverse populations. Here I will use the Coriell Personalize Medicine Collaborative (CPMC) as a case study to evaluate how well reported genetic risk variants for complex disease tag surrounding variants across population samples in the CPMC and in the 1000 Genomes Phase 3 data. I will also discuss the translational implications of simulated tag SNP performance.
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Tuesday, April 12, 2016
Joseph Kawash
CCIB Student
TITLE: Comparative Genome Analysis Utilizing Machine Learning Techniques
ABSTRACT: NGS has appeared as a powerful tool to query genotypes. It is widely used for genome re-sequencing allowing one to find differences between genomes especially between tumor/normal pairs. Identifying these mutations with acute confidence is key to being able to ascertain important differences. However, changes in sequencing methods or algorithm use can lead to widely varying output. Differing variety and amplitude of noise inherently present in the NGS data (from GC biases, PCR biases, sample heterogeneity, etc.) often hinders the ability to make accurate predictions. Traditional methods of detecting genome variations can become increasingly difficult to improve if greater consideration of feature variance and interactions are not taken into account. These interactions can be difficult to predict or account for when using simple filtering methods. Several possible factors may need to be considered under unique circumstances that increase algorithm complexity and cannot be easily accounted for manually. Presently we have begun to expand and test our existing algorithm with a machine learning module that utilizes gradient tree boosting. Machine learning provides an edge in creating a comprehensive set of parameters that are transferable between samples for analysis. These interactions and data transformations could be key to reliable prediction of calls through a wide range of sequencing conditions. We believe that by using publicly available validated data combined with machine learning we can identify the requisite key parameters and subsequent feature interactions of various mutation types. This enables us to create a robust algorithm that accurately detects genomic variants across differing genome samples.
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Tuesday, April 5, 2016
Sean McQuade
CCIB Student
TITLE: Sensitivity of human Cholesterol Metabolic Networks, and Analysis of Cis-Regulatory Modules via Cell Lineage Asymmetry.
ABSTRACT: Two Projects will be discussed involving biological networks. The first is an analysis of reaction pathways in human cholesterol metabolism, based on techniques from Bernhard Palsson’s, “Systems Biology”. In the analysis, we find a stoichiometric matrix and use the kernel of this matrix to describe possible equilibria (sets of flows on the network which represent a Steady State). We then perturb a given flow to determine the sensitivity of the flow on the network. Our goal is to understand the nature of the network’s response to the perturbations. We work in collaboration with the Translational Informatics department of Sanofi, a pharmaceutical company.
The second project is a theoretical analysis of a cell lineage. We are testing an analytic method to infer spatial activity of Cis-Regulatory Modules(CRMs), based on the data provided by a specific experiment. The experiment has been done before, and we believe an understanding of the spatial distribution of CRM activity can be obtained from this high throughput, quantitative data. We combine two methods of limiting candidate cell groups that drive CRM expression. The first method is called the “Probabilistic” method, and the second is called “Rank ordered profile reconstruction”. The latter method implements a simulation of the cell Lineage of the organism to be tested. In this case the organism is a Purple Sea Urchin.
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Tuesday, March 29, 2016
Min Kyung Min
CCIB Student
TITLE: E-Flux2 and SPOT: Validated Methods for Inferring Intracellular Metabolic Flux Distributions from Transcriptomic Data
ABSTRACT:
Understanding how the activity of intracellular metabolic pathways changes in response to environmental or genetic perturbations is important to figure out how the living cell controls its metabolism and other cellular processes. Although intracellular metabolic flux (i.e. reaction rate) distributions are desirable information to this end, it is challenging to directly measure them. Several computational methods thus have been developed to predict system-wide and condition-specific intracellular metabolic fluxes by integrating transcriptomic data which is relatively easy to obtain, with genome-scale metabolic models. While powerful in many settings, existing methods have several shortcomings, and it is unclear which method has the best accuracy in general because of limited validation against experimentally measured fluxes.
We developed a general optimization strategy for inferring intracellular metabolic flux distributions from transcriptomic data coupled with genome-scale metabolic reconstructions. It consists of two different template models called DC (determined carbon source model) and AC (all possible carbon sources model) and two different new methods called E-Flux2 (E-Flux method combined with minimization of l2 norm) and SPOT (Simplified Pearson cOrrelation with Transcriptomic data), which can be chosen and combined depending on the availability of knowledge on carbon source or objective function.
The predictive accuracy of algorithm was then validated by calculating the Pearson correlation between predicted fluxes and measured fluxes. To this end, we compiled 20 experimental conditions (11 in E. coli and 9 in S. cerevisiae), of transcriptome measurements coupled with corresponding central carbon metabolism intracellular flux measurements, which is the largest dataset assembled to date. In both organisms, our method outperformed a representative sample of competing methods.
In several collaborative research, our method has been used to help better understand differences in metabolism among cells, each of which is with a phenotype of interest. Easy-to-use implementations of E-Flux2 and SPOT are available as part of the open-source package MOST (https://most.ccib.rutgers.edu/).
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Tuesday, March 22, 2016
Dr. Michael Levine
Princeton University
TITLE: Computational and quantitative analysis of animal enhancers.
ABSTRACT: Enhancers are short segments of DNA, typically 50-500 bp in length, that activate and repress gene expression in response to a variety of intrinsic and external signals. The human genome is thought to contain ~1 million enhancers, and sequence polymorphisms in these enhancers are thought to be a major source of population variation and predilection to disease. I will discuss several basic properties of enhancers, based on live-imaging studies in Drosophila and high throughput assays in the protovertebrate, Ciona. Several topics will be considered, including the regulation of transcriptional bursts, and the suboptimization of developmental enhancers mediating tissue-specific patterns of gene activity.
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Tuesday, March 8, 2016
Dr. Alicia Ebert
University of Vermont
TITLE: FGF8 signaling is required for proper vascularization of the zebrafish retina
ABSTRACT: Fibroblast growth factors (FGFs) are critical in many aspects of embryonic development and other cellular functions including apoptosis, cell adhesion, and proliferation. FGF8a, specifically, is known to work in coordination with FGF3 to initiate retinal ganglion cell (RGC) differentiation (Martinez-Morales et al., 2005). We identified FGF8a mRNA expression in the RGCs themselves a day after differentiation. Inhibition of FGF8 using the acerebellar (ace) mutant zebrafish, we show a decrease in retinal proliferation leading to smaller eyes. We also identified FGFR1b expression in the adjacent vascular tissue. Inhibiting FGFR1b with an antisense morpholino led to a phenocopy of FGF8 mutants. Loss of either the ligand (FGF8) or the receptor (FGFR1b) led to mispatterned retinal vasculature with decreases in blood flow through the eye. Our working model is that FGF8 secreted from the RGCs stimulates vascularization in the retina allowing for nutrient provisions to the developing nervous tissue. This neurovascular interaction is unique and has not been previously described in the retina.
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Tuesday, February 23, 2016
Daniel Pinolini
CCIB Student
Title: Melatonin Synthesis Pathway in Neurospora Crassa
Abstract: Melatonin is a hormone that has been shown to be secreted rhythmically and anticipates the daily onset of darkness, and its receptors have been shown to play a major role in the treatment of various sleep disorders. While the synthesis and response pathways are fairly well characterized in humans, little to no research has been done to characterize the same pathways in the model organism Neurospora crassa. Previous research has, however, revealed that melatonin is secreted in N. crassa. Preliminary data has shown that exposure to melatonin in constant conditions elicits the same effect on observable circadian rhythm in N. crassa as previously demonstrated in humans. It is hoped that through further research, this circadian effect can be confirmed, the fungal melatonin synthesis and response pathways can be characterized in N. crassa, and that the model organism Neurospora crassa can be established as a viable testing ground for new pharmaceutical compounds for the melatonin receptor related treatment of various sleep disorders.
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February 16, 2016
Dr. Kyung-Jae Won
University of Pennsylvania
Title: Finding Orders in Heterogeneity: Studying X Chromosome Inactivation using Single Cell Transcriptome
Abstract: We studied X chromosome inactivation (XCI) during the human embryonic cell (hESC) differentiation using single-cell transcriptomic data. Cells were collected in hESCs and the differentiated cells by treating ESCs with BMP4 (Diffs). As expected, the expression levels of the master regulator (Oct4, Sox2, and Nanog) in ESCs were reduced drastically in Diffs. Interestingly, the expression level of Xist, a long intergenic non-coding RNA that controls XCI, was not correlated well with these master regulators. Classifying genes based on the Xist expression levels, we found a group of genes that are potentially regulated by Xist in ESCs. More importantly, we observed that many genes including the genes known to subject to XCI were not entirely inactivated in the inactivated X chromosome. Our analysis using single cell transcriptomic data expanded our knowledge about XCI. In addition, I present a study about pancreatic cell development using single cell transcriptomic data.
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Tuesday, February 9, 2016
Dr. Brad Davidson
Swarthmore College
Title: Mitotic membrane turnover: inscribing heart cell fate during division.
Abstract: Cell fate decisions can be impacted by trafficking of signal components. Recent studies have overturned the long-held assumption that trafficking is shutdown during mitosis. Thus, mitotic trafficking of signaling components may play a profound, largely unrecognized role in embryonic and stem cell fate. We have gained significant insights regarding the interplay between division and signaling by examining embryonic development in the basal chordate, Ciona intestinalis. The extreme cellular simplicity of Ciona embryos facilitates high-resolution, in vivo analysis of inductive signaling mechanisms. In particular, we focus on Fibroblast Growth Factor (FGF) dependent induction of the heart progenitor lineage. We have found that mitotic redistribution of FGF receptors (FGFRs) promotes differential heart progenitor induction. We are currently investigating the role of mitotic kinases in FGF receptor localization.
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Tuesday, February 2, 2016
Catherine Guay
CCIB Student
TITLE: Functional adaptation of the maternal skeleton to reproduction and lactation
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Tuesday, January 26, 2016
Dr. Xaiowei Sherry Liu
University of Pennsylvania
TITLE: Functional adaptation of the maternal skeleton to reproduction and lactation
ABSTRACT: The maternal skeleton serves as an important source of calcium for fetal/infant bone growth and undergoes substantial changes during pregnancy and lactation. Despite a substantial increase in bone formation following weaning, bone mass is only partially restored. However, most epidemiology studies report that the number of pregnancies and the duration of lactation have either no long-term adverse effect, or have a positive effect on fracture risk later in life. This represents a paradox that reproduction and lactation reduce bone mass without increasing future risk of fractures. To uncover the mechanisms behind this paradox, we compared the mechanical integrity between the highly load-bearing bone regions and other sites post-lactation, and demonstrated reduced bone loss and more efficient recovery in the load bearing vs. non-load bearing region. Furthermore, although post-lactation rats have lower bone mass than virgin rats, they have a distinct bone structural phenotype and a much slower rate of bone loss when exposed to estrogen deficiency by ovariectomy (OVX). These discoveries suggested that skeletal changes that occur during reproduction and lactation represent a physiological adaptation of bone structure, which minimizes the impact of reproduction on the skeleton’s mechanical function and prepares the skeleton to be more resistant to estrogen deficiency induced bone loss later in life. These amazing adaptive mechanisms in bone may help to protect the maternal skeleton from future menopausal bone loss.
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Tuesday, December 8, 2015
Spyros Karaiskos
CCIB Student
TITLE: Transfer RNA-derived fragments in Drosophila and their potential targets
ABSTRACT: We present rather unusual suspects in the world of gene regulation–transfer RNA fragments (tRFs). Such fragments have previously escaped detection or have been generally ignored as noise due to low count numbers in of small RNA libraries. While the focus in the analysis of such libraries has been primarily on microRNAs (miRNAs), recent studies have reported findings of fragments of transfer RNAs (tRFs) across a range of organisms. We had originally investigated the age-related dynamics of miRNA loading into different RNA-induced silencing complexes (RISC) of Drosophila melanogaster using small RNA-Seq. Here we describe tRFs found in the same transcriptome libraries, and focus on their structural and functional features that make these fragments similar to miRNAs. Similar to miRNAs tRFs have distinct isoforms with precise ends preferentially originating from 5′ or 3′ end of a precursor molecule (tRNA). Analogously to the seed sequences in miRNAs, we observe that tRF ends possess short 7-mer sequences matching conserved regions across 12 Drosophila genomes, preferentially in 3′ UTRs but also in introns and exons. Like miRNAs, tRFs display specific isoform loading into Ago1 and Ago2 and thus likely function in RISC complexes. And finally, as is the case with miRNAs, we observe the levels of tRF loading into Ago1 and Ago2 to differ considerably and both tRF expression and loading appear to be age-dependent, indicating potential regulatory changes from young to adult organisms. We found that Drosophila tRF reads mapped to tRNA genes for all 20 amino acids, while previous studies have usually reported fragments from only a few tRNAs. Moreover, we detected fragments of both nuclear and mitochondrial tRNAs, while only the former have been described. Following the similarities with miRNAs and based on complementarity with conserved Drosophila genome regions we described seed sequence found in the most abundant tRFs. Further, we identified their possible targets with matches in the Drosophila melanogaster 3’UTR regions. Strikingly, these potential target genes of the most abundant tRFs show significant Gene Ontology enrichment in development and neuronal function. This observation suggests that involvement of tRFs in the RNA interfering pathway may play a role in brain activity or brain changes with age.
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Tuesday, December 1, 2015
Dr. Robert Best
National Institutes of Science
TITLE: How well do atomistic simulations of folding compare to theoretical expectations, and to experiments?
ABSTRACT: It has recently become possible to perform all-atom molecular simulations, in explicit solvent, of proteins folding and unfolding at equilibrium. These unbiased trajectories provide a ‘movie’ of protein folding which can be used to test directly the assumptions made in developing theoretical models for folding. I will discuss two such assumptions, firstly that only native contacts are important in determining folding mechanism, and secondly that folding can be modeled as diffusion on a low-dimensional free energy surface. We can also ask how well the results of the simulations agree with experimental data. To this end, we have developed a method for obtaining protein folding phi-values from simulation, allowing us to test the observed mechanism against experiment. We find reasonable agreement in many cases, but a surprising heterogeneity in folding mechanism is hidden by the ensemble-average phi-values.
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Tuesday, November 24, 2015
Dr. Tianhai Tian
Monash University
TITLE: Simplified model for molecular process of multi-step reactions
ABSTRACT: Many molecular processes include a number of detailed chemical reactions. Example of this type process include gene transcription, translation and molecule degradation. Currently these multi-step reactions are modelled by a single step reaction or chemical reaction with time delay. However, numerical results suggest that these models cannot describe multi-step process accurately. In this work we will discuss two new methods for modelling multi-step reactions.
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Tuesday, November 17, 2015
Dr. Marc Halfon
University of Buffalo
TITLE: Regulatory Genomics of Model and Non-Model Organisms
ABSTRACT: Regulation of gene expression is essential for proper metazoan development and homeostasis, yet our knowledge of regulatory DNA sequences—transcriptional cis-regulatory modules (CRMs)—is strikingly limited. This is particularly true for non-model organisms, in which the reagents and resources necessary for large-scale CRM discovery are often lacking. We have developed the SCRMshaw algorithm, which leverages extensive available knowledge of Drosophila regulatory sequences to enable CRM discovery in both closely and distantly related insects. SCRMshaw has great potential for studying the regulatory genomics of both model and non-model organisms, including enabling rapid annotation of the regulatory genomes of newly-sequenced insects, distinguishing between divergent and convergent regulatory sequence evolution, and understanding the mechanisms underlying the evolution and neofunctionalization of developmental regulatory networks.
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Tuesday, November 10, 2015
Yinghui Zhong
Drexel University
TITLE: Metal ion binding-mediated drug delivery mechanism
ABSTRACT: We developed a novel drug delivery mechanism: metal ion binding-mediated interaction for sustained release of hydrophilic small molecule drugs with high metal ion binding affinity. Minocycline Hydrochloride (MH) is a tetracycline derivative antibiotic with anti-inflammatory, anti-oxidative, and anti-apoptotic properties. Moreover, it has demonstrated great therapeutic potential in treating debilitating spinal cord injury and neurodegenerative diseases. MH can chelate divalent metal ions such as Ca2+ and Mg2+ ions which can enhance its stability in aqueous environment without affecting its biological activities. Dextran sulfate (DS) is a biodegradable polysaccharide that also has a high binding affinity for Ca2+ and Mg2+ ions. We found that the metal ions can be used as the linkers to attach MH to DS molecules. Utilizing this property, we have developed two drug delivery systems: layer-by-layer (LbL) thin film coatings and self-assembled insoluble complex for sustained release of MH. We will discuss the potential mechanisms that mediate the interactions between DS, MH and metal ions, which lead to the complex formation between a charged polymer and a neutral small drug molecule. We will also discuss the application of these complexes in spinal cord repair.
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Tuesday, November 3, 2015
Sweta Sharma
CCIB Student
TITLE: Empirical Evidence Supporting a Systematic Approach to Gene Network Identification
ABSTRACT: A major cellular systems biology challenge of the past decade has been the development of a comprehensive model for gene regulatory networks (GRNs). Particularly, there is growing impetus for the extraction of regulatory information from expression data as it becomes increasingly available and accurate. Identifying networks from such information requires deciphering direct interactions from indirect ones. For instance, if gene A regulates gene B and B regulates gene C, then changing A’s expression will directly affect B’s expression while indirectly affecting C’s.
Recently, Birget et al proposed a systematic approach for network identification. They consider a binary model that captures the non-linear dependencies of GRNs and reverse-engineer the network using assignments (perturbations to the expression level of a single gene) and whole transcriptome steady-state expression measurements. Under this model, their approach achieves identification of acyclic networks with worst-case complexity costs in terms of assignments and measurements that scale quadratically with the size of the network. For networks with cycles, the worst-case complexity cost scales cubically.
We conduct a proof-of-concept experiment for this approach by reverse-engineering a five-gene sub-network of the outer-membrane protein regulator (ompR) in E. coli. Through assignments achieved by gene deletions and expression measurements from qPCR, we successfully identify the regulatory relationships and discern direct from indirect interactions. We also performed computational experiments on in silico networks derived from known regulatory relationships in E. coli and S. cerevisiae, where gene regulation is thermodynamically modeled using the system of ODEs that was used to generate data for previous DREAM challenges. We achieve 100% identification for noiseless acyclic networks of size ranging from 100 to 4,000 genes. For noisy acyclic E. coli networks of size 100, we obtain an AUPR of .95. This is significantly improved from the .71 AUPR obtained by the top performer in the DREAM3 inference challenge for acyclic in silico networks. Furthermore, we achieve this using ten-fold fewer assignments and measurements. For noisy cyclic E. coli networks of size 100, we obtain an AUPR of .75, compared to .45 for the top performer in the DREAM4 InSilico_Size100 sub-challenge containing cyclic networks. We achieve this using roughly the same number of assignments and half as many measurements.
Taken together, our results imply that the reverse engineering method of Birget et al is not only experimentally feasible but uses reasonable resources. It can therefore serve as the basis for systematic, accurate reverse engineering of large-scale gene regulatory networks.
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Tuesday, October 20, 2015
Dr. Jia Song
University of Delaware
TITLE: microRNA cross-regulation of signaling pathways
ABSTRACT: In early development, cell specification and pattern formation are controlled by cross-regulation of gene regulatory networks (GRN) and signaling pathways. Signaling morphogen gradients are critical regulators that need to be tightly controlled in order to ensure that the precise organization of the embryo is achieved. This research addresses the overarching hypothesis that microRNAs (miRNAs) perform this critical regulatory function. The miRNAs are small non-coding RNAs that repress the translation and reduce the stability of target mRNAs in animal cells. microRNA-31 (miR-31) has been found to play a role in cancer, bone formation, and lymphatic development. However, the function of miR-31 in embryogenesis is not known. We examined the regulatory role of miR-31 in early development of the sea urchin. We found that miR-31 is expressed in all stages of development and its knockdown (KD) results in decrease in embryo size, formation of extra cells and cell clumps in the blastocoelar space of the embryo and disrupts the patterning and function of the primary mesenchyme cells (PMCs), which form the embryonic skeleton spicules. Using bioinformatics approach and luciferase reporter constructs, we identified miR-31 to repress directly Pmar1, Alx1, Snail and VegfR7 within the PMC GRN. Further, blocking the miR-31-mediated repression of Alx1 and/or VegfR7 genes in the developing embryo resulted in defects in PMC patterning and skeletogenesis. The majority of the mislocalized PMCs in miR-31 KD embryos did not express VegfR10, indicating that miR-31 regulated VegfRs within the PMCs. In addition, miR-31 indirectly suppresses Vegf3 expression in the ectoderm. These results indicate that miR-31 coordinately suppresses genes within the PMCs and in the ectoderm to impact PMC patterning and skeletogenesis.
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Tuesday, October 13, 2015
Dr. Sean Ekins
Collaborative Drug Discovery
TITLE: Open Science for Rare and Neglected Diseases
ABSTRACT: As a computational scientist I have been fortunate to work with many collaborators to generate biological data to train and test my models. We are in a position with increasing amounts of data becoming accessible in the public domain to generate models and predictions relevant to finding molecules for many diseases. For example databases like ChEMBL and PubChem become a potential goldmine when combined with computational algorithms that can be applied quickly to identify new molecules to test. Over the past 8 years I have made extensive use of public datasets for screening against M. tuberculosis, T. Cruzi, responsible for tuberculosis and Chagas disease, respectively. This work has lead to collaborators testing a small number of compounds and finding novel actives. This approach is more broadly applicable to other diseases. The subsequent publication of the models and molecules into the public domain has also been addressed by developing tools on the desktop which can enable the sharing of machine learning models, and use on mobile devices. Many of the lessons learned have also been applied to work on rare diseases. For example, utilizing some of the high throughput screening data generated to identify inhibitors of the Ebola virus has been used to create machine learning models to find new molecules not previously tested. For many rare diseases there are huge data gaps and other approaches need to be taken. In spite of the many incentives offered it is likely most of the rare diseases do not present big commercial opportunities and hence there is definitely a place for more openness and collaboration to bring ideas from the lab to the clinic quickly.
Learning objectives: 1. Demonstrate how open data can be used to create models, identify new leads and how this is increasingly accessible to anyone 2. Describe experiences working on rare and neglected diseases and what could be done to increase success in bringing ideas to the clinic.
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Tuesday, October 6, 2015
Wenyi Wang
CCIB, Rutgers-Camden
TITLE: Quantitative Nanostructure Toxicity Relationship
ABSTRACT: The toxicity of nanomaterials has become a major concern due to its increased usage in a variety of applications (e.g., electronics, cosmetics, and medicine). Nanoparticles have different sizes, shapes, chemical compositions and surface modifications, making it difficult to simulate the toxicity as opposed to small organic compounds. Furthermore, there is a lack of comprehensive data and standardized toxicity endpoints on nanoparticles necessary for modeling the toxicity of compounds. I gathered and curated several data points related to nanotoxicity, along with the crucial parameters mentioned above. Using the datasets, I aim at designing Quantitative Nanostructure Toxicity Relationship (QNTR) approaches to develop validated and predictive models. Specifically, I will develop novel approaches to calculate the surface properties, as well as overall chemical properties of nanoparticles, integrate the various parameters with the conventional Quantitative Structure-Activity Relationship (QSAR) modeling process and publish the predictors. The novel approaches will be applicable to various nanomaterials and endpoints, while the established models and predictors will be used to prioritize potentially toxic nanomaterials.
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Tuesday, September 29, 2015
Daniel Russo
CCIB, Rutgers-Camden
TITLE: CIIPro: An online cheminformatics portal for large scale chemical data analysis
ABSTRACT: The massive amount of chemical data that currently exists in this big data era is difficult to extract and hard to rely on. To this end, we developed a public Chemical In vitro In vivo Profiling (CIIPro) portal that can automatically extract biological data from public resources (i.e., PubChem) for compounds based on user input. Unlike querying a typical chemical database, a novel algorithm in the portal allows users to query compounds with a target activity (e.g., specific animal toxicity testing results), extracts biological data based on the in vitro-in vivo correlation, and outputs the data in a format conducive to research. The resulting biological data for target compounds can be used for modeling purposes. For example, the CIIPro portal can identify the chemical and biological similarity between compounds based on their chemical structure and optimized biological profile. This portal was used to develop multiple novel predictive models for complex biological activities (e.g., complex animal toxicity endpoints). The CIIPro portal is free and accessible through the internet at ciipro.rutgers.edu.
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Tuesday, September 22, 2015
Nicole Pope
University of Pennsylvania
TITLE: The TGF-? like ligand, Gurken, shapes the formation of dorsal eggshell structures in Drosophila species
ABSTRACT: The epidermal growth factor receptor (EGFR) signaling pathway is an essential regulator of tissue development across animals. In Drosophila melanogaster, the EGFR ligand, Gurken (GRK), is a central regulator of dorsal ventral and anterior posterior axes formation. During Drosophila oogenesis, gurken is restricted around the oocyte nucleus. The nucleus positions are dynamic, initially at the posterior end, and later it moves to a dorsal position. Consequently, the combination of nuclear movement and ligand secretion, generates a transient signal in the overlaying follicle cells through a uniformly expressed receptor. The follicle cells form the 3D eggshell structures, including the respiratory filaments, the dorsal appendages, and the lumen-like dorsal ridge. These structures reflect the numerous signaling pathways’ activities, including EGFR. Interestingly, the pattern of EGFR activation is different among Drosophila species. These patterns are consistent with GRK’s distribution and are consistent with the different positions and shapes of eggshell morphologies. The sequence of GRK is highly evolved among species. We hypothesize that changes in the GRK protein enable different persistence of the ligand, which control the length of the transient signal, and consequently the formation of the corresponding morphologies. Using experimental and computational tools, we are studying the mechanisms underlying GRK distributions among species.
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Tuesday, September 15, 2015
Dr. Feng Gai
CCIB, Rutgers-Camden
TITLE: Probing Protein Hydrogen Bonding Dynamics via 2D IR Spectroscopy
ABSTRACT: Hydrogen bonds (HBs) play a critical role in determining the structure, function and folding dynamics of proteins. In this talk, we will discuss how linear and nonlinear infrared spectroscopic methods, in conjunction with unnatural amino acid-based infrared probes, can be used to study the dynamics of such HBs in a site-specific manner.
2014-2015 Seminars
Tuesday, April 28, 2015
Robert Marmion
Rutgers University-Camden
TITLE: Wishful thinking is regulated by a P-MAD binding site that varies among Drosophila species
ABSTRACT: The Drosophila eggshell is an established model to study cell signaling, tissue patterning, and morphogenesis. The bone morphogenetic protein (BMP) signaling pathway is a crucial regulator of tissue growth during multiple stages of Drosophila development. We found the type II receptor, wishful thinking (wit), to be dynamically and non-uniformly expressed in the follicle cells, which are a mono-layer of epithelial cells engulfing the developing oocyte. wit is transcriptionally regulated by BMP signaling and necessary for BMP signal transduction within the follicle cells. We studied gene regulation by BMP via studying the cis-regulatory module (CRM) responsible for wit expression. Here we describe a binding site for P-MAD, the transcription factor of the BMP pathway, which differs from the literature description. We utilized D. virilis, a species separated by 45 million years of evolution from D. melanogaster. Interestingly, wit patterning differs both in width and dynamics. We recapitulated this pattern in D. melanogaster by expression driven by the homologous CRM. We suggest that spacing between MAD & MED binding sites underlies the patterning differences.
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Tuesday, April 21, 2015
Dr. Lan Yang
Washington University
Title: Whispering-Gallery Microresonators and Microlasers for nanoscale sensing and beyond
Abstract: Optical sensors based on Whispering-Gallery-Mode (WGM) resonators have emerged as front-runners for label-free, ultra-sensitive detection of nanoscale materials and structures due to their superior capability to significantly enhance the interactions of light with the sensing targets. A WGM resonator traps light in circular orbits in a way similar to a whisper, i.e., a sound wave, traveling along a circular wall, an effect found in the whispering gallery of St. Paul’s Cathedral in London. The basis for resonator sensors is that the physical associations and interactions of nanomaterials on the surface of a high-Q optical WGM resonator alter the trajectory and lifetime of photons in a way that can be measured and quantified. I will first present a laser-assisted processing method to create Si-chip based optical microresonators with Q-factors in excess of 100 million. Sol-gel process will be introduced as a convenient and efficient method to incorporate optical gain dopants into the oxide layer deposited on a silicon wafer, providing a route to achieve arrays of microlasers on silicon wafer with emission spectral windows from visible to infrared. I will then present a recent discovery of using ultra-high-Q microresonators and microlasers for ultra-sensitive self-referencing detection and sizing of single virion, dielectric and metallic nanoparticles. I will also discuss using optical gains in a microlaser to improve the detection limit beyond the reach of a passive microresonator. These recent advancements in WGM microresonators will enable a new class of ultra-sensitive and low-power sensors for investigating the properties and kinetic behaviors of nanomaterials, nanostructures, and nanoscale phenomena. In the end, I will discuss exploration of fundamental physics, such as parity-time symmetry and light-matter interactions around exceptional point (EP) in high-quality WGM resonators, which can be used to achieve a new generation of optical systems enabling unconventional control of light flow.
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Tuesday, April 7, 2015
Marlene Kim
Rutgers University-Camden
TITLE: Mechanism Profiling of Hepatotoxicity Caused by Oxidative Stress Using the Antioxidant Response Element Reporter Gene Assay Models and Big Data Sources
ABSTRACT: Hepatotoxicity accounts for a substantial number of drugs withdrawn from the market. Traditional animal models used to detect hepatotoxicity are expensive and time consuming. Alternative in vitro methods, especially cell-based high-throughput screening (HTS) studies, have provided the research community with rich toxicity data. Among the various assays used to screen potential toxicants, the Antioxidant Response Element beta lactamase reporter gene assay (ARE-bla), which identifies chemicals that have the potential to induce oxidative stress, has been used to test over 10,000 compounds from the Tox21 program. The ARE-bla computational model and HTS data from big data sources (e.g., PubChem) were used to profile environmental and pharmaceutical compounds with hepatotoxic data. Quantitative Structure-Activity Relationship models were developed based on ARE-bla data. The models predicted the potential oxidative stress response for known liver toxicants when there was no ARE-bla data available. Liver toxicants were used as probe compounds to search PubChem Bioassay and generate a response profile, which contained thousands of bioassays (>10 million data points). By ranking the in vitro-in vivo correlations (IIC), the most relevant bioassay(s) related to hepatotoxicity were identified. The liver toxicants profile contained the ARE-bla and relevant PubChem assays. Toxicity pathway maps for well-known toxicants were created by identifying chemical features that existed only in compounds with high IIC. The chemical IIC profiling created an opportunity to fully explore the source-to-outcome continuum of modern experimental toxicology using cheminformatics approaches and big data sources.
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Tuesday, March 31, 2015
Nastassia Pouradier Duteil
Rutgers University-Camden
TITLE: Modeling Gurken and dpERK distributions in Drosophila melanogaster oogenesis.
ABSTRACT: Tissue patterning and cell fate determination are guided by a multitude of cell signaling pathways working independently and in cooperation. At this level of complexity, computational simulations can serve as an efficient tool to narrow down potential mechanisms. We study tissue patterning in Drosophila, which is a commonly used model organism for cell signaling, tissue patterning, and morphogenesis. More specifically, we aim to understand the mechanisms leading to the formation of a particular eggshell structure, the dorsal ridge. The dorsal ridge is present with various lengths in the species D. nebulosa and willistoni, and is absent in D. melanogaster. It has been shown that the distribution of the TGF-alpha like ligand Gurken in the egg chamber, the precursor to the mature eggshell, is not only consistent with the patterns of the eggshell structure, it is also responsible to mediate dorsal ridge formation. We propose a mathematical model to explain the evolution of the Gurken distribution during oogenesis in D. melanogaster. Gurken is secreted from near the oocyte nucleus into the perivitalline space surrounding the oocyte. It then binds to the EGF receptors in the overlying follicle cells, internalizes, and initiate a signaling cascade of phosphorilations monitored by dpERK. These processes occur over the course of ~27h, during which the Drosophila egg chamber undergoes morphological changes, including the relative movement of the oocyte nucleus with respect to the follicle cells, and the considerable growth and elongation of the oocyte. Previously, the diffusion of Gurken was modeled to determine its impact on follicle cells’ patterning, however, the dynamics of oocyte nucleus location was not considered. Our contribution is to consider the oocyte nucleus as a moving source of Gurken. We are also developing a mathematical framework that will allow us to account for the growth of the oocyte.
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Tuesday, March 24, 2015
Sruthi Murlidaran
Rutgers University-Camden
TITLE: Effects of genetic variance and general anesthetics on ligand gated ion channels.
ABSTRACT: GABA(A) receptors are critical for proper transmission of inhibitory signals in the central nervous system, and are common targets of anesthetics, neurosteroids and thyroid hormone. Several naturally occurring mutations in such receptors can cause an increased likelihood of seizures.
Molecular Dynamics simulations with atomic resolution were conducted on a GABA(A) receptor model based on the GluCl template, including a single nucleotide polymorphism (SNP) K289M associated with increased likelihood of febrile seizures. Receptors with the K289M SNP had statistically significant differences in numerous conformational variables, including pore radius. From these results we propose a molecular mechanism for rapid but unstable closure of GABA(A) receptors, which would be likely to cause the flickering effect reported in single channel recordings. Mechanisms underlying modulation were also investigated using Molecular Dynamics simulations. Photoaffinity labeling and mutagenesis studies have suggested numerous potential binding sites for modulators of GABA(A) receptors such as general anesthetics and neurosteroids, but relative affinities of the modulators for the individual sites are challenging to characterize experimentally. Molecular dynamics simulations and the alchemical free energy perturbation technique were used to calculate the isolated affinities of general anesthetics such as propofol and sevoflurane, for the intersubunit sites in the transmembrane domain.
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Tuesday, March 10, 2015
Dr. Curt Breneman
Dean, School of Science
Rensselaer Polytechnic Institute
TITLE: “A Fast Tour of Predictive Cheminformatics: From Drug Discovery to Materials Informatics.”
ABSTRACT: Given the data rich environment present in many fields of study, it has become imperative to develop methods and “Best Practice” workflows that can extract information from these data repositories that enable “Data to Action” design strategies. Some of the first major efforts in this area took place in the domain of drug discovery, where many mistakes were made and lessons were learned that now serve us well within that original field as well as in bioinformatics and materials informatics applications. This presentation will emphasize the principles involved in developing and validating data-driven models that incorporate “just enough physics” to have meaningful domains of applicability. Examples and anecdotes will be plentiful…
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Tuesday, March 3, 2015
Mr. Vincent Smeraglia
Executive Director, Strategic Alliances
Office of Translational Sciences
TITLE: What is Translational Sciences?
ABSTRACT: Rutgers Translational Sciences (RTS) is designed to assist biomedical research faculty create and build interdisciplinary collaborations. RTS connects intellectual property, marketing and contracts capabilities to build strategic alliances with the private sector. By providing access to facilities and expertise in molecular design and synthesis, nonclinical imaging, histopathology and toxicology, drug discovery screening, structural biology and related technologies, RTS is committed to meeting the needs of university and industry collaborators.
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February 24, 2015
Sweta Sharma
Rutgers University–Camden
TITLE: Empirical Evidence Supporting a Theoretical Approach to Gene Network Identification
ABSTRACT: An important challenge in systems biology is the reverse engineering of biological networks. For gene regulatory networks, expression profile data for networks under various conditions are often used to identify regulatory interactions. Predictions from the best-performing existing network inference methods are plagued with low accuracy, often due to an inability to discern direct from indirect interactions. Recently, a theoretical model has been proposed to ascertain whether expression data is sufficient for identifying the network and, if so, how many single-gene perturbations (assignments) and whole-genome expression profile measurements are necessary. They show that for an n gene network, identification can be achieved with quadratic complexity in terms of these assignments and measurements.
Using a five-gene sub-network of the E. coli K-12 strain, we show in vivo evidence of the models effectiveness in discerning direct from indirect interactions. We also perform computational experiments on dynamical in silico E.coli and S. cerevisiae sub-networks generated by the same method as those used for previous DREAM challenges. Networks were probed by making assignments, specifically modifying the expression value of a gene to a fixed value, and then measuring the corresponding steady-state expression profile. Assuming no molecular or experimental noise, we achieve 100% identification for networks of size ranging up to 4,000 genes. We further assess performance with varying model assumptions such as additive Gaussian noise in the expression data and the presence of cycles in the network. For a 100-gene network with noise, we achieve an AUPR of .95 using ten-fold fewer assignments and measurements as compared to the DREAM3 top performer (AUPR .75).
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February 17, 2015
Dr. Luis Cruz Cruz
Associate Professor, Physics Department
Drexel University
TITLE: Possible Connection between Neuronal Locations and Network Firing Patterns: A Computational Approach
ABSTRACT: In normal aging, our brains suffer from progressive loss of function expressed as a gradual deterioration in memory, learning, and concentration. This is an important scientific and social problem as an increasingly larger percentage of our population ages. The causes of this cognitive decline remain unclear and cannot be assigned to any particular mechanism, unlike neurodegenerative disorders such as Alzheimer’s disease where the cause of progressive cognitive impairments is the massive and progressive loss of cortical neurons. However, normal aging causes subtle alterations in positional correlations between neurons in some brain areas, specifically within microcolumns: arrays of interconnected neurons which may constitute fundamental computational units in the brain. This age-related loss of correlations is hypothesized to be a result of subtle deteriorating processes that change the underlying organization of the neuronal architecture and that may affect network firing dynamics. Using a dynamical model applied to virtual 3D representations of neuronal arrangements, micron-sized random displacements in neuronal positions are shown to account for the experimentally measured loss of
organization in aged brains and to correlate with age-related cognitive decline. In addition, by using an empirically-based computational framework, a connection between neuronal positions, topology of connectivity, and network firing dynamics will be presented.
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February 10, 2015
Harish Swaminathan
Rutgers University-Camden
TITLE: Computational methods for the interpretation of forensic DNA samples
ABSTRACT: Repetitive sequences in the human genome called Short Tandem Repeats (STRs) are used in human identification for forensic purposes. An STR profile developed from a biological sample collected at a crime scene is compared with that of a person of interest or run against a database to check for a match. Interpretation of STR profiles is problematic because of dropout, allele overlap and PCR amplification artifacts like stutter. The goal of this research is to develop computational methods and tools for analysis of STR profiles that are robust to these phenomena and that utilize quantitative peak height information captured in profiles. These methods are expected to improve significantly on existing methods for analysis of STR profiles, particularly in cases of low amounts of template DNA or where there are many contributors. The first aim of this research is to characterize the signal, noise and stutter peak heights and study their dependence on template DNA amount. Our second aim is to develop a method to identify the number of contributors (NOC) to a DNA sample. We developed a computational method called NOCIt that calculates the a posteriori probability (APP) on the number of contributors to a forensic sample. NOCIt takes into account signal peak heights, population allele frequencies, allele dropout and stutter. Among the samples tested, NOCIt had a higher accuracy than two of the existing methods to determine the NOC. The ultimate objective of mixture interpretation is to determine whether a person of interest contributed to the sample. Though methods have been developed to tackle this problem by deconvolving mixtures, they are generally not suitable for complex mixtures that contain significant amounts of dropout, stutter and allele sharing. Our third aim is to develop a computational tool (MatchIt) to directly calculate the LR for a person of interest and to compute a p value for the LR.
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Tuesday, February 3rd
Dr. Joel Freundlich
Rutgers University–Newark
TITLE: A Novel Intersection of Computation, Chemistry, and Biology in the Study of Tuberculosis
Tuberculosis, due to Mycobacterium tuberculosis infection, represents a global health pandemic. New drugs are needed to combat resistance to existing therapeutics, mycobacterial persistence, and latent infection. In particular, novel therapeutic targets must be validated. We propose an innovative combination of cheminformatics, chemistry, and biology to find novel small molecule antituberculars, understand their effects on M. tuberculosis from both the pathway and biological target perspectives, and demonstrate their potential to seed new drug discovery strategies.
His research interests are: The design and synthesis of chemical tools and their use in conjunction with biological methods to study infectious diseases (currently tuberculosis and malaria).
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CANCELLED: Due to Inclement Weather
Tuesday, January 27th
Robert Best
National Institute for Health
TITLE: How well do all-atom folding simulations conform to the “funnel” picture of protein folding?
Cell fate determination is dependent upon the reliable integration of extracellular signaling cues into appropriate transcriptional outputs. When starved for nutrition, Saccharomyces cerevisiae can enter one of two differentiation pathways, meiosis or filamentous growth. Post-translational histone modifications are central to regulating the complex transcriptional programs that underlie cell fate decisions. Histones can be modified in a number of ways including acetylation, phosphorylation, ubiquitination, and methylation; dynamic and reversible combinations of these modifications allow communication between chromatin and the RNA pol II holoenzyme complex. While histone H3 Lys4 methylation is the best studied methyl mark, questions remain regarding how its regulation impacts cell fates. In this talk, I will describe our genetic, molecular, biochemical and genomic results that detail how the CDK subcomplex of the RNA pol II holoenzyme interacts with the histone H3 Lys4 methylation regulators SET1 and JHD2 to guide cell fate decisions in yeast.
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Tuesday, January 20th
Dr. David Liberles
Department of Biology and Center for Computational Genetics and Genomics
Temple University
TITLE: Lineage-specific processes of genome diversification
Computational genomics is now generating very large volumes of data that have the potential to be used to address important questions in both basic biology and biomedicine. Addressing important biological questions becomes possible when mechanistic models rooted in biochemistry and evolutionary/population genetic processes are developed. Examples are described on problems involving the inference of duplicate gene retention mechanisms to apply in a gene tree/species tree reconciliation setting and towards understanding the role of gene duplication and deletion in cancer progression. Additionally, two classes of models for characterizing amino acid transitions during protein evolution are presented, one rooted in population genetics and the other rooted in protein physical chemistry. Ultimately, the development of such models adds to our tool box to enable inference of lineage-specific processes in comparative genomics.
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Tuesday, November 18th
Catherine Guay
Doctoral Student, CCIB, Rutgers University–Camden
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Tuesday, November 11th
Michael Law, PhD
Dept of Molecular Biology, School of Osteopathic Medicine
Rowan University
TITLE: Tipping the Balance: How CDK and histone methylation guide cell fate decisions
Cell fate determination is dependent upon the reliable integration of extracellular signaling cues into appropriate transcriptional outputs. When starved for nutrition, Saccharomyces cerevisiae can enter one of two differentiation pathways, meiosis or filamentous growth. Post-translational histone modifications are central to regulating the complex transcriptional programs that underlie cell fate decisions. Histones can be modified in a number of ways including acetylation, phosphorylation, ubiquitination, and methylation; dynamic and reversible combinations of these modifications allow communication between chromatin and the RNA pol II holoenzyme complex. While histone H3 Lys4 methylation is the best studied methyl mark, questions remain regarding how its regulation impacts cell fates. In this talk, I will describe our genetic, molecular, biochemical and genomic results that detail how the CDK subcomplex of the RNA pol II holoenzyme interacts with the histone H3 Lys4 methylation regulators SET1 and JHD2 to guide cell fate decisions in yeast.
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Tuesday, October 28th
Dr Jinglin Fu
Assistant Professor, Rutgers–Camden
TITLE: BioInspired Molecular Complexes on DNA nanoscaffolds
Living systems have evolved complex macromolecular nanostructure networks to mediate a range of cellular activities with high efficiency and specificity, including metabolic pathways, signaling transduction, gene expression and regulation. Many of these macromolecular systems are spatially organized with precisely controlled position and orientation of biomolecule components, which facilitate the functionality. Such examples include the substrate channeling in the multienzyme cascades and the light harvesting systems in photosynthesis. If these systems can be mimicked and constructed artificially, they could be applied to the realization of smart nanobioreactors and devices that have utility in the production of high-value chemicals, the conversion of a variety of energy forms, and the development of new bio-diagnostics. These studies will also increase our fundamental knowledge of biomolecule selfassembly and cellular metabolism in general. Recently, DNA has emerged as promising molecular scaffolds to organize macromolecule structures on the nanoscale with controlled geometries and nanomechanical capabilities. In this talk, I will present a DNA scaffold-directed strategy to assemble artificial biomolecular complex systems with precisely controlled spatial arrangements, and mimic biological functions for applications in catalysis, bioenergy and biomedicine.
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Tuesday, October 21st
Rob Marmian
CCIB, Rutgers–Camden
TITLE: Space and time at the molecular level: Lessons from Wishful thinking
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Tuesday, October 14th
Dr. Sean Smith
Post-Doctoral Researcher, CCIB, Rutgers – Camden
TITLE: Structural Variant Detection Using Whole Genome Sequencing
Structural variants are variations in chromosome structure that include deletions, insertions, duplications, inversions, and translocations. Structural variants have been linked to several diseases including cancer. This talk will describe how structural variants are detected using whole genome sequencing data and describe current work developing the structural variant detection algorithms GROM and GROM-RD.
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Tuesday, October 7th
Dr. Sudha Moorthy
Scientist at PMC Advanced Technologies
TITLE: Vibrio cholerae: Lifestyle choices seen through the “genomics” lens
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Tuesday, September 30th
Dr Laura Scheinfeldt
Research Scientist, Coriell Institute for Medical Research
TITLE: Using the Coriell Personalized Medicine Collaborative data to conduct a genome-wide association study of sleep duration
Sleep is critical to health and functionality, and several studies have investigated the inherited component of insomnia and other sleep disorders using genome-wide association study (GWAS) methodologies. However, genome-wide studies focused on sleep duration are less common. I will present sleep data from participants in the Coriell Personalized Medicine Collaborative (CPMC)(n=3948) together with demographic and lifestyle variables, and genome-wide SNP data to better understand genetic contributions to variation in sleep duration. I used stepwise ordered logistic regression with the following predictive variables: age, gender, weight, physical activity, physical activity at work, smoking status, alcohol consumption, ethnicity, and ancestry (as measured by principal components analysis). The strongest candidate loci replicated several genomic regions that were previously identified in GWAS related to sleep duration (TSHZ2, ABCC9, FBXO15) and narcolepsy (NFATC2 and SALL4). In addition, I have identified novel candidate genes for involvement in sleep duration including SORCS1 and ELOVL2. These results demonstrate that the self-reported data collected through the CPMC is robust, and even with a modest sample size, genome-wide association analysis has identified biologically interpretable genetic candidate genes involved in sleep duration. More generally, this study contributes to a better understanding of the complexity of human sleep variation.
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Tuesday, September 23rd
Dr Percival Zhang
Biological Systems Engineering Department
Virginia Polytechnic Institute and State University
TITLE: Biotechnology Paradigm Shift: From Living Microorganisms to in vitro Synthetic Biosystems
The largest production challenge of biocommodities (e.g., biofuels, renewable chemicals, food, and feed) is the economically viable production with satisfying three manufacturing criteria: high product yield, easy product separation, and fast productivity. In vitro synthetic biosystems for biomanufacturing (ivSB2) are the implementation of complicated biological reactions via the in vitro assembly of numerous standardized and exchangeable enzymes or their complexes and/or (biomimetic) cofactors. Compared to microbial fermentation, ivSB2 features manufacturing advantages, such as high product yield, fast reaction rate, easy access and control for open systems, tolerance of toxic compounds and broad reaction conditions.
In this talk, I will introduce the concept of this disruptive technology platform, present its four major applications: (i) the highest yield production of hydrogen for C5 and C6 sugars; (ii) enzymatic transformation of cellulose to amylose, a value-added co-product, and (iii) the generation of electricity via a sugar biobattery; as well as develop building blocks — synthetic metabolons and engineered redox enzymes working on biomimetic cofatcors. In a word, ivSB2 could lead to a biotechnology paradigm shift, especially in the sustainability revolution pertaining to the energy-water-food nexus.
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Tuesday, September 16th
Leonid Chindelevitch
Postdoctoral Fellow
Harvard School of Public Health
TITLE: Probing Networks to Understand Nature
Networks are a fundamental tool for understanding the intricate interconnections that govern biological systems. This talk will describe two ways in which networks, in combination with mathematical models and algorithmic techniques, can yield valuable biological insights.
Causal regulatory networks help reveal the hidden regulators of gene expression patterns. To facilitate their analysis we established an efficient method for evaluating the significance of the overlap of ternary signals, which generalizes Fisher’s exact test. We used this method to analyze a large-scale causal regulatory network and uncovered new regulators of cardiac hypertrophy.
Metabolic networks help identify novel drug targets. We uncovered structural features of these networks that had been missed by previous researchers, and developed a theoretical framework based on duality for analyzing them in a consistent fashion. We used this theoretical framework to create a new metabolic network for Mycobacterium tuberculosis by algorithmically merging two existing networks, and identified several putative drug targets.
2013-2014 Seminars:
Wednesday, April 30th
Dr Mary Mullins
Cell and Developmental Biology
Perelman School of Medicine, University of Pennsylvania
TITLE: Dynamics and Shaping of a BMP Signaling Gradient
The vertebrate embryonic dorsoventral (DV) axis is patterned by a bone morphogenetic protein (BMP) activity gradient during blastula and gastrula stages. This BMP morphogen gradient is shaped by BMP antagonists emanating from dorsal regions that block signaling dorsally and lead to a gradient of signaling with highest levels ventrally. Quantitation of this gradient, defining its range and the dynamics of its formation, as well as its modulation during gastrulation has not been investigated. We quantified in every cell of the embryo at 30-minute intervals the nuclear intensities of phosphorylated-Smad5 (P-Smad) protein, the BMP signal transducing protein. We use automated algorithms to identify the thousands of individual nuclei present at each embryonic time point, and to measure their corresponding P-Smad intensities. The quantitative dynamics of the gradient in blastula and gastrula stages will be presented, as well as the distinct roles of BMP antagonists in shaping this gradient.
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Wednesday April 30th
Matthew Niepielko
CCIB Ph.D. Candidate
TITLE: Changes in BMP and EGFR signaling components underlie the evolution of Drosophila eggshell morphologies.
FIRST EVER CCIB Ph.D. Thesis Defense at Rutgers–Camden
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Thursday, April 17th
Tejashree Redij
TITLE: Studies on the biochemical and molecular mechanisms of thiourea mediated abiotic stress tolerance inBrassica juncea L.
Salinity and drought are the major abiotic stresses responsible for reducing the crop productivity and hence, worldwide research is focused to develop strategies for enhancing plant stress tolerance. In this context, the present study was initiated to evaluate the ameliorative potential of thiourea [TU; a non-physiological thiol based ROS scavenger] in Brassica juncea under salt and PEG stress. The entire study was performed on pot experiment. The concentration of 175mg/50g soil of NaCl, 1g/50g of soil of PEG (and 50mg/50g of soil thiourea was used throughout the experiment. The analyses were performed at two developmental stages viz. at seedling (1, 2 and 3 d after stress) and seed soaking (1 and 6 h after stress). In seedlings, thiourea supplementation along with NaCl or PEG stress was found to maintain the reduced redox state, which in turn favored the coordinated action of various enzymatic and non-enzymatic antioxidants and pro-oxidants to reduce the oxidative damage inside the plants. In seeds, thiourea supplementation differentially regulates the expression of a set of genes already known as markers of stress tolerance. The effect of thiourea was also found to be dependent upon ABA and calcium based signaling. Thus, the present study highlights the use of thiourea application as a technology for enhancing NaCl and PEG stress tolerance in Indian mustard.
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Tuesday, April 15th
Kuhn Ip
CCIB
TITLE: Mathematical Modeling of Bacterial Metabolism
The ubiquity of microbes in our lives has made research into microbial metabolism an important are of study. The biochemical reactions that comprise metabolism determine the consumption and production of chemical compounds and thus, directly relate to efficiency of microbial industrial fermentation. Metabolism and its regulation is also a key determinant in the virulence and survival of pathogenic microbes.
The key to engineering microbial metabolism for a particular purpose is finding targets for manipulation or modification to study experimentally. Microbial metabolism is complex and effects of modifications to a metabolic pathway are not necessarily localized and may be indirect. Trying to study metabolism as a whole entity by investigating all potential sets of modifications experimentally is infeasible. Thus, mathematical models combined with computational search algorithms have been used to ease the experimental burden by directing laboratory efforts towards promising candidates in a systematic and efficient fashion.
Constraint-based modelling, where metabolism is represented as a directed stoichiometric network of constrained biochemical reactions, is one such widely used framework for predicting metabolic phenotype.
Here, we present projects that consider two issues with the application of predictive modelling to metabolic engineering.
First, the complexity of genome-scale metabolic models affects our ability to interpret the predictions generated. A useful tool for understanding and manipulating cellular metabolism is decomposition into elementary flux modes, which systematically organize metabolic networks into potential pathways associated with biochemical functions. Its utility, however, is severely limited since the number of modes increases exponentially with the size of the network. We developed a new method for decomposition that can easily operate on genome-scale metabolic networks. We demonstrated the utility of our method for metabolic engineering of Escherichia coli and for understanding the survival of Mycobacterium tuberculosis during infection.
Second, the underlying model must successfully capture the effects of manipulations on the metabolic behavior of an organism. With constraint-based modelling techniques, it is not immediately clear how to model the correct behavior of inserted heterologous genes under artificial control. We developed a modelling method that we call Proportional Flux Forcing (PFF) to model artificially induced enzymatic genes within constraint-based modelling schemes. We applied PFF in conjunction with flux balance analysis-based computational strain optimization to yield non-obvious genetic manipulation strategies that significantly increase free fatty acid production in Escherichia coli with an artificially induced heterologous thioesterase.
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Tuesday, April 15th
Martin Golubitsky
Mathematical Biosciences Institute
Ohio State University
Patterns of Synchrony: From Animal Gaits to Binocular Rivalry
This talk will discuss previous work on quadrupedal gaits and recent work on a generalized model for binocular rivalry proposed by Hugh Wilson. Both applications show how rigid phase-shift synchrony in periodic solutions of coupled systems of differential equations can help understand high level collective behavior in the nervous system. For gaits the symmetries predict unexpected gaits and for binocular rivalry the symmetries predict unexpected percepts.
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Elizabeth Demaray
Floraborgs, Victimless Leather: Work Samples From the Field of Art and Science Collaboration
Prof. Elizabeth Demaray manufactures alternative forms of housing for hermit crabs, cultures lichen on the sides of skyscrapers in New York City and, with the engineer Dr. Qingze Zou, is currently building the IndaPlant Project: An Act of Trans-Species Giving in which light-sensing robotic floraborgs allow houseplants to roam freely in a domestic environment, in search of sunlight. Demaray will present her work along with a brief overview of other artwork currently being made in the field of art and science collaboration. This emerging field of practice creates opportunities to heighten scientific literacy in the general public while illuminating the research of individual scientists. Demaray will additionally addresses the challenges and rewards inherent in soliciting institutional support, dealing with the media and presenting work in this genre of art making.
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Tuesday, March 11th
Joanna Slusky
TITLE: The Ins and Outs of Membrane Proteins
Membrane proteins comprise 30% of all proteins and are the majority of modern drug targets. My talk will span both the inner and outer membrane and will use methodologies ranging from molecular biology to protein design to bioinformatics. I will discuss I) how charge affects membrane protein insertion and topology in the inner membrane, II) the relative forces responsible for membrane protein-protein interactions, and III) how charge location clarifies a mechanism for protein insertion in the outer membrane.
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Monday, March 10th
Christopher C Govern
Postdoctoral Researcher
FOM Institute AMOLF Amsterdam, NL
TITLE: Fundamental limits on the precision of cellular sensing
Cells can sense the concentrations of chemicals in their environments with remarkable precision, rivaling the sensory abilities of the best machines that humans build. Yet, they are built very differently than typical machines, with complex networks of molecular interactions and considerable noise in their outputs. While much is known about sensory machines in general, we do not understand how cells actually build them out of molecular components or the design constraints cells face in doing so. I first discuss how common signaling motifs affect the precision of cellular sensing, leading to the specific motifs that can achieve fundamental sensing limits. I then consider design trade-offs that emerge between the performance of these networks and the resources required to build and operate them at the molecular level, like energy (fuel), copies of signaling proteins, and signal processing time.
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Thursday, March 6th
Amin S. Ghabrial
Dept of Cell and Developmental Biology
University of Pennsylvania, School of Medicine
TITLE: “Tube morphogenesis”
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Tuesday, March 4th
Buz Barstow
TITLE: Understanding the Bridge Between Renewable Electricity and Biological Metabolism
Electrosynthesis is a new approach to the production of renewable fuels and chemicals that combines the energy capture efficiency of renewable electricity with the metabolic versatility of biology. Naturally occurring electroactive microbes, that in the wild participate in the geochemical cycling of metals, provide the biological foundation for the uptake of renewable electricity into metabolism. However, despite recent advances, very few of these microorganisms, and others that offer unique capabilities to synthetic biology, can be easily genetically engineered and the systems biology of their unique behavior remains poorly understood. In this talk, I will describe the use of novel high-throughput genetic screens and sequencing methods to discover genes and molecular pathways that underly electron uptake in the model electroactive organism Shewanella oneidensis MR-1.
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Thursday, February 27th
Vyacheslav Labunskyy
TITLE: New insights into translational regulation by the unfolded protein response and its role in aging revealed by ribosome profiling
Impaired protein function caused by protein misfolding and aggregation has been implicated in the development of age-related diseases and regulation of lifespan. Accumulation of misfolded proteins in the endoplasmic reticulum, a cellular organelle responsible for protein folding and trafficking, activates protective signaling pathways that restore protein homeostasis. One such conserved signaling pathway is mediated by the protein misfolding sensor Ire1p and the transcription factor Hac1p, which up-regulate endoplasmic reticulum chaperones, oxidative folding components and factors that facilitate degradation of misfolded proteins to alleviate increased protein folding demand. Here, we describe the role of the unfolded protein response (UPR) signaling pathway and its downstream targets in regulation of lifespan in yeast. While the loss of Ire1p itself had little effect on lifespan, we found that selective inactivation of the individual protein folding and maturation factors led to increased longevity. We also provide evidence that this increased longevity depends on functional Ire1p and is associated with constitutive activation of upstream UPR signaling. We applied RNA-seq and ribosome profiling coupled with next generation sequencing to elucidate the mechanism by which the UPR regulates longevity. Ribosome profiling is based on deep sequencing of ribosome-protected mRNA fragments and provides quantitative information on translation at the genome-wide level. Using this method, we performed detailed characterization of translational changes that are associated with increased UPR activity and identified a set of stress response factors up-regulated in the long-lived mutants. In addition to activation of known UPR targets, we observed induction of other cytoprotective pathways that result in resistance of cells to multiple stresses. These findings establish a novel role for UPR in multiple stress resistance and identify a signaling network that couples stress resistance to longevity.
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Tuesday, February 25th
Dr. Orly Levitan
Environmental Biophysics and Molecular Ecology Program,
Institute of Marine and Coastal Sciences, Rutgers University, New Brunswick
TITLE: The Intelligent Design of Algal Biofuels
Although marine microalgae account for <1% of the photosynthetic biomass they account for > 45% of the global primary production. Over geological time scales, ca. 10-4 to 10-5 % of their organic carbon becomes incorporated into petroleum reservoirs. Each year, humans extract ~1 million years worth of accumulated oil, leading not only to an inevitable depletion of the reservoirs but to a massive increase in atmospheric CO2. Owing to their high productivity-to-biomass ratio, algae have been long considered as leading candidates for biodiesel feedstock. Indeed, lipids derived from fossils of a specific algal taxon, the diatoms, are a major component of the highest quality petroleum. Diatoms are an extremely successful taxon in the contemporary oceans and accumulate triacylglycerols (TAG) as storage lipids. In the late 1990’s, genetic tools were developed for diatoms, and several fully sequenced genomes are now available in the public domain with more in the pipeline. These tools have enabled us to use diatoms as a platform for developing high lipid producing cells using a synthetic biological approach.
Using biochemistry, molecular biology, physiology, and biophysical tools, I took an integrative approach to understand the ‘carbon decision tree’ of the model diatom, Phaeodactylum tricornutum, and to denote several potential regulatory nodes that could alter its cellular carbon and energy allocation. Transcriptomic analysis revealed key pathways involved in the remodeling of intermediary metabolism of carbon and nitrogen that allow for the increase in lipid production. I have used molecular and advances transformation tools, to generate several knock-down (RNAi approach) and over-expression plasmids and target some of the postulated regulatory nodes of the central carbon and nitrogen metabolic pathways. The results have led to new cell lines with increased lipid production. An integrative analysis of the transformants indicates that manipulation of the diatom’s cellular carbon flow could be achieved by modification of the nitrogen assimilation pathway, TAG biosynthesis, and by overexpression of specific transcription factors. To displace the total U.S. consumption of fossil fuels, algae must be grown at a scale that yields approximately 20 million barrels of oil per day. Based on my work, I propose that by using genetically engineered diatoms as a platform, it is possible to achieve annual lipid yields that will enable the production of economically viable and environmentally sustainable biofuels for the transportation sector of the economy.
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Thursday, February 20th
Abid Saleem
TITLE: “Localization of Brain Bio-Iodine”
&
Scott Davis
TITLE: “Starflag”
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Wednesday, February 19th
CCIB Qualifying Exam
Thesis Director: Dr Hao Zhu
Marlene Kim
TITLE: PROFILING ENVIRONMENTAL CHEMICALS THAT INDUCE THE ANTIOXIDANT RESPONSE ELEMENT (ARE) SIGNALING PATHWAY USING A NOVEL QUANTITATIVE STRUCTURE IN VITRO-IN VIVO RELATIONSHIP (QSIIR) APPROACH
Oxidative stress causes cell damage which can lead to a variety of neurodegenerative diseases. Reactive oxygen species (ROS) that cause oxidative stress can trigger the transcription of antioxidative enzymes found in the antioxidant response element (ARE) signaling pathway. This pathway is essential for alleviating cell injury, but the mechanisms that lead to cytotoxicity and animal toxicity are still unclear. Previously, a cell-based ARE beta-lactamase reporter gene assay was used to screen over 2,800 compounds from the National Toxicology Program (NTP) and Environmental Protection Agency (EPA) libraries in a Quantitative High Throughput Screening (qHTS) format in efforts to evaluate environmental chemicals that activate the ARE pathway. These compounds were identified and used to develop predictive ARE models using Quantitative Structure-Activity Relationship (QSAR) approaches. The resulting models can be used to virtually screen other compounds of interest (e.g. Tox21 chemical library). Unfortunately, most QSAR models cannot be used to predict animal toxicity. Therefore, we hope to improve the current QSAR models by incorporating more biological information, especially in vitro information, to solve this problem. The integration of chemical and biological information will be used to develop Quantitative Structure In Vitro-In Vivo Relationship (QSIIR) approaches. The in vitro and in vivo relationships established in this step could be used to develop predictive models for complicated animal toxicity endpoints (e.g. neurotoxicity). Furthermore, the final alternative computational toxicity predictors could be used to prioritize potentially toxic compounds, that induce the ARE pathway, for experimental animal tests.
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Thursday, February 13th
Joanna Slusky
TITLE: The Ins and Outs of Membrane Proteins
Membrane proteins comprise 30% of all proteins and are the majority of modern drug targets. My talk will span both the inner and outer membrane and will use methodologies ranging from molecular biology to protein design to bioinformatics. I will discuss I) how charge affects membrane protein insertion and topology in the inner membrane, II) the relative forces responsible for membrane protein-protein interactions, and III) how charge location clarifies a mechanism for protein insertion in the outer membrane.
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Tuesday, February 11th
Dr. Kevin Chen
BioMaPS Institute for Quantitative Biology
Department of Genetics, Rutgers University, NB
TITLE: Spectral Learning of Hidden Markov Models for Genomic Segmentation
Since 93% of disease-related variants in the human genome reside outside of protein coding genes, it is important to develop computational techniques to predict which of these variants are most likely to be causal for the disease. Our group mainly uses computational techniques to interpret variation in the genome that affects the function of non-coding RNAs and gene regulatory elements. In this talk, I will describe our recent results on extending and implementing a novel class of spectral learning algorithms for Hidden Markov Models and related graphical models, with the goal of segmenting the genome into distinct chromatin.
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Thursday, February 6th
Ruchi Lohia
TITLE: Prediction of the effects of the Val66Met polymorphism on the conformational ensemble of an intrinsically disordered protein, Brain-Derived Neurotrophic Factor
The discovery of Intrinsically Disordered Proteins (IDP) has challenged the structure-function paradigm and forced us to find new ways for identifying functional mechanisms of proteins. Studies show that IDPs can function while being partly disordered or may fold once they bind to their receptors. Disease-associated Single Nucleotide Polymorphisms (SNP) are common in the disordered regions of proteins, but not much is known about their effect on the protein structure. Brain Derived Neurotrophic Factor (BDNF) belongs to the family of neurotrophins, and facilitates neurogenesis in its short (mature) form but apoptosis in its long (pro) form. A common (found in 4% of the United States population) SNP that results in the Val66Met mutation in the disordered N terminus domain of the long form of BDNF (proBDNF) has been associated with various neuropsychiatric disorders such as bipolar disorder and Parkinson’s and Alzheimer’s diseases. In order to explore the effect of this SNP on protein structure and dynamics, we conducted Molecular dynamics simulations to identify the effect of the above SNP on likely conformations of proBDNF. Although IDPs have been identified to change their conformations rapidly, many also exhibit some residual secondary structure, which might be biased towards the bound conformation. To construct the ensemble of proBDNF in both forms, large-scale fully atomistic replica exchange calculations of both the Val and Met forms of proBDNF were carried out. We find significant differences in the secondary structure available to Val and Met forms of the protein in the region surrounding the SNP, with results that agree with recent NMR studies. This suggests a position specific residue-type dependence of the residual secondary structure of proBDNF, which might account for functional compromise.
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Tuesday, February 4th
Dr. Darius Balciunas
Department of Biology, Temple University
TITLE: Conditional gene traps for genetic analysis of regeneration in zebrafish
Regeneration is one of the most fascinating and biomedically relevant biological processes. Studies implicating classical developmental pathways- Wnt, Fgf, Bmp and retinoic acid- in regeneration underscore the overlap between genetic control of development and regeneration, necessitating the use of conditional mutants to study regeneration. However, conditional mutants are only readily generated in the mouse, which has rather poor regenerative capacity. Conversely, in vertebrate model systems with extensive regenerative capacity, such as the zebrafish, very few conditional mutants exist. To address this chiasmus in regeneration biology, we have constructed a fully conditional gene trap vector for use in zebrafish. Several mutants affecting development of the cardiovascular system have been recovered from our pilot screens, including an insertional allele of tbx5a. Our data indicate that in addition to playing an essential role in pectoral fin and heart development, tbx5a plays an essential role in cardiac regeneration. Our future studies will focus on thorough characterization of regeneration defect in tbx5a mutants, while also testing regenerative capacity of other conditional cardiovascular mutants.
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Thursday, January 30th
Kyle Jenkins
TITLE: Biomusicology: An Interdisciplinary Look at the Crossroads of Science and Art
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Tuesday, January 28th
Zhijun Li, Ph.D.
TITLE: Three-dimensional Structure Modeling of Transmembrane Proteins
Transmembrane proteins account for 30% of the human genome and are important drug targets. Elucidating the three-dimensional structures of membrane proteins is crucial to understanding their structure-function relationships and to their structure-based drug design. In disparity to their biological significance, the structure of most membrane proteins remains unknown, comprising less than 1% of the total structures in the Protein Data Bank. This is mainly due to the challenge of experimental structure determination of membrane proteins. As a result, computational modeling plays an important role in the studies of membrane proteins and in structure-based drug design efforts targeting these proteins.
In this talk, three ongoing projects from our lab that is related to structure modeling of helical membrane proteins will be presented: (1) Developing an objective measure for quality assessment of membrane protein structures/models through bioinformatics approaches; (2) Developing a computational approach for conformational sampling to improve the standard homology modeling techniques; and (3) Applying computational modeling techniques to the chemoreceptors and their regulatory enzyme.
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Thursday, January 23rd
Rachel Sohn
TITLE: Memory Reconsolidation as a Treatment Option for Opioid Dependency
Drug dependency, specifically to opioids, is a detrimental disease which has no known cure at the current time. Research has shown that abnormalities within the mesolimbic pathway in the brain can account for the negative behaviors associated with users. Posttraumatic stress disorder is another malady that is affected by the mesolimbic pathway, specifically to anomalies in the amygdala. By further researching this pathway, more conclusions can be drawn and pharmacological ways of treating the disease can be developed. Recent studies have focused on memory reconsolidation, a process of recalling memories and altering them. Using propranolol and clonidine, patients with PTSD showed great improvement in their quality of life as well as displayed minimal symptoms. Although not yet proven, this technique could be applied to those who are opioid dependent as a way to eliminate the triggering memories that cause a patient to relapse.
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Thursday, November 7th, 2013
Dr. Bing Yan
School of Chemistry and Chemical Engineering
Shangdong University, China
TITLE: Modulation of the Biological Activities of Nanoparticles
Nanomaterials are widely used in various industrial sectors, biomedicine, and more than 1300 consumer products. Although there is still no unified safety regulation, their potential toxicity is a major concern worldwide. We discovered that nanoparticles target and enter human cells, perturb cellular signaling pathways, affect various cell functions, and cause malfunctions in animals. Because the majority of atoms in nanoparticles are on the surface, chemistry modification on their surface may change its biological properties significantly. We modified nanoparticle surface using a nano-combinatorial chemistry approach. Novel nanoparticles were discovered to possess reduced toxicity, enhanced cancer targeting ability, or increased cell differentiation regulation. Quantitative nanostructure-activity relationships (QNAR) models have been built and applied for predicting biocompatible nanoparticles.
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Thursday, October 31st, 2013
Sulbha Choudhari
Center for Computational & Integrative Biology, Rutgers University-Camden
TITLE: Insights into Glacial Ecosystem using Metagenomics
The temperature in the Arctic region has been increasing in the recent past accompanied by melting of its glaciers. We took a snapshot of the current microbial inhabitation of an Alaskan glacier (which can be considered as one of the simplest possible ecosystems) by using metagenomic sequencing of 16S rRNA recovered from ice/snow samples. Somewhat contrary to our expectations and earlier estimates, a rich and diverse microbial population of more than 2,500 species was revealed including several species of Archaea that has been identified for the first time in the glaciers of the Northern hemisphere. The most prominent bacterial groups found were Proteobacteria, Bacteroidetes, and Firmicutes. Firmicutes were not reported in large numbers in a previously studied Alpine glacier but were dominant in an Antarctic subglacial lake. Principal component analysis of nucleotide word frequency revealed distinct sequence clusters for different taxonomic groups in the Alaskan glacier community and separate clusters for the glacial communities from other regions of the world. Comparative analysis of the community composition and bacterial diversity present in the Byron glacier in Alaska with other environments showed larger overlap with an arctic soil than with a high arctic lake, indicating patterns of community exchange and suggesting that these bacteria may play an important role in soil development during glacial retreat.
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Tuesday, October 29th, 2013
William J. Welsh, Ph.D.
N. H. Edelman Professor in Bioinformatics
Department of Pharmacology, Rutgers University
Robert Wood Johnson Medical School
TITLE: Computational Approaches to Accelerate Drug Discovery
The recent emergence of the translational medicine paradigm has imposed a high premium on advanced computational platforms to accelerate the discovery of new diagnostic and therapeutic agents. We thus introduce two computational tools, Shape Signatures and Avalanche, developed in the Welsh laboratory that offer a central portal for drug discovery, virtual screening of chemical libraries, and predictive toxicology. Among their many attractive features, Shape Signatures and Avalanche are extremely fast, are accessible via a user-friendly GUI, and can handle any number and type of molecular species. Our current Shape Signatures and Avalanche chemical databases comprise over 3 million commercially available organic compounds including natural products and FDA-approved small molecule drugs. I will present Case Studies, taken from my research laboratory, to demonstrate the utility of Shape Signatures and Avalanche within an integrated drug discovery project. Examples will be drawn from projects aimed at the discovery of novel treatments for cancer, pain, and infectious, as well as cosmetic agents.
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Thursday, October 24th, 2013
Dr. Wei Xu
Department of Oncology
McArdle Laboratory for Cancer Research, University of Wisconsin
TITLE: Developing in vitro and in vivo models for probing environmental estrogens’ action via estrogen receptor dimers
Many environmental estrogens so called xenoestrogens mimic the action of endogenous estrogens to regulate the risk of breast cancer. The effects of xenoestrogens are mediated by functional estrogen receptor dimers. Although numerous biochemical evidence exist in support of ERa/b heterodimer formation, the functions of heterodimers were completely unknown.
We have developed Bioluminescence Resonance Energy Transfer (BRET) assay to study intermolecular interactions of ERa/b heterodimers. Further, this assay was used for high throughput screening and identified several ERa/b heterodimer inducing compounds.
These compounds allow us to reveal the growth inhibitory function of ERa/b heterodimers and in silico modeling identifies possible pharmacophore conferring ERa/b selectivity. Finally, I will discuss recent animal models developed to screen ligands specific to ERa and ERb in vivo.
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Thursday, October 17, 2013
Ammar Naqvi
Rutgers University-Camden
TITLE: Patterns of microRNA changes in Aging and Neurodegeneration
microRNAs (miRNAs) are 20~24nt small RNAs that impact a variety of biological processes, from development to age-associated events. To study the role of miRNAs in aging, studies have profiled the levels of miRNAs with time. However, evidence suggests that miRNAs show heterogeneity in length and sequences in different biological contexts. Here, by examining the expression pattern of miRNAs by northern blot analysis, we found that Drosophila miRNAs show distinct isoform pattern changes with age. Surprisingly, an increase of some miRNAs reflects increased 2′-O-methylation of select isoforms. Small RNA deep-sequencing revealed a global increase of miRNAs loaded into Ago2, but not into Ago1, with age. In addition, only specific miRNA isoforms showed increased loading into Ago2, but not Ago1, indicating a mechanism for differential loading of miRNAs between Ago1 and Ago2 with age. Mutations in Hen1 and Ago2, which lack 2′-O-methylation of miRNAs, result in accelerated neurodegeneration and shorter lifespan, suggesting an impact of the age-associated increase of 2′-O-methylation of small RNAs on age-associated processes. Our study highlights that miRNA 2′-O-methylation at the 3’end is modulated by differential partitioning of miRNAs between Ago1 and Ago2 with age, and that this process might impact age-associated processes in Drosophila.
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Thursday, October 10th, 2013
Dr. Paolo Zunino
Department of Mechanical Engineering and Materials Science
University of Pittsburgh
TITLE: Computational models for fluid and chemical exchange between microcirculation and tissue interstitium
Reduced models of fluid flow or mass transport in heterogeneous media are often adopted in the computational approach when the geometrical configuration of the system at hand is too complex. A paradigmatic example in this respect is blood flow through a network of capillaries surrounded by a porous interstitium. We numerically address this biological system by a computational model based on the Immersed Boundary Method (IBM), a technique originally proposed for the solution of complex fluid-structure interaction problems. Exploiting the large aspect ratio of the system, we avoid resolving the complex 3D geometry of the submerged vessels by representing them with a 1D geometrical description of their centerline and the resulting network [1,2].
Cancer employs mass transport as a fundamental mechanism of coordination and communication and the physics of mass transport within body compartments and across biological barriers differentiates cancer from healthy tissues [3]. Mass transport is also at the basis of cancer pharmacological treatment Delivery of diagnostic and therapeutic agents differs dramatically between tumor and normal tissues. In contrast to healthy tissue, tumors exhibit interstitial hypertension, which is caused by the high permeability of tumor vessels in combination with the lack of functional lymphatic vessels in the tumor interstitial space.
The analysis of fluid and chemicals exchange in vascularized tumors is a relevant application of the model proposed here. We will use it to study fluid and mass exchange between the capillaries and the interstitial volume, as well as to compare different modalities to deliver chemotherapy drugs to the tumor mass, including using nanoparticles as delivery vectors.
[1] D’Angelo, C., Quarteroni, A. On the coupling of 1D and 3D diffusion-reaction equations. Application to tissue perfusion problems (2008) Mathematical Models and Methods in Applied Sciences, 18 (8), pp. 1481-1504.
[2] L.Cattaneo, P.Zunino, Computational models for fluid exchange between microcirculation and tissue interstitium. Networks and Heterogeneous Media (2013). MOX Report 25/2013
[3] Baxter, L.T., Jain, R.K. Transport of fluid and macromolecules in tumors. I. Role of interstitial pressure and convection (1989) Microvascular Research, 37 (1), pp. 77-104.
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Thursday, September 26th, 2013
Dr. Laura Scheinfeldt
Coriell Institute for Medical Research
Coriell Personalized Medicine Collaborative
TITLE: An integrated genomic approach to the study of human adaptation to high altitude
The recent availability of genomic data and related methodologies has resulted in several genome-wide studies of human adaptation across geographically diverse populations. Such studies have provided novel candidate genes and pathways putatively involved in adaptation to different environments, and in particular, to high altitude. However, one of the limitations of these studies is the high false positive rate for candidate loci, and additional work is needed to translate the strongest results into biologically interpretable adaptive candidate variants. I will discuss methods that may help to recover biological signals of adaptation to high altitude in Ethiopia and ways in which I have incorporated complementary phenotypic data. I will also present results from studies of other high altitude regions across the world and discuss the combined findings.
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Tuesday, September 24th, 2013
Dr. Eric Klein
Dept. of Biology
Rutgers University-Camden
TITLE: Mechanical signaling pathways in bacteria
The dynamic regulation of virulence is an essential aspect of pathogenesis that holds great promise for combating infectious disease. While the overwhelming emphasis of the field to date has been on the chemical cues that mediate bacteria-host interactions, there is evidence indicating that bacteria can also sense and respond to their mechanical environment. In particular, I have demonstrated that uropathogenic Escherichia coli (UPEC) induce a novel gene expression program in response to adhesion to stiff surfaces. While the mechanism by which fimbriae attach to their targets is well understood, little is known about how fimbrial attachment may trigger pathogenic gene expression. Using a variety of genetic and microscopy-based approaches, my work will focus on how mechanical forces stimulate adhesion-mediated gene expression, the mechanism of bacterial mechanotransduction, and the role of mechanical signaling in pathogenic infection. Ultimately, my goal is to identify components of the mechanotransduction pathway as targets for the development of novel therapeutics for use as alternatives to traditional antibiotics.
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Thursday, September 19th, 2013
Dr. Luca Larini
Dept. of Physics, Rutgers University-Camden
TITLE: Bridging length and time scales in complex materials
Complex systems (such as biomolecules or glasses) are characterized by multiple time and length scales that are associated with different properties of the system under examination. As a consequence, simplified models (referred to as coarse-grained models) can be constructed that retain only the most relevant physical properties at a given scale. These models can be expected to render both theory and simulations more tractable. In this talk I will present examples of successful coarse-grained models and techniques applied to glass transition, biomolecules and simple liquids. These examples will be used to introduce basic notions of the theory and the algorithms employed in multiscale modeling.
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Tuesday, September 17th, 2013
Dr. Jinglin Fu
Dept. of Chemistry, Rutgers University-Camden
TITLE: Spatially Interactive Biomolecular Networks Organized by Nucleic Acid Nanostructures
The cellular activities of all organisms are governed by a variety of multi-step chemical conversion events, or biochemical pathways that exhibit extraordinary yield and specificity. Biological systems have evolved numerous mechanisms to regulate this process including molecular scaffolding and spatial confinement, where the function of a pathway is critically dependent on the relative position, orientation, and quantity of participating enzymes. Understanding the effect of spatial arrangement on pathway activity in multi-enzyme systems will not only advance our knowledge of fundamental biomolecule networks, but is also important to translate biochemical reaction pathways to non-cellular environment applications. Toward this goal, DNA nanostructures, that pieces of DNA can come together and spontaneously assemble into sophisticated structures, have been employed as assembly scaffolds to organize multiple enzymes with precisely-controlled spatial positions and orientation. This makes it possible to investigate and mimic several important mechanisms for multi-enzyme pathways, including the dependence of the distance between the enzymes on the overall reaction rate and specificity
(Fig.1A), the ‘Swing Arm’-organized substrate channeling (Fig.1B) and the regulatory enzymatic circuitry (Fig.1C). These approaches and principles provide a predictable framework to create efficient and regulatory nanoscale catalytic complexes, which will find utility in the development of the catalysts for chemical synthesis, bioenergy, diagnostic and therapeutic applications.
2012-2013 Seminars:
Thursday, January 24, 2013, Science Building Lecture Hall
Extracting Essential Features of Biological Signaling Networks
Speaker: Dr Natalie Arkus, Postdoctoral Fellow, Department of Physics and Astronomy, University of Pennsylvania
Abstract: Because biological signaling networks have many components, it has become common to model such networks using large systems of coupled ordinary differential equations. However, there is as yet no simple way of determining how solutions to large systems depend on their parameters. In contrast, large systems of differential equations describing electronic circuits are routinely reduced to simpler systems that quantitatively capture circuit behavior using lumped parameters for resistance, capacitance, and inductance. We show that biological signaling networks can similarly be reduced to systems involving a few equations and effective parameters. The effective parameters lump the system’s many components together, yielding a simplified system that contains within it information on all of the many components. We apply this method to a model from the literature of heat shock response in E. coli consisting of 31 equations and 48 parameters. We reduce this model to just 1 equation and 3 effective parameters. The reduced system quantitatively agrees with the original, and demonstrates that feedback loops do not necessarily confer a faster heat shock response at lower cost, as had been claimed. We discuss the application of this method to other models from recent literature, such as that of Beta-catenin degradation in the Wnt signaling network – where a model of 19 equations is reduced to 1-3 equations (depending on the initial conditions), and the features that determine the rate of beta-catenin degradation are extracted.
Tuesday, January 29, 2013, Armitage Hall Faculty Lounge
A Control Theory Approach to Engineering Biomolecular Networks
Speaker: Dr Domitillo del Vecchio, W. M. Keck Career Development Professor in Biomedical Engineering, Associate Professor, Laboratory for Information and Decision Systems (LIDS), Department of Mechanical Engineering, MIT
Abstract: The past decade has seen tremendous advances in the fields of Systems and Synthetic Biology to the point that de novo creation of simple biomolecular networks, or “circuits”, in living organisms to control their behavior has become a reality. A near future is envisioned in which re-engineered bacteria will turn waste into energy and kill cancer cells in ill patients. To meet this vision, one key challenge must be tackled, namely designing biomolecular networks that can realize substantially more complex functionalities than those currently available.
A promising approach to analyzing or designing complex networks is to modularly connect simple components whose behavior can be isolated from that of the surrounding modules. The assumption underlying this approach is that the behavior of a component does not change upon interconnection. This is often taken for granted in fields such as electrical engineering, in which insulating amplifiers enforce modular behavior by suppressing impedance effects. This triggers the fundamental question of whether a modular approach is viable in biomolecular circuits. Here, we address this research question and illustrate how, just as in many mechanical, hydraulic, and electrical systems, impedance-like effects are found in biomolecular systems. These effects, which we call retroactivity, dramatically alter the behavior of a component upon interconnection. We illustrate how, similarly to what is performed in electrical networks, one can reduce the description of an arbitrarily complex system by calculating equivalent retroactivities to the input. By merging disturbance rejection and singular perturbation techniques, we provide an approach that exploits the structure of biomolecular networks to design insulating amplifiers, which buffer systems from retroactivity effects. We provide experimental demonstration of our theory on a reconstituted protein modification cycle extracted from bacterial signal transduction and on a synthetic biology circuit in vivo.
Thursday, February 14, 2013, Science Building Lecture Hall
The Tox21 Program
Speaker: Menghang Xia, Ph.D., Group Leader, Cellular Toxicity & Signaling, NIH Chemical Genomics Center
Abstract: To meet the needs of toxicity testing in the 21st century, the National Toxicology Program (NTP), the NIH Chemical Genomics Center (NCGC), the U.S. Environmental Protection Agency (EPA), and the U.S. Food and Drug Administration (FDA) formed the Tox21 partnership. The goals of Tox21 are to identify mechanisms of compound action at the cellular level, prioritize chemicals for further toxicological evaluation, and develop useful predictive models of in vivo biological response. In the presentation, I will describe the Tox21 program, qHTS-based compound testing and the various Tox21 screening assays that have been validated and screened at the NCGC.
Thursday, March 7, 2013, 12:20pm – 1:20pm, Lecture Hall, Science Building
Patterning challenges for optional sex: the case of reproductive polyphenism in aphids
Speaker: Dr Gregory Davis, Assistant Professor, Biology, Bryn Mawr College
Abstract: The pea aphid, Acyrthosiphon pisum, exhibits several environmentally cued, discrete, alternate phenotypes (polyphenisms) during its life cycle. In the case of the reproductive polyphenism, differences in day length determine whether mothers will produce daughters that reproduce either sexually by laying fertilized eggs (oviparous sexual reproduction), or asexually by allowing oocytes to complete embryogenesis within the mother without fertilization (viviparous parthenogenesis). Oocytes and embryos that are produced asexually develop more rapidly, are yolk-free, and much smaller than oocytes and embryos that are produced sexually. Perhaps most striking, the process of oocyte differentiation is truncated in the case of asexual/viviparous development, potentially precluding interactions between the oocyte and surrounding follicle cells that might take place during sexual/oviparous development. Given the important patterning roles that oocyte-follicle cell interactions play in Drosophila, these overt differences suggest that there may be underlying differences in the molecular mechanisms of pattern formation. We have found differences in the expression of torso-like, as well as activated MAP kinase, suggesting that there are important differences in the hemipteran version of the terminal patterning system between viviparous and oviparous development. Establishing such differences in the expression of patterning genes between these developmental modes is a first step toward understanding how a single genome manages to direct patterning events in such different embryological contexts.
Wednesday, March 27, 2013, Campus Center Multi-Purpose Room
Fast Algorithms for Brownian Dynamics Simulation with Hydrodynamic Interactions
Speaker: Dr Shidong Jiang, Associate Professor, Department of Mathematical Sciences, New Jersey Institute of Technology
Abstract: In the Brownian dynamics simulation with hydrodynamic interactions, the motion of the Brownian particles can be described via a stochastic differential equation (SDE). The change of the displacement vectors of Brownian particles in the popular Ermak-McCammon algorithm for Brownian dynamics simulation consists of two parts: a deterministic part which is proportional to the product of the Rotne-Prager-Yamakawa (RPY) tensor and the given external forces; and a hydrodynamically correlated random part whose covariance is proportional to the RPY tensor. For an arbitrary N-particle configuration, the computation cost of the classical algorithms for computing the deterministic part is quadratic in N; and the computational cost for generating random vectors with a specific covariance is cubic in N. These form the bottleneck for long term large-scale Brownian dynamic simulation.
In this talk, we will first present two fast multipole methods (FMM) for computing the deterministic part. We then discuss several methods of generating random vectors whose covariance matrix is proportional to the RPY tensor. The performance of our algorithms will be illustrated via several numerical examples. The algorithms are expected to be useful for the study of diffusion limited reactions, polymer dynamics, protein folding, and particle coagulation.
This is joint work with Zhi Liang at NJIT, Zydrunas Gimbutas & Leslie Greengard at NYU, & Jingfang Huang at UNC at Chapel Hill.
Thursday, March 28, 2013, 12:20pm – 1:20pm, Lecture Hall, Science Building
Mathematical Models for Cancer Treatments – The Role of the Vasculature and the Immune System in Optimal Protocols for Cancer Therapies
(joint research with Urszula Ledzewicz, Southern Illinois University)
Speaker: Dr Heinz Schättler, Dept. of Electrical and Systems Engineering, Washington University, St Louis
Abstract: A systematic study of cancer treatments requires that we take into account not only the tumor and its growth, but also its microenvironment which comprises the cancerous cells, (sensitive and resistant to the treatment), healthy cells, tumor vasculature, immune system and more. In this talk, I will discuss some mathematical models that include increasingly more complex aspects of the tumor microenvironment such as tumor heterogeneity, angiogenic signaling, and tumor immune system interactions. These models will be analyzed from a dynamical systems point of view in the context of the optimal control problem of designing treatment protocols. Using methods of geometric optimal control, syntheses of optimal solutions will be described for some of these models. As more and more aspects of the tumor microenvironment are taken into account, optimal solutions change from bang-bang solutions (which correlate with the standard medical practice of giving chemotherapeutic agents in maximum tolerated doses) to administration schedules that favor singular controls (which administer agents at specific time varying reduced dose rates). This raises the possibility of metronomic administrations of agents (at low concentrations over prolonged periods without any major interruptions), an alternative scheduling approach that has shown some success in pediatric cancers. The talk will also address some of the mathematical challenges that arise in the analysis of these generally highly nonlinear, multi-input control systems.
2011 – 2012 Seminars:
Monday, December 12, 2011, (BSB 334)
Characterization of ultradian rhythms in adult male rats through EEG analyses and in Neuorspora crassa through growth data analyses
Speaker: Steve Moffett, Ph.D. candidate, CCIB, Rutgers University
Abstract: The brain gives rise to rhythms on different time scales, including circadian or ultradian. In humans, hormone secretion is known to follow an ultradian rhythm of approximately 90 minutes. To date, ultradian rhythms have not been well-characterized in rodents. Adult male rats were prepared with electrodes for electroencephalography (EEG) and in the neck musculature for electromyography. After recovery, the EEG signal was recorded for 48-hours in a 12/12 L/D cycle. Following data acquisition, a window fast-Fourier transform (FFT) of EEG data was computed for 30-second epochs. The percentages of total power in 1-4 Hz, 4-8 Hz, or 1-8 Hz frequency bands were separately plotted by epoch over the course of the study. An ultradian rhythm in percent total power was apparent in the plots for each of the frequency ranges. To quantitate the ultradian rhythm, trained observers independently determined the time of occurrence of local minima in the window FFT plot. The means of measurements of periods between minima for given observers ranged from 9.4 to 13.2 minutes. A similar study was conducted on the growth data of the model circadian organism Neurospora crassa.
February 6, 2012
Systematic Structural Mass Spectrometry: Probing Structures of Membrane Skeletons Using Chemical Crosslinking, Mass Spectrometry and Homology Modeling
Speaker: David W. Speicher, Ph.D., Casper Wistar Professor in Computational and Systems Biology
Director, Center for Systems and Computational Biology, The Wistar Institute
Abstract: Crystallographic and NMR techniques have produced high resolution structures of many protein domains, small proteins and some protein complexes. However, few high resolution structures exist for proteins or protein complexes larger than 100 kDa, and no high resolution structures exist for the majority of proteins expressed by the human genome. A strategy for systematically determining novel protein structures is to use homology modeling since one or more high resolution structures exist for most protein folds. However, major challenges include: distinguishing between multiple plausible models, improving accuracy of predicted models, and experimental validation of models. A few distance constraints from chemical crosslinks, particularly “zero-length” linkers, can effectively address all of these challenges, and recent advances in tandem mass spectrometry (MS/MS) have improved in-depth analysis of complex peptide mixtures. Despite these advances, chemical crosslinkers remain under-utilized because there are no effective software tools for identification of peptides crosslinked by zero-length crosslinkers. While analysis of small proteins and protein complexes can be performed through manual review of the MS/MS data, this approach is very time consuming and tedious for moderate-sized proteins and impractical for large proteins and protein complexes. To address this roadblock, we recently developed software and a multi-tiered MS/MS analysis strategy that eliminates subjective, tedious manual review of MS/MS data, identifies more crosslinks, increases the confidence of crosslink assignments, and enables analysis of much larger proteins and protein complexes. We are applying this strategy to the systematic analysis of spectrin and other protein complexes in the membrane skeleton, a two-dimensional network on the cytoplasmic face of the membrane that provides membrane flexibility and integrity in red cells as well as other cell types. A long term goal is the development of a comprehensive medium resolution structure for the entire red cell membrane skeleton.
March 19, 2012
What I Saw When I Watched Some Evolution
Speaker: Dr. Michael Desai, Assistant Professor, Department of Organismic and Evolutionary Biology, Harvard University
Abstract: Evolutionary adaptation proceeds by the accumulation of beneficial mutations. We often think of these beneficial mutations as being rare, and adaptation is then characterized by a sequence of “selective sweeps”: a beneficial mutation occurs, spreads through an entire population, then later another beneficial mutation occurs, and so on. This simple picture is the basis for much of our intuition about adaptive evolution, and underlies a number of practical techniques for analyzing sequence data. Yet many large and mostly asexual populations — including a wide variety of unicellular organisms and viruses — live in a very different world. In these populations, beneficial mutations are common, and frequently interfere or cooperate with one another as they all attempt to sweep simultaneously. This radically changes the way these populations adapt: rather than an orderly sequence of selective sweeps driven by single strongly beneficial mutations, evolution is a constant swarm of competing and interfering mutations. The fate of any individual mutation depends on how it interacts with this background of other variation; no single mutation drives adaptation by itself. I will describe a new experimental system developed to directly visualize some aspects of these dynamics, and describe the results of 1000 generations of experimental evolution of 600 budding yeast populations. We see intriguing signatures of complicated patterns of interference between mutations, as well as the unexpected spontaneous evolution of stable polymorphisms in some populations. I will describe some further experiments to show how this dynamics depends on factors such as population subdivision and patterns of epistasis. If time allows I will also describe new theoretical work which predicts how many beneficial mutations collectively lead to variation in fitness within the population, and how each mutation interacts with this variation to determine its ultimate fate.
April 9, 2012
Automated Annotation of Chemical Names in the Literature
Speaker: Jun Zhang, PhD, Post-doc with Hao Zhu, Assistant Professor, Chemistry, Rutgers-Camden
Abstract: A significant portion of the biomedical and chemical literature refers to small molecules. The accurate identification and annotation of compound name that are relevant to the topic of the given literature can establish links between scientific publications and various chemical and life science databases. Manual annotation is the preferred method for these works because well-trained indexers can understand the paper topics as well as recognize key terms. However, considering the hundreds of thousands of new papers published annually, an automatic annotation system with high precision and relevance can be a useful complement to manual annotation. An automated chemical name annotation system, MeSH Automated Annotations (MAA), was developed to annotate small molecule names in scientific abstracts with tunable accuracy. This system aims to reproduce the MeSH term annotations on biomedical and chemical literature that would be created by indexers. To reduce the false-positive annotations, MAA incorporated several filters to remove “incorrect” annotations caused by nonspecific, partial, and low relevance chemical names. Accurate chemical name annotation can help researchers not only identify important chemical names in abstracts, but also match unindexed and unstructured abstracts to chemical records. The current work is tested against MEDLINE, but the algorithm is not specific to this corpus and it is possible that the algorithm can be applied to papers from chemical physics, material, polymer and environmental science, as well as patents, biological assay descriptions and other textual data.
Monday, April 23, 2012, 12:10 – 1:10 pm, BSB 117
Modeling and simulations of single-stranded RNA viruses
Speaker: Mustafa Burak Boz, Georgia Institute of Technology, Ph.D. Candidate in Physical Chemistry
Abstract: We investigate the assembly of Satellite Tobacco Mosaic Virus (STMV) using coarse-grained models. We use multi-level coarse-grained representations to decrease the computational expenses and adequately represent the different parts of the viral structure. The RNA coarse-grained model is generated from a proposed secondary structure [1]. The RNA model has one pseudo-atom (bead) per residue. The coarse-grained model for the capsid contains twenty triangular units, each of which also contains three flexible positively charged protein tails. The assembly process as well as the stability of the virus mainly depends on RNA-protein and protein-protein interactions. The protein tails are attracted to the RNA by electrostatic interactions while the capsid proteins are weakly attracted with each other by hydrophobic interactions. We model RNA-protein interactions with a Debye-HÃŒckel potential and protein-protein interaction with a Lennard-Jones potential. We vary values of these two interactions to find regions where the virus is stable and will self-assemble. Finally, we investigate the assembly of the virus using molecular dynamics. These simulations help us understand the individual roles of these two interactions on viral assembly.
Ref: 1. Schroeder JS, Stone, WJ et.al. Biophysical Journal,101: p. 167-175, 2011
Thursday, April 26, 2012, 12:30 – 1:30 pm, Science Bldg, Science Lecture Hall
Investigating functional roles of circadian rhythms in Neurospora crassa employing mathematical modeling and experimental validations
Speaker: Christian Hong, PhD, Assistant Professor, The Department of Molecular & Cellular Physiology, University of Cincinnati
Abstract: Fundamental cellular processes that maintain most organisms’ health and survival include cell cycle, DNA damage response, and circadian rhythms. Cell cycle is equipped with multiple checkpoints for controlled growth, DNA rep-lication, and divisions. DNA damage response (DDR) mechanisms control cell fate by either repairing single or double strand breaks, or triggering apoptosis for programmed cell death when the damage is fatal. Last, but not least, is circadian rhythm that keeps track of time of a day, and plays a central role in most organ-isms for setting the sleep/wake cycle, feeding rhythms, and other daily activities. These distinct molecular mechanisms communicate with each other and create a complex bio-molecular network to optimize conditions for cells to grow and adapt to the surrounding environment. We explore functional roles of circadian rhythms in other cellular processes such as cell cycle employing mathematical modeling and experimental validations using a modeling organism Neurospora crassa.
Monday, April 30, 2012, 12:10 – 1:10 pm
Immune-based identification of drug resistance in Mycobacterium tuberculosis: Shifting a paradigm or tilting at windmills?
Speaker: Gregory P. Bisson, MD, MSCE, Assistant Professor of Medicine and Epidemiology, Division of Infectious Diseases, Senior Scholar, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania School of Medicine
Abstract: Nearly 2 billion people, one third of the World’s population, are at risk of active tuberculosis (TB) because they are latently infected with the causative agent Mycobacterium tuberculosis ( Mtb ) , and almost 2 million TB-related deaths occur annually. A major barrier to TB control is that detection of Mtb drug resistance requires collection of microbiological samples, which are not available in many cases of active disease and in all cases of latent infection. Treatment of latent Mtb infection, a necessary approach to global TB control efforts, cannot be properly targeted if drug resistance is suspected, which represents a major knowledge gap. A central tenet of microbiology and infectious diseases holds that identification of antibiotic resistance in human pathogens requires direct access to the organism. In this talk, the hypothesis that the host adaptive cellular immune system responds to changes in the proteome of pathogens that specifically occur as a result of genetic mutation(s) conferring drug resistance will be presented, using rpoB mutation and rifampicin resistance in Mtb as an example. The host immune system is not expected to consistently and specifically recognize epitope differences conferred by SNPs in rpoB , but data from model organisms phylogenetically related to Mtb indicate that rpoB mutation activates dormant gene networks not expressed by wild-type strains. The possibility that Mtb strains share conserved, ancestral responses of competition interference will be considered in light of preliminary proteomics data, and the public health implications of the approach will be discussed
Thursday, June 21, 2012, 1:00pm – 2:00pm, Campus Center – South BC Conference Room
Information Session on Entrepreneurial Collaborations
Speaker: CCIB member faculty in conjunction with several faculty members of the School of Business
Abstract: The CCIB, in conjunction with several faculty members of the School of Business, is sponsoring a one-hour session to address possible collaborations on entrepreneurial endeavors, start-ups, etc., as well as the potential for related graduate course offerings and seminars. All interested parties are welcome to attend.
The format for the meeting would be an initial general conversation on programmatic collaborations. This will be followed by a series of 5-10 minute presentations on projects of CCIB researchers and discussions with the members of the Business School on potential entrepreneurial ventures related to the research. Please reply to Karen Taylor at kdt41@camden.rutgers.edu if you would like to make a research presentation at the meeting. RSVP is requested via email to karen if you plan to attend. Thank you.
Monday, June 25, 2012
The Defence Science and Technology Organisation (DSTO) is part of Australia’s Department of Defence and Australian Government’s lead agency charged with applying science and technology to protect and defend Australia and its national interests ( https://www.dsto.defence.gov.au ).
Speaker: Dr Gulay Mann BSc (Hons), Ph.D., Research Advisor to Defence Science Institute, Principal Research Scientist, Human Protection & Performance Division, Defence Science and Technology Organisation, Australia.
Abstract: In January 2002, Dr Mann commenced work with CSIRO Division of Plant Industry at the Black Mountain laboratories in Canberra. Her research activities included: a) The application of molecular/genetic analyses to investigate the genes responsible for complex dough attributes; b) Processing of novel cereal grains for maximum health benefit; c) Genetic basis of wheat quality; d) Development of improved analytical methods for key wheat quality traits; and e) Development of a laboratory scale baking facility. She has written numerous papers and conference proceedings on dough rheology, wheat and end product quality.
Dr Mann joined DSTO’ss Human Protection and Performance Division in February 2006 as an S&T 7 (corresponding to Principal Research Scientist), as Capability Leader of Defence Nutrition and Food Technology. Dr Mann provided scientific leadership for DSTO’ss research programs in Nutrition & Dietetics and Food Science & Technology, and also supervised the management of the freeze dried meal production line.
In March 2009, Dr Mann took up a career development position at the DSTO Headquarters as the Director Strategic Planning and Coordination in Science Strategy and Policy Branch. In this role, she involved in the strategic coordination and science planning across DSTO as well as providing policy advice on the defence science and technology capabilities needed to achieve Defence’s broad objectives and priorities.
Since September 2011, Dr Mann is developing an enabling research program in Synthetic Biology.