2015 CCIB Annual Retreat

Friday, October 16, 2015
9 a.m. – 5 p.m.
Waterfront Technology Center

(200 Federal Street, Camden, NJ)

Retreat Group Photo








Chancellor, Rutgers-Camden – Dr. Pheobe Haddon


Dean, Faculty of Arts and Sciences, Rutgers-Camden – Dr. Kris Lindemeyer


Director, CCIB – Dr. Desmond Lun

 (Abstracts for all speakers can be found below)


Keynote Speaker: Princeton University – Dr. Stas Shvartsman

Title: Dynamics of inductive signaling






Speaker: CCIB – Dr. Hao Zhu

Title: Advance the Predictive ADME-Tox Modeling by Big Data



Speaker: Rowan School of Medicine – Dr. Eric Moss

Title: MicroRNA regulation during development



Speaker: CCIB – Dr. Simeon Kotchoni

Title: Botanical resources in CCIB: Re-emergence of natural products for drug discovery



Lunch provided by CCIB



Poster Session

 (Abstracts for all posters can be found below)  


Speaker: CCIB Best Student Paper 2015 – Rob Marmion

Title: Chorion Patterning: A Window into Gene Regulation and Drosophila Species’ Relatedness






Speaker:  CCIB – Dr. Grace Brannigan

Title: Mechanisms of small molecule action on membrane proteins



Speaker:  CCIB – Dr. Jongmin Nam

Title: A new functional cis-regulatory genomics tool for systems biology



Closing Remarks – Dr. Desmond Lun



KEYNOTE SPEAKER – DR. STAS SHVARTSMAN – Lewis-Sigler Institute for Integrative Genomics, Princeton University

Title: Dynamics of inductive signaling

Abstract: Transient activation of the highly conserved extracellular-signal-regulated kinase (ERK) establishes precise patterns of cell fates in developing tissues. Quantitative parameters of these transients are essentially unknown, but a growing number of studies suggest that changes in these parameters can lead to a broad spectrum of developmental abnormalities. We provide a detailed quantitative picture of an ERK-dependent inductive signaling event in the early Drosophila embryo, an experimental system that offers unique opportunities for high-throughput studies of developmental signaling. Our analysis reveals a spatiotemporal pulse of ERK activation that is consistent with a model in which transient production of a short-ranged ligand feeds into a simple signal interpretation system. The pulse of ERK signaling acts as a switch in controlling the expression of the ERK target gene. The quantitative approach that led to this model, based on the integration of data from fixed embryos and live imaging, can be extended to other developmental systems patterned by transient inductive signals.


DR. HAO ZHU – Department of Chemistry, Rutgers-Camden

Title: Advance the Predictive ADME-Tox Modeling by Big Data

Abstract: In the current big data era, there are massive amounts of data existing in public resources for drugs and drug candidates. The two relevant challenges for modern drug research and development (R&D) are: 1) reliable data exists, but it is difficult to locate; 2) good data will not guarantee good decisions.

This project addresses the above challenges in predictive modeling of drug molecules by developing innovative computational approaches to automatically generate bioprofiles from public big data sources; robust computational models to accurately predict drug Absorption, Distribution, Metabolism and Exclusion (ADME) and toxicity (Tox); and potential in vitro-in vivo relationships that could be used to reveal mechanisms of actions (MOA).

This project will greatly benefit the whole research community, especially the small pharmaceutical companies and all university drug R&D laboratories because of the following reasons:

1) The automatic profiling tool, a new data search engine, will greatly populate the key pharmaceutical and toxicological information for compounds of pharmaceutical interest;

2) Predictive computational models developed from bioprofiles for complex drug toxicity endpoints can be used to directly evaluate the toxicity potential of new drug molecules and greatly reduce the experimental cost of drug R&D procedure;

3) The chemical in vitro-in vivo profiling (CIIPro) web portal allows the users from scientific communities to search the public big data pool against their own drug candidates, generate their own models, and most importantly, use their own in-house experimental toxicity data to define in vitro-in vivo relationships.


DR. ERIC MOSS – Molecular Biology, Rowan University

Title: MicroRNA regulation during development

Abstract: Pluripotent stem cells of mammals and the larval of the nematode Caenorhabditis share a set of factors that controls the sequence of gene expression changes that occur as cells form tissues. In the worm, this pathway is characterized by the activities of two microRNA families, the lin-4 and let-7 families, that sequentially repress specific targets that encode various regulators of gene expression. One of these targets, LIN-28, is itself a microRNA regulator and a fascinating entry point to investigate the genetic regulation of pluripotency as well as microRNA pathways. Over many years, we have been investigating how LIN-28 and microRNAs work together to control developmental timing. We learned that LIN-28 has two sequential and molecularly distinct activities: one that involves the direct inhibition of let-7 and the other that is let-7-independent. LIN-28’s two activities appear to be essential for its role in controlling the succession of distinct cell fates that are produced by a common pool of stem cells. To help us dissect LIN-28’s function, we have worked out to use RNA circles that act as microRNA sponges. RNA circles occur naturally and a few seem to have evolved for this very purpose. We have modeled our artificial sponges on the naturally occurring ones. Our recent experiments have led us to conclude that much of LIN-28’s activity during neural progenitor development activity occurs independently of the let-7 microRNA, underscoring the importance of the let-7-independent targets.


DR. SIMEON KOTCHONI – Biology Department, Rutgers-Camden

Title: Botanical resources in CCIB: Re-emergence of natural products for drug discovery

Abstract:  In many countries around the globe, the treatment of many human diseases relies on the application of highly effective, plant-based remedies — some time largely unknown to the western world. A large number of these plant species are currently threatened with extinction. In light of these realities, we launched a consortium research platform made of national and international scientists and began the collection of valuable medicinal seeds/plants species from as many geographical locations as possible with the overarching goal of creating a well-established, globally representative, and accessible repository for medicinal plant species that are currently threatened by habitat loss and over exploitation. In this way, the “seed bank” which is currently housed in Dr. Kotchoni’s lab in CCIB will promote seed/sample availability to the scientific community, thereby facilitating and accelerating the process of natural drug discovery. Of the plants collected thus far, investigations into the properties of three classes – plants traditionally used to treat anxiety disorders, diabetes, and cancer – have yielded very promising results. In this presentation, the importance of the plant-based natural drug discovery, data collection workflow, data processing and targeted compound lead will be discussed.


MR. ROB MARMION – CCIB Ph.D. Student, Rutgers-Camden

Recipient of the 2015 CCIB Best Paper Award

Title: Chorion Patterning: A Window into Gene Regulation and Drosophila

Abstract: Species’ Relatedness Changes in gene regulation are associated with the evolution of morphologies. However, the specific sequence information controlling gene expression is largely unknown and discovery is time and labor consuming. We use the intricate patterning of follicle cells to probe species’ relatedness in the absence of sequence information. We focus on one of the major families of genes that pattern the Drosophila eggshell, the Chorion protein (Cp). Systematically screening for the spatiotemporal patterning of all nine Cp genes in three species (Drosophila melanogaster, D. nebulosa, and D. willistoni), we found that most genes are expressed dynamically during mid and late stages of oogenesis. Applying an annotation code, we transformed the data into binary matrices that capture the complexity of gene expression. Gene patterning is sufficient to predict species’ relatedness, consistent with their phylogeny. Surprisingly, we found that expression domains of most genes are different among species, suggesting that Cp regulation is rapidly evolving. In addition, we found a morphological novelty along the dorsal most side of the eggshell, the dorsal ridge. Our matrix analysis placed the dorsal ridge domain in a cluster of epidermal growth factor receptor associated domains, which was validated through genetic and chemical perturbations. Expression domains are regulated cooperatively or independently by signaling pathways, supporting that complex patterns are combinatorially assembled from simple domains.


DR. GRACE BRANNIGAN – Physics Department, 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 . 


DR. JONGMIN NAM – Biology Department, Rutgers-Camden 

Title: A new functional cis-regulatory genomics tool for systems biology

Abstract: We have formulated a new method that can simultaneously measure quantitative and spatial activities of many cis-regulatory modules (CRMs) in a single experiment. The new method takes advantage of stochastic, mosaic incorporation of linear reporter constructs in embryos in conjunction with the law of large numbers; that is, when a large number of random incorporation events are considered, the overall patterns of mosaic DNA incorporations between different CRMs will become virtually identical. Therefore, while reporter expression in each embryo is governed both by random clones of cells harboring the CRM::reporter construct and by the intrinsic activity of the CRM, the quantitative profile of single-embryo resolution reporter expressions in a large number of embryos is solely determined by the intrinsic spatial activity of the CRM, which can be used as the “fingerprint”. We validated this theory in purple sea urchin embryos by applying a high-throughput method for single-embryo resolution reporter assays.



Daniel Banker – Rowan University           

Title: Absorption of Gold Nanoparticles as a means to Probe Cell Activity and Structure

Abstract: Surface treatment of gold nanoparticles (AuNPs) enable those particles to cross the cellular wall. Once within the cell, absorption spectral shifts, predicted by the Mie Theory equation, occur because the dielectric constant of the intracellular matrix differs from that surrounding the cell. This phenomena will lead to particle agglomeration (due to changes in surface energy of the particles) and shifts in the Mie absorption spectra. UV-visible spectrophotometry is used to analyze the absorption gold nanoparticles. Using a modified Mie Theory equation for calculation of extinction coefficients, theoretical and experimental data were compared. An explanation of dielectric constant of gold and how various factors (such as dielectric constant of the surrounding medium, particle size, and particle shape) alter the absorption wavelength will also be discussed.


Catherine Guay – CCIB, Rutgers-Camden           

Title: Elusive causal linkages revealed by combinatorial cis-regulatory perturbations of univin

Abstract: In Strongylocetrotus purpuratus, the maternal TGF-beta ligand univin is essential for initiation of the early ectoderm gene regulatory network (GRN). The inputs for zygotic expression of univin during early and mid-blastula stages were previously unknown. In an earlier study, a high-throughput screen for cis-regulatory modules (CRMs) revealed two CRMs of univin, one in the 5’ proximal region (5P) and one in the intron (INT). Perturbation assays demonstrated that 5P and INT respectively responded to nodal and soxb1 perturbations, but endogenous univin expression did not respond to either perturbation in early and mid-blastula stages. Interestingly, in the gastrula stage, both nodal and soxb1 were previously reported as inputs for univin. This discrepancy led us to ask if 5P and INT were relevant to univin expression in the blastula stage. To address this question, we generated BACs containing 180kb of the univin locus. A BAC containing a double deletion of the 5P and INT CRMs (dAll) abolished reporter expression, suggesting that 5P and INT are the only CRMs of univin within the 180kb locus. A BAC containing a single deletion of INT (dINT) retained a level of reporter expression comparable to the wild type. Interestingly, the dINT BAC gained responsiveness to nodal  perturbation compared to the wild type construct. Together, our results suggest that both CRMs are relevant to univin expression and neither is essential in normal embryos. Our further cis-regulatory analyses showed  that nodal  directly controls univin, while soxb1 is likely to be an indirect regulatory of univin. Given the importance of univin in early embryos, reciprocal buffering of the two CRMs may function as a ‘fail-safe’ system to ensure normal development. In addition, similar elusive regulatory interactions that are mediated by multiple CRMs may be more common than appreciated and may have previously been overlooked in gene expression analysis.


Joe Kawash & Sean Smith – CCIB, Rutgers-Camden

Title: GROM-RD: resolving genomic biases to improve read depth detection of copy number variants.

Abstract: Copy number variants (CNVs), amplifications or deletions of genome segments, are an important contributor of phenotypic variation. The advent of next-generation sequencing (NGS) has prompted read depth analysis as an essential tool for the detection of CNVs. However, the predictive capabilities of existing algorithms using genome read coverage is frequently hindered by the various biases in NGS platforms. Additionally, imprecise breakpoint identification somewhat limits the utility of read depth tools. We created GROM-RD, an algorithm that analyzes multiple biases in read coverage to detect CNVs in NGS data. After using existing GC bias correction methods we found lingering non-uniform variance across distinct GC regions and developed a novel approach to normalize such variance. By adjusting for repeat bias and using a two-pipeline masking approach GROM-RD is able to detect CNVs in complex and repetitive segments that otherwise complicate CNV detection, as well as improve sensitivity in less complicated regions. GROM-RD employs a CNV search using size-varying overlapping windows to improve breakpoint resolution, a typical weakness of RD methods . Compared to two widely used programs based on read depth methods, CNVnator and RDXplorer, GROM-RD was observed to improve CNV detection and breakpoint accuracy.


Julie Keklak – CCIB, Rutgers-Camden

Title: Adhesion mediated signal transduction in E. coli  


Andrew Kilroy – Rowan University

Title: Layer-by-Layer Assembly Onto Gold Nanoparticles of Various Size

Abstract: This research focuses on the potential applications of coated gold nanoparticles in medicine. By coating gold nanoparticles in layers of polyelectrolytes, with a final layer of antibodies which targets chemicals uniquely exhibited by cancer cells, we eventually hope to selectively attach the nanoparticles to the cancer cells. The coated nanoparticles are assembled through layer-by-layer coulombic attraction due to the passive zeta potential of the particle and the charged nature of the polyelectrolytes. This poster will explore the potential usefulness of variously sized nanoparticles with various thickness of polyelectrolyte layers.


Min Kyung Kim – CCIB, Rutgers-Camden

Title: E-Flux2 and SPOT: Validated methods for inferring intracellular metabolic flux distributions from transcriptomic data

Abstract: Several methods 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 no method is clearly the best in terms of accuracy against experimentally measured intracellular fluxes.

We present a general optimization strategy for inferring intracellular metabolic flux distributions from transcriptomic data. It consists of two different template models called DC and AC and two new methods called E-Flux2 and SPOT, which can be chosen and combined depending on the availability of knowledge on carbon source or objective function. We examined E. coli and S. cerevisiae as representative prokaryotic and eukaryotic microorganisms respectively. The predictive accuracy of our algorithm was validated by calculating the uncentered Pearson correlation between predicted fluxes and measured fluxes. To this end, we compiled 20 experimental conditions, of transcriptome measurements coupled with corresponding central carbon metabolism intracellular flux measurements determined by 13C metabolic flux analysis, which is the largest dataset assembled to date. In both organisms, our method achieves an average correlation coefficient ranging from 0.59 to 0.87, outperforming a representative sample of competing methods. It not only achieves higher accuracy, but it also has many other desirable characteristics including applicability to a wide range of experimental conditions, production of a unique solution, and fast running time. Easy-to-use implementations of E-Flux2 and SPOT are available as part of the open-source package MOST (http://most.ccib.rutgers.edu/).


Ruchi Lohia – CCIB, Rutgers-Camden      

Title: Prediction of the effects of Val66Met polymorphism on the conformational ensemble of an intrinsically disordered protein, Brain Derived Neurotrophic factor

Abstract:  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. 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 conformation ensembles. 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. To construct the ensemble of prodomain in both forms, large-scale fully atomistic replica exchange calculations of both the Val and Met forms of prodomain were carried out. We find significant differences in the secondary structure and global conformations available to Val and Met forms of the protein. This points the dramatic effects of a single point mutation on conformational ensemble of disordered protein, which might account for it’s functional compromise. 


Sean  McQuade – CCIB, Rutgers-Camden            

Title: Deriving Spatial CRM Activity via High Throughput Quantitative Methods

Abstract: Understanding of how  cis –Regulatory modules(CRMs) control gene expression patterns will provide us with critical information to  better comprehend gene regulation.  We propose a fast way to characterize 1,000’s of  CRMs in a single experiment which provides a method to distinguish CRMs which are active in distinct cells.  The method proposed will also provide quantitative and spatial information, as in, which specific groups of cells drive expression of a given CRM.


Steve Moffett – CCIB, Rutgers-Camden

Title: Investigating the effects of thyroid hormones, benzodiazepines, and cholesterol on the GABAA receptor in the Xenopus oocyte

Abstract: The GABAA receptor, the most prevalent inhibitory receptor in the brain, can be inhibited by thyroid hormone (T3). After injection RNAs of the ?1?1?2 –subunit cRNA mixture into Xenopus oocytes, we will verify the presence of GABAA receptor expression through perfusion of GABA in conjunction with the benzodiazepine diazepam. In our functional experiments, we aim to detect interactions between T3 and ivermectin, a putative anti-parasitic and GABAA receptor agonist which has been crystallographically-determined to bind at the subunit interface of a homologous receptor. We will also detect any interactions between T3 and the structurally-related neurosteroid allopregnenolone, as computational models have placed its binding site at the subunit interface in the chloride channel GluCl.


Sruthi Murlidaran – CCIB, Rutgers-Camden    

Title: Relative affinities of positive and negative modulators of heteromeric GABA(A) receptors for pseudo-symmetric intersubunit binding sites

Abstract:  Type-A ?-aminobutyric acid receptors (GABAARs) are pentameric ligand-gated ion channels that are ubiquitous to the central nervous system (CNS) and critical for regulating neuronal excitability. These inhibitory receptors, gated by ?-aminobutyric acid (GABA), can be potentiated and also directly activated by certain exogenous and endogenous lipophilic small molecules. Among the various endogenous modulators are derivatives of cholesterol called neurosteroids; neurosteroids that positively modulate GABAARs can function as natural sedatives, anesthetics, anxiolytics and anti-convulsants.  The mechanisms underlying modulation of GABAARs by neurosteroids and exogenous modulators like general anesthetics are still poorly understood, in part due to the location of likely quasi-symmetric interaction sites in the transmembrane domain of the receptor, as well as inconclusive results from mutagenesis experiments. In particular, the extent to which binding modes of positively and negatively modulating neurosteroids overlap is unknown.  Here we use molecular dynamics simulations and the thermodynamically rigorous alchemical free energy perturbation technique to rank pseudo-symmetric intersubunit binding modes by affinity for the positively modulating neurosteroid allopregnanolone, the negatively modulating neurosteroid pregnenolone sulfate, and a novel analog with complex dose-response: triiodothyronine (thyroid hormone).  Differential rankings of intersubunit sites for positive and negative modulating neurosteroids, triiodothyronine, and the general anesthetics propofol and sevoflurane, are interpreted in the context of their distinctive functional effects.


Nicole Pope and Nastassia  Pouradier Duteil – CCIB, Rutgers-Camden          

Title: Dynamic position of TGF-alpha ligand source shapes the patterning of EGFR activation in epithelial cells

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 has a dynamic position: it is initially at the posterior end, and later 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, or the dorsal appendages. These structures reflect the numerous signaling pathways’ activities, including EGFR. Using experimental and computational tools, we identified several parameters that shape the distribution of GRK.  These parameters include the rate of diffusion, the internalization of the EGFR, and the effects of inhibitors on the pathway.  Using these parameters, we were able to model the distribution of GRK and the activated diphosphorylated ERK (dpERK). 


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.


Reza Salari – CCIB, Rutgers-Camden

Title: Calculation of Cholesterol Binding Affinity for Pentameric Ligand-gated Ion Channels

Abstract: Cholesterol has been shown to play a critical role in the function of ion channels, and many types of ion channels function in cholesterol-rich membranes. Several eukaryotic pentameric ligand-gated ion channels, including the nicotinic acetylcholine receptor and the GABAA receptor, are highly sensitive to the cholesterol content of reconstitution mixtures or native membranes, however the details of the cholesterol interaction with these channels, i.e. to what extent it’s due to indirect effects of cholesterol on the bulk membrane properties vs. direct cholesterol binding to the protein, remains largely uncharacterized. Based on the crystal structure of the glutamate-gated chloride channel (GluCl) from C. elegans in complex with ivermectin, we recently proposed that cholesterol molecules could bind to these intersubunit sites in the homologous GABAA receptor, in a pose analogous to that of ivermectin, and tested this model using Molecular Dynamics (MD) simulations. Here we employ an explicit, asymptotically exact computational free energy calculation method to calculate the binding affinity of cholesterol for these sites on GluCl. The computed affinity predicts these sites to be mostly occupied at physiological concentrations of cholesterol.


Sweta Sharma – CCIB, Rutgers-Camden

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.


Liam Sharp – CCIB, Rutgers-Camden

Title: Effects of quasi-native lipid composition on membrane domain formation induced by nicotinic acetylcholine receptors

Abstract:  Nicotinic acetylcholine receptors (nAChRs) are pentameric Ligand Gated Ion Channels that are critical to signaling across synapses and the neuromuscular junction; such signaling is facilitated by high densities of nAChRs in the post-synaptic membrane.  Organization of nAChRs, including partitioning behavior in membranes containing distinct lipid domains, is poorly characterized.  Numerous experimental studies have shown nAChR gain-of-function likely caused by direct interactions with cholesterol, but a significant role for lipid domains has been suggested by nAChR gain-of-function upon cholesterol depletion. Furthermore, the opportunity for direct interactions will likely have a complex dependence on the extent of domain formation in the membrane, which has not been previously addressed. In the present research, we use Molecular Dynamics Simulations with coarse-grained resolution via the MARTINI model to investigate concentrations of cholesterol and other lipids local to nAChRs embedded in complex model membranes with a range of head groups and degrees of unsaturation. Cholesterol and unsaturated lipids are observed binding in deep ‘non-annular’ sites in the nAChR bundle (based on the 2BG9 cryo-EM structure), consistent with our previous predictions. nAChR partitions, however, into cholesterol-poor phases, resulting in dynamic exchange between cholesterol and unsaturated phospholipids, as well as a non-monotonic dependence of the number of direct cholesterol interactions on the cholesterol concentration in the membrane bulk.


Sai Shashank Chavali – CCIB, Rutgers-Camden

Title: Effects of NADH excluded volume and induced N-terminal conformational change on ion translocation across VDAC

Abstract: The Voltage Dependent Anion Channel (VDAC) is a mitochondrial outer membrane protein that mediates transfer of ions and small metabolites. It also allows apoptotic factors like cytochrome C into the cytoplasm, thereby playing a crucial role in mediating programmed cell death (apoptosis). Previous studies have indicated that Nicotinamide adenine dinucleotide in its reduced form (NADH) but not its oxidized form (NAD+) reduces conduction through VDAC. However,  there is no available non-conducting structure of VDAC, and the mechanism of modulation by NADH remains poorly understood. Here we use long, fully-atomistic molecular dynamics simulations to study NADH binding to VDAC and its effect on channel dynamics and ion translocation. Simulations of VDAC were conducted using an NMR structure determined in the presence of NADH (and provided by Dr. Sebastian Hiller), and with NADH bound as in that structure, deprotonated in silico to NAD+, or removed entirely.  We observed dissociation of NAD+ within 100 ns, while NADH remained bound, suggesting that insensitivity of VDAC to NAD+ reflects a significantly lowered affinity of NAD+ for the VDAC pore.   In VDAC with NADH entirely removed from the complex before simulation, the N-terminal loop dramatically changed its conformation over the course of the simulations, eventually approaching its conformation in structures experimentally determined in an apo-state.  The present results are consistent with a mechanism in which NADH reduces conduction by partial pore block, while concurrently forcing a conformational change of the N-terminus. Potential direct contributions of the N-terminal loop to modulating conduction, such as reduction of the favorable ion density in the pore, are also discussed.


Harish Swaminathan – CCIB, Rutgers-Camden              

Title: MatchIt: A Continuous DNA Interpretation Method that Generates Likelihood Ratio Distributions

Abstract: In forensic DNA interpretation, the Likelihood Ratio (LR) is often used to convey the strength of a match. Expanding on binary and semi-continuous methods that do not use all of the quantitative data contained in an electropherogram, fully continuous methods to calculate the LR have been created. These fully continuous methods utilize all of the information captured in the electropherogram, including the peak heights. Recently, methods that calculate the distribution of the LR using semi-continuous methods have also been developed.  The LR distribution has been proposed as a way of studying the robustness of the LR, which varies depending on the probabilistic model used for its calculation. For example, the LR distribution can be used to calculate the p-value, which is the probability that a randomly chosen individual results in a LR greater than the LR obtained from the person-of-interest (POI). Hence, the p-value is a statistic that is different from, but related to, the LR; and it may be interpreted as the false positive rate resulting from a binary hypothesis test between the prosecution and defense hypotheses. Here, we present MatchIt, a method that combines the twin features of a fully continuous model to calculate the LR and its distribution, conditioned on the defense hypothesis, along with an associated p-value. MatchIt incorporates dropout, noise and stutter (reverse and forward) in its calculation. As calibration data, MatchIt uses single source samples with known genotypes and calculates a LR for a specified POI on a question sample, along with the LR distribution and a p-value. The method was tested on 306, 1-, 2- and 3-person experimental samples containing between 0.016 and 1 ng of template DNA. This represents the largest empirical study to evaluate the LR and p-value on laboratory-generated data that we know of. Our data allows us to evaluate changes in these two statistics with respect to the complexity of the sample, to facilitate discussions regarding complex DNA mixture interpretation. We observed that the amount of template DNA from the contributor impacted the LR – small LRs resulted from contributors with low template masses. Moreover, as expected, we observed a decrease of p-values as the LR increased. A p-value of 10-9, the lowest possible in our testing, was achieved in all the cases where the LR was greater than 108. We tested the repeatability of MatchIt by running all samples in duplicate and found the results to be repeatable.


Wenyi Wang – CCIB, Rutgers-Camden

Title: From QSAR to big data: developing mechanism driven predictive models for animal toxicity

Abstract: High Throughput Screening studies have provided the scientific community with rich toxicology data that is currently so complex that it is difficult to process using traditional data analysis techniques. However, all toxicity-related studies, including bioassays retrievable through big data sources, should be evaluated as a possible alternative to animal tests. The goal of this project was to develop novel computational approaches and predictive oxidative stress-induced liver toxicity models using publically available big data sources. Since compounds that are both biologically and chemically similar are considered likely to have similar toxicity mechanisms/effects, their response profile was used to predict animal toxicity and/or prioritize toxicants for in vivo tests. This was done using a Weighted Estimate of Biological Similarity (WEBS) tool, which uses the toxicity pathways constructed from the biological and chemical response profiles to calculate the similarity between two compounds. One of the major causes of liver toxicity is oxidative stress induced by electrophilic compounds. Therefore, assays related to oxidative stress, such as the Antioxidant Response Element reporter gene assays, were used to develop mechanism models for liver toxicity. The validation procedure showed that the resulting models have high predictivity (Correct Classification Rate>0.6-0.85) for hepatotoxicity, especially for electrophilic compounds with certain structural features. The mechanism profiles and predictive models can be used to predict the toxicity of environmental compounds (e.g., the Tox21 library). Furthermore, this study illustrates the benefits of using multiple toxicity bioassays in the current big data era.




     Coffee and Tea (Regular and Decaf), Juice (Cranberry and Orange Juice) & Bottled Water

     Assorted Muffins( Blueberry, Corn)

     Assorted Yogurts and Fresh Fruit


     Coffee and Tea (Regular and Decaf), Coke, Sprite, Ice Tea & Bottled Water

     Greek Pistachio

     Spinach Pie ½ Vegan

     Greek Meat Balls with Oven Potatoes,

     (Vegan: Artichoke a la Polita

           (w/ potatoes, carrots, Olive Oil & Lemon Juice)



     Coffee and Tea (Regular and Decaf), Coke, Sprite, Ice Tea & Bottled Water

     Cookies, Crudites Platter with Dips



2014 CCIB Annual Retreat – coming soon

2013 CCIB Annual Retreat – coming soon