Workshop 2015

On Friday, October 9, the Center for Computational and Integrative Biology (CCIB) at Rutgers-Camden and the School of Environmental and Biological Sciences (SEBS) in New Brunswick jointly hosted a Research Collaboration Workshop at the Rutgers-Camden campus. 

The focus of the workshop was to act as a catalyst in research collaborations and explore ways of cooperating on graduate education.







Welcome from Joseph Martin (CCIB) and Bob Goodman (SEBS)


Overview and Goals of Retreat – Desmond Lun (CCIB) and Debashish Bhattacharya (SEBS)


Talk 1: CCIB – Eric Klein (Biology)


Talk 2: SEBS – Yana Bromberg (Microbiology)


Talk 3: CCIB – Desmond Lun (Computer Science)


Talk 4: SEBS – Manish Parashar (RDI2)


Talk 5: CCIB – Grace Brannigan (Physics)


Poster Session


Lunch – provided by Center for Computational & Integrative Biology


Talk 6: SEBS – Costantino Vetriani (Microbiology)


Talk 7: CCIB – Hao Zhu (Chemistry)


Talk 8: SEBS – Siobain Duffy (Ecology/Evolution)


Talk 9: CCIB – Nawaf Bou-Rabee (Mathematics)


Talk 10: SEBS – Debashish Bhattacharya (Ecology and Evolution)


Break and tour / Posters


Breakout session: Discussion of PhD program, content, and administration


Closing thoughts – Desmond Lun and Debashish Bhattacharya


TALK 1: Adhesion as a regulator of bacterial pathogenicity by Eric Klein

TALK 3: Computational Biology Research in the Camden Department of Computer Science by Desmond Lun

TALK 4: Rutgers Discovery Informatics Institute (RDI2) by Manish Parashar




Anthony Cooper – Molecular Dynamics Researcher, Rutgers-Camden          

Title: Experimental and Computational Study of Beta-Galactosidase Inhibition

Abstract: In this study, we combine experiments and simulations to design novel inhibitors of enzymes.  We aim to characterize the inhibition mechanism which we show to be dependent on the aggregation of inhibitor peptides.  As a model system we chose to use Beta-galactosidase.  We selected four peptides out of 10,000 initially screened using microarrays and that show the greatest Michaelis-Menten constant and highest solubility.  Molecular dynamics simulations were performed to identify the exact mechanism of action of these peptides.  We show that the positive residues, like arginine and lysine, are crucial for inhibiting enzyme activity.  According to simulations, these residues are also responsible for the conformations adopted by the peptide in solution.  Dynamic light scattering study revealed that the aggregation of peptides with the enzyme takes place and is responsible for inhibiting enzyme activity.


Fatima Foflonker – Graduate Student, Ecology, Evolution and Natural Resources, SEBS        

Title: The unexpected extremophile: tolerance to fluctuating salinity in the green alga Picochlorum


Min Kyung Kim – PhD Student, CCIB, Rutgers-Camden            

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


Dibyendu Kumar – Genetics Core Director, Waksman Institute, Rutgers University         

Title: Cleaving, Cutting, and Splicing: Identifying Genomic Features


Ruchi Lohia – PhD Student, CCIB, Rutgers-Camden     

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


Tianran Li – Graduate Student, CCIB, Rutgers-Camden

Title: Structural basis of cholesterol transport of NPC2

Abstract: Niemann-Pick type C (NPC) disease is characterized by the accumulation of cholesterol and other lipids in the late endo/lysosomal compartment, and is caused by defects in either of two genes that encode for the proteins NPC1 and NPC2.  NPC2 is a 16kDa soluble lysosomal protein that binds cholesterol. Previous work have shown We used  fluorescence-based assays to monitor the kinetics of cholesterol transfer from NPC2 to model membranes, from membranes to NPC2, and transfer of the cholesterol analog, dehydroergosterol (DHE), between membranes. Results show that NPC2 rapidly transports cholesterol to/from phospholipid vesicles via direct interaction with the membranes, and rates are greatly enhanced by the unique lysosomal phospholipid lyso-bisphosphatidic acid (LBPA). Site-directed mutagenesis, assessed using the sterol transfer assays and studies of npc2-/- fibroblasts, has suggested that at least two membrane interacting domains may be present on the surface of NPC2, and be necessary for its cholesterol transport properties.  A turbidity assay was used to test the possibility that NPC2 is able to interact with greater than one membrane simultaneously; a dose-dependent increase in turbidity upon addition of NPC2 to unilamellar vesicles indicates that NPC2 can cause vesicle-vesicle interaction, supporting the hypothesis that the surface of NPC2 contains two membrane interaction domains.  To examine the hypothesized protein-membrane interactions associate with NPC2 function on an atomic-level, we used the crystal structures of bovine apo and holo NPC2 as initial structures in a series of mid-range molecular dynamics simulations. Simulations with an explicit model membrane showed that while both states of NPC2 interact direct directly with the model membrane, each has different mechanisms and orientations of binding. Results gives an explicit portrayal of a previous orientation prediction based on an implicit membrane model by the OPM database and supports the hypothesis that NPC2 contains multiple membrane interaction domains.


Oscar Marin   

Title: A novel algorithm for computing cross-sections in ion-mobility measurements

Abstract: Ion mobility mass spectrometry allows the characterization of the molecular structure of complex systems, such as proteins, with high precision. In order to assign a well-defined structure to the mobility measurement (usually reported in terms of their cross-sections), it is required to undertake cumbersome calculations. In this study we will show an efficient way of computing cross-sections starting from the atomic structure of a molecule. 


Sean McQuade            

Title: Deriving spatial CRM activity via high throughput quantitative methods


 Sruthi Murlidaran      

Title: Affinity Calculations for Lipophilic Modulators Binding to Isolated Sites on GABA(A) Receptors


 Micaiah Muthee

Title: Aggregation propensity of the microtubule binding region of the tau protein

Abstract: The Alzheimer’s disease is an irreversible, progressive brain disorder that slowly destroys memory and thinking skills, which eventually leads to the ability to not able to carry out the simplest tasks. The Alzheimer’s disease is characterized by the formation of protein aggregates both within and outside of the brain’s cells, the neurons. Within the neurons, the aggregation of the protein tau leads to the destruction of the microtubules in the axon of the neuron. Tau belongs to a group of proteins referred to as Microtubule-Associated Proteins. It is extremely flexible and is classified as an intrinsically unstructured protein (IDP) due to its low propensity to form secondary structure. Tau promotes tubulin assembly into microtubules thereby stabilizing the cytoskeleton of the axon of the neurons. The microtubule binding region of tau consists of 4 pseudo-repeats. In this study, we will focus on the aggregation propensity of two fragments. The PHF43 contains the third pseudo-repeat and has been shown experimentally to aggregate readily. The R2+ and its mutant R2+/P301L contain the second pseudo-repeat, which is a region where mutations are associated with various form of dementia.


Nicole Pope

Title: Dynamics of EGFR activation by a transitioning ligand


 Dana Price      

Title: Group II introns and twintrons in the unicellular red alga Porphyridium purpureum


Sweta Sharma              

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.


Lena Struwe   

Title: Patterns of plant species diversity and sexual reproduction in extreme urban environments


Rafael  Valentin           

Title: Development and use of a real-time PCR assay for detection of brown marmorated stinkbug through eDNA