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What is computational and integrative biology? At a general level, computational biology is the study of biological systems using tools from traditional biomedical disciplines such as biology, chemistry and physics integrated with methods for analysis of interacting complex systems frommathematics and computer science. In particular, computational analysis is a prominent aspect of integrative biology, which analyzes a large number of interacting biological variables to obtain a fuller overall understanding of complex biological systems. The goal of integrative biology is to extract broad quantitative organizational principles that can relate interactions of component parts to macroscopic behaviors of the complex system. Examples of areas of investigation particularly amenable to an integrative approach include ecological and physiological systems. The Rutgers’ Center for Computational and Integrative Biology (CCIB) emphasizes the development of mathematical models for these biological systems, application of the models to data from laboratory and field investigations, the adjustment of the model based on its fit to and predictive value for experimental results and the subsequent modification of the experimental design based on the predictions of the model.

Research Directions

The CCIB is comprise of over 40 nationally and internationally recognized scholars and researchers, many of whom have received prestigious awards and honors. The CCIB regularly receives research funding from the National Science Foundation (NSF) and the National Institutes of Health (NIH). Some of the CCIB’s current research directions include:

  • Developmental Biology and Cell Signaling
  • Enhancer Analysis and Gene Regulatory Networks
  • Structural Modeling of Proteins
  • Biological Rhythms (circadian and ultradian)
  • Biofuels and sustainable energy
  • Predictive toxicology and pharmacology
  • Metagenomics
  • Movement of large groups and complex systems

The CCIB is always interested in exploring new research directions and is happy to lend its computational expertise to external collaborators. Please contact us if you are interested in discussing current or potential projects.

Opportunities for Support by CCIB

Faculty members of CCIB can apply for support via a CCIB Research Fellowship Application form, to promote interdisciplinary research, Faculty Traveling Grant based on matching funds to the Dean’s Traveling Grant, and Faculty Lunch to promote collaboration that may lead to grant submission.  CCIB graduate students can apply for a Student Traveling Grant based on matching funds to the Dean’s Traveling Grant. 


NSF Grant Awards

RUI: Mechanisms of Modulation of GABA(A) receptors by Steroids and Lipophilic Hormones #1330728
Grace Brannigan (PI), Joseph V. Martin (co-PI)

CAREER: Dynamics and Diversity of Bone Morphogenetic Protein Signaling in Epithelial Cells #1149144
Nir Yakoby (PI)

MRI: Acquisition of a High-Performance Computing Cluster for the Interdisciplinary Research in Computational and Integrative Biology #1126052
Andrey Grigoriev (PI), Kwangwon Lee, Joe Martin, Michael Palis (Co-Principal Investigators)

Q-STEP: Community of Quantitative Scientists #0856435
Joe Martin (PI), Alex Roche; Daniel Bubb; Haydee Herrera (Co-Principal Investigators)

Mechanisms of Nanoparticle Generation by Laser Ablation of Thin Films in Liquids #1300920
Daniel Bubb (PI)

Transforming Potential into Promise: A Depth-First Approach   CCF-1433220
Rajiv Gandhi (PI)

Quantitative Trait Loci for Circadian Rhythm in Neurospora Crassa (Continuation)   MCB-0946860 2/2
Kwangwon Lee (PI)

REU site: Computational Biology Summer Program at Rutgers-Camden  DBI-1263163 1/3
Benedetto Piccoli (PI), Joe Martin (Co-Principal Investigators)

CPS: Synergy: Collaborative Research: Control of Vehicular Traffic Flow via Low Density Autonomous Vehicles  CNS-1446715
Benedetto Piccoli (PI)

Collaborative RUI: Quadrilateral Surface Meshes with Provable Quality Guarantees   CCF-1422004
Suneeta Ramaswami (PI)


NIH Grant Awards

Area: Mechanisms Underlying EGFR Signaling Distribution in Epithelial Tissues  1R15GM101597-01A1
Nir Yakoby (PI); Benedetto Piccoli (Co-PI)

Energy-based cold tolerance in genetically tractable eukaryotic systems 115GM093685-01R
Dan Shain (PI); Nir Yakoby (co-Principal Investigator)

AREA: Predictive Computational Acute Toxicity Modeling by Profiling Chemicals with Public Bioassay Data   1R15ES023148-01 1/1  
Hao Zhu (PI); 


Other Grant Awards

Interactions of inhaled anesthetics with macromolecules
Grace Brannigan (PI)

Army Research Young Investigator
Jinglin Fu (PI)

Predictive Quantitative Structure Activity Relationship (QSAR) modeling of reproductive and developmental toxicity using integrated chemical and biological (HTS profiles) descriptors of molecules
Hao Zhu (PI)

Profiling chemicals based on public bioassay data for the development of predictive computational acute toxicity model
Hao Zhu (PI)

Establishing exclusion criteria  and the significance of inclusion:  Developing tools for the interpre- tation of complex DNA mixtures 4500001685
Desmond Lun (PI)

Identification of gene networks:  An approach based on mathematical modeling
Desmond Lun (PI)

High-accuracy   integration of transcriptomics  and  metabolic  modeling  with  application to the metabolic  engineering  of Escherichia coli
Desmond Lun (PI)

Establishing exclusion criteria  and the significance of inclusion:  Developing tools for the interpre- tation of complex DNA mixtures
Desmond Lun (PI)

Low-Template DNA Mixture Interpretation: Determining the Number of Contributors
Desmond Lun (PI)