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 from mathematics 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 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.
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
- 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 the Grant editing services to increase the quality of grant submission (more details in the link). CCIB supports Faculty Traveling based on matching funds to the CCIB Faculty Travel Grant.
>CCIB graduate students can apply for a Student Traveling Grant based on matching funds to the Dean’s Graduate Travel Grant >(use the Dean’s Traveling Grant Form to apply). All funding opportunities depend on budget availability.
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)
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)
Transforming Potential into Promise: A Depth-First Approach CCF-1433220
Rajiv Gandhi (PI)
REU site: Computational Biology Summer Program at Rutgers-Camden DBI-1263163 1/3
Benedetto Piccoli (PI), Joe Martin (Co-Principal Investigators)
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)
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)