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.
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.