As the current revolution in genomics and system-level biology unfolds, it is becoming increasingly apparent that strong and deep links exist between molecular/cellular biology and modern statistical mechanics. Extensive studies in molecular biology during the past few decades have yielded detailed information on the molecular components of complex biological systems, and their properties are beginning to be probed by modern high-throughput technologies. The goal of post-genome biology is to make biology quantitative and predictive at the system level. Statistical mechanics provides an ideal framework to relate the properties of the molecular components of a system with the observable (system-level) behaviors of that system. Statistical mechanics refers to the use of statistical and mathematics tools to analyze large populations of particles (e.g., the nucleotide and amino acid sequences of a gene and protein, respectively), in concert with mechanics, which deals with the motions and interactions of these particles (amino acids and nucleotides). Statistical mechanics provides a framework for relating the atomic and molecular properties of individual atoms and molecules with the macroscopic or bulk properties of macromolecules (proteins, DNA) that are observed everyday. By examining intricate atomic/molecular details statistical biophysicists can develop models to explain and predict macromolecular behavior (function); for example, the binding of a regulatory protein to a gene controlling the expression of a cancer-causing gene, or the folding of a protein into its correct, native, thermodynamically stable shape which in turn conveys its specific function. In essence, "gene regulatory networks" research puts a quantitative face onto biological phenomena.
Gene regulatory networks research within CTBP encompasses three major research areas: bioinformatics, evolutionary dynamics and molecular networks. There are many parallels between statistical mechanics and the analysis of genomic and proteomic sequences, and CTBP researchers are capitalizing upon these parallels to develop novel tools and approaches to analyze DNA and protein sequences, explain system-level behaviors, and create predictive models that explain and/or simulate behaviors. These models are particularly useful in that you can simulate perturbations in environments (e.g., changes in protein structure, movement of metabolites within/between cells, etc.) and study changes in behavior virtually. Models are invaluable tools in understanding natural biological processes (e.g., gene expression and cancer cell growth) and the development of cures (e.g., drugs, protein/gene therapies, etc.) for dysfunctional behaviors.
Representative CTBP Research Publications
Other CTBP Research Areas: