Collaborative Research - Biochemically-constrained Genomic Signal Processing (BioGSP): A Multi-Scale Interdisciplinary Approach to Regulatory Network Inference

Columbia University, New York NY

Investigators

Abstract

Columbia University and the University of California Berkley are awarded grants for the development of novel analytical tools that bridge interdisciplinary gaps between engineering and biological sciences by facilitating a synergistic integration of top-down statistical signal processing theory approaches with bottom-up methods that characterize biological networks as collections of basic biomolecular interactions. The former are the subject of the emerging engineering discipline: Genomic Signal Processing (GSP), while the latter are the domain of classical biochemistry/biophysics. The investigators recognize that the number of putative signal processing mechanisms that needs to be analyzed by GSP for a given biological system could be significantly reduced when their consistency with biochemical/biophysical laws is demanded. The resulting Biochemically-constrained GSP (BioGSP) approach is thus able to produce results on par with traditional GSP methods, but is significantly more efficient as well as assured to be in compliance with key molecular properties of biological mechanisms. Biological systems consist of molecules and molecular complexes, whose interactions comprise intricate circuits and networks. Knowledge of their structure and function can lead to powerful new ways of controlling biological mechanisms, which may potentially enable new approaches to remedying faults in natural biological processes as well as to engineering denovo synthetic biomolecular designs. Recent advancements in experimental techniques have allowed us an unprecedented view of how these systems are structured. However, detailed understanding their function remains a challenge due, in large part to the scale and complexity of networks involved as well as the nonlinear nature of biochemical interactions among the various molecular species. This issue is particularly acute for genetic networks - both because of their importance to biological systems development and operation as well as due to the often complex regulatory patterns they employ. Further information about the project may be found at the PI web sites at http://www.ee.columbia.edu/~wangx/ and http://genomics.lbl.gov/index.html.