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Research in molecular biology is shifting fundamentally towards the study of complex, multi-component systems, or networks, that underlie the living cell. These networks are described and modeled in terms of their components, component interactions, regulatory properties, sub-networks or pathways, and system dynamics in steady-state and in response… more »

Research in molecular biology is shifting fundamentally towards the study of complex, multi-component systems, or networks, that underlie the living cell. These networks are described and modeled in terms of their components, component interactions, regulatory properties, sub-networks or pathways, and system dynamics in steady-state and in response to perturbations. Areas where networks research is becoming increasingly important are 1) biochemical pathways of metabolism, 2) interactions of proteins with DNA to regulate gene transcription, and 3) signaling pathways for cellular response to hormones and other molecules which modify activity and control early development.

The networks approach has developed in conjunction with the shift to high-throughput data generating techniques (microarrays, chromatin immunoprecipitation, two-hybrid studies, mass spectrometry, etc.). As a result, it is necessarily dependent on the development of advanced computational and mathematical tools to pool, organize, and analyze vast quantities of data. Networks in the abstract have mathematical properties that allow inferences about their connections, missing components, and dynamics. High-throughput data combined with comparative genomics supply the foundation for these inferences as they apply to biological networks.

The BU Bioinformatics IGERT supports training and research in biological networks. This can be divided into three interrelated sub-areas: regulatory networks, metabolic networks, and protein-protein interaction networks. Across the sub-areas we have leading experts in computational and mathematical methods and outstanding experimentalists studying important underlying biological functions. Strong collaborations, which combine essential biological motivation and validation with theoretical work, exist within and across the sub-areas. « less