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Network Bioinformatics: Regulatory Motifs, Modules,
and Mechanism
C. Rao
A number of technological innovations are yielding unprecedented data on
the networks of biochemical, genetic, and biophysical reactionsthat underlie
cellular behavior and failure. These networks are composed of hundreds to
thousands of chemical species and structures, interacting via nonlinear and
possibly stochastic physical
processes. A central goal of modern biology is to optimally use the
data on these networks to understand how their design leads to the
observed cellular behaviors and failures. Ultimately, this knowledge
should enable cellular engineers to redesign cellular processes to
meet industrial needs (such as optimal natural product synthesis), aid
in choosing the most effective targets for pharmaceuticals, and tailor
treatment for individual genotypes. The size and complexity of these
networks and the inevitable lack of complete data, however, makes
reaching these goals extremely difficult. If it proves possible to
modularize these networks into functional subnetworks, then these
smaller networks may be amenable to direct analysis and might serve as regulatory
motifs. These motifs, recurring elements of control, may
help to deduce the structure and function of partially known networks
and form the basis for fulfilling the goals described above. This talk
discusses a number of approaches undertaken by our research group toidentify
and analyze regulatory motifs in intracellular networks.
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