Transcriptional Regulatory Networks from the Bottom Down

Ilya Shmulevich (May 9, 2012)

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Abstract

Regulatory networks of biomolecular interactions in cells govern virtually all cellular behaviors and functions. Modern measurement technologies are being used to generateinformation on many types of interactions, involving transcriptional and microRNA regulatory networks, signaling networks, and cytokine networks. Temporal measurements of gene and protein expression levels and chromatin modifications, coupled with data fusion strategies that incorporate computational predictions of regulatory mechanisms on the basis of other types of information, such as nucleic acid sequence, can be used to constructdynamical system models of these networks. The analysis and simulation of such models, in conjunction with experimental validation, sheds light on biological function and paves the way toward rational and systematic control strategies intended to drive a diseased system toward a desired state by means of targeted interventions. At the same time, such systems approaches permit new biological observables that reflect system-level behavior that cannot be understood by studying individual sets of interactions. Cellular decision making, maintenance of homeostasis and robustness, sensitivity to diverse types of information in the presence of environmental variability, and coordination ofcomplex macroscopic behavior are examples of such emergent systems-level behavior. Information theoretic approaches combined with elements of dynamical systems theory, such as phase transitions and structure dynamics relationships, are promising frameworks for studying fundamental principles governing living systems at all scales of organization.