Dynamical Motifs in Gene Regulatory Networks
Michael Driscoll, Boston University
The stunning varieties of cellular function, from chemotaxis in bacteria to cell-type specification in mammals, are founded upon complex interaction networks of proteins, metabolites, and cis-regulatory genetic sequences.
In genetic regulatory networks, genes act as nodes connected in a directed fashion when the protein product of one gene influences the expression of a second. While the large-scale topologies of these networks have recently been described [1], their dynamics remain little understood. Yet it has been recognized that the dynamics of a regulatory network can have a critical effect on cellular behavior[2-3].
Drawing on a previous study which characterized common topological motifs[4], we have developed first-order models of small, sparsely connected transcriptional networks in order to characterize and explore “dynamical motifs” found in regulatory gene networks. These models incorporate key properties observed for naturally occurring biological networks.
These models have a number of applications. They serve to generate biologically relevant “test networks” against which algorithms, such as those intended to reverse engineer a networks' connectivity[5], can be applied. They also frame our experimental observations about existing, well-characterized networks. Finally, these models can aid our ability to control and engineer regulatory networks in cells, in vitro and in vivo[6].
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Reverse Engineering Genetic Networks and Identifying Compound Mode of Action via Expression Profiling.
(submitted; under review at Science)
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Abstract Author(s): Michael Driscoll