title: Optimization-based inference for temporally evolving networks with applications in biology.
abstract: The problem of identifying dynamics of biological networks is of critical importance in order to understand biological systems. In this article, we propose a data-driven inference scheme to identify temporally evolving network representations of genetic networks. In the formulation of the optimization problem, we use an adjacency map as a priori information and define a cost function that both drives the connectivity of the graph to match biological data as well as generates a sparse and robust network at corresponding time intervals. Through simulation studies of simple examples, it is shown that this optimization scheme can help capture the topological change of a biological signaling pathway, and furthermore, might help to understand the structure and dynamics of biological genetic networks.
authors.affiliation: Department of Mechanical Engineering, University of California, Berkeley, CA 94720-1770, USA.
authors.forename: Young Hwan
authors.initials: YH
authors.lastname: Chang
authors.forename: Joe
authors.initials: J
authors.lastname: Gray
authors.forename: Claire
authors.initials: C
authors.lastname: Tomlin
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D005260:Female; D053263:Gene Regulatory Networks; D006801:Humans; D008954:Models, Biological; D017711:Nonlinear Dynamics; D018719:Erb-b2 Receptor Tyrosine Kinases / Q000378:metabolism; D015398:Signal Transduction
chemical_list: D018719:Erb-b2 Receptor Tyrosine Kinases