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.
Original language | English (US) |
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Pages (from-to) | 1307-1323 |
Number of pages | 17 |
Journal | Journal of Computational Biology |
Volume | 19 |
Issue number | 12 |
DOIs | |
State | Published - Dec 1 2012 |
Keywords
- gene regulatory networks
- inference of dynamic models
- temporally evolving networks
ASJC Scopus subject areas
- Modeling and Simulation
- Molecular Biology
- Genetics
- Computational Mathematics
- Computational Theory and Mathematics