Optimizing regulation functions in gene network identification

Guilhem Richard, A. Agung Julius, Calin Belta

Abstract: This paper is concerned with the problem of identifying a discrete-time dynamical system model for a gene regulatory network with unknown topology using time series gene expression data. The topology of such a network can be characterized by a set of regulation hypotheses, one for each gene. In our earlier work, we formulated a convex optimization method to select the regulation hypotheses (and hence the network topology). In this paper, we further optimize the dynamics of the inferred network. Specifically, for a given topology, we minimize the ℓ2 distance between the experimental data and the model prediction. We illustrate the performance of our algorithm by identifying models for gene networks with known topology.

External IDs:dblp:conf/cdc/RichardJB13