TY - JOUR
TI - Heuristic approach to sparse approximation of gene regulatory networks
AU - Andrecut, M.
AU - Huang, S.
AU - Kauffman, S. A.
T2 - Journal of Computational Biology: A Journal of Computational Molecular Cell Biology
AB - Determining the structure of the gene regulatory network using the information in genomewide profiles of mRNA abundance, such as microarray data, poses several challenges. Typically, "static" rather than dynamical profile measurements, such as those taken from steady state tissues in various conditions, are the starting point. This makes the inference of causal relationships between genes difficult. Moreover, the paucity of samples relative to the gene number leads to problems such as overfitting and underconstrained regression analysis. Here we present a novel method for the sparse approximation of gene regulatory networks that addresses these issues. It is formulated as a sparse combinatorial optimization problem which has a globally optimal solution in terms of l(0) norm error. In order to seek an approximate solution of the l(0) optimization problem, we consider a heuristic approach based on iterative greedy algorithms. We apply our method to a set of gene expression profiles comprising of 24,102 genes measured over 79 human tissues. The inferred network is a signed directed graph, hence predicts causal relationships. It exhibits typical characteristics of regulatory networks organism with partially known network topology, such as the average number of inputs per gene as well as the in-degree and out-degree distribution.
DA - 2008/11//
PY - 2008
DO - 10.1089/cmb.2008.0087
DP - PubMed
VL - 15
IS - 9
SP - 1173
EP - 1186
J2 - J. Comput. Biol.
LA - eng
SN - 1557-8666
KW - Algorithms
KW - Computer Simulation
KW - Gene Expression Profiling
KW - Gene Regulatory Networks
KW - Humans
KW - Models, Genetic
KW - Oligonucleotide Array Sequence Analysis
KW - RNA, Messenger
KW - Stochastic Processes
ER -