INFERRING GENE REGULATORY NETWORKS FROM TIME-ORDERED GENE EXPRESSION DATA OF BACILLUS SUBTILIS USING DIFFERENTIAL EQUATIONS

Pacific Symposium on Biocomputing 2003

Michiel JL De Hoon ; Seiya Imoto ; Kazuo Kobayashi ; Naotake Ogasawara ; Satoru Miyano

PID  :  13784

来源: CEUR

【 摘 要 】

We describe a new method to infer a gene regulatory network, in terms of a linear system of differential equations, from time course gene expression data. As biologically the gene regulatory network is known to be sparse, we expect most coefficients in such a linear system of differential equations to be zero. In previously proposed methods, the number of nonzero coefficients in the system was limited based on ad hoc assumptions. Instead, we propose to infer the degree of sparsenessof the gene regulatory network from the data, where we use Akaike’s Information Criterion to determine which coefficients are nonzero. We apply our method to MMGE time course data of Bacillus subtilis.