title: Inferring gene regulatory networks from time-ordered gene expression data of Bacillus subtilis using differential equations.
abstract: 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 sparseness of 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.
authors.affiliation: Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan.
authors.forename: Michiel J L
authors.initials: MJ
authors.lastname: de Hoon
authors.forename: Seiya
authors.initials: S
authors.lastname: Imoto
authors.forename: Kazuo
authors.initials: K
authors.lastname: Kobayashi
authors.forename: Naotake
authors.initials: N
authors.lastname: Ogasawara
authors.forename: Satoru
authors.initials: S
authors.lastname: Miyano
mesh_terms: D001412:Bacillus subtilis / Q000235:genetics* / Q000378:metabolism; D001499:Bayes Theorem; D016000:Cluster Analysis; D003470:Culture Media; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D015964:Gene Expression Regulation, Bacterial; D005798:Genes, Bacterial; D016014:Linear Models; D008957:Models, Genetic; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data
chemical_list: D003470:Culture Media