Inferring gene regulatory networks from time-ordered gene expression data of Bacillus subtilis using differential equations.
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.
de Hoon
Michiel J L
MJ
Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan.
Imoto
Seiya
S
Kobayashi
Kazuo
K
Ogasawara
Naotake
N
Miyano
Satoru
S
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
D003470:Culture Media