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Inferring gene regulatory networks from time-ordered gene expression data of bacillus subtilis using differential equations

By Michiel De Hoon, Seiya Imoto and Satoru Miyano


Abstract. Recently, cDNA microarray experiments have generated large amounts of gene expression data. In time-ordered gene expression data, the expression levels are measured at several points in time following some experimental manipulation. A gene regulatory network can be inferred by describing the gene expression data in terms of a linear system of differential equations. 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 to infer a linear system of differential equations, some ad hoc assumptions are made to limit the number of nonzero coefficients in the system. Instead, we propose to infer the degree of sparseness of the gene regulatory network from the data, where we determine which coefficients are nonzero by using Akaike’s Information Criterion.

Year: 2003
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