Function approximation approach to the inference of reduced NGnet models of genetic networks

BMC Bioinformatics
S KimuraMariko Hatakeyama

Abstract

The inference of a genetic network is a problem in which mutual interactions among genes are deduced using time-series of gene expression patterns. While a number of models have been proposed to describe genetic regulatory networks, this study focuses on a set of differential equations since it has the ability to model dynamic behavior of gene expression. When we use a set of differential equations to describe genetic networks, the inference problem can be defined as a function approximation problem. On the basis of this problem definition, we propose in this study a new method to infer reduced NGnet models of genetic networks. Through numerical experiments on artificial genetic network inference problems, we demonstrated that our method has the ability to infer genetic networks correctly and it was faster than the other inference methods. We then applied the proposed method to actual expression data of the bacterial SOS DNA repair system, and succeeded in finding several reasonable regulations. When our method inferred the genetic network from the actual data, it required about 4.7 min on a single-CPU personal computer. The proposed method has an ability to obtain reasonable networks with a short computational time. As a high ...Continue Reading

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Citations

Feb 4, 2012·Journal of Computational Biology : a Journal of Computational Molecular Cell Biology·Xinyi YangChristine Nardini
Feb 5, 2009·Bioinformatics·Shuhei KimuraMariko Hatakeyama
Jun 20, 2013·BMC Bioinformatics·Ahsan Raja ChowdhuryNguyen Xuan Vinh
Jun 12, 2012·Biochimica Et Biophysica Acta·Nguyen Xuan VinhPramod P Wangikar
May 18, 2011·IEEE/ACM Transactions on Computational Biology and Bioinformatics·Kyriakos Kentzoglanakis, Matthew Poole
Jan 16, 2008·BMC Bioinformatics·Shuhei KimuraMariko Hatakeyama
Jan 1, 2016·3 Biotech·A S Jereesh, V K Govindan

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Software Mentioned

PEACE1
NGnet

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