A self-organizing state space approach to inferring time-varying causalities between regulatory proteins

Authors:
Osamu Hirose;Kentaro Shimizu
Affiliations:
Bioinformation Engineering Laboratory, Department of Biotechnology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan;Bioinformation Engineering Laboratory, Department of Biotechnology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan
Venue:
ITBAM'10 Proceedings of the First international conference on Information technology in bio- and medical informatics
Year:
2010

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Abstract

A number of methods based on time-dependent state space models have been proposed for inferring time-varying gene regulatory networks. These methods are capable of detecting a relatively small number of topological changes in gene regulatory networks. However, they are insufficient since there is a greater number of changes in the gene regulatory mechanisms; the function of a regulatory protein frequently changes due to post-translational modification, such as protein phosphorylation and ATP-binding.We propose a self-organizing state space approach to inferring consecutive changes in causalities between regulatory proteins from gene expression data. Hidden regulatory proteins are identified using a test-based method from genome-wide protein-DNA binding data. Application of this approach to cell cycle data demonstrated its effectiveness.