A Gene Network Inference Method from Continuous-Value Gene Expression Data of Wild-Type and Mutants

Abstract

In this paper we introduce a new inference method of a gene regulatory network from steadystate gene expression data. Our method determines a regulatory structure consistent with an observeds et of steady-statee xpressionp rofiles, e ach generated fromw ild-typea nd single deletion mutant of the target network.O ur method derivest he regulatoryr elationshipsi n the networku sing a graph theoretic approach. The advantage of our method is to be able to deal with continuous values of steady-state data, while most of the methods proposed in past use a Boolean network model with binary data. Performance of our method is evaluated on simulated networks with varying the size of networks, i ndegreeo f eachg ene, and the data characteristics (continuous-value/binary), and is compared with that of predictor method proposed by Ideker et al. As a result, we show the superiorityo f usingc ontinuousv aluest o binary values, a nd the performanceo f our method is much better than that of the predictor method.