Finding module-based gene networks with state-space models - Mining high-dimensional and short time-course gene expression data

Abstract

This study explores some problems to analyze time-course gene expression data by state-space models (SSMs). One problem is regarding the methods of parameter estimation and determination of the dimension of the internal state variable. Although several methods have been applied, there are few literature studies which with to compare them. Thus, this paper gives a brief review of the existing literature that use the SSM to analyze the gene expression time-course data. Another problem is the identifiability of the model. If the parameters of SSMs are simply estimated without any constraints for parameter space, they lack identifiability. To identify a system uniquely, it requires a specific algorithm to estimate the parameters with some constraints. For that purpose, an identifiable form of SSMs and an algorithm for estimating parameters are derived. The last problem is the extraction of biological information by interpreting the estimated parameters, such as mechanism of gene regulations at the module level. For that one, this paper explores methods to extract further information using the estimated parameters, that is, reconstruction of a module network from time-course gene expression data