A Kalman-filter based approach to identification of time-varying gene regulatory networks.

ABSTRACT:

Motivation

Conventional identification methods for gene regulatory networks (GRNs) have overwhelmingly adopted static topology models, which remains unchanged over time to represent the underlying molecular interactions of a biological system. However, GRNs are dynamic in response to physiological and environmental changes. Although there is a rich literature in modeling static or temporally invariant networks, how to systematically recover these temporally changing networks remains a major and significant pressing challenge. The purpose of this study is to suggest a two-step strategy that recovers time-varying GRNs.

Results

It is suggested in this paper to utilize a switching auto-regressive model to describe the dynamics of time-varying GRNs, and a two-step strategy is pro

SUBMITTER: Xiong J 

PROVIDER: S-EPMC3792119 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature