Bioinformatics Advance Access originally published online on March 26, 2008
Bioinformatics 2008 24(10):1286-1292; doi:10.1093/bioinformatics/btn107
Linear time-varying models can reveal non-linear interactions of biomolecular regulatory networks using multiple time-series data
1Department of Aerospace Engineering, University of Glasgow, Glasgow G12 8QQ, 2Systems Biology Lab, 3Department of Engineering, 4Department of Biology, University of Leicester, Leicester, LE1 7RH, UK and 5Department of Bio and Brain Engineering and KI for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), 335 Gwahangno, Yuseong-gu, Daejeon 305-701, Republic of Korea
*To whom correspondence should be addressed.
 |
Abstract |
|---|
Motivation: Inherent non-linearities in biomolecular interactions make the identification of network interactions difficult. One of the principal problems is that all methods based on the use of linear time-invariant models will have fundamental limitations in their capability to infer certain non-linear network interactions. Another difficulty is the multiplicity of possible solutions, since, for a given dataset, there may be many different possible networks which generate the same time-series expression profiles.
Results: A novel algorithm for the inference of biomolecular interaction networks from temporal expression data is presented. Linear time-varying models, which can represent a much wider class of time-series data than linear time-invariant models, are employed in the algorithm. From time-series expression profiles, the model parameters are identified by solving a non-linear optimization problem. In order to systematically reduce the set of possible solutions for the optimization problem, a filtering process is performed using a phase-portrait analysis with random numerical perturbations. The proposed approach has the advantages of not requiring the system to be in a stable steady state, of using time-series profiles which have been generated by a single experiment, and of allowing non-linear network interactions to be identified. The ability of the proposed algorithm to correctly infer network interactions is illustrated by its application to three examples: a non-linear model for cAMP oscillations in Dictyostelium discoideum, the cell-cycle data for Saccharomyces cerevisiae and a large-scale non-linear model of a group of synchronized Dictyostelium cells.
Availability: The software used in this article is available from http://sbie.kaist.ac.kr/software
Contact: ckh{at}kaist.ac.kr
Supplementary information: Supplementary data are available at Bioinformatics online.
Associate Editor: Martin Bishop
Received on January 16, 2008; revised on March 7, 2008; accepted on March 22, 2008
CiteULike 
Connotea 
Del.icio.us What's this?