Linear time-varying models can reveal non-linear interactions of biomolecular regulatory networks using multiple time-series data

Kim, J., Heslop-Harrison, P., Postlethwaite, I., Bates, D.G. and Cho, K. (2008) Linear time-varying models can reveal non-linear interactions of biomolecular regulatory networks using multiple time-series data. Bioinformatics, 24(10), pp. 1286-1292. (doi: 10.1093/bioinformatics/btn107)

Abstract

<b>Motivation:</b> 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. <b>Results:</b> 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 <i>Dictyostelium discoideum</i>, the cell-cycle data for Saccharomyces cerevisiae and a large-scale non-linear model of a group of synchronized <i>Dictyostelium</i> cells.

• Item Type: Articles
• Keywords: Biomolecular network inferring, time-varying model.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Kim, Dr Jongrae
• Authors: Kim, J., Heslop-Harrison, P., Postlethwaite, I., Bates, D.G., and Cho, K.
• Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science; Q Science > Q Science (General)
• College/School: College of Science and Engineering > School of Engineering > Biomedical Engineering; College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity
• Journal Name: Bioinformatics
• ISSN: 1367-4803
• ISSN (Online): 1460-2059