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

Kim, Jongrae, Bates, Declan, Postlethwaite, Ian, Heslop-Harrison, Pat and Cho, Kwang-Hyun (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. ISSN 1367-4803

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Official URL: http://dx.doi.org/10.1093/bioinformatics/btn107

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

Item Type: Article
Subjects: H400 Aerospace Engineering
Department: Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
Depositing User: Sarah Howells
Date Deposited: 25 Apr 2012 12:02
Last Modified: 10 Aug 2015 11:31
URI: http://nrl.northumbria.ac.uk/id/eprint/6471

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