DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Jongrae | ko |
dc.contributor.author | Bates, Declan G. | ko |
dc.contributor.author | Postlethwaite, Ian | ko |
dc.contributor.author | Heslop-Harrison, Pat | ko |
dc.contributor.author | Cho, Kwang-Hyun | ko |
dc.date.accessioned | 2013-03-08T03:13:35Z | - |
dc.date.available | 2013-03-08T03:13:35Z | - |
dc.date.created | 2012-02-06 | - |
dc.date.created | 2012-02-06 | - |
dc.date.issued | 2008-05 | - |
dc.identifier.citation | BIOINFORMATICS, v.24, no.10, pp.1286 - 1292 | - |
dc.identifier.issn | 1367-4803 | - |
dc.identifier.uri | http://hdl.handle.net/10203/91960 | - |
dc.description.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. | - |
dc.language | English | - |
dc.publisher | OXFORD UNIV PRESS | - |
dc.subject | ENGINEERING GENE NETWORKS | - |
dc.subject | EXPRESSION PROFILES | - |
dc.subject | OSCILLATIONS | - |
dc.subject | IDENTIFICATION | - |
dc.subject | PERTURBATIONS | - |
dc.subject | INFERENCE | - |
dc.subject | PROTEIN | - |
dc.subject | YEAST | - |
dc.title | Linear time-varying models can reveal non-linear interactions of biomolecular regulatory networks using multiple time-series data | - |
dc.type | Article | - |
dc.identifier.wosid | 000255756500010 | - |
dc.identifier.scopusid | 2-s2.0-43349086434 | - |
dc.type.rims | ART | - |
dc.citation.volume | 24 | - |
dc.citation.issue | 10 | - |
dc.citation.beginningpage | 1286 | - |
dc.citation.endingpage | 1292 | - |
dc.citation.publicationname | BIOINFORMATICS | - |
dc.identifier.doi | 10.1093/bioinformatics/btn107 | - |
dc.contributor.localauthor | Cho, Kwang-Hyun | - |
dc.contributor.nonIdAuthor | Kim, Jongrae | - |
dc.contributor.nonIdAuthor | Bates, Declan G. | - |
dc.contributor.nonIdAuthor | Postlethwaite, Ian | - |
dc.contributor.nonIdAuthor | Heslop-Harrison, Pat | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordPlus | ENGINEERING GENE NETWORKS | - |
dc.subject.keywordPlus | EXPRESSION PROFILES | - |
dc.subject.keywordPlus | OSCILLATIONS | - |
dc.subject.keywordPlus | IDENTIFICATION | - |
dc.subject.keywordPlus | PERTURBATIONS | - |
dc.subject.keywordPlus | INFERENCE | - |
dc.subject.keywordPlus | PROTEIN | - |
dc.subject.keywordPlus | YEAST | - |
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