| Linear time-varying models can reveal non-linear interactions of biomolecular regulatory networks using multiple time-series data. | |
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MedLine Citation:
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PMID: 18367478 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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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 |
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Authors:
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Jongrae Kim; Declan G Bates; Ian Postlethwaite; Pat Heslop-Harrison; Kwang-Hyun Cho |
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Publication Detail:
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Type: Journal Article; Research Support, Non-U.S. Gov't Date: 2008-03-26 |
Journal Detail:
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Title: Bioinformatics (Oxford, England) Volume: 24 ISSN: 1367-4811 ISO Abbreviation: Bioinformatics Publication Date: 2008 May |
Date Detail:
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Created Date: 2008-05-08 Completed Date: 2008-06-04 Revised Date: 2009-11-04 |
Medline Journal Info:
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Nlm Unique ID: 9808944 Medline TA: Bioinformatics Country: England |
Other Details:
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Languages: eng Pagination: 1286-92 Citation Subset: IM |
Affiliation:
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Department of Aerospace Engineering, University of Glasgow, Glasgow, UK. |
Export Citation:
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| MeSH Terms | |
Descriptor/Qualifier:
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Algorithms Computer Simulation Feedback / physiology Gene Expression Profiling / methods* Gene Expression Regulation / physiology* Linear Models Models, Biological* Nonlinear Dynamics Proteome / metabolism* Signal Transduction / physiology* |
| Chemical | |
Reg. No./Substance:
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0/Proteome |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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