Document Detail


Identification of metabolic network models from incomplete high-throughput datasets.
MedLine Citation:
PMID:  21685069     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
MOTIVATION: High-throughput measurement techniques for metabolism and gene expression provide a wealth of information for the identification of metabolic network models. Yet, missing observations scattered over the dataset restrict the number of effectively available datapoints and make classical regression techniques inaccurate or inapplicable. Thorough exploitation of the data by identification techniques that explicitly cope with missing observations is therefore of major importance.
RESULTS: We develop a maximum-likelihood approach for the estimation of unknown parameters of metabolic network models that relies on the integration of statistical priors to compensate for the missing data. In the context of the linlog metabolic modeling framework, we implement the identification method by an Expectation-Maximization (EM) algorithm and by a simpler direct numerical optimization method. We evaluate performance of our methods by comparison to existing approaches, and show that our EM method provides the best results over a variety of simulated scenarios. We then apply the EM algorithm to a real problem, the identification of a model for the Escherichia coli central carbon metabolism, based on challenging experimental data from the literature. This leads to promising results and allows us to highlight critical identification issues.
Authors:
Sara Berthoumieux; Matteo Brilli; Hidde de Jong; Daniel Kahn; Eugenio Cinquemani
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  Bioinformatics (Oxford, England)     Volume:  27     ISSN:  1367-4811     ISO Abbreviation:  Bioinformatics     Publication Date:  2011 Jul 
Date Detail:
Created Date:  2011-06-20     Completed Date:  2011-10-27     Revised Date:  2013-06-28    
Medline Journal Info:
Nlm Unique ID:  9808944     Medline TA:  Bioinformatics     Country:  England    
Other Details:
Languages:  eng     Pagination:  i186-95     Citation Subset:  IM    
Affiliation:
INRIA Grenoble-Rhône-Alpes, Montbonnot, France. sara.berthoumieux@inria.fr
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MeSH Terms
Descriptor/Qualifier:
Algorithms*
Computational Biology / methods
Escherichia coli / metabolism*
Metabolic Networks and Pathways*
Models, Biological
Comments/Corrections

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