ArticleBioinformatics (Oxford, England)2011
Identification of metabolic network models from incomplete high-throughput datasets.
Article in Bioinformatics (Oxford, England), 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- A new selective force driving metabolic gene clustering.mSystems · 2024Article
- Qualitative Modeling, Analysis and Control of Synthetic Regulatory Circuits.Methods in molecular biology (Clifton, N.J.) · 2021Review
- Estimation methods for heterogeneous cell population models in systems biology.Journal of the Royal Society, Interface · 2018Review
- Modular bond-graph modelling and analysis of biomolecular systems.IET systems biology · 2016Article
- Mass spectrometry-based workflow for accurate quantification of Escherichia coli enzymes: how proteomics can play a key role in metabolic engineering.Molecular & cellular proteomics : MCP · 2014Article
- On the identifiability of metabolic network models.Journal of mathematical biology · 2013Article
- Identification of aberrant pathways and network activities from high-throughput data.Briefings in bioinformatics · 2012Review
- Controversies in modern evolutionary biology: the imperative for error detection and quality control.BMC genomics · 2012Article
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5 authors.
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Abstract
motivationHigh-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.
resultsWe 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.
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