title: Identification of metabolic network models from incomplete high-throughput datasets.
abstract: BACKGROUND
  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.affiliation: INRIA Grenoble-Rhône-Alpes, Montbonnot, France. sara.berthoumieux@inria.fr
authors.forename: Sara
authors.initials: S
authors.lastname: Berthoumieux
authors.forename: Matteo
authors.initials: M
authors.lastname: Brilli
authors.forename: Hidde
authors.initials: H
authors.lastname: de Jong
authors.forename: Daniel
authors.initials: D
authors.lastname: Kahn
authors.forename: Eugenio
authors.initials: E
authors.lastname: Cinquemani
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods; D004926:Escherichia coli / Q000378:metabolism*; D053858:Metabolic Networks and Pathways; D008954:Models, Biological