Identification of metabolic network models from incomplete high-throughtput datasets

English

Séminaire Probabilités & Statistique

23/06/2011 - 14:00 Sara Berthoumieux (Equipe IBIS, INRIA Grenoble-Rhône-Alpes) Salle 1 - Tour IRMA

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. Thorough exploitation of the data by identification techniques that explicitly cope with missing observations is therefore of major importance. 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. We evaluate performance of our methods by comparison to existing approaches and 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.