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MuSE – Multi-Omics and Metabolic models iNtegration to study growth Transition in Escherichia coli

MuSE – Multi-Omics and Metabolic models iNtegration to study growth Transition in Escherichia coli

Delphine ROPERS, Inria Grenoble Rhône Alpes 

Living organisms adapt their physiology to environmental and genetic perturbations through a wide reshuffling of their metabolism. At the level of metabolic networks, this implies modifications in the fluxes through biochemical reactions, which result from catalytic protein–metabolite and regulatory interactions at the genetic, post-transcriptional, allosteric, and kinetic levels. Advanced –omics technologies allow to monitor these changes. The biggest challenge nowadays is to integrate the data and especially to make sense of them. Indeed, given that information may be incomplete or noisy, it is far from trivial to infer the precise part of the metabolism that was directly affected by the perturbation. Although there exist methods that attempt to solve this problem, none is fully satisfying from a biological point of view or efficient from an algorithmic perspective, which may again limit any biological insight to be gained from –omics data. The MuSE project aims at addressing both issues by bringing together two teams of different expertise, who know each other enough to facilitate the difficult inter-discipline dialog, but who have never concretely worked together yet.
What each team can thus gain from the other is fully new in relation to what they have been able to do separately in the past. The main expected result is a method that finds the cause-consequence chain between a given perturbation and the reorganization of the metabolism. The method is based on the representation of a metabolic network as a directed hypergraph, with or without stoichiometry, to extract the subnetwork playing a role in the change of metabolite concentrations or/and change of enzymatic expression, which for the sake of simplicity, will be called the metabolic hyperstory associated to such a change. The method will be able to use absolute metabolomics and transcriptomics data, and will be applied as proof-of-concept to analyze the post-transcriptional regulation of the carbon central metabolism of the bacterium Escherichia coli.A