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Data-driven 13 C-fluxomics towards ab initio reconstruction of metabolic networks
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Edité par CCSD -
International audience. Metabolic networks reconstruction is a valuable tool for understanding biological systems, byidentifying the link between the structure of the network, its organization and its functional properties.The reconstruction process, usually based on the genome, provide a comprehensive model thatrepresents an organism’s entire set of biochemical reactions. Alternative experimental reconstructionapproaches, aim at reconstructing the active metabolic networks directly from metabolomeobservation, in a way that is orthogonal and complementary to in silico approaches. In this work, wehave developed a new data-driven approach for ab initio metabolic reconstruction based on isotopictracing experiments. Isotopic labeling, especially with 13C isotopic tracers, has long been used to studythe structure and the activity of metabolic pathways. Our approach, IsoMet for Isotopic drivenMetabolic reconstruction, is based on the interpretation of untargeted metabolomics data collectedin 13C-labeling dynamic experiments. It aims at providing direct access to the active metabolic networkof an organism, specific to a cell type in a given context, without prior considerations. IsoMet sharesconcepts with non-stationary 13C-fluxomics approaches, such as label propagation simulation and fluxcalculation. Its basic principle consists to construct and test the ability of different network topologiesto explain observed labeling dynamic data, leading to the construction of active metabolicsubnetworks. IsoMet is generic and can be applied to various stable isotope tracers (13C, 15N, etc) anddifferent type of isotopic measurement (MS, MS/MS, NMR, etc). It is intended to be applicable tovarious biological models, ranging from poorly known organisms to complex systems.
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