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Artificial Metabolic Networks: enabling neural computation with metabolic networks

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Abstract

Metabolic networks have largely been exploited as mechanistic tools to predict the behavior of microorganisms with a defined genotype in different environments. However, flux predictions by constraint-based modeling approaches are limited in quality unless labor intensive experiments including the measurement of media intake fluxes, are performed. Using machine learning instead of an optimization of biomass flux-on which most existing constraint-based methods are basedprovides ways to improve flux and growth rate predictions. In this paper, we show how Recurrent Neural Networks can surrogate constraint-based modeling and make metabolic networks suitable for backpropagation and consequently be used as an architecture for machine learning. We refer to our hybrid-mechanistic and neural network-models as Artificial Metabolic Networks (AMN). We showcase AMN and illustrate its performance with an experimental dataset of Escherichia coli growth rates in 73 different media compositions. We reach a regression coefficient of R 2 =0.78 on crossvalidation sets. We expect AMNs to provide easier discovery of metabolic insights and prompt new biotechnological applications.
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Dates and versions

hal-03613655 , version 1 (18-03-2022)
hal-03613655 , version 2 (31-10-2022)

Licence

Attribution - NonCommercial - NoDerivatives - CC BY 4.0

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Léon Faure, Bastien Mollet, Wolfram Liebermeister, Jean-Loup Faulon. Artificial Metabolic Networks: enabling neural computation with metabolic networks. 2022. ⟨hal-03613655v2⟩
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