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Artificial Metabolic Networks: enabling neural computation with metabolic networks

Léon Faure, Bastien Mollet, Wolfram Liebermeister, View ORCID ProfileJean-Loup Faulon
doi: https://doi.org/10.1101/2022.01.09.475487
Léon Faure
1University of Paris-Saclay, Saclay, France
2MICALIS Institute INRAe, Jouy-en-Josas, France
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Bastien Mollet
2MICALIS Institute INRAe, Jouy-en-Josas, France
3ENS Lyon, Lyon, France
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Wolfram Liebermeister
1University of Paris-Saclay, Saclay, France
4MaIAGE, INRAe, Jouy-en-Josas, France
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Jean-Loup Faulon
1University of Paris-Saclay, Saclay, France
2MICALIS Institute INRAe, Jouy-en-Josas, France
5LSSB Laboratory, Genoscope, CEA, CNRS
6Manchester Institute of Biotechnology, University of Manchester, Manchester, UK
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  • ORCID record for Jean-Loup Faulon
  • For correspondence: Jean-loup.Faulon@inrae.fr
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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 based – provides 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 R2=0.78 on cross-validation sets. We expect AMNs to provide easier discovery of metabolic insights and prompt new biotechnological applications.

Competing Interest Statement

The authors have declared no competing interest.

  • Abbreviations

    AMN
    Artificial Metabolic Networks
    ANN
    Artificial Neural Network
    CBM
    Constraint-Based Modelling
    (p)FBA
    (parsimonious) Flux Balance Analysis
    LP(QP)
    Linear (Quadradic) Programming
    MFA
    Metabolic Flux Analysis
    ML
    Machine Learning
    MM
    Mechanistic Modelling
    RNN
    Recurrent Neural Network
  • Copyright 
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    Posted January 11, 2022.
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    Artificial Metabolic Networks: enabling neural computation with metabolic networks
    Léon Faure, Bastien Mollet, Wolfram Liebermeister, Jean-Loup Faulon
    bioRxiv 2022.01.09.475487; doi: https://doi.org/10.1101/2022.01.09.475487
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    Artificial Metabolic Networks: enabling neural computation with metabolic networks
    Léon Faure, Bastien Mollet, Wolfram Liebermeister, Jean-Loup Faulon
    bioRxiv 2022.01.09.475487; doi: https://doi.org/10.1101/2022.01.09.475487

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