PT  - JOURNAL ARTICLE
AU  - Léon Faure
AU  - Bastien Mollet
AU  - Wolfram Liebermeister
AU  - Jean-Loup Faulon
TI  - Artificial Metabolic Networks: enabling neural computation with metabolic networks
AID  - 10.1101/2022.01.09.475487
DP  - 2022 Jan 01
TA  - bioRxiv
PG  - 2022.01.09.475487
4099  - http://biorxiv.org/content/early/2022/01/11/2022.01.09.475487.short
4100  - http://biorxiv.org/content/early/2022/01/11/2022.01.09.475487.full
AB  - 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 StatementThe authors have declared no competing interest.AMNArtificial Metabolic NetworksANNArtificial Neural NetworkCBMConstraint-Based Modelling(p)FBA(parsimonious) Flux Balance AnalysisLP(QP)Linear (Quadradic) ProgrammingMFAMetabolic Flux AnalysisMLMachine LearningMMMechanistic ModellingRNNRecurrent Neural Network