Artificial Metabolic Networks: enabling neural computation with metabolic networks
Résumé
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.
Mots clés
Artificial Neural Network
Metabolic Network
Mechanistic Modeling
Metabolic Flux Analysis
Scientific Machine Learning
Hybrid Modeling
Artificial Metabolic Network 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
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