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