Apr 7, 2020

Machine Learning and Optimal Control of Enzyme Activities to Preserve Solvent Capacity in the Cell

BioRxiv : the Preprint Server for Biology
S. BrittonWilliam Cannon

Abstract

Experimental measurements or computational model predictions of the post-translational regulation of enzymes needed in a metabolic pathway is a difficult problem. Consequently, regulation is mostly known only for well-studied reactions of central metabolism in various model organisms. In this study, we utilize two approaches to predict enzyme regulation policies and investigate the hypothesis that regulation is driven by the need to maintain the solvent capacity in the cell. The first predictive method uses a statistical thermodynamics and metabolic control theory framework while the second method is performed using a hybrid optimization-reinforcement learning approach. Efficient regulation schemes were learned from experimental data that either agree with theoretical calculations or result in a higher cell fitness using maximum useful work as a metric. Model predictions provide the following novel general principles: (1) the regulation itself causes the reactions to be much further from equilibrium instead of the common assumption that highly non-equilibrium reactions are the targets for regulation; (2) regulation is used to maintain the concentrations of both immediate and downstream product concentrations rather than to main...Continue Reading

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Mentioned in this Paper

Study
Genome
Genetic Association Studies
Autosome
Genotype Determination
Gene Mutant
Phenotype Determination

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