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 use 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. As previously hypothesized, regulation is herein shown to control the concentrations of both immediate and downstream product concentrations at physiological levels. Model predictions provide the following two 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; and (2) the minimal regulation needed to maintain metabolite levels at physiological concentrations maximizes the free energy dissipation rate instead of preserving a specific energy charge. The resulting energy dissipation rate is an emergent property of regulation which may be represented by a high value of the adenylate energy charge. In addition, the predictions demonstrate that the amount of regulation needed can be minimized if it is applied at the beginning or branch point of a pathway, in agreement with common notions. The approach is demonstrated for three pathways in the central metabolism of E. coli (gluconeogenesis, glycolysis-tricarboxylic acid (TCA) and pentose phosphate-TCA) that each require different regulation schemes. It is shown quantitatively that hexokinase, glucose 6-phosphate dehydrogenase and glyceraldehyde phosphate dehydrogenase, all branch points of pathways, play the largest roles in regulating central metabolism.

Highlights

  • While our understanding of regulation of transcription and post-transcriptional processes has blossomed in the past 25 years due to advances in high-throughput experimental technologies such as RNA expression, ChIP-Seq and mass spectrometry-based proteomics, our understanding of post-translational regulation has advanced [1,2,3,4], but not as rapidly or as far [5].Fifty years ago, it was postulated that the purpose of post-translational regulation in metabolism is to either maintain a balance of the energy charge of the adenylate pool [6], or to control solvent properties [7]

  • We solve the prediction problem of which enzyme to regulate by a novel combination of methods from statistical thermodynamics, control theory and reinforcement learning (RL)

  • We have shown that this problem is solved, again as suggested by Atkinson, by reducing the activity of either the enzyme catalysing the reaction or upstream enzymes. While this role of regulation has recognized by some in the metabolic control analysis (MCA) research community and elsewhere [15], the wider biological literature predominantly discusses the notion that enzymes are posttranslationally regulated to control flux or maintain an energy charge within a narrow range [43,44,45], despite a number of exceptions regarding the latter [46,47]

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Summary

Introduction

It was postulated that the purpose of post-translational regulation in metabolism is to either maintain a balance of the energy charge of the adenylate pool [6], or to control solvent properties [7]. Atkinson recognized that the maintenance of physiological concentrations of metabolites may well be the most pressing problem of metabolic control [7]. Metabolite concentrations are both a function of the reaction kinetics and a molecule’s standard chemical potential, of which the latter varies only over a small range for each individual metabolite because solution conditions inside a cell vary over a small range. The set of enzymes which are post-translationally regulated is relatively wellconserved across species as well [3], despite the fact that the rate constants for the same enzymes can vary dramatically [8]

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