Prediction of Metabolite Concentrations, Rate Constants and Post-Translational Regulation Using Maximum Entropy-Based Simulations with Application to Central Metabolism of Neurospora crassa

Cannon, W. Roger; Zucker, Jeremy; Baxter, Douglas J.; Kumar, Neeraj; Baker, Scott E.; Hurley, Jennifer M.; Dunlap, Jay C.

doi:10.3390/pr6060063

Cited by 14 publications

(19 citation statements)

References 46 publications

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“…The initial step is to determine steady state concentrations without applying regulation by using numerical optimization of the respective ordinary differential equations on a convex energy surface. The convex energy surface for metabolic dynamics is obtained by assuming that the time dependence is the same for all reactions in the Marcelin-de Donder dynamical force equation for mass action kinetics 15 . Due to the assumption that the reactions all occur on the same time scale, the thermodynamic odds of each reaction (Methods, Eqn.…”

Section: Resultsmentioning

confidence: 99%

“…Eqn. (9) is the Marcelin-de Donder equation 15,35 , which describes the forward and reverse reactions as being functions of the time independent odds of the reaction and the rate of change of the odds. Given a metabolic model with Z reactions, M metabolic species, and N total particles, we formulate the flux through each reaction using Eqn.…”

Section: Convex Optimization Approach For Obtaining Metabolic Steady mentioning

confidence: 99%

“…When all metabolite values are brought into agreement with experimental observations, rate constants can be determined, if desired, using Eqn. (15). Alternately, the influence of the activities can directly be incorporated into the rate constants.…”

Section: /21mentioning

confidence: 99%

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

Britton

Alber

Cannon

2020

Preprint

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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 maintain a specific energy charge; and (3) the minimal regulation needed to maintain metabolite levels at physiological concentrations also results in the maximal energy production rate that can be obtained at physiological conditions. The resulting energy production 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-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.

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Section: Resultsmentioning

confidence: 99%

Section: Convex Optimization Approach For Obtaining Metabolic Steady mentioning

confidence: 99%

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

Britton

Alber

Cannon

2020

Preprint

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“…The first paper addresses the problem of model-based experimental design based on global parameter sensitivity measures and demonstrates the application of the methods through a case study on the synthesis of a precursor for protein kinase inhibitors [16]. The second contribution describes the development of a maximum entropy-based simulation method for analysis of metabolic pathways with application to the central metabolism of the mold Neurospora crassa [17].…”

Section: Methodsmentioning

confidence: 99%

Special Issue on Feature Papers for Celebrating the Fifth Anniversary of the Founding of Processes

Henson

2019

Processes

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“…The level of uncertainty can be measured using the entropy concept and may be interpreted as fuzziness or randomness. In order to analyze randomness in decision-making problems applied to different fields, various techniques which use risk measures or information measures have been developed [38,39].…”

Section: Circular Economy Processesmentioning

confidence: 99%

Modeling the Circular Economy Processes at the EU Level Using an Evaluation Algorithm Based on Shannon Entropy

Buṣu

Bușu

2018

Processes

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In this paper we propose a methodology to study circular economy processes based on mathematical modelling. In open-ended systems, waste could be converted back to recycling, transforming the economy from linear to circular. The concept of entropy and the second law of thermodynamics give the argument for a scale reduction of material circulation. As humans extract more and more energy and matter for the economy, the degree of entropy is likely to increase. Based on the findings of economic studies on the implications of industrialization in the case of growing economies, this study aims at evaluating circular economy processes at the European Union (EU) level using a Shannon-Entropy-based algorithm. An entropy-based analysis was conducted for the 28 European Union countries during the time frame 2007–2016. The modelling process consists of constructing a composite indicator which is composed of a weighted sum of all indicators developed by an algorithm based on Shannon Entropy. The weights assigned to each indicator in our analysis measure the significance of each indicator involved in the development of the composite indicator. The results are similar to the international rakings, consolidating and confirming the accuracy and reliability of this approach.

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Prediction of Metabolite Concentrations, Rate Constants and Post-Translational Regulation Using Maximum Entropy-Based Simulations with Application to Central Metabolism of Neurospora crassa

Cited by 14 publications

References 46 publications

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

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

Special Issue on Feature Papers for Celebrating the Fifth Anniversary of the Founding of Processes

Modeling the Circular Economy Processes at the EU Level Using an Evaluation Algorithm Based on Shannon Entropy

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