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Data-driven model of glycolysis identifies the role of allostery in maintaining ATP homeostasis

Choe, Mangyu and Einav, Tal and Phillips, Rob and Titov, Denis V. (2022) Data-driven model of glycolysis identifies the role of allostery in maintaining ATP homeostasis. . (Unpublished)

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The specific roles of allostery in regulating metabolism are not well understood. Here, we develop a data-driven mathematical model of mammalian glycolysis that uses enzyme rate equations and coupled ordinary differential equations. The key components of our model are the rate equations for allosterically regulated enzymes based on the Monod-Wyman-Changeux model that we derive using a rigorous analysis of thousands ofin vitrokinetic measurements. The resulting model recapitulates the properties of glycolysis observed in live cells and shows that the specific function of allosteric regulation is to maintain high and stable concentrations of ATP, while glycolysis without allosteric regulation is fully capable of producing ATP and ensuring that ATP hydrolysis generates energy. Our data-based modeling approach provides a roadmap for a better understanding of the role of allostery in metabolism regulation.One-Sentence SummaryThe glycolysis model based on allosteric enzyme rate equations recapitulates properties of glycolysis observed in live cells.

Item Type:Report or Paper (Discussion Paper)
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URLURL TypeDescription Paper ItemCode
Choe, Mangyu0000-0002-8889-7494
Einav, Tal0000-0003-0777-1193
Phillips, Rob0000-0003-3082-2809
Titov, Denis V.0000-0001-5677-0651
Additional Information:The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license. We thank the students and instructors of the Marine Biological Laboratory course on Physical Biology of the Cell for their comments on the early version of this project; participants of the Kavli Institute for Theoretical Physics workshop on Cellular Energetics for fruitful discussions; Bradley Webb for discussions about PFK regulation; and members of Titov and Phillips labs for helpful suggestions. This research used the Savio computational cluster resource provided by the Berkeley Research Computing program at the University of California, Berkeley (supported by the UC Berkeley Chancellor, Vice Chancellor for Research, and Chief Information Officer). Funding: National Institutes of Health grant DP2 GM132933 (DVT) National Institutes of Health grant 5R35 GM118043-7 (RP) Damon Runyon Cancer Research Foundation Fellowship DRQ 01-20 (TE). Author contributions: Conceptualization: RP, DVT Data curation: MC, TE, DVT Methodology: MC, TE, DVT Investigation: MC, TE, DVT Funding acquisition: RP, DVT Supervision: RP, DVT Writing – original draft: DVT Writing – review & editing: MC, TE, RP, DVT. Data and materials availability: All data are available in the main text or the supplementary materials. The Julia code for the glycolysis model that reproduces all the figures in the main text is deposited at The authors have declared no competing interest.
Funding AgencyGrant Number
NIHDP2 GM132933
NIH5R35 GM118043-7
Damon Runyon Cancer Research FoundationDRQ 01-20
Record Number:CaltechAUTHORS:20230316-182632000.47
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:120160
Deposited By: George Porter
Deposited On:20 Mar 2023 20:11
Last Modified:20 Mar 2023 20:11

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