Transitions and thermodynamics on species graphs of chemical reaction networks

Chemical reaction network (CRN) theory is widely utilized to model and analyze a wide range of biochemical phenomena. Structural transformations and reductions serve as essential tools to better understand how topological properties of a CRN are related with specific functions. In this context, graph-based representations of CRNs have been explored; however, their connections to concentration dynamics and thermodynamics remain elusive. In this study, we propose a natural transformation from the classical complex-reaction graph to a species-transition graph, leveraging the conservation laws of the stoichiometric matrix. By introducing transition matrices on the species graph, the concentration dynamics of CRNs are reinterpreted as the differences between the physically observable inflow and outflow of species. This approach enables the formal lumping of multiple reactions into fewer interactions, which we demonstrate using a realistic metabolic model of E. coli. Additionally, we define species-specific thermodynamic quantities on the species graphs and establish bounds relating them to conventional reactionwise quantities. The specieswise bounds of conventional driving forces and entropy production rates can outperform the bounds determined by reactionwise flux fluctuations, as illustrated through numerical simulations with the Brusselator model. The proposed framework unifies graph-based representations of CRNs with concentration dynamics and thermodynamic principles, offering novel insights and computational advantages for analyzing complex biochemical networks.

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Publication Details

Journal
Physical review. E
Published
2025-10-08
DOI
https://doi.org/10.1103/p42m-8bqy
Primary Topic
Microbial Metabolic Engineering and Bioproduction
Type
article
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article

Transitions and thermodynamics on species graphs of chemical reaction networks

Tetsuya J. Kobayashi, Dimitri Loutchko, Keisuke Sugie
Physical review. E
Microbial Metabolic Engineering and Bioproduction
article

Transitions and thermodynamics on species graphs of chemical reaction networks

Tetsuya J. Kobayashi, Dimitri Loutchko, Keisuke Sugie
article en

Abstract

Chemical reaction network (CRN) theory is widely utilized to model and analyze a wide range of biochemical phenomena. Structural transformations and reductions serve as essential tools to better understand how topological properties of a CRN are related with specific functions. In this context, graph-based representations of CRNs have been explored; however, their connections to concentration dynamics and thermodynamics remain elusive. In this study, we propose a natural transformation from the classical complex-reaction graph to a species-transition graph, leveraging the conservation laws of the stoichiometric matrix. By introducing transition matrices on the species graph, the concentration dynamics of CRNs are reinterpreted as the differences between the physically observable inflow and outflow of species. This approach enables the formal lumping of multiple reactions into fewer interactions, which we demonstrate using a realistic metabolic model of E. coli. Additionally, we define species-specific thermodynamic quantities on the species graphs and establish bounds relating them to conventional reactionwise quantities. The specieswise bounds of conventional driving forces and entropy production rates can outperform the bounds determined by reactionwise flux fluctuations, as illustrated through numerical simulations with the Brusselator model. The proposed framework unifies graph-based representations of CRNs with concentration dynamics and thermodynamic principles, offering novel insights and computational advantages for analyzing complex biochemical networks.

Physical review. EVol. 112(4)
Okinawa Institute of Science and Technology Graduate University (JP), The University of Tokyo (JP)
Openalex Percentile: Top 75%
Microbial Metabolic Engineering and Bioproduction
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Transitions and thermodynamics on species graphs of chemical reaction networks — Tetsuya J. Kobayashi, Dimitri Loutchko, et al. · Physical review. E (2025) | TGRS Research Map | TGRS