Gears in Chemical Reaction Networks: Optimizing Energy Transduction Efficiency
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866918102206251008 |
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| author | Bilancioni, Massimo Esposito, Massimiliano |
| author_facet | Bilancioni, Massimo Esposito, Massimiliano |
| contents | Similarly to gear systems in vehicles, most chemical reaction networks (CRNs) involved in energy transduction have at their disposal multiple transduction pathways, each characterized by distinct efficiencies. We conceptualize these pathways as `chemical gears' and demonstrate their role in refining the second law of thermodynamics. This allows us to determine the optimal efficiency of a CRN, and the gear enabling it, solely based on its topology and operating conditions, defined by the chemical potentials of its input and output species. By suitably tuning reaction kinetics, a CRN can be engineered to self-regulate its gear settings, maintaining optimal efficiency under varying external conditions. We demonstrate this principle in a biological context with a CRN where enzymes function as gear shifters, autonomously adapting the system to achieve near-optimal efficiency across changing environments. Additionally, we examine artificial molecular motors and reveal that their gearing mechanisms are far from optimally regulated. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_17960 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Gears in Chemical Reaction Networks: Optimizing Energy Transduction Efficiency Bilancioni, Massimo Esposito, Massimiliano Molecular Networks Statistical Mechanics Similarly to gear systems in vehicles, most chemical reaction networks (CRNs) involved in energy transduction have at their disposal multiple transduction pathways, each characterized by distinct efficiencies. We conceptualize these pathways as `chemical gears' and demonstrate their role in refining the second law of thermodynamics. This allows us to determine the optimal efficiency of a CRN, and the gear enabling it, solely based on its topology and operating conditions, defined by the chemical potentials of its input and output species. By suitably tuning reaction kinetics, a CRN can be engineered to self-regulate its gear settings, maintaining optimal efficiency under varying external conditions. We demonstrate this principle in a biological context with a CRN where enzymes function as gear shifters, autonomously adapting the system to achieve near-optimal efficiency across changing environments. Additionally, we examine artificial molecular motors and reveal that their gearing mechanisms are far from optimally regulated. |
| title | Gears in Chemical Reaction Networks: Optimizing Energy Transduction Efficiency |
| topic | Molecular Networks Statistical Mechanics |
| url | https://arxiv.org/abs/2405.17960 |