Bilancioni Massimo, Esposito Massimiliano
Complex Systems and Statistical Mechanics, Department of Physics and Materials Science, University of Luxembourg, 30 Avenue des Hauts-Fourneaux, L-4362, Esch-sur-Alzette, Luxembourg.
Nat Commun. 2025 Jul 1;16(1):5765. doi: 10.1038/s41467-025-60787-1.
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 analyze the gear system of an artificial molecular motor, identifying numerous counterproductive gears and providing insights into its transduction capabilities and optimization.
与车辆中的齿轮系统类似，大多数参与能量转换的化学反应网络（CRN）都有多种转换途径可供使用，每种途径都具有不同的效率。我们将这些途径概念化为“化学齿轮”，并展示它们在完善热力学第二定律方面的作用。这使我们能够仅根据CRN的拓扑结构和操作条件（由其输入和输出物种的化学势定义）来确定其最佳效率以及实现该效率的齿轮。通过适当地调整反应动力学，可以设计CRN以自我调节其齿轮设置，在变化的外部条件下保持最佳效率。我们在一个生物环境中的CRN中展示了这一原理，其中酶充当换挡器，自主地使系统适应不断变化的环境以实现接近最佳的效率。此外，我们分析了一种人工分子马达的齿轮系统，识别出许多适得其反的齿轮，并深入了解其转换能力和优化方法。
