da Cunha Éverton F, Kraakman Yanna J, Kriukov Dmitrii V, van Poppel Thomas, Stegehuis Clara, Wong Albert S Y
Department of Molecules and Materials, Faculty of Science and Technology, University of Twente Drienerlolaan 5 Enschede 7522 NH The Netherlands.
MESA+ Institute and BRAINS (Center for Brain-inspired Nano Systems), University of Twente Drienerlolaan 5 Enschede 7522 NH The Netherlands.
Chem Sci. 2025 Jan 8;16(7):3099-3106. doi: 10.1039/d4sc05234j. eCollection 2025 Feb 12.
Network measures have proven very successful in identifying structural patterns in complex systems (, a living cell, a neural network, the Internet). How such measures can be applied to understand the rational and experimental design of chemical reaction networks (CRNs) is unknown. Here, we develop a procedure to model CRNs as a mathematical graph on which network measures and a random graph analysis can be applied. We used an enzymatic CRN (for which a mass-action model was previously developed) to show that the procedure provides insights into its network structure and properties. Temporal analyses, in particular, revealed when feedback interactions emerge in such a network, indicating that CRNs comprise various reactions that are being added and removed over time. We envision that the procedure, including the temporal network analysis method, could be broadly applied in chemistry to characterize the network properties of many other CRNs, promising data-driven analysis of future molecular systems of ever greater complexity.
网络测量在识别复杂系统（如活细胞、神经网络、互联网）中的结构模式方面已被证明非常成功。然而，尚不清楚如何应用这些测量方法来理解化学反应网络（CRN）的合理设计和实验设计。在这里，我们开发了一种程序，将CRN建模为一个数学图，在该图上可以应用网络测量和随机图分析。我们使用了一个酶促CRN（之前已开发出其质量作用模型）来表明该程序能够深入了解其网络结构和特性。特别是时间分析揭示了这种网络中反馈相互作用何时出现，这表明CRN包含随着时间推移不断添加和去除的各种反应。我们设想，包括时间网络分析方法在内的该程序可广泛应用于化学领域，以表征许多其他CRN的网络特性，有望对未来更复杂的分子系统进行数据驱动的分析。
