细胞的代谢网络异常复杂，涉及各种途径之间的复杂相互作用。本研究提出了一个计算模型，使用排队理论将糖酵解、戊糖磷酸通路（PPP）、脂肪酸β氧化和三羧酸循环（TCA循环）整合起来。该模型利用文献中的代谢物浓度和酶动力学常数数据，计算微观尺度上各个反应发生的概率，可以看作是宏观尺度上的反应速率。但需要注意的是，该模型存在一些局限性，包括没有考虑代谢物参与的所有反应。因此，本研究使用遗传算法（GA）估计了这些外部过程的影响。尽管存在这些局限性，我们的模型达到了高准确性和稳定性，能够实时观测代谢物浓度的变化。这种模型可以帮助更好地理解细胞中的生化反应机制，进而有助于老化、癌症、代谢性疾病和神经退行性疾病的预防和治疗。© 2023. Springer Nature Limited.

The metabolic network of a living cell is highly intricate and involves complex interactions between various pathways. In this study, we propose a computational model that integrates glycolysis, the pentose phosphate pathway (PPP), the fatty acids beta-oxidation, and the tricarboxylic acid cycle (TCA cycle) using queueing theory. The model utilizes literature data on metabolite concentrations and enzyme kinetic constants to calculate the probabilities of individual reactions occurring on a microscopic scale, which can be viewed as the reaction rates on a macroscopic scale. However, it should be noted that the model has some limitations, including not accounting for all the reactions in which the metabolites are involved. Therefore, a genetic algorithm (GA) was used to estimate the impact of these external processes. Despite these limitations, our model achieved high accuracy and stability, providing real-time observation of changes in metabolite concentrations. This type of model can help in better understanding the mechanisms of biochemical reactions in cells, which can ultimately contribute to the prevention and treatment of aging, cancer, metabolic diseases, and neurodegenerative disorders.© 2023. Springer Nature Limited.