Zhang Tongli
Department of Pharmacology and Systems Physiology, College of Medicine, University of Cincinnati, Cincinnati, OH, United States.
Front Genet. 2021 Sep 9;12:656508. doi: 10.3389/fgene.2021.656508. eCollection 2021.
Heterogeneity among individual patients presents a fundamental challenge to effective treatment, since a treatment protocol working for a portion of the population often fails in others. We hypothesize that a computational pipeline integrating mathematical modeling and machine learning could be used to address this fundamental challenge and facilitate the optimization of individualized treatment protocols. We tested our hypothesis with the neuroendocrine systems controlled by the hypothalamic-pituitary-adrenal (HPA) axis. With a synergistic combination of mathematical modeling and machine learning (ML), this integrated computational pipeline could indeed efficiently reveal optimal treatment targets that significantly contribute to the effective treatment of heterogeneous individuals. What is more, the integrated pipeline also suggested quantitative information on how these key targets should be perturbed. Based on such ML revealed hints, mathematical modeling could be used to rationally design novel protocols and test their performances. We believe that this integrated computational pipeline, properly applied in combination with other computational, experimental and clinical research tools, can be used to design novel and improved treatment against a broad range of complex diseases.
个体患者之间的异质性对有效治疗提出了根本性挑战，因为适用于一部分人群的治疗方案在其他人群中往往会失败。我们假设，结合数学建模和机器学习的计算流程可用于应对这一根本性挑战，并促进个性化治疗方案的优化。我们以由下丘脑 - 垂体 - 肾上腺（HPA）轴控制的神经内分泌系统来检验我们的假设。通过数学建模和机器学习（ML）的协同结合，这种集成的计算流程确实能够有效地揭示出对异质性个体的有效治疗有显著贡献的最佳治疗靶点。此外，该集成流程还给出了关于如何调节这些关键靶点的定量信息。基于机器学习揭示的这些线索，数学建模可用于合理设计新方案并测试其性能。我们相信，这种集成的计算流程，与其他计算、实验和临床研究工具合理结合应用，可用于设计针对广泛复杂疾病的新型和改进治疗方法。
