Identifying the network of biochemical interactions that underpin disease pathophysiology is a key hurdle in drug discovery. While many components involved in these biological processes are identified, how components organize differently in health and disease remains unclear. In chemical engineering, mechanistic modeling provides a quantitative framework to capture our understanding of a reactive system and test this knowledge against data. Here, we describe an emerging approach to test this knowledge against data that leverages concepts from probability, Bayesian statistics, and chemical kinetics by focusing on two related inverse problems. The first problem is to identify the causal structure of the reaction network, given uncertainty as to how the reactive components interact. The second problem is to identify the values of the model parameters, when a network is known a priori.

中文翻译：

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基于计算机模型的推理：一种用于设计更好药物的逆问题的新兴方法。


识别支撑疾病病理生理学的生化相互作用网络是药物发现的关键障碍。虽然参与这些生物过程的许多成分已被确定，但成分在健康和疾病中如何以不同的方式组织仍不清楚。在化学工程中，机械建模提供了一个定量框架来捕获我们对反应系统的理解并根据数据测试这些知识。在这里，我们描述了一种新兴方法，通过关注两个相关的逆问题，利用概率、贝叶斯统计和化学动力学的概念来根据数据测试这些知识。第一个问题是在反应成分如何相互作用的不确定性下确定反应网络的因果结构。第二个问题是当网络先验已知时确定模型参数的值。