Identification of Dominant Paths for Biochemical Reaction Networks from Incomplete Data

斯坦福大学的约翰·罗斯得到了化学部理论和计算化学项目的支持，以表彰他开发方法和工具来确定复杂生化网络中的主要反应路径。该方法基于广义Fisher定律，结合重整化群方法，并应用于响应实验。介绍了从响应实验收集的不完全数据中识别主要反应路径的统计方法。这些方法可以分析生化反应中不完整的实验数据。这项工作的更广泛影响涉及到国际合作，将这些方法应用于几个生物系统的研究：酵母新陈代谢和血液凝固的研究。预计凝血模型将在临床软件程序中实施，以改进对口服凝血剂剂量的预测。

John Ross of Stanford University is supported by an award from the Theoretical and Computational Chemistry program within the Division of Chemistry for the development of methods and tools to determine dominant reaction pathways in complex biochemical networks. The approach is based on the use of a generalized Fisher law, combined with a renormalization group approach, and applied to response experiments. Statistical approaches for identifying dominant reaction pathways from incomplete data collected from response experiments have been introduced. These methods permit the analysis of incomplete experimental data in biochemical reactions.The broader impacts of this work involve an international collaboration to apply these methods to study several biological systems: to the study of yeast metabolism and blood coagulation. It is expected that the blood coagulation model will be implemented in clinical software programs for improved prediction of the dosage of oral coagulants.