Leiderman Karin, Sindi Suzanne S, Monroe Dougald M, Fogelson Aaron L, Neeves Keith B
Department of Applied Mathematics and Statistics, Colorado School of Mines, Golden, Colorado.
Department of Applied Mathematics, University of California, Merced, Merced, California.
Semin Thromb Hemost. 2021 Mar;47(2):129-138. doi: 10.1055/s-0041-1722861. Epub 2021 Feb 26.
Computational models of various facets of hemostasis and thrombosis have increased substantially in the last decade. These models have the potential to make predictions that can uncover new mechanisms within the complex dynamics of thrombus formation. However, these predictions are only as good as the data and assumptions they are built upon, and therefore model building requires intimate coupling with experiments. The objective of this article is to guide the reader through how a computational model is built and how it can inform and be refined by experiments. This is accomplished by answering six questions facing the model builder: (1) Why make a model? (2) What kind of model should be built? (3) How is the model built? (4) Is the model a "good" model? (5) Do we believe the model? (6) Is the model useful? These questions are answered in the context of a model of thrombus formation that has been successfully applied to understanding the interplay between blood flow, platelet deposition, and coagulation and in identifying potential modifiers of thrombin generation in hemophilia A.
在过去十年中，止血和血栓形成各个方面的计算模型有了显著增加。这些模型有潜力做出预测，从而揭示血栓形成复杂动态过程中的新机制。然而，这些预测的质量取决于其建立所依据的数据和假设，因此模型构建需要与实验紧密结合。本文的目的是引导读者了解如何构建计算模型，以及它如何通过实验得到信息并得到完善。这通过回答模型构建者面临的六个问题来实现：（1）为什么要构建模型？（2）应该构建哪种类型的模型？（3）如何构建模型？（4）该模型是一个“好”模型吗？（5）我们相信这个模型吗？（6）这个模型有用吗？这些问题是在一个血栓形成模型的背景下回答的，该模型已成功应用于理解血流、血小板沉积和凝血之间的相互作用，以及识别甲型血友病中凝血酶生成的潜在调节因子。
