Department of Mathematics, University of California Davis (K.G.L.).
Department of Mathematics and Computer Science, Coe College, Cedar Rapids, IA (M.T.S.).
Arterioscler Thromb Vasc Biol. 2021 Jan;41(1):79-86. doi: 10.1161/ATVBAHA.120.314648. Epub 2020 Oct 29.
Bleeding frequency and severity within clinical categories of hemophilia A are highly variable and the origin of this variation is unknown. Solving this mystery in coagulation requires the generation and analysis of large data sets comprised of experimental outputs or patient samples, both of which are subject to limited availability. In this review, we describe how a computationally driven approach bypasses such limitations by generating large synthetic patient data sets. These data sets were created with a mechanistic mathematical model, by varying the model inputs, clotting factor, and inhibitor concentrations, within normal physiological ranges. Specific mathematical metrics were chosen from the model output, used as a surrogate measure for bleeding severity, and statistically analyzed for further exploration and hypothesis generation. We highlight results from our recent study that employed this computationally driven approach to identify FV (factor V) as a key modifier of thrombin generation in mild to moderate hemophilia A, which was confirmed with complementary experimental assays. The mathematical model was used further to propose a potential mechanism for these observations whereby thrombin generation is rescued in FVIII-deficient plasma due to reduced substrate competition between FV and FVIII for FXa (activated factor X).
在 A 型血友病的临床分类中，出血频率和严重程度差异很大，其变异的起源尚不清楚。要解决凝血中的这个谜团，需要生成和分析由实验输出或患者样本组成的大型数据集，这两者都受到可用性有限的限制。在这篇综述中，我们描述了一种计算驱动的方法如何通过生成大型合成患者数据集来绕过这些限制。这些数据集是通过在正常生理范围内改变模型输入、凝血因子和抑制剂浓度，使用机械数学模型创建的。从模型输出中选择了特定的数学指标，作为出血严重程度的替代衡量标准，并进行了统计分析，以进一步探索和假设生成。我们重点介绍了我们最近的一项研究结果，该研究采用这种计算驱动的方法来确定 FV（因子 V）是轻度至中度 A 型血友病中凝血酶生成的关键调节剂，这一结果通过补充实验检测得到了证实。该数学模型进一步被用于提出一种潜在的机制，即由于 FV 和 FVIII 对 FXa（激活的因子 X）的底物竞争减少，FVIII 缺乏的血浆中的凝血酶生成得到挽救。
