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纤维蛋白聚合动力学建模与参数子集选择及其在创伤愈合中的应用。

Modeling and Parameter Subset Selection for Fibrin Polymerization Kinetics with Applications to Wound Healing.

机构信息

Department of Mathematics, North Carolina State University, Box 8205, Raleigh, NC, 27695-8205, USA.

Joint Department of Biomedical Engineering, North Carolina State University and The University of North Carolina at Chapel Hill, Raleigh, NC, 27695, USA.

出版信息

Bull Math Biol. 2021 Mar 22;83(5):47. doi: 10.1007/s11538-021-00876-6.

Abstract

During the hemostatic phase of wound healing, vascular injury leads to endothelial cell damage, initiation of a coagulation cascade involving platelets, and formation of a fibrin-rich clot. As this cascade culminates, activation of the protease thrombin occurs and soluble fibrinogen is converted into an insoluble polymerized fibrin network. Fibrin polymerization is critical for bleeding cessation and subsequent stages of wound healing. We develop a cooperative enzyme kinetics model for in vitro fibrin matrix polymerization capturing dynamic interactions among fibrinogen, thrombin, fibrin, and intermediate complexes. A tailored parameter subset selection technique is also developed to evaluate parameter identifiability for a representative data curve for fibrin accumulation in a short-duration in vitro polymerization experiment. Our approach is based on systematic analysis of eigenvalues and eigenvectors of the classical information matrix for simulations of accumulating fibrin matrix via optimization based on a least squares objective function. Results demonstrate robustness of our approach in that a significant reduction in objective function cost is achieved relative to a more ad hoc curve-fitting procedure. Capabilities of this approach to integrate non-overlapping subsets of the data to enhance the evaluation of parameter identifiability are also demonstrated. Unidentifiable reaction rate parameters are screened to determine whether individual reactions can be eliminated from the overall system while preserving the low objective cost. These findings demonstrate the high degree of information within a single fibrin accumulation curve, and a tailored model and parameter subset selection approach for improving optimization and reducing model complexity in the context of polymerization experiments.

摘要

在止血阶段的伤口愈合,血管损伤导致内皮细胞损伤,启动涉及血小板的凝血级联反应,并形成富含纤维蛋白的血栓。随着这一级联反应的结束,凝血酶的激活发生,可溶性纤维蛋白原转化为不溶性聚合纤维蛋白网络。纤维蛋白聚合对于止血和随后的伤口愈合阶段至关重要。我们开发了一种协同酶动力学模型,用于体外纤维蛋白基质聚合,捕捉纤维蛋白原、凝血酶、纤维蛋白和中间复合物之间的动态相互作用。还开发了一种定制的参数子集选择技术,以评估在短时间体外聚合实验中纤维蛋白积累的代表性数据曲线的参数可识别性。我们的方法基于对经典信息矩阵的特征值和特征向量的系统分析,该矩阵用于通过基于最小二乘目标函数的优化来模拟积累的纤维蛋白基质。结果表明,我们的方法具有鲁棒性,与更特定的曲线拟合过程相比,相对目标函数成本显著降低。还证明了这种方法能够集成数据的非重叠子集的能力,以增强对参数可识别性的评估。筛选不可识别的反应速率参数,以确定是否可以在不影响低目标成本的情况下从整个系统中消除个别反应。这些发现表明,在单个纤维蛋白积累曲线中存在高度信息,以及一种定制的模型和参数子集选择方法,用于在聚合实验的背景下提高优化和降低模型复杂性。

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