School of Chemical, Materials and Biomedical Engineering, University of Georgia, Athens, USA.
Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, USA.
Neural Netw. 2024 Dec;180:106732. doi: 10.1016/j.neunet.2024.106732. Epub 2024 Sep 19.
Blood coagulation, which involves a group of complex biochemical reactions, is a crucial step in hemostasis to stop bleeding at the injury site of a blood vessel. Coagulation abnormalities, such as hypercoagulation and hypocoagulation, could either cause thrombosis or hemorrhage, resulting in severe clinical consequences. Mathematical models of blood coagulation have been widely used to improve the understanding of the pathophysiology of coagulation disorders, guide the design and testing of new anticoagulants or other therapeutic agents, and promote precision medicine. However, estimating the parameters in these coagulation models has been challenging as not all reaction rate constants and new parameters derived from model assumptions are measurable. Although various conventional methods have been employed for parameter estimation for coagulation models, the existing approaches have several shortcomings. Inspired by the physics-informed neural networks, we propose Coagulo-Net, which synergizes the strengths of deep neural networks with the mechanistic understanding of the blood coagulation processes to enhance the mathematical models of the blood coagulation cascade. We assess the performance of the Coagulo-Net using two existing coagulation models with different extents of complexity. Our simulation results illustrate that Coagulo-Net can efficiently infer the unknown model parameters and dynamics of species based on sparse measurement data and data contaminated with noise. In addition, we show that Coagulo-Net can process a mixture of synthetic and experimental data and refine the predictions of existing mathematical models of coagulation. These results demonstrate the promise of Coagulo-Net in enhancing current coagulation models and aiding the creation of novel models for physiological and pathological research. These results showcase the potential of Coagulo-Net to advance computational modeling in the study of blood coagulation, improving both research methodologies and the development of new therapies for treating patients with coagulation disorders.
血液凝固是一个涉及一系列复杂生化反应的过程，是血管损伤部位止血的关键步骤。凝血异常，如高凝和低凝，可能导致血栓形成或出血，从而导致严重的临床后果。血液凝固的数学模型已广泛用于改善对凝血障碍病理生理学的理解、指导新型抗凝剂或其他治疗剂的设计和测试，并促进精准医学。然而，由于并非所有反应速率常数和从模型假设中推导出的新参数都可测量，因此估计这些凝血模型中的参数一直具有挑战性。尽管已经采用了各种传统方法来进行凝血模型的参数估计，但现有的方法存在一些缺点。受物理启发的神经网络的启发，我们提出了 Coagulo-Net，它将深度神经网络的优势与血液凝固过程的机制理解相结合，以增强血液凝固级联的数学模型。我们使用具有不同复杂程度的两个现有凝血模型来评估 Coagulo-Net 的性能。我们的模拟结果表明，Coagulo-Net 可以根据稀疏的测量数据和受噪声污染的数据有效地推断未知的模型参数和物种动态。此外，我们表明 Coagulo-Net 可以处理合成数据和实验数据的混合物，并改进现有的凝血数学模型的预测。这些结果表明 Coagulo-Net 在增强当前凝血模型和辅助为生理和病理研究创建新模型方面具有潜力。这些结果展示了 Coagulo-Net 在推进血液凝固研究中的计算建模方面的潜力，改善了研究方法和开发治疗凝血障碍患者的新疗法。
