Automated reduction of blood coagulation models.

Area of Science:

• Biophysics
• Computational Biology
• Biomathematics

Background:

• Mathematical modeling of thrombosis relies on complex coagulation cascade simulations.
• These detailed models are computationally intensive, difficult to parameterize, and challenging to solve numerically.
• Prior research indicates that simpler models may capture essential coagulation dynamics.

Purpose of the Study:

• To introduce an automated framework for generating reduced-order models of blood coagulation.
• To systematically determine the minimal set of species and reaction rates needed to accurately represent coagulation dynamics.

Main Methods:

• Developed a nested optimization framework for automated model reduction.
• Outer optimization identifies key species, while inner optimization refines reaction rates.
• Tested the framework on a 34-species coagulation model to assess fidelity.

Main Results:

• A nine-species reduced-order model effectively reproduced the thrombin dynamics of the benchmark 34-species model.
• This reduced model maintained accuracy across various tissue factor concentrations.
• Further reduction in species compromised the accurate simulation of thrombin generation.

Conclusions:

• The automated framework enables efficient development of reduced-order coagulation models.
• A nine-species model is sufficient for capturing essential thrombin dynamics in thrombosis modeling.
• This approach facilitates more tractable and computationally feasible simulations of clotting processes.