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

Katherine J Pearce1, Kimberly Nellenbach2, Ralph C Smith1

  • 1Department of Mathematics, North Carolina State University, Box 8205, Raleigh, NC, 27695-8205, USA.

Bulletin of Mathematical Biology
|March 22, 2021
PubMed
Summary

This study introduces a new kinetic model for fibrin polymerization, crucial for wound healing. The developed method efficiently identifies key parameters, simplifying complex polymerization processes.

Keywords:
Fibrin polymerizationKinetics modelParameter identifiabilitySubset selectionWound healing

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Area of Science:

  • Biophysics
  • Biochemistry
  • Computational Biology

Background:

  • Fibrin polymerization is essential for hemostasis and wound healing, involving fibrinogen conversion to fibrin by thrombin.
  • Understanding fibrin matrix formation dynamics is critical for studying coagulation and bleeding disorders.

Purpose of the Study:

  • To develop a cooperative enzyme kinetics model for in vitro fibrin matrix polymerization.
  • To create a parameter subset selection technique for evaluating parameter identifiability in fibrin accumulation experiments.

Main Methods:

  • Developed a kinetic model capturing interactions between fibrinogen, thrombin, fibrin, and intermediate complexes.
  • Employed systematic analysis of eigenvalues/eigenvectors of the information matrix for parameter identifiability.
  • Utilized a least squares objective function for optimization and simulations of fibrin accumulation.

Main Results:

  • The developed approach significantly reduced objective function cost compared to traditional curve-fitting.
  • Demonstrated the ability to integrate data subsets to enhance parameter identifiability evaluation.
  • Successfully screened unidentifiable reaction rate parameters, enabling model simplification while maintaining low objective cost.

Conclusions:

  • A single fibrin accumulation curve contains substantial information for kinetic modeling.
  • The tailored model and parameter subset selection approach improves optimization and reduces complexity in polymerization studies.