The Art and Science of Building a Computational Model to Understand Hemostasis
Abstract
Computational models of various facets of hemostasis and thrombosis have increased substantially in the last decade. These models have the potential to make predictions that can uncover new mechanisms within the complex dynamics of thrombus formation. However, these predictions are only as good as the data and assumptions they are built upon, and therefore model building requires intimate coupling with experiments. The objective of this article is to guide the reader through how a computational model is built and how it can inform and be refined by experiments. This is accomplished by answering six questions facing the model builder: (1) Why make a model? (2) What kind of model should be built? (3) How is the model built? (4) Is the model a “good” model? (5) Do we believe the model? (6) Is the model useful? These questions are answered in the context of a model of thrombus formation that has been successfully applied to understanding the interplay between blood flow, platelet deposition, and coagulation and in identifying potential modifiers of thrombin generation in hemophilia A.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Feb 26, 2021
- Source ID
- 10.1055/s-0041-1722861
Entities
People
- Aaron L. Fogelson
- Dougald M. Monroe
- Karin Leiderman
- Keith B. Neeves
- Suzanne S. Sindi
Organizations
- Army Research Office
- Colorado School of Mines
- National Institutes of Health
- National Science Foundation
- University of California
- University of Colorado
- University of North Carolina at Chapel Hill
- University of Utah