The Art and Science of Building a Computational Model to Understand Hemostasis

Overview

Journal Semin Thromb Hemost

Publisher Thieme

Specialties Cardiology & Vascular Diseases
Hematology

Date 2021 Mar 3

PMID 33657623

Citations 7

Authors

Karin Leiderman

Suzanne S Sindi

Dougald M Monroe

Aaron L Fogelson

Keith B Neeves

Affiliations

Soon will be listed here.

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.

Citing Articles

A new look at TFPI inhibition of factor X activation.

Santiago F, Kaur A, Bride S, Monroe D, Leiderman K, Sindi S PLoS Comput Biol. 2024; 20(11):e1012509.

PMID: 39546494 PMC: 11567595. DOI: 10.1371/journal.pcbi.1012509.

Coagulo-Net: Enhancing the mathematical modeling of blood coagulation using physics-informed neural networks.

Qian Y, Zhu G, Zhang Z, Modepalli S, Zheng Y, Zheng X Neural Netw. 2024; 180:106732.

PMID: 39305783 PMC: 11578045. DOI: 10.1016/j.neunet.2024.106732.

Decoding thrombosis through code: a review of computational models.

Grande Gutierrez N, Mukherjee D, Bark Jr D J Thromb Haemost. 2023; 22(1):35-47.

PMID: 37657562 PMC: 11064820. DOI: 10.1016/j.jtha.2023.08.021.

Physiological Modeling of Hemodynamic Responses to Sodium Nitroprusside.

Rinehart J, Coeckelenbergh S, Srivastava I, Cannesson M, Joosten A J Pers Med. 2023; 13(7).

PMID: 37511714 PMC: 10381667. DOI: 10.3390/jpm13071101.

Clinical Predictors and Prediction Models for rFVIII-Fc Half Life in Real-World People with Severe Hemophilia A.

Chang C, Chiou S, Weng T, Lin P, Lai S, Tsai C J Clin Med. 2023; 12(6).

PMID: 36983209 PMC: 10053229. DOI: 10.3390/jcm12062207.

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