Topological data analysis distinguishes parameter regimes in the Anderson-Chaplain model of angiogenesis

Angiogenesis is the process by which blood vessels form from pre-existing vessels. It plays a key role in many biological processes, including embryonic development and wound healing, and contributes to many diseases including cancer and rheumatoid arthritis. The structure of the resulting vessel ne...

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Bibliographic Details
Published in:PLoS computational biology 2021-06, Vol.17 (6), p.e1009094-e1009094
Main Authors: Nardini, John T, Stolz, Bernadette J, Flores, Kevin B, Harrington, Heather A, Byrne, Helen M
Format: Article
Language:English
Subjects:
Algorithms
Angiogenesis
Animals
Arthritis
Biological activity
Biology and Life Sciences
Blood vessels
Blood Vessels - anatomy & histology
Blood Vessels - growth & development
Blood Vessels - physiology
Cell adhesion & migration
Chemotaxis
Computational Biology
Computational mathematics
Computer applications
Computer Simulation
Data analysis
Electronic data processing
Embryogenesis
Embryonic growth stage
Fibroblasts
Humans
Mathematical models
Medicine and Health Sciences
Models, Cardiovascular
Morphology
Neoplasms - blood supply
Neovascularization
Neovascularization, Pathologic
Neovascularization, Physiologic
Nutrients
Parameters
Physical Sciences
Physiological aspects
Research and Analysis Methods
Rheumatoid arthritis
Simulation
Spatio-Temporal Analysis
Tissues
Topology
Vascular endothelial growth factor
Wound healing
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Summary:Angiogenesis is the process by which blood vessels form from pre-existing vessels. It plays a key role in many biological processes, including embryonic development and wound healing, and contributes to many diseases including cancer and rheumatoid arthritis. The structure of the resulting vessel networks determines their ability to deliver nutrients and remove waste products from biological tissues. Here we simulate the Anderson-Chaplain model of angiogenesis at different parameter values and quantify the vessel architectures of the resulting synthetic data. Specifically, we propose a topological data analysis (TDA) pipeline for systematic analysis of the model. TDA is a vibrant and relatively new field of computational mathematics for studying the shape of data. We compute topological and standard descriptors of model simulations generated by different parameter values. We show that TDA of model simulation data stratifies parameter space into regions with similar vessel morphology. The methodologies proposed here are widely applicable to other synthetic and experimental data including wound healing, development, and plant biology.
ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1009094