Modelling of chemotactic sprouting endothelial cells through an extracellular matrix

Front Bioeng Biotechnol. 2023 Jun 8:11:1145550. doi: 10.3389/fbioe.2023.1145550. eCollection 2023.

Abstract

Sprouting angiogenesis is a core biological process critical to vascular development. Its accurate simulation, relevant to multiple facets of human health, is of broad, interdisciplinary appeal. This study presents an in-silico model replicating a microfluidic assay where endothelial cells sprout into a biomimetic extracellular matrix, specifically, a large-pore, low-concentration fibrin-based porous hydrogel, influenced by chemotactic factors. We introduce a novel approach by incorporating the extracellular matrix and chemotactic factor effects into a unified term using a single parameter, primarily focusing on modelling sprouting dynamics and morphology. This continuous model naturally describes chemotactic-induced sprouting with no need for additional rules. In addition, we extended our base model to account for matrix sensing and degradation, crucial aspects of angiogenesis. We validate our model via a hybrid in-silico experimental method, comparing the model predictions with experimental results derived from the microfluidic setup. Our results underscore the intricate relationship between the extracellular matrix structure and angiogenic sprouting, proposing a promising method for predicting the influence of the extracellular matrix on angiogenesis.

Keywords: angiogenesis; biomimmetic; chemotaxis; endothelial cells; extracellular matrix; in silico model; mathematical models; phase field.

Grants and funding

This work was supported in part by the Spanish Ministry of Science and Innovation (MICINN) and the State Research Agency (AEI) through the projects RTI 2018-097038-B-C22, PID 2019-106063GB-I00, PID2021-124575OB-I00 and CONACyT through the project 283279. JF-T is supported by Generalitat de Catalunya/AGAUR 2018-DI-064. AL-C thanks The Spanish Ministry of Universities for the fellowship FPU17/06161. JR-A acknowledges support from DGAPA-UNAM grant IA-100823. AH-M is supported by Generalitat de Catalunya/AGAUR 2021SGR00450. Authors also thank for the grant from the University of Barcelona to publish in open access.