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Lookup NU author(s): Jean de Montigny, Dr Roman BauerORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2020 The Authors. This paper develops a three-dimensional in silico hybrid model of cancer, which describes the multi-variate phenotypic behaviour of tumour and host cells. The model encompasses the role of cell migration and adhesion, the influence of the extracellular matrix, the effects of oxygen and nutrient availability, and the signalling triggered by chemical cues and growth factors. The proposed in silico hybrid modelling framework combines successfully the advantages of continuum-based and discrete methods, namely the finite element and agent-based method respectively. The framework is thus used to realistically model cancer mechano-biology in a multiscale fashion while maintaining the resolution power of each method in a computationally cost-effective manner. The model is tailored to simulate glioma progression, and is subsequently used to interrogate the balance between the host cells and small sized gliomas, while the go-or-grow phenotype characteristic in glioblastomas is also investigated. Also, cell–cell and cell–matrix interactions are examined with respect to their effect in (macroscopic) tumour growth, brain tissue perfusion and tumour necrosis. Finally, we use the in silico framework to assess differences between low-grade and high-grade glioma growth, demonstrating significant differences in the distribution of cancer as well as host cells, in accordance with reported experimental findings.
Author(s): de Montigny J, Iosif A, Breitwieser L, Manca M, Bauer R, Vavourakis V
Publication type: Article
Publication status: Published
Journal: Methods
Year: 2021
Volume: 185
Pages: 94-104
Print publication date: 01/01/2021
Online publication date: 23/01/2020
Acceptance date: 14/01/2020
Date deposited: 28/07/2020
ISSN (print): 1046-2023
ISSN (electronic): 1095-9130
Publisher: Academic Press Inc.
URL: https://doi.org/10.1016/j.ymeth.2020.01.006
DOI: 10.1016/j.ymeth.2020.01.006
Data Access Statement: https://figshare.com/projects/In_silico_hybrid_FEM-ABM_model/67343
PubMed id: 31981608
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