In:
PLOS Computational Biology, Public Library of Science (PLoS), Vol. 17, No. 2 ( 2021-2-10), p. e1008266-
Abstract:
Increasingly complex in silico modeling approaches offer a way to simultaneously access cancerous processes at different spatio-temporal scales. High-level models, such as those based on partial differential equations, are computationally affordable and allow large tumor sizes and long temporal windows to be studied, but miss the discrete nature of many key underlying cellular processes. Individual-based approaches provide a much more detailed description of tumors, but have difficulties when trying to handle full-sized real cancers. Thus, there exists a trade-off between the integration of macroscopic and microscopic information, now widely available, and the ability to attain clinical tumor sizes. In this paper we put forward a stochastic mesoscopic simulation framework that incorporates key cellular processes during tumor progression while keeping computational costs to a minimum. Our framework captures a physical scale that allows both the incorporation of microscopic information, tracking the spatio-temporal emergence of tumor heterogeneity and the underlying evolutionary dynamics, and the reconstruction of clinically sized tumors from high-resolution medical imaging data, with the additional benefit of low computational cost. We illustrate the functionality of our modeling approach for the case of glioblastoma, a paradigm of tumor heterogeneity that remains extremely challenging in the clinical setting.
Type of Medium:
Online Resource
ISSN:
1553-7358
DOI:
10.1371/journal.pcbi.1008266
DOI:
10.1371/journal.pcbi.1008266.g001
DOI:
10.1371/journal.pcbi.1008266.g002
DOI:
10.1371/journal.pcbi.1008266.g003
DOI:
10.1371/journal.pcbi.1008266.g004
DOI:
10.1371/journal.pcbi.1008266.g005
DOI:
10.1371/journal.pcbi.1008266.g006
DOI:
10.1371/journal.pcbi.1008266.g007
DOI:
10.1371/journal.pcbi.1008266.g008
DOI:
10.1371/journal.pcbi.1008266.g009
DOI:
10.1371/journal.pcbi.1008266.t001
DOI:
10.1371/journal.pcbi.1008266.s001
DOI:
10.1371/journal.pcbi.1008266.s002
DOI:
10.1371/journal.pcbi.1008266.s003
DOI:
10.1371/journal.pcbi.1008266.s004
DOI:
10.1371/journal.pcbi.1008266.s005
DOI:
10.1371/journal.pcbi.1008266.s006
DOI:
10.1371/journal.pcbi.1008266.r001
DOI:
10.1371/journal.pcbi.1008266.r002
DOI:
10.1371/journal.pcbi.1008266.r003
DOI:
10.1371/journal.pcbi.1008266.r004
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2021
detail.hit.zdb_id:
2193340-6
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