Quantitative cell-based model predicts mechanical stress response of growing tumor spheroids over various growth conditions and cell lines
Author(s) -
Paul Van Liedekerke,
Johannes Neitsch,
Tim Johann,
Kévin Alessandri,
Pierre Nassoy,
Dirk Drasdo
Publication year - 2019
Publication title -
plos computational biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.628
H-Index - 182
eISSN - 1553-7358
pISSN - 1553-734X
DOI - 10.1371/journal.pcbi.1006273
Subject(s) - cell growth , spheroid , biological system , cell , growth curve (statistics) , cell culture , stress (linguistics) , biophysics , materials science , chemistry , biology , mathematics , biochemistry , linguistics , philosophy , econometrics , genetics
Model simulations indicate that the response of growing cell populations on mechanical stress follows the same functional relationship and is predictable over different cell lines and growth conditions despite experimental response curves look largely different. We develop a hybrid model strategy in which cells are represented by coarse-grained individual units calibrated with a high resolution cell model and parameterized by measurable biophysical and cell-biological parameters. Cell cycle progression in our model is controlled by volumetric strain, the latter being derived from a bio-mechanical relation between applied pressure and cell compressibility. After parameter calibration from experiments with mouse colon carcinoma cells growing against the resistance of an elastic alginate capsule, the model adequately predicts the growth curve in i) soft and rigid capsules, ii) in different experimental conditions where the mechanical stress is generated by osmosis via a high molecular weight dextran solution, and iii) for other cell types with different growth kinetics from the growth kinetics in absence of external stress. Our model simulation results suggest a generic, even quantitatively same, growth response of cell populations upon externally applied mechanical stress, as it can be quantitatively predicted using the same growth progression function.
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