Quantitative cell-based model predicts mechanical stress response of growing tumor spheroids over various growth conditions and cell lines

PLoS Comput Biol. 2019 Mar 8;15(3):e1006273. doi: 10.1371/journal.pcbi.1006273. eCollection 2019 Mar.


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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Cell Line, Tumor
  • Cell Shape / physiology
  • Computational Biology
  • Humans
  • Mechanotransduction, Cellular / physiology*
  • Mice
  • Models, Biological*
  • Spheroids, Cellular / physiology*
  • Tumor Cells, Cultured / physiology*

Grant support

This work was supported by INSERM Physicancer (https://www.inserm.fr/), ITMO INVADE (France), the EU 7th Framework Programme (Notox, http://notox-sb.eu/), Bundesministerium für Bildung und Forschung (Virtual Liver Network - https://fair-dom.org/partners/virtual-liver-network-vln/, LiSym, LEBERSIMULATOR), Agence Nationale de la Recherche (iLite). P.N. acknowledges financial support from INCA (grant 2012-1-PL BIO-09-IO) and from the ANR (http://www.agence-nationale-recherche.fr/) Blanc SVSE5 “Invaders”. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.