American Association for Cancer Research
00085472can153232-sup-158756_1_supp_3341867_w218wr.pdf (1.38 MB)

Figure S1 from Mathematical Modeling Reveals That Changes to Local Cell Density Dynamically Modulate Baseline Variations in Cell Growth and Drug Response

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journal contribution
posted on 2023-03-31, 00:09 authored by James M. Greene, Doron Levy, Sylvia P. Herrada, Michael M. Gottesman, Orit Lavi

Detailed information of the mathematical model calibrations . Panel A) Initial and final configurations of the experimental data used for model comparison. Panel B) Attraction and repulsion potentials used in the SDE model. Panel C) Quiescent transition rates.



John Simon Guggenheim Memorial Foundation



Cell-to-cell variations contribute to drug resistance with consequent therapy failure in cancer. Experimental techniques have been developed to monitor tumor heterogeneity, but estimates of cell-to-cell variation typically fail to account for the expected spatiotemporal variations during the cell growth process. To fully capture the extent of such dynamic variations, we developed a mechanistic mathematical model supported by in vitro experiments with an ovarian cancer cell line. We introduce the notion of dynamic baseline cell-to-cell variation, showing how the emerging spatiotemporal heterogeneity of one cell population can be attributed to differences in local cell density and cell cycle. Manipulation of the geometric arrangement and spatial density of cancer cells revealed that given a fixed global cell density, significant differences in growth, proliferation, and paclitaxel-induced apoptosis rates were observed based solely on cell movement and local conditions. We conclude that any statistical estimate of changes in the level of heterogeneity should be integrated with the dynamics and spatial effects of the baseline system. This approach incorporates experimental and theoretical methods to systematically analyze biologic phenomena and merits consideration as an underlying reference model for cell biology studies that investigate dynamic processes affecting cancer cell behavior. Cancer Res; 76(10); 2882–90. ©2016 AACR.

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