Modeling intrinsic heterogeneity and growth of cancer cells
Authored by Doron Levy, James M Greene, King Leung Fung, Paloma S Souza, Michael M Gottesman, Orit Lavi
Date Published: 2015
DOI: 10.1016/j.jtbi.2014.11.017
Sponsors:
United States National Institutes of Health (NIH)
United States National Science Foundation (NSF)
John Simon Guggenheim Memorial Foundation
Platforms:
MATLAB
Model Documentation:
Other Narrative
Mathematical description
Model Code URLs:
Model code not found
Abstract
Intratumoral heterogeneity has been found to be a major cause of drug
resistance. Cell-to-cell variation increases as a result of
cancer-related alterations, which are acquired by stochastic events and
further induced by environmental signals. However, most cellular
mechanisms include natural fluctuations that are closely regulated, and
thus lead to asynchronization of the cells, which causes intrinsic
heterogeneity in a given population. Here, we derive two novel
mathematical models, a stochastic agent-based model and an
integro-differential equation model, each of which describes the growth
of cancer cells as a dynamic transition between proliferative and
quiescent states. These models are designed to predict variations in
growth as a function of the intrinsic heterogeneity emerging from the
durations of the cell-cycle and apoptosis, and also include cellular
density dependencies. By examining the role all parameters play in the
evolution of intrinsic tumor heterogeneity, and the sensitivity of the
population growth to parameter values, we show that the cell-cycle
length has the most significant effect on the growth dynamics. In
addition, we demonstrate that the agent-based model can be approximated
well by the more computationally efficient integro-differential
equations when the number of cells is large. This essential step in
cancer growth modeling will allow us to revisit the mechanisms of
multidrug resistance by examining spatiotemporal differences of cell
growth while administering a drug among the different sub-populations in
a single tumor, as well as the evolution of those mechanisms as a
function of the resistance level. Published by Elsevier Ltd.
Tags
Dynamics
apoptosis
Drug-resistance
Multidrug-resistance