Abstract
Tumor growth from a single transformed cancer cell up to a clinically apparent mass spans many spatial and temporal orders of magnitude. Implementation of cellular automata simulations of such tumor growth can be straightforward but computing performance often counterbalances simplicity. Computationally convenient simulation times can be achieved by choosing appropriate data structures, memory and cell handling as well as domain setup. We propose a cellular automaton model of tumor growth with a domain that expands dynamically as the tumor population increases. We discuss memory access, data structures and implementation techniques that yield high-performance multi-scale Monte Carlo simulations of tumor growth. We discuss tumor properties that favor the proposed high-performance design and present simulation results of the tumor growth model. We estimate to which parameters the model is the most sensitive, and show that tumor volume depends on a number of parameters in a non-monotonic manner.
Highlights
Simulating complex multi-scale cellular automata is still a great challenge despite advances in computational power of modern computers in recent years
In typical tumor growth models many parameters need to be estimated in high-dimensional parameter sweeps and sensitivity analysis needs to be performed to study parameter influence on overall dynamics
Each cell type can divide symmetrically to produce two daughter cells with parental phenotype. Both populations are coupled through asymmetric division of cancer stem cells, which with probability 1 - ps produces a cancer stem cell and a non-stem cancer cell, which inherits an initial proliferation potential ρ = ρmax that decreases with each subsequent non-stem cell division (Figure 1(a))
Summary
College of Inter-Faculty Individual Studies in Mathematics and Natural Sciences, University of Warsaw, Warsaw, Poland. Lee Moffitt Cancer Center and Research Institute, Tampa, USA. Received September 20, 2013; revised October 20, 2013; accepted October 27, 2013
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