Publish in OALib Journal
APC: Only $99
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.