2013/09/23 by Jan Poleszczuk, Poleszczuk, Jan, Heiko Enderling +1
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · #92-08 #B.8.2 #Cell Behavior (q-bio.CB) #Cellular Automata and Applications #FOS: Biological sciences #Gene Regulatory Network Analysis #J.3 #Mathematical Biology Tumor Growth #Microtubule and mitosis dynamics #Quantitative Methods (q-bio.QM)
paper · pdf · doi:10.48550/arxiv.1309.6015
openalex publication_date 2013/09/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Tumor growth from a single transformed cancer cell up to a clinically\napparent mass spans many spatial and temporal orders of magnitude.\nImplementation of cellular automata simulations of such tumor growth can be\nstraightforward but computing performance often counterbalances simplicity.\nComputationally convenient simulation times can be achieved by choosing\nappropriate data structures, memory and cell handling as well as domain setup.\nWe propose a cellular automaton model of tumor growth with a domain that\nexpands dynamically as the tumor population increases. We discuss memory\naccess, data structures and implementation techniques that yield\nhigh-performance multi-scale Monte Carlo simulations of tumor growth. We\npresent simulation results of the tumor growth model and discuss tumor\nproperties that favor the proposed high-performance design.\n