Experimental evidence suggests that a tumor's environment may be critical to designing successful therapeutic protocols: Modeling interactions between a tumor and its environment could improve our understanding of tumor growth and inform approaches to treatment. This paper describes an efficient, flexible, hybrid cellular automaton-based implementation of numerical solutions to multiple time-scale reaction-diffusion equations, applied to a model of tumor proliferation. The growth and maintenance of cells in our simulation depend on the rate of cellular energy (ATP) metabolized from nearby nutrients such as glucose and oxygen. Nutrient consumption rates are functions of local pH as well as local concentrations of oxygen and other fuels. The diffusion of these nutrients is modeled using a novel variation of random-walk techniques. Furthermore, we detail the effects of three boundary update rules on simulations, describing their effects on computational efficiency and biological realism. Qualitative and quantitative results from simulations provide insight on how tumor growth is affected by various environmental changes such as micro-vessel density or lower pH, both of high interest in current cancer research.
List of Figures 4.1 Tumor cell population before diffusion adjustments 4.2 Tumor cell population after diffusion adjustments 4.3 Starting tumor population 4.4 Healthy cell population without growth limiter 4.5 Zero-flux boundary conditions 4.6 Tumor cells: low nutrient concentrations at boundary 4.7 Tumor cells: high nutrient concentrations at boundary 5.1 Steady-state locations with different threshold settings 5.2 Steady-state locations at different timesteps 6.1 Example of the hierarchical data structure 6.2 Suppression of updates using the data structure 7.1 Cell population difference between modified and canonical simulations 7.2 Location of update suppression 7.3 Locations of cell population differences 7.4 Change in an equivalence region over time 7.5 Equivalence region of an outer tissue block 7.6 Non-equivalent tissue locations 7.7 Equivalence region created from reasonable result states 7.8 Example of translation with single element-based equivalence 7.9 Example of translation with block-based equivalence 8.1 Data structure with bitwise operations 8.2 Gene regulatory network model
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.