ArticleApplied mathematics2014
A High-Performance Cellular Automaton Model of Tumor Growth with Dynamically Growing Domains.
Article in Applied mathematics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Calibrating tumor growth and invasion parameters with spectral spatial analysis of cancer biopsy tissues.NPJ systems biology and applications · 2024Article
- Inspiring a convergent engineering approach to measure and model the tissue microenvironment.Heliyon · 2024Review
- A Review of Mathematical and Computational Methods in Cancer Dynamics.Frontiers in oncology · 2022Review
- Selection-driven tumor evolution with public goods leads to patterns of clonal expansion consistent with neutral growth.iScience · 2021Article
- Uncertainty quantification on a spatial Markov-chain model for the progression of skin cancer.Journal of mathematical biology · 2020Article
- Dynamic Load Balancing Strategy for Parallel Tumor Growth Simulations.Journal of integrative bioinformatics · 2019Article
- A Review of Cell-Based Computational Modeling in Cancer Biology.JCO clinical cancer informatics · 2019Review
- Sampling from single-cell observations to predict tumor cell growthOncotarget · 2017Article
- Cell adhesion heterogeneity reinforces tumour cell dissemination: novel insights from a mathematical model.Biology direct · 2017Article
- Article
- Agent-Based Modeling of Cancer Stem Cell Driven Solid Tumor Growth.Methods in molecular biology (Clifton, N.J.) · 2016Article
- Cancer Stem Cell Plasticity as Tumor Growth Promoter and Catalyst of Population Collapse.Stem cells international · 2016Article
- Extremely high genetic diversity in a single tumor points to prevalence of non-Darwinian cell evolution.Proceedings of the National Academy of Sciences of the United States of America · 2015Article
- Evolution and phenotypic selection of cancer stem cells.PLoS computational biology · 2015Article
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Authors and funding
2 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.