ArticleJournal of theoretical biology2008
A hybrid cellular automaton model of clonal evolution in cancer: the emergence of the glycolytic phenotype.
Article in Journal of theoretical biology, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers.
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Who cites it
61 citing papers in PubMed.
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- A circuit for secretion-coupled cellular autonomy in multicellular eukaryotic cells.Molecular systems biology · 2023Article
- Agent-based methods facilitate integrative science in cancer.Trends in cell biology · 2023Review
- Physics-based tissue simulator to model multicellular systems: A study of liver regeneration and hepatocellular carcinoma recurrence.PLoS computational biology · 2023Article
- AnJournal of the Royal Society, Interface · 2023Article
- Hybrid modelling of biological systems: current progress and future prospects.Briefings in bioinformatics · 2022Review
- Impact of tumor-parenchyma biomechanics on liver metastatic progression: a multi-model approach.Scientific reports · 2021Article
- A New Mathematical Model for Controlling Tumor Growth Based on Microenvironment Acidity and Oxygen Concentration.BioMed research international · 2021Article
- Tumor Growth Dynamic Modeling in Oncology Drug Development and Regulatory Approval: Past, Present, and Future Opportunities.CPT: pharmacometrics & systems pharmacology · 2020Review
- Inferring Tumor Proliferative Organization from Phylogenetic Tree Measures in a Computational Model.Systematic biology · 2020Article
- Investigation of solid tumor progression with account of proliferation/migration dichotomy via Darwinian mathematical model.Journal of mathematical biology · 2020Article
- Changes in CT Radiomic Features Associated with Lymphocyte Distribution Predict Overall Survival and Response to Immunotherapy in Non-Small Cell Lung Cancer.Cancer immunology research · 2020Article
- Model genotype-phenotype mappings and the algorithmic structure of evolution.Journal of the Royal Society, Interface · 2019Article
- Three-dimensional tumor growth in time-varying chemical fields: a modeling framework and theoretical study.BMC bioinformatics · 2019Article
- Mathematical Models of Cancer Cell Plasticity.Journal of oncology · 2019Review
- Integrating in vitro experiments with in silico approaches for Glioblastoma invasion: the role of cell-to-cell adhesion heterogeneity.Scientific reports · 2018Article
- Glioma growth modeling based on the effect of vital nutrients and metabolic products.Medical & biological engineering & computing · 2018Article
1 more citing papers are in PubMed but not listed here.
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Abstract
We present a cellular automaton model of clonal evolution in cancer aimed at investigating the emergence of the glycolytic phenotype. In the model each cell is equipped with a micro-environment response network that determines the behaviour or phenotype of the cell based on the local environment. The response network is modelled using a feed-forward neural network, which is subject to mutations when the cells divide. This implies that cells might react differently to the environment and when space and nutrients are limited only the fittest cells will survive. With this model we have investigated the impact of the environment on the growth dynamics of the tumour. In particular, we have analysed the influence of the tissue oxygen concentration and extra-cellular matrix density on the dynamics of the model. We found that the environment influences both the growth and the evolutionary dynamics of the tumour. For low oxygen concentration we observe tumours with a fingered morphology, while increasing the matrix density gives rise to more compact tumours with wider fingers. The distribution of phenotypes in the tumour is also affected, and we observe that the glycolytic phenotype is most likely to emerge in a poorly oxygenated tissue with a high matrix density. Our results suggest that it is the combined effect of the oxygen concentration and matrix density that creates an environment where the glycolytic phenotype has a growth advantage and consequently is most likely to appear.
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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.