ArticleFrontiers in oncology2013
Cellular potts modeling of tumor growth, tumor invasion, and tumor evolution.
Article in Frontiers in oncology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 77 papers.
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77 citing papers in PubMed, 227 citations in OpenAlex.
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- Validating the predictions of mathematical models describing tumor growth and treatment response.ArXiv · 2025Article
- Improving 3D deep learning segmentation with biophysically motivated cell synthesis.Communications biology · 2025Article
- Direct visualization of emergent metastatic features within an ex vivo model of the tumor microenvironment.Life science alliance · 2025Article
- Tissue stresses caused by invasive tumour: a biomechanical model.Journal of the Royal Society, Interface · 2025Article
- A Multicellular Mechanochemical Model to Investigate Tumor Microenvironment Remodeling and Pre-Metastatic Niche Formation.Cellular and molecular bioengineering · 2024Article
- Morphological Stability for in silico Models of Avascular Tumors.Bulletin of mathematical biology · 2024Article
- A seven-step guide to spatial, agent-based modelling of tumour evolution.Evolutionary applications · 2024Review
- On the possibility of engineering social evolution in microfluidic environments.Biophysical journal · 2024Article
- CellularPotts.jl: simulating multiscale cellular models in Julia.Bioinformatics (Oxford, England) · 2024Article
- Modeling Physical Forces Experienced by Cancer and Stromal Cells Within Different Organ-Specific Tumor Tissue.IEEE journal of translational engineering in health and medicine · 2024Article
17 more citing papers are in PubMed but not listed here.
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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite a growing wealth of available molecular data, the growth of tumors, invasion of tumors into healthy tissue, and response of tumors to therapies are still poorly understood. Although genetic mutations are in general the first step in the development of a cancer, for the mutated cell to persist in a tissue, it must compete against the other, healthy or diseased cells, for example by becoming more motile, adhesive, or multiplying faster. Thus, the cellular phenotype determines the success of a cancer cell in competition with its neighbors, irrespective of the genetic mutations or physiological alterations that gave rise to the altered phenotype. What phenotypes can make a cell "successful" in an environment of healthy and cancerous cells, and how? A widely used tool for getting more insight into that question is cell-based modeling. Cell-based models constitute a class of computational, agent-based models that mimic biophysical and molecular interactions between cells. One of the most widely used cell-based modeling formalisms is the cellular Potts model (CPM), a lattice-based, multi particle cell-based modeling approach. The CPM has become a popular and accessible method for modeling mechanisms of multicellular processes including cell sorting, gastrulation, or angiogenesis. The CPM accounts for biophysical cellular properties, including cell proliferation, cell motility, and cell adhesion, which play a key role in cancer. Multiscale models are constructed by extending the agents with intracellular processes including metabolism, growth, and signaling. Here we review the use of the CPM for modeling tumor growth, tumor invasion, and tumor progression. We argue that the accessibility and flexibility of the CPM, and its accurate, yet coarse-grained and computationally efficient representation of cell and tissue biophysics, make the CPM the method of choice for modeling cellular processes in tumor development.
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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.