ArticleCancer informatics2015
Multiscale Model of Colorectal Cancer Using the Cellular Potts Framework.
Article in Cancer informatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Improving 3D deep learning segmentation with biophysically motivated cell synthesis.Communications biology · 2025Article
- Bridging from single to collective cell migration: A review of models and links to experiments.PLoS computational biology · 2020Review
- Unification of aggregate growth models by emergence from cellular and intracellular mechanisms.Royal Society open science · 2020Article
- Modelling the effect of subcellular mutations on the migration of cells in the colorectal crypt.BMC bioinformatics · 2020Article
- Multiscale Tumor Modeling With Drug Pharmacokinetic and Pharmacodynamic Profile Using Stochastic Hybrid System.Cancer informatics · 2018Review
- Combining radiation with hyperthermia: a multiscale model informed byJournal of the Royal Society, Interface · 2018Article
- A Model for Adult Organ Resizing Demonstrates Stem Cell Scaling through a Tunable Commitment Rate.Biophysical journal · 2017Article
- Comparing individual-based approaches to modelling the self-organization of multicellular tissues.PLoS computational biology · 2017Article
- In silico modeling for tumor growth visualization.BMC systems biology · 2016Article
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Abstract
Colorectal cancer (CRC) is one of the major causes of death in the developed world and forms a canonical example of tumorigenesis. CRC arises from a string of mutations of individual cells in the colorectal crypt, making it particularly suited for multiscale multicellular modeling, where mutations of individual cells can be clearly represented and their effects readily tracked. In this paper, we present a multicellular model of the onset of colorectal cancer, utilizing the cellular Potts model (CPM). We use the model to investigate how, through the modification of their mechanical properties, mutant cells colonize the crypt. Moreover, we study the influence of mutations on the shape of cells in the crypt, suggesting possible cell- and tissue-level indicators for identifying early-stage cancerous crypts. Crucially, we discuss the effect that the motility parameters of the model (key factors in the behavior of the CPM) have on the distribution of cells within a homeostatic crypt, resulting in an optimal parameter regime that accurately reflects biological assumptions. In summary, the key results of this paper are 1) how to couple the CPM with processes occurring on other spatial scales, using the example of the crypt to motivate suitable motility parameters; 2) modeling mutant cells with the CPM; 3) and investigating how mutations influence the shape of cells in the crypt.
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