ArticleBioinformatics (Oxford, England)2023
SMITH: spatially constrained stochastic model for simulation of intra-tumour heterogeneity.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 19 citations in OpenAlex.
- Agent-Based Simulations of Lung Tumor Evolution Suggest That Ongoing Cell Competition Drives Realistic Clonal Expansions.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A new cancer progression model: From synthetic tumors to real data and back.PLoS computational biology · 2026Article
- tugMedi: simulator of cancer-cell evolution for personalized medicine based on the genomic data of patients.NPJ systems biology and applications · 2026Article
- Spatial interactions modulate tumor growth and immune infiltration.NPJ systems biology and applications · 2024Article
- Real-time identification of epistatic interactions in SARS-CoV-2 from large genome collections.Genome biology · 2024Article
- Oncotree2vec - a method for embedding and clustering of tumor mutation trees.Bioinformatics (Oxford, England) · 2024Article
- Spatial interactions modulate tumor growth and immune infiltration.Research square · 2024Article
- A seven-step guide to spatial, agent-based modelling of tumour evolution.Evolutionary applications · 2024Review
- Spatial interactions modulate tumor growth and immune infiltration.bioRxiv : the preprint server for biology · 2024Article
- Molecular Genetic Profile of Myelofibrosis: Implications in the Diagnosis, Prognosis, and Treatment Advancements.Cancers · 2024Review
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
motivationSimulations of cancer evolution are highly useful to study the effects of selection and mutation rates on cellular fitness. However, most methods are either lattice-based and cannot simulate realistically sized tumours, or they omit spatial constraints and lack the clonal dynamics of real-world tumours.
resultsStochastic model of intra-tumour heterogeneity (SMITH) is an efficient and explainable model of cancer evolution that combines a branching process with a new confinement mechanism limiting clonal growth based on the size of the individual clones as well as the overall tumour population. We demonstrate how confinement is sufficient to induce the rich clonal dynamics observed in spatial models and cancer samples across tumour types, while allowing for a clear geometric interpretation and simulation of 1 billion cells within a few minutes on a desktop PC. AVAILABILITY AND IMPLEMENTATION: SMITH is implemented in C# and freely available at https://bitbucket.org/schwarzlab/smith. For visualizations, we provide the accompanying Python package PyFish at https://bitbucket.org/schwarzlab/pyfish. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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