ArticlePLoS computational biology2021
A mesoscopic simulator to uncover heterogeneity and evolutionary dynamics in tumors.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 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
- In silico models in oncology, neurology, and epidemiology: systems-level and multiscale perspectives.NPJ systems biology and applications · 2026Review
- tugMedi: simulator of cancer-cell evolution for personalized medicine based on the genomic data of patients.NPJ systems biology and applications · 2026Article
- Mathematical model of tumor-macrophage dynamics in glioma to advance myeloid-targeted therapies.Computers in biology and medicine · 2025Article
- Mathematical model of tumor-macrophage dynamics in glioma to advance myeloid-targeted therapies.bioRxiv : the preprint server for biology · 2025Article
- Spatiotemperal Dynamics of Osteoarthritis: Bridging Insights from Bench to Bedside.Aging and disease · 2024Review
- Characterising Cancer Cell Responses to Cyclic Hypoxia Using Mathematical Modelling.Bulletin of mathematical biology · 2024Article
- AMBER: A Modular Model for Tumor Growth, Vasculature and Radiation Response.Bulletin of mathematical biology · 2024Article
- Radiation necrosis after radiation therapy treatment of brain metastases: A computational approach.PLoS computational biology · 2024Article
- Growth exponents reflect evolutionary processes and treatment response in brain metastases.NPJ systems biology and applications · 2023Article
- Data-driven spatio-temporal modelling of glioblastoma.Royal Society open science · 2023Review
- On optimal temozolomide scheduling for slowly growing glioblastomas.Neuro-oncology advancesArticle
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Authors and funding
9 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Increasingly complex in silico modeling approaches offer a way to simultaneously access cancerous processes at different spatio-temporal scales. High-level models, such as those based on partial differential equations, are computationally affordable and allow large tumor sizes and long temporal windows to be studied, but miss the discrete nature of many key underlying cellular processes. Individual-based approaches provide a much more detailed description of tumors, but have difficulties when trying to handle full-sized real cancers. Thus, there exists a trade-off between the integration of macroscopic and microscopic information, now widely available, and the ability to attain clinical tumor sizes. In this paper we put forward a stochastic mesoscopic simulation framework that incorporates key cellular processes during tumor progression while keeping computational costs to a minimum. Our framework captures a physical scale that allows both the incorporation of microscopic information, tracking the spatio-temporal emergence of tumor heterogeneity and the underlying evolutionary dynamics, and the reconstruction of clinically sized tumors from high-resolution medical imaging data, with the additional benefit of low computational cost. We illustrate the functionality of our modeling approach for the case of glioblastoma, a paradigm of tumor heterogeneity that remains extremely challenging in the clinical setting.
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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.