ArticleJournal of the Royal Society, Interface2019
Accurate and efficient discretizations for stochastic models providing near agent-based spatial resolution at low computational cost.
Article in Journal of the Royal Society, Interface, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed.
- Article
- Article
- From homogeneity to heterogeneity: Refining stochastic simulations of gene regulation.Computational and structural biotechnology journal · 2025Article
- Quantifying collective motion patterns in mesenchymal cell populations using topological data analysis and agent-based modeling.Mathematical biosciences · 2024Article
- Article
- Travelling-Wave and Asymptotic Analysis of a Multiphase Moving Boundary Model for Engineered Tissue Growth.Bulletin of mathematical biology · 2022Article
- Agent-based computational modeling of glioblastoma predicts that stromal density is central to oncolytic virus efficacy.iScience · 2022Article
- A global method for fast simulations of molecular dynamics in multiscale agent-based models of biological tissues.iScience · 2022Article
- Autocrine signaling can explain the emergence of Allee effects in cancer cell populations.PLoS computational biology · 2022Article
- Incorporating domain growth into hybrid methods for reaction-diffusion systems.Journal of the Royal Society, Interface · 2021Article
- Learning differential equation models from stochastic agent-based model simulations.Journal of the Royal Society, Interface · 2021Article
- A mesoscopic simulator to uncover heterogeneity and evolutionary dynamics in tumors.PLoS computational biology · 2021Article
- Unpacking the Allee effect: determining individual-level mechanisms that drive global population dynamics.Proceedings. Mathematical, physical, and engineering sciences · 2020Article
- Identifying density-dependent interactions in collective cell behaviour.Journal of the Royal Society, Interface · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Understanding how cells proliferate, migrate and die in various environments is essential in determining how organisms develop and repair themselves. Continuum mathematical models, such as the logistic equation and the Fisher-Kolmogorov equation, can describe the global characteristics observed in commonly used cell biology assays, such as proliferation and scratch assays. However, these continuum models do not account for single-cell-level mechanics observed in high-throughput experiments. Mathematical modelling frameworks that represent individual cells, often called agent-based models, can successfully describe key single-cell-level features of these assays but are computationally infeasible when dealing with large populations. In this work, we propose an agent-based model with crowding effects that is computationally efficient and matches the logistic and Fisher-Kolmogorov equations in parameter regimes relevant to proliferation and scratch assays, respectively. This stochastic agent-based model allows multiple agents to be contained within compartments on an underlying lattice, thereby reducing the computational storage compared to existing agent-based models that allow one agent per site only. We propose a systematic method to determine a suitable compartment size. Implementing this compartment-based model with this compartment size provides a balance between computational storage, local resolution of agent behaviour and agreement with classical continuum descriptions.
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Registered trials
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.