ArticleJournal of the Royal Society, Interface2021
Learning differential equation models from stochastic agent-based model simulations.
Article in Journal of the Royal Society, Interface, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
24 citing papers in PubMed.
- Virtual twins and the future of human developmental biology.NPJ systems biology and applications · 2026Review
- Enhancing generalizability of model discovery across parameter space with multi-experiment equation learning for biological systems.PLoS computational biology · 2026Article
- Hybrid modelling using simulation and machine learning in healthcare.Computers & operations research · 2026Review
- Advances in surrogate modeling for biological agent-based simulations: trends, challenges, and future prospects.Journal of mathematical biology · 2025Review
- Inference and prediction for stochastic models of biological populations undergoing migration and proliferation.Journal of the Royal Society, Interface · 2025Review
- An agent-based model of COVID- 19 in the food industry for assessing public health and economic impacts of infection control strategies.Scientific reports · 2025Article
- Control of medical digital twins with artificial neural networks.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Article
- Mathematical modeling of multicellular tumor spheroids quantifies inter-patient and intra-tumor heterogeneity.NPJ systems biology and applications · 2025Article
- Optimal control of agent-based models via surrogate modeling.PLoS computational biology · 2025Article
- Random walk models in the life sciences: including births, deaths and local interactions.Journal of the Royal Society, Interface · 2025Review
- Data-driven model discovery and model selection for noisy biological systems.PLoS computational biology · 2025Article
- Linking discrete and continuous models of cell birth and migration.Royal Society open science · 2024Article
- Article
- Inferring Stochastic Rates from Heterogeneous Snapshots of Particle Positions.Bulletin of mathematical biology · 2024Article
- Discrete and continuous mathematical models of sharp-fronted collective cell migration and invasion.Royal Society open science · 2024Article
- Connecting Agent-Based Models with High-Dimensional Parameter Spaces to Multidimensional Data Using SMoRe ParS: A Surrogate Modeling Approach.Bulletin of mathematical biology · 2023Article
- Article
- Learning hydrodynamic equations for active matter from particle simulations and experiments.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Review
- Learning mean-field equations from particle data using WSINDy.Physica D. Nonlinear phenomena · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Agent-based models provide a flexible framework that is frequently used for modelling many biological systems, including cell migration, molecular dynamics, ecology and epidemiology. Analysis of the model dynamics can be challenging due to their inherent stochasticity and heavy computational requirements. Common approaches to the analysis of agent-based models include extensive Monte Carlo simulation of the model or the derivation of coarse-grained differential equation models to predict the expected or averaged output from the agent-based model. Both of these approaches have limitations, however, as extensive computation of complex agent-based models may be infeasible, and coarse-grained differential equation models can fail to accurately describe model dynamics in certain parameter regimes. We propose that methods from the equation learning field provide a promising, novel and unifying approach for agent-based model analysis. Equation learning is a recent field of research from data science that aims to infer differential equation models directly from data. We use this tutorial to review how methods from equation learning can be used to learn differential equation models from agent-based model simulations. We demonstrate that this framework is easy to use, requires few model simulations, and accurately predicts model dynamics in parameter regions where coarse-grained differential equation models fail to do so. We highlight these advantages through several case studies involving two agent-based models that are broadly applicable to biological phenomena: a birth-death-migration model commonly used to explore cell biology experiments and a susceptible-infected-recovered model of infectious disease spread.
Indexed as
Identifiers
What OpenQuestion holds
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.