ArticleBulletin of mathematical biology2017
Optimization and Control of Agent-Based Models in Biology: A Perspective.
Article in Bulletin of mathematical biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
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Who cites it
41 citing papers in PubMed, 91 citations in OpenAlex.
- Integrative agent-based modeling of cutaneous lupus: single-cell benchmarking and in silico evaluation of B-cell therapies.Briefings in bioinformatics · 2026Article
- Experimentally calibrated multiscale model predicts schedule dependent drug combination effects.NPJ systems biology and applications · 2026Article
- The heme scavenger hemopexin protects against lung injury during aspergillosis by mitigating release of neutrophil extracellular traps.JCI insight · 2025Article
- Stochastic pharmacodynamics of a heterogeneous tumour-cell population.Journal of pharmacokinetics and pharmacodynamics · 2025Article
- Smart epidemic control: A hybrid model blending ODEs and agent-based simulations for optimal, real-world intervention planning.PLoS computational biology · 2025Article
- Control of medical digital twins with artificial neural networks.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Article
- Optimal control of agent-based models via surrogate modeling.PLoS computational biology · 2025Article
- Article
- The Wound Environment Agent-based Model (WEABM): a digital twin platform for characterization and complex therapeutic discovery for volumetric muscle loss.bioRxiv : the preprint server for biology · 2024Article
- Article
- Quantifying collective motion patterns in mesenchymal cell populations using topological data analysis and agent-based modeling.Mathematical biosciences · 2024Article
- SimService: a lightweight library for building simulation services in Python.Bioinformatics (Oxford, England) · 2024Article
- Bridging the gap between mechanistic biological models and machine learning surrogates.PLoS computational biology · 2023Review
- Delayed skeletal muscle repair following inflammatory damage in simulated agent-based models of muscle regeneration.PLoS computational biology · 2023Article
- ChemChaste: Simulating spatially inhomogeneous biochemical reaction-diffusion systems for modeling cell-environment feedbacks.GigaScience · 2022Article
- Preparing for the next COVID: Deep Reinforcement Learning trained Artificial Intelligence discovery of multi-modal immunomodulatory control of systemic inflammation in the absence of effective anti-microbials.bioRxiv : the preprint server for biology · 2022Article
- In silico design of recombinant multi-epitope vaccine against influenza A virus.BMC bioinformatics · 2022Article
- A laboratory and simulation platform to integrate individual life history traits and population dynamics.Nature computational science · 2022Article
- Coarse-grained Stochastic Model of Myosin-Driven Vesicles into Dendritic Spines.SIAM journal on applied mathematics · 2022Article
- Preparing for the next pandemic: Simulation-based deep reinforcement learning to discover and test multimodal control of systemic inflammation using repurposed immunomodulatory agents.Frontiers in immunology · 2022Article
Corrections and comments
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Authors and funding
10 authors at 10 institutions in 1 country.
Funding
Abstract
Agent-based models (ABMs) have become an increasingly important mode of inquiry for the life sciences. They are particularly valuable for systems that are not understood well enough to build an equation-based model. These advantages, however, are counterbalanced by the difficulty of analyzing and using ABMs, due to the lack of the type of mathematical tools available for more traditional models, which leaves simulation as the primary approach. As models become large, simulation becomes challenging. This paper proposes a novel approach to two mathematical aspects of ABMs, optimization and control, and it presents a few first steps outlining how one might carry out this approach. Rather than viewing the ABM as a model, it is to be viewed as a surrogate for the actual system. For a given optimization or control problem (which may change over time), the surrogate system is modeled instead, using data from the ABM and a modeling framework for which ready-made mathematical tools exist, such as differential equations, or for which control strategies can explored more easily. Once the optimization problem is solved for the model of the surrogate, it is then lifted to the surrogate and tested. The final step is to lift the optimization solution from the surrogate system to the actual system. This program is illustrated with published work, using two relatively simple ABMs as a demonstration, Sugarscape and a consumer-resource ABM. Specific techniques discussed include dimension reduction and approximation of an ABM by difference equations as well systems of PDEs, related to certain specific control objectives. This demonstration illustrates the very challenging mathematical problems that need to be solved before this approach can be realistically applied to complex and large ABMs, current and future. The paper outlines a research program to address them.
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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.