Optimal control of agent-based models via surrogate modeling.

IF 3.8 2区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

PLoS Computational Biology Pub Date : 2025-01-14 eCollection Date: 2025-01-01 DOI:10.1371/journal.pcbi.1012138

Luis L Fonseca, Lucas Böttcher, Borna Mehrad, Reinhard C Laubenbacher

引用次数: 0

Abstract

This paper describes and validates an algorithm to solve optimal control problems for agent-based models (ABMs). For a given ABM and a given optimal control problem, the algorithm derives a surrogate model, typically lower-dimensional, in the form of a system of ordinary differential equations (ODEs), solves the control problem for the surrogate model, and then transfers the solution back to the original ABM. It applies to quite general ABMs and offers several options for the ODE structure, depending on what information about the ABM is to be used. There is a broad range of applications for such an algorithm, since ABMs are used widely in the life sciences, such as ecology, epidemiology, and biomedicine and healthcare, areas where optimal control is an important purpose for modeling, such as for medical digital twin technology.

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通过代理建模对基于代理的模型进行优化控制。

本文描述并验证了一种解决基于代理的模型（ABM）的最优控制问题的算法。对于给定的 ABM 和给定的最优控制问题，该算法以常微分方程组（ODE）的形式推导出一个代理模型（通常是低维模型），求解代理模型的控制问题，然后将其转回原始 ABM。它适用于相当普遍的 ABM，并根据需要使用的 ABM 信息，为 ODE 结构提供了多种选择。这种算法的应用范围很广，因为 ABM 广泛应用于生命科学领域，如生态学、流行病学、生物医学和医疗保健，在这些领域中，最优控制是建模的重要目的，如医学数字孪生技术。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

PLoS Computational Biology BIOCHEMICAL RESEARCH METHODS-MATHEMATICAL & COMPUTATIONAL BIOLOGY

CiteScore

7.10

自引率

4.70%

发文量

820

审稿时长

2.5 months

期刊介绍： PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.