Norton Kerri-Ann, Bergman Daniel, Jain Harsh Vardhan, Jackson Trachette
Computational Sciences Program, Bard College, 30 Campus Road, Annandale-on-Hudson, 12504, NY, USA.
Institute for Genome Sciences, University of Maryland School of Medicine, 655 W. Baltimore Street, Baltimore, 21201, MD, USA.
ArXiv. 2025 Apr 15:arXiv:2504.11617v1.
Agent-based modeling (ABM) is a powerful computational approach for studying complex biological and biomedical systems, yet its widespread use remains limited by significant computational demands. As models become increasingly sophisticated, the number of parameters and interactions rises rapidly, exacerbating the so-called "curse of dimensionality" and making comprehensive parameter exploration and uncertainty analyses computationally prohibitive. Surrogate modeling provides a promising solution by approximating ABM behavior through computationally efficient alternatives, greatly reducing the runtime needed for parameter estimation, sensitivity analysis, and uncertainty quantification. In this review, we examine traditional approaches for performing these tasks directly within ABMs-providing a baseline for comparison-and then synthesize recent developments in surrogate-assisted methodologies for biological and biomedical applications. We cover statistical, mechanistic, and machine-learning-based approaches, emphasizing emerging hybrid strategies that integrate mechanistic insights with machine learning to balance interpretability and scalability. Finally, we discuss current challenges and outline directions for future research, including the development of standardized benchmarks to enhance methodological rigor and facilitate the broad adoption of surrogate-assisted ABMs in biology and medicine.
基于主体的建模（ABM）是一种用于研究复杂生物和生物医学系统的强大计算方法，但其广泛应用仍受到巨大计算需求的限制。随着模型变得越来越复杂，参数和相互作用的数量迅速增加，加剧了所谓的“维度诅咒”，使得全面的参数探索和不确定性分析在计算上变得难以实现。代理建模通过计算效率高的替代方法近似ABM行为，为解决这一问题提供了一个有前景的方案，大大减少了参数估计、敏感性分析和不确定性量化所需的运行时间。在这篇综述中，我们首先研究在ABM中直接执行这些任务的传统方法——提供一个比较基准——然后综合生物和生物医学应用中代理辅助方法的最新进展。我们涵盖基于统计、机理和机器学习的方法，重点介绍将机理见解与机器学习相结合以平衡可解释性和可扩展性的新兴混合策略。最后，我们讨论当前面临的挑战并概述未来研究方向，包括开发标准化基准以提高方法的严谨性，并促进代理辅助ABM在生物学和医学中的广泛应用。
