Advances in surrogate modeling for biological agent-based simulations: trends, challenges, and future prospects.

Area of Science:

• Computational Biology
• Biomedical Informatics
• Complex Systems Modeling

Background:

• Agent-based modeling (ABM) is crucial for understanding complex biological and biomedical systems.
• High computational demands and the curse of dimensionality limit ABM's widespread application.
• Parameter exploration and uncertainty analysis in ABMs are often computationally prohibitive.

Purpose of the Study:

• To review traditional and surrogate-assisted methods for analyzing agent-based models (ABMs).
• To synthesize recent advancements in surrogate-assisted methodologies for biological and biomedical applications.
• To identify challenges and future research directions for surrogate-assisted ABMs.

Main Methods:

• Examination of traditional computational approaches for ABM analysis.
• Synthesis of statistical, mechanistic, and machine learning-based surrogate modeling techniques.
• Emphasis on hybrid strategies combining mechanistic insights with machine learning.

Main Results:

• Surrogate modeling provides computationally efficient pathways for ABM analysis.
• Emerging hybrid approaches balance interpretability and scalability in surrogate-assisted ABMs.
• Current challenges include methodological rigor and standardization.

Conclusions:

• Surrogate-assisted ABMs offer a promising solution to computational limitations in complex systems research.
• Further development of standardized benchmarks is needed to enhance rigor and adoption.
• Future research should focus on integrating mechanistic understanding with advanced machine learning for broader applicability.