A Comprehensive Review of Agent-Based Methodologies and Applications in Cell Biology.

Abstract

The need to understand the emergent properties of complex biological systems requires computational frameworks that can bridge the gap from stochastic molecular events to tissue-level macroscopic responses. Agent-Based Modeling (ABM) has proven to be an effective mechanistic modeling tool in this field. It provides an advantage over traditional continuum modeling methods by allowing individual components of cells and subcellular structures to be modeled as discrete entities. This review aims to offers a thorough synthesis of the current state of ABM methodologies in the field, with special focus on its potential in modeling different aspects of cellular behavior and mechanotransduction events. Additionally, this paper delivers an analytical perspective on recent advances in modeling and parameterization strategies, including the integration of machine learning techniques into these modeling frameworks. Such algorithmic advances have facilitated multiscale model calibration, though translating these acceleration techniques into strict physical predictions remains constrained by data distribution boundaries and overfitting risks. This review provides a comprehensive overview of the current state of affairs in this field by combining different modeling strategies with validation methods. Finally, we discuss some of the challenges in this field, particularly regarding computational reproducibility, and outline possible avenues for future development in ABM as a robust, scale-bridging tool in systems biomedicine.