Agent-based modeling (ABM), also termed 'Individual-based modeling (IBM)', is a computational approach that simulates the interactions of autonomous entities (agents, or individual cells) with each other and their local environment to predict higher level emergent patterns. A literature-derived rule set governs the actions of each individual agent. While this technique has been widely used in the ecological and social sciences, it has only recently been applied in biomedical research. The purpose of this review is to provide an introduction to ABM as it has been used to study complex multi-cell biological phenomena, underscore the importance of coupling models with experimental work, and outline future challenges for the ABM field and its application to biomedical research. We highlight a number of published examples of ABM, focusing on work that has combined experimental with ABM analyses and how this pairing produces new understanding. We conclude with suggestions for moving forward with this parallel approach.
Thorne et al. (Thu,) studied this question.