Phenotype switching in a global method for agent-based models of biological tissue

Abstract

Agent-based models (ABMs) are an increasingly important tool for understanding the complexities presented by phenotypic and spatial heterogeneity in biological tissue. The resolution a modeler can achieve in these regards is unrivaled by other approaches. However, this comes at a steep computational cost limiting either the scale of such models or the ability to explore, parameterize, analyze, and apply them. When the models involve molecular-level dynamics, especially cell-specific dynamics, the limitations are compounded. We have developed a global method for solving these computationally expensive dynamics significantly decreases the computational time without altering the behavior of the system. Here, we extend this method to the case where cells can switch phenotypes in response to signals in the microenvironment. We find that the global method in this context preserves the temporal population dynamics and the spatial arrangements of the cells while requiring markedly less simulation time. We thus add a tool for efficiently simulating ABMs that captures key facets of the molecular and cellular dynamics in heterogeneous tissue. Author summaryAgent-based models (ABMs) are an important tool for understanding how cells and molecular compounds interact to produce complex, emergent behavior. The principal feature of ABMs that set them apart from other types of models is their ability to capture the diversity of cells in a tissue. However, this feature comes at the cost of long simulation times, reducing the ability to apply the findings of these models to improve our understanding of living organisms. We present here a means of simulating ABMs using a more efficient method, called the global method, when the cells are undergoing discrete, phenotypic changes in response to molecular cues. We demonstrate that the global method preserves the key features of the ABM while performing simulations much faster. This allows for more efficient testing of biological hypotheses in a mathematical framework that captures key facets of the diversity in biological tissue.