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

Area of Science:

• Computational Biology
• Systems Biology
• Bioinformatics

Background:

• Agent-based models (ABMs) are crucial for studying biological tissue complexity, offering unparalleled resolution for phenotypic and spatial heterogeneity.
• However, ABMs face significant computational costs, limiting model scale and analysis, especially for molecular and cell-specific dynamics.

Purpose of the Study:

• To extend a previously developed global method for computationally expensive dynamics in ABMs.
• To assess the method's efficacy in simulating heterogeneous tissue with cells capable of phenotype switching in response to microenvironmental signals.

Main Methods:

• Developed and applied a global computational method to solve complex dynamics in agent-based models.
• Extended the method to incorporate cell phenotype switching based on microenvironmental cues.
• Compared simulation time and system behavior with and without the global method.

Main Results:

• The global method significantly decreased simulation time for agent-based models.
• The extended method accurately preserved temporal population dynamics and spatial cell arrangements.
• Computational efficiency was achieved without altering the fundamental behavior of the simulated biological system.

Conclusions:

• The enhanced global method provides an efficient tool for simulating agent-based models of heterogeneous biological tissue.
• This approach effectively captures critical molecular and cellular dynamics, including phenotype plasticity.
• Enables larger-scale simulations and more comprehensive analysis of complex biological systems.