Topologically-based parameter inference for agent-based model selection from spatiotemporal cellular data.

ABSTRACT: Advances in spatiotemporal single-cell imaging have enabled detailed observations of cell population dynamics and intercellular interactions. However, translating these rich data sets into mechanistic insight remains a significant challenge. Agent-based models (ABMs) are a bottom-up computational framework for investigating the emergent behavior of cell populations that can arise from rules defining the interactions between individual neighboring cells, while topological data analysis (TDA) provides robust descriptors of spatial organization. We present TOPAZ (TOpologically-based Parameter inference for Agent-based model optimiZation), a computational pipeline that integrates TDA with approximate Bayesian computation (ABC) and Bayesian model selection to identify biologically plausible ABM

SUBMITTER: Wenzel AR 

PROVIDER: S-EPMC12259179 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature