Image-based systems biology integrates microscopy and computational modeling to quantitatively analyze biological processes in space and time. While traditional systems biology has focused on omics-scale data and biochemical networks, this field extends the approach to spatially resolved cellular and molecular dynamics. By combining automated image acquisition, quantitative feature extraction, and image-derived modeling, researchers can link experimental imaging data with predictive, mechanistic models.

This special issue of Cytometry Part A presents advances across multiple scales, from molecular colocalization and protein interaction analysis to tissue- and organism-level imaging. Studies include model selection for reaction-diffusion dynamics, multiscale modeling of stem cell organization, automated segmentation of neuronal networks, image-based high-throughput screening, and quantitative frameworks for analyzing molecular interactions and cytoskeletal assembly.

Together, these works demonstrate how image-based systems biology bridges experimental imaging and theoretical modeling, enabling a deeper understanding of spatial organization, cellular behavior, and molecular networks within complex biological systems.