Modeling reaction–diffusion processes in simplified to complex astroglial branchlet geometries using different simulation tools

Computational modeling of spatial biochemical processes in neuroscience is increasingly important as advances in experimental and imaging techniques provide detailed insights into cellular organization. Reaction–diffusion models are commonly used to study the diffusion and interactions of ions and molecules within neuronal cells, and multiple open-access simulation platforms have been developed to support such analysis. However, a comprehensive analysis of commonly used simulation tools, including MCell, STEPS, NeuroRD, and NEURON, remains lacking, particularly for realistic glial cell geometries, as these tools were originally developed for neurons. Here, we present a systematic analysis of these four simulation environments by implementing equivalent calcium diffusion and calcium–calmodulin reaction–diffusion models across a range of spatial domains. These include simplified geometries (cylinders and rectangular cuboids) as well as biologically realistic astroglial morphologies derived from electron microscopy data, including mouse Bergmann glia and mouse and human cortical astrocytes. Using matched kinetic parameters and diffusion constants, we examine simulation outputs and morphological variations across platforms under comparable conditions. We find that all tools produce overall consistent results for the systems considered, indicating that each can reliably capture diffusion and reaction–diffusion dynamics in simplified and complex geometries. At the same time, we show that the structural heterogeneity of branchlets from human astrocytes and mouse Bergmann glia gives rise to distinct calcium signaling, demonstrating that fine-scale morphology critically shapes astroglial dynamics and must be accurately captured by simulation frameworks. In addition, notable differences emerge in geometry representation, implementation strategies, computational aspects, and usability of the tools. By highlighting key trade-offs between geometric fidelity, flexibility, and computational cost in modeling intracellular biochemical processes, these findings provide practical guidance for selecting appropriate simulation tools. More broadly, our results support the use of reaction–diffusion modeling in morphologically realistic astrocytic environments and contribute to enabling mechanistic studies of localized signaling events at synaptic and perisynaptic sites.