Neuronal branching is a critical aspect of neural development and function, as the form of a neuron directly influences its connectivity and functional properties. While numerous molecular factors have been identified that modulate neuronal morphology, the mechanistic link between molecular activity and emergent branched structures remains poorly understood. Computational modeling offers a framework to bridge this gap by enabling systematic exploration of how specific parameters influence morphological outcomes. In this work, we introduce a fast and flexible generative simulation platform “BranchMorphoGen” that captures the development of branched neuronal morphologies. It reproduces a broad spectrum of branched structures based on observed growth rules, including de novo branching, fluctuating tip elongation, self-avoidance, diameter scaling, and branch straightening. Moreover, in this study, we demonstrate that branch hierarchies (primary, secondary, and higher orders) emerge as a consequence of branch straightening coupled with regulation of diameter. While our software reproduces the measured branching structure of Drosophila Class IV dendritic arborization neurons, it also recapitulates other branched networks, including purkinje cells, retinal ganglion cells, and starburst amacrine cells. Additionally, our software allows users to explore high-dimensional parameter spaces efficiently and to simulate how molecular-level perturbations can impact global neuronal architecture. By providing a tunable and extensible tool for hypothesis generation, this platform opens new avenues for mechanistic investigations into neuronal growth and morphogenesis.
Sutradhar et al. (Sun,) studied this question.