Abstract
<jats:p>Dendrites develop branching patterns that are critical for their function, yet the mechanisms guiding arbor morphology remain incompletely understood, and quantitative models predicting how signals guide morphology remain limited. The ddaE neuron in Drosophila larvae is a proprioceptive sensory neuron with a characteristic asymmetric dendrite arbor that exhibits posterior-biased branching. We developed a computational model using Dynamical Graph Grammar (DGG) to simulate ddaE dendrite development as a graph-based dynamical system, using a single morphogen gradient to establish arbor architecture. Our simulations of ddaE dendrites, guided by the spatial gradient of the Teneurin-m (Ten-m) morphogen combined with resource constraints and self-avoidance rules, accurately recapitulate the morphological features of biological ddaE neurons, including primary branch orientation, posterior bias, branch tree distributions, and branch length statistics. We find that the response to a single morphogen gradient is sufficient to guide the computerized dendritic arbor. Null model analyses demonstrate that simulated arbors exhibit non-random spatial and topological organization consistent with biological constraints. Our results demonstrate that rules based on a single morphogen gradient are sufficient to generate complex asymmetric dendritic patterns and provide a validated computational framework for testing perturbations in silico.</jats:p>