In silico model of axonal pathfinding during spinal cord regeneration in zebrafish larvae.

Functional spinal cord repair in zebrafish is governed by regeneration-favorable biochemical and mechanical cues within the lesion microenvironment. Alterations in extracellular matrix composition and stiffness are closely associated with axon regeneration and its failure. However, experimentally dissecting the interplay between mechanical signals and axonal regrowth in vivo remains technically challenging. Here, we present an agent-based modeling framework to simulate stiffness-mediated axonal growth trajectories across the lesion. We use this model to explore potential mechanisms underlying the characteristic growth patterns observed during zebrafish spinal cord regeneration. Experimental growth profiles of regenerated axons were averaged and compared with mean simulation profiles obtained using different mechanosensing parameter sets. Among the tested parameter combinations, a common parameter set yielded the highest profile overlap at 48 hours post lesion. These exploratory comparisons demonstrate that the model can generate selected features of the observed spatial signal distributions of regenerated axons. Hence, our computational framework provides an in silico platform for investigating the role of mechanical cues in axon regeneration in the injured zebrafish spinal cord.