Simulation Data - In silico model of axonal pathfinding during spinal cord regeneration in zebrafish larvae

Overview This dataset contains the agent-based model (ABM) simulation outputs underlying the figures of In silico model of axonal pathfinding during spinal cord regeneration in zebrafish larvae by Oskar Neumann, Mathar Kravikass, Nora John, Rahul Gopalan Ramachandran, Paul Steinmann, Vasily Zaburdaev, Daniel Wehner and Silvia Budday. In the model, axonal pathfinding is represented as stiffness-guided (durotactic) neurite growth through a deformable extracellular matrix. Neurites extend from neurons and navigate a prescribed substrate stiffness profile, where local stiffness modulates neurite growth speed and directionality through tunable coupling functions. The files generate the representative trajectories and parameter studies shown in the manuscript. Simulation output format Each simulation is stored as a series of plain-text snapshot files (snapXXX.txt), one per recorded time point, where the trailing number indexes the simulation progress and the highest-numbered snapshot is the final state. Every row describes one particle, including its position, type, radius, and identifier, and the files import directly into OVITO. Particle types share a common colour scheme: extracellular-matrix particles (gray), neurons (red), neurites (yellow), and neurite leading beads (blue). Repository structure The upload is grouped by the figure or analysis that each set of simulations supports. Figure 7, stiffness-pattern test cases (1 top stiff bottom soft, 2 radial inner soft, 3 radial inner stiff, 4 x-shaped bands, 5 cascading bands). Each folder corresponds to one stiffness pattern and contains the corresponding data for panels of the mechanosensing comparison figure, labelled a2 to e5. Rows a to e correspond to the five stiffness patterns in the order listed above, and columns 2 to 5 correspond to the four mechanosensing conditions: no mechanosensing (2), stiffness-modulated growth speed with straight growth (3), stiffness-modulated directionality (4), and stiffness-modulated growth speed and directionality (5). Figure S1, coupling-function forms. Sixteen simulations on the cascading stiffness profile that vary the mathematical form of the two stiffness couplings. Folders are named beta_<form>_n_<form>, where the first token is the form of the link (β) coupling and the second the form of the cone-angle (n) coupling, with form being log, pow, lin, or step for logistic, power, linear, and smoothstep respectively. Figure 8, coupling-strength sweep. A 5×5 grid on the cascading stiffness profile that varies the strength of both stiffness-dependent couplings simultaneously. Folders are named beta_XX_n_YY, where the numeric suffix encodes the parameter value multiplied by ten. Here β_link takes the values 0.0, 2.5, 7.5, 12.5, 25.0 (encoded 00, 25, 75, 125, 250) and n takes 0.0, 0.5, 2.5, 5.0, 10.0 (encoded 00, 05, 25, 50, 100). Figure 9 and 10, progessing wound edge study. A nested sweep over the cone angle (cone_angle_30, cone_angle_75), the coupling strength β (beta_25, beta_75, beta_125), and the exponent n (n_05, n_25, n_50), using the same value-times-ten encoding. Each parameter combination contains five independent stochastic realisations, sim_1 to sim_5.