Reproducible Agent-Based Simulations of Protein Aggregation: A FAIR Implementation

Aberrant protein aggregation is implicated in many neurodegenerative diseases and is strongly modulated by intracellular spatial constraints such as macromolecular crowding and clearance. Computational studies of aggregation, however, frequently lack the documentation and provenance required for independent reproduction. We present a spatial, lattice-based agent‑based model of intracellular protein aggregation implemented in Julia and packaged as a research software object aligned with the FAIR (Findable, Accessible, Interoperable, and Reusable) principles. Individual monomers undergo reversible activation, oligomer nucleation, aggregate growth, and optional oligomer clearance; stochastic movement and local encounters on a 3D face-centered cubic lattice capture spatial heterogeneity and crowding. The accompanying repository includes centralized parameters, machine-readable metadata, version-pinned dependencies, example runs, and automated post-simulation analysis. Ensemble simulations reproduce canonical aggregation phases (lag, nucleation, growth, saturation) and illustrate that oligomer removal reduces the final aggregate burden. The model also supports configurable macromolecular crowding via spherical obstacles, enabling systematic exploration of crowding effects on aggregation kinetics. Runtime benchmarking shows that 300 independent simulations (1,000 monomers; 5,000 timesteps), executed as 15 concurrent single-threaded jobs on institutional high-performance computing resources, completed in approximately 25 h of wall-clock time, enabling parameter sweeps and ensemble averaging. Together, the model and its FAIR packaging provide a reproducible template for transparent, extensible agent-based computational biology.