Stochastic polymerisation models have been employed to study the self-assembly of amyloid fibres or synthetic selfassembling systems, such as e.g. 1,3,5-benzenetricarboxamides (BTAs) or phenylenevinylenes. Such models can predict the evolution of the aggregate population, resulting in polymerisation curves that are validated by fitting the signals detected in experiments based on hypotetical polymerization schemes. At atomistic resolution, first-principles molecular dynamics (MD) simulations were recently demonstrated useful for the study of supramolecular polymers. Coarse-grained MD (CG-MD) simulations have been also employed to study complex supramolecular polymer systems, reaching relevant space and time-scales for the study of their dynamics. In particular, combined with enhanced sampling methods, such models allow to study the molecular exchange between the supramolecular structures and their surroundings, the exchange pathways, and the response of these materials to external stimuli. Such advanced computational approaches are mostly employed to study isolated assemblies in solution. But the dynamics of a system involving multiple assemblies (e.g., monomers/oligomers exchange between fibres, or fibres fragmentation and recombination) remains typically overlooked. To understand how supramolecular polymers communicate with each other in a realistic system, it is necessary to simulate multiple assemblies in dynamic, supramolecular equilibrium conditions, studying their complex, concerted behavior with sufficiently high resolution.