Date created: | Last Updated:

Category: Project

Description: Computational modeling of assembly is challenging for many systems because their timescales vastly exceed computational limitations. This work introduces the MultiMSM, which is a general framework that uses Markov state models (MSMs) to enable simulating self-assembly and self-organization on timescales that are orders of magnitude longer than those accessible to brute force dynamics simulations. In contrast to previous MSM approaches to simulating assembly, the framework describes simultaneous assembly of many clusters and the consequent depletion of free subunits or other small oligomers. The algorithm accounts for changes in transition rates as concentrations of monomers and intermediates evolve over the course of the reaction. Using two model systems, we show that the MultiMSM accurately predicts the concentrations of the full ensemble of intermediates on the long timescales required for reactions to reach equilibrium. Included in this project are the post processed trajectory files to train the MultiMSM, as well as scripts to perform all the analyses described in the corresponding manuscript and generate the figures included therein. Requires the use of two Python libraries which can be found at https://github.com/onehalfatsquared/MultiMSM and https://github.com/onehalfatsquared/SAASH