Markov State Model Approach to Simulate Self-Assembly

TL;DR

This paper introduces MultiMSM, a Markov state model framework that efficiently simulates long-timescale self-assembly processes, accounting for multiple clusters and concentration changes, enabling rapid predictions of assembly dynamics.

Contribution

The paper presents a novel MultiMSM framework that extends MSMs to simulate simultaneous assembly of many clusters with evolving concentrations, significantly improving efficiency and scalability.

Findings

Accurately predicts intermediate concentrations over long timescales.

Enables simulation of multiple concentrations without additional sampling.

Calculates free energy, nucleation times, and entropy production efficiently.

Abstract

Computational modeling of assembly is challenging for many systems because their timescales vastly exceed those accessible to simulations. This article describes 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. Importantly, after constructing a MultiMSM for one system concentration, a wide range of other concentrations can be simulated without any further sampling. This capability allows for orders of magnitude additional speed up. In addition, the method enables highly efficient calculation of quantities such as free energy profiles, nucleation timescales, flux along the ensemble of assembly pathways, and entropy production rates. Identifying contributions of individual transitions to entropy production rates reveals sources of kinetic traps. The method is broadly applicable to systems with equilibrium or nonequilibrium dynamics, and is trivially parallelizable and thus highly scalable.