Salvato in:
| Autori principali: | , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2405.02467 |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866911866442219520 |
|---|---|
| author | Trubiano, Anthony Hagan, Michael F. |
| author_facet | Trubiano, Anthony Hagan, Michael F. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_02467 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Markov State Model Approach to Simulate Self-Assembly Trubiano, Anthony Hagan, Michael F. Soft Condensed Matter 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. |
| title | Markov State Model Approach to Simulate Self-Assembly |
| topic | Soft Condensed Matter |
| url | https://arxiv.org/abs/2405.02467 |