title: "Cross-graining": efficient multi-scale simulation via Markov state models.
abstract: Accurate and efficient methods to simulate biomolecular systems at multiple levels of detail simultaneously are an ongoing challenge for the simulation community. Here we present a new method for multi-scale simulation where a complex system can be partitioned into two loosely-coupled sub-systems, one coarse-grained and one atomistic. If the coupling between the coarse-grained and atomistic systems can be encoded into discrete states that interconvert slowly, we can construct a Markov state model where we approximate any given transition P[(s(i),t(j))->(s(k),t(1))] in the joint space of the coarse-grained and atomistic systems as the product of two orthogonal transitions P(s(i)->s(k) mid R: t(j)) and P(t(j)->t(1) mid R: s(j)). We provide a formalism for constructing such models and describe how they may be applied to multi-scale simulation of membrane proteins. This "cross-graining" methodology may provide a general means to efficiently simulate mixed-scale systems.
authors.affiliation: Departments of Chemistry and Structural Biology, Stanford University Stanford, CA 94305, USA.
authors.forename: Peter M
authors.initials: PM
authors.lastname: Kasson
authors.forename: Vijay S
authors.initials: VS
authors.lastname: Pande
mesh_terms: D019295:Computational Biology; D003198:Computer Simulation; D008390:Markov Chains; D008565:Membrane Proteins / Q000737:chemistry; D008958:Models, Molecular; D056004:Molecular Dynamics Simulation
chemical_list: D008565:Membrane Proteins