A method for efficient Bayesian optimization of self-assembly systems from scattering data.

ABSTRACT:

Background

The ability of collections of molecules to spontaneously assemble into large functional complexes is central to all cellular processes. Using the viral capsid as a model system for complicated macro-molecular assembly, we develop methods for probing fine details of the process by learning kinetic rate parameters consistent with experimental measures of assembly. We have previously shown that local rule based stochastic simulation methods in conjunction with bulk indirect experimental data can meaningfully constrain the space of possible assembly trajectories and allow inference of experimentally unobservable features of the real system.

Results

In the present work, we introduce a new Bayesian optimization framework using multi-Gaussian process model regression. We

SUBMITTER: Thomas M 

PROVIDER: S-EPMC5994016 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature