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

Overview of attention for article published in BMC Systems Biology, June 2018
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Title
A method for efficient Bayesian optimization of self-assembly systems from scattering data
Published in
BMC Systems Biology, June 2018
DOI 10.1186/s12918-018-0592-8
Pubmed ID
Authors

Marcus Thomas, Russell Schwartz

Abstract

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. In the present work, we introduce a new Bayesian optimization framework using multi-Gaussian process model regression. We also extend our prior work to encompass small-angle X-ray/neutron scattering (SAXS/SANS) as a possibly richer experimental data source than the previously used static light scattering (SLS). Method validation is based on synthetic experiments generated using protein data bank (PDB) structures of cowpea chlorotic mottle virus. We also apply the same approach to computationally cheaper differential equation based simulation models. We present a flexible approach for the global optimization of computationally costly objective functions associated with dynamic, multidimensional models. When applied to the stochastic viral capsid system, our method outperforms a current state of the art black box solver tailored for use with noisy objectives. Our approach also has wide applicability to general stochastic optimization problems.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 25 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 6 24%
Researcher 4 16%
Student > Ph. D. Student 3 12%
Unspecified 2 8%
Student > Bachelor 1 4%
Other 5 20%
Unknown 4 16%
Readers by discipline Count As %
Engineering 4 16%
Chemistry 4 16%
Agricultural and Biological Sciences 2 8%
Unspecified 2 8%
Medicine and Dentistry 2 8%
Other 4 16%
Unknown 7 28%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 09 June 2018.
All research outputs
#20,520,426
of 23,088,369 outputs
Outputs from BMC Systems Biology
#1,011
of 1,144 outputs
Outputs of similar age
#288,643
of 328,957 outputs
Outputs of similar age from BMC Systems Biology
#25
of 33 outputs
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