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A method for efficient Bayesian optimization of self-assembly systems from scattering data
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 learnin...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5994016/ https://www.ncbi.nlm.nih.gov/pubmed/29884203 http://dx.doi.org/10.1186/s12918-018-0592-8 |
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author | Thomas, Marcus Schwartz, Russell |
author_facet | Thomas, Marcus Schwartz, Russell |
author_sort | Thomas, Marcus |
collection | PubMed |
description | 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 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. CONCLUSIONS: 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. |
format | Online Article Text |
id | pubmed-5994016 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-59940162018-07-05 A method for efficient Bayesian optimization of self-assembly systems from scattering data Thomas, Marcus Schwartz, Russell BMC Syst Biol Methodology Article 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 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. CONCLUSIONS: 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. BioMed Central 2018-06-08 /pmc/articles/PMC5994016/ /pubmed/29884203 http://dx.doi.org/10.1186/s12918-018-0592-8 Text en © The Author(s) 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Methodology Article Thomas, Marcus Schwartz, Russell A method for efficient Bayesian optimization of self-assembly systems from scattering data |
title | A method for efficient Bayesian optimization of self-assembly systems from scattering data |
title_full | A method for efficient Bayesian optimization of self-assembly systems from scattering data |
title_fullStr | A method for efficient Bayesian optimization of self-assembly systems from scattering data |
title_full_unstemmed | A method for efficient Bayesian optimization of self-assembly systems from scattering data |
title_short | A method for efficient Bayesian optimization of self-assembly systems from scattering data |
title_sort | method for efficient bayesian optimization of self-assembly systems from scattering data |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5994016/ https://www.ncbi.nlm.nih.gov/pubmed/29884203 http://dx.doi.org/10.1186/s12918-018-0592-8 |
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