Learning dynamical information from static protein and sequencing data

Learning dynamical information from static protein and sequencing data

Reconstructing system dynamics on complex high-dimensional energy landscapes from static experimental snapshots remains challenging. Here, the authors introduce a framework to infer the essential dynamics of physical and biological systems without need for time-dependent measurements.

Bibliographic Details
Main Authors: Philip Pearce, Francis G. Woodhouse, Aden Forrow, Ashley Kelly, Halim Kusumaatmaja, Jörn Dunkel
Format: Article
Language:English
Published: Nature Publishing Group 2019-11-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-019-13307-x
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spelling doaj-ea15b726de5c44c49d48fefe125fa6ad2021-05-11T12:14:20ZengNature Publishing GroupNature Communications2041-17232019-11-011011810.1038/s41467-019-13307-xLearning dynamical information from static protein and sequencing dataPhilip Pearce0Francis G. Woodhouse1Aden Forrow2Ashley Kelly3Halim Kusumaatmaja4Jörn Dunkel5Department of Mathematics, Massachusetts Institute of TechnologyMathematical Institute, University of OxfordDepartment of Mathematics, Massachusetts Institute of TechnologyDepartment of Physics, Durham UniversityDepartment of Physics, Durham UniversityDepartment of Mathematics, Massachusetts Institute of TechnologyReconstructing system dynamics on complex high-dimensional energy landscapes from static experimental snapshots remains challenging. Here, the authors introduce a framework to infer the essential dynamics of physical and biological systems without need for time-dependent measurements.https://doi.org/10.1038/s41467-019-13307-x
collection DOAJ
language English
format Article
sources DOAJ
author Philip Pearce
Francis G. Woodhouse
Aden Forrow
Ashley Kelly
Halim Kusumaatmaja
Jörn Dunkel
spellingShingle Philip Pearce
Francis G. Woodhouse
Aden Forrow
Ashley Kelly
Halim Kusumaatmaja
Jörn Dunkel
Learning dynamical information from static protein and sequencing data
Nature Communications
author_facet Philip Pearce
Francis G. Woodhouse
Aden Forrow
Ashley Kelly
Halim Kusumaatmaja
Jörn Dunkel
author_sort Philip Pearce
title Learning dynamical information from static protein and sequencing data
title_short Learning dynamical information from static protein and sequencing data
title_full Learning dynamical information from static protein and sequencing data
title_fullStr Learning dynamical information from static protein and sequencing data
title_full_unstemmed Learning dynamical information from static protein and sequencing data
title_sort learning dynamical information from static protein and sequencing data
publisher Nature Publishing Group
series Nature Communications
issn 2041-1723
publishDate 2019-11-01
description Reconstructing system dynamics on complex high-dimensional energy landscapes from static experimental snapshots remains challenging. Here, the authors introduce a framework to infer the essential dynamics of physical and biological systems without need for time-dependent measurements.
url https://doi.org/10.1038/s41467-019-13307-x
work_keys_str_mv AT philippearce learningdynamicalinformationfromstaticproteinandsequencingdata
AT francisgwoodhouse learningdynamicalinformationfromstaticproteinandsequencingdata
AT adenforrow learningdynamicalinformationfromstaticproteinandsequencingdata
AT ashleykelly learningdynamicalinformationfromstaticproteinandsequencingdata
AT halimkusumaatmaja learningdynamicalinformationfromstaticproteinandsequencingdata
AT jorndunkel learningdynamicalinformationfromstaticproteinandsequencingdata
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