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.
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2019-11-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-019-13307-x |
| _version_ | 1818682370090336256 |
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| author | Philip Pearce Francis G. Woodhouse Aden Forrow Ashley Kelly Halim Kusumaatmaja Jörn Dunkel |
| author_facet | Philip Pearce Francis G. Woodhouse Aden Forrow Ashley Kelly Halim Kusumaatmaja Jörn Dunkel |
| author_sort | Philip Pearce |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-17T10:17:46Z |
| format | Article |
| id | doaj.art-ea15b726de5c44c49d48fefe125fa6ad |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-12-17T10:17:46Z |
| publishDate | 2019-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-ea15b726de5c44c49d48fefe125fa6ad2022-12-21T21:52:53ZengNature PortfolioNature 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 |
| 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 |
| title | 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_short | Learning dynamical information from static protein and sequencing data |
| title_sort | learning dynamical information from static protein and sequencing data |
| url | https://doi.org/10.1038/s41467-019-13307-x |
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