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Physics-informed deep learning characterizes morphodynamics of Asian soybean rust disease
Medicines and agricultural biocides are often discovered using large phenotypic screens across hundreds of compounds, where visible effects of whole organisms are compared to gauge efficacy and possible modes of action. However, such analysis is often limited to human-defined and static features. He...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8571353/ https://www.ncbi.nlm.nih.gov/pubmed/34741028 http://dx.doi.org/10.1038/s41467-021-26577-1 |
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author | Cavanagh, Henry Mosbach, Andreas Scalliet, Gabriel Lind, Rob Endres, Robert G. |
author_facet | Cavanagh, Henry Mosbach, Andreas Scalliet, Gabriel Lind, Rob Endres, Robert G. |
author_sort | Cavanagh, Henry |
collection | PubMed |
description | Medicines and agricultural biocides are often discovered using large phenotypic screens across hundreds of compounds, where visible effects of whole organisms are compared to gauge efficacy and possible modes of action. However, such analysis is often limited to human-defined and static features. Here, we introduce a novel framework that can characterize shape changes (morphodynamics) for cell-drug interactions directly from images, and use it to interpret perturbed development of Phakopsora pachyrhizi, the Asian soybean rust crop pathogen. We describe population development over a 2D space of shapes (morphospace) using two models with condition-dependent parameters: a top-down Fokker-Planck model of diffusive development over Waddington-type landscapes, and a bottom-up model of tip growth. We discover a variety of landscapes, describing phenotype transitions during growth, and identify possible perturbations in the tip growth machinery that cause this variation. This demonstrates a widely-applicable integration of unsupervised learning and biophysical modeling. |
format | Online Article Text |
id | pubmed-8571353 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-85713532021-11-15 Physics-informed deep learning characterizes morphodynamics of Asian soybean rust disease Cavanagh, Henry Mosbach, Andreas Scalliet, Gabriel Lind, Rob Endres, Robert G. Nat Commun Article Medicines and agricultural biocides are often discovered using large phenotypic screens across hundreds of compounds, where visible effects of whole organisms are compared to gauge efficacy and possible modes of action. However, such analysis is often limited to human-defined and static features. Here, we introduce a novel framework that can characterize shape changes (morphodynamics) for cell-drug interactions directly from images, and use it to interpret perturbed development of Phakopsora pachyrhizi, the Asian soybean rust crop pathogen. We describe population development over a 2D space of shapes (morphospace) using two models with condition-dependent parameters: a top-down Fokker-Planck model of diffusive development over Waddington-type landscapes, and a bottom-up model of tip growth. We discover a variety of landscapes, describing phenotype transitions during growth, and identify possible perturbations in the tip growth machinery that cause this variation. This demonstrates a widely-applicable integration of unsupervised learning and biophysical modeling. Nature Publishing Group UK 2021-11-05 /pmc/articles/PMC8571353/ /pubmed/34741028 http://dx.doi.org/10.1038/s41467-021-26577-1 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Cavanagh, Henry Mosbach, Andreas Scalliet, Gabriel Lind, Rob Endres, Robert G. Physics-informed deep learning characterizes morphodynamics of Asian soybean rust disease |
title | Physics-informed deep learning characterizes morphodynamics of Asian soybean rust disease |
title_full | Physics-informed deep learning characterizes morphodynamics of Asian soybean rust disease |
title_fullStr | Physics-informed deep learning characterizes morphodynamics of Asian soybean rust disease |
title_full_unstemmed | Physics-informed deep learning characterizes morphodynamics of Asian soybean rust disease |
title_short | Physics-informed deep learning characterizes morphodynamics of Asian soybean rust disease |
title_sort | physics-informed deep learning characterizes morphodynamics of asian soybean rust disease |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8571353/ https://www.ncbi.nlm.nih.gov/pubmed/34741028 http://dx.doi.org/10.1038/s41467-021-26577-1 |
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