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Latent space of a small genetic network: Geometry of dynamics and information
The high-dimensional character of most biological systems presents genuine challenges for modeling and prediction. Here we propose a neural network–based approach for dimensionality reduction and analysis of biological gene expression data, using, as a case study, a well-known genetic network in the...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
National Academy of Sciences
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9245618/ https://www.ncbi.nlm.nih.gov/pubmed/35737842 http://dx.doi.org/10.1073/pnas.2113651119 |
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author | Seyboldt, Rabea Lavoie, Juliette Henry, Adrien Vanaret, Jules Petkova, Mariela D. Gregor, Thomas François, Paul |
author_facet | Seyboldt, Rabea Lavoie, Juliette Henry, Adrien Vanaret, Jules Petkova, Mariela D. Gregor, Thomas François, Paul |
author_sort | Seyboldt, Rabea |
collection | PubMed |
description | The high-dimensional character of most biological systems presents genuine challenges for modeling and prediction. Here we propose a neural network–based approach for dimensionality reduction and analysis of biological gene expression data, using, as a case study, a well-known genetic network in the early Drosophila embryo, the gap gene patterning system. We build an autoencoder compressing the dynamics of spatial gap gene expression into a two-dimensional (2D) latent map. The resulting 2D dynamics suggests an almost linear model, with a small bare set of essential interactions. Maternally defined spatial modes control gap genes positioning, without the classically assumed intricate set of repressive gap gene interactions. This, surprisingly, predicts minimal changes of neighboring gap domains when knocking out gap genes, consistent with previous observations. Latent space geometries in maternal mutants are also consistent with the existence of such spatial modes. Finally, we show how positional information is well defined and interpretable as a polar angle in latent space. Our work illustrates how optimization of small neural networks on medium-sized biological datasets is sufficiently informative to capture essential underlying mechanisms of network function. |
format | Online Article Text |
id | pubmed-9245618 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | National Academy of Sciences |
record_format | MEDLINE/PubMed |
spelling | pubmed-92456182022-12-22 Latent space of a small genetic network: Geometry of dynamics and information Seyboldt, Rabea Lavoie, Juliette Henry, Adrien Vanaret, Jules Petkova, Mariela D. Gregor, Thomas François, Paul Proc Natl Acad Sci U S A Physical Sciences The high-dimensional character of most biological systems presents genuine challenges for modeling and prediction. Here we propose a neural network–based approach for dimensionality reduction and analysis of biological gene expression data, using, as a case study, a well-known genetic network in the early Drosophila embryo, the gap gene patterning system. We build an autoencoder compressing the dynamics of spatial gap gene expression into a two-dimensional (2D) latent map. The resulting 2D dynamics suggests an almost linear model, with a small bare set of essential interactions. Maternally defined spatial modes control gap genes positioning, without the classically assumed intricate set of repressive gap gene interactions. This, surprisingly, predicts minimal changes of neighboring gap domains when knocking out gap genes, consistent with previous observations. Latent space geometries in maternal mutants are also consistent with the existence of such spatial modes. Finally, we show how positional information is well defined and interpretable as a polar angle in latent space. Our work illustrates how optimization of small neural networks on medium-sized biological datasets is sufficiently informative to capture essential underlying mechanisms of network function. National Academy of Sciences 2022-06-22 2022-06-28 /pmc/articles/PMC9245618/ /pubmed/35737842 http://dx.doi.org/10.1073/pnas.2113651119 Text en Copyright © 2022 the Author(s). Published by PNAS. https://creativecommons.org/licenses/by-nc-nd/4.0/This article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) . |
spellingShingle | Physical Sciences Seyboldt, Rabea Lavoie, Juliette Henry, Adrien Vanaret, Jules Petkova, Mariela D. Gregor, Thomas François, Paul Latent space of a small genetic network: Geometry of dynamics and information |
title | Latent space of a small genetic network: Geometry of dynamics and information |
title_full | Latent space of a small genetic network: Geometry of dynamics and information |
title_fullStr | Latent space of a small genetic network: Geometry of dynamics and information |
title_full_unstemmed | Latent space of a small genetic network: Geometry of dynamics and information |
title_short | Latent space of a small genetic network: Geometry of dynamics and information |
title_sort | latent space of a small genetic network: geometry of dynamics and information |
topic | Physical Sciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9245618/ https://www.ncbi.nlm.nih.gov/pubmed/35737842 http://dx.doi.org/10.1073/pnas.2113651119 |
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