Latent space of a small genetic network: Geometry of dynamics and information

Rabea Seyboldt, Juliette Lavoie, Adrien Henry, Jules Vanaret, Mariela D. Petkova, Thomas Gregor, Paul Francois

Research output: Contribution to journalArticlepeer-review

Abstract

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.Theresulting 2Ddynamics 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 onmedium-sized biological datasets is sufficiently informative to capture essential underlying mechanisms of network function.

Original languageEnglish (US)
Article numbere2113651119
JournalProceedings of the National Academy of Sciences of the United States of America
Volume119
Issue number26
DOIs
StatePublished - Jun 28 2022

All Science Journal Classification (ASJC) codes

  • General

Keywords

  • Drosophila gap genes
  • developmental biology
  • dimensionality reduction
  • machine learning
  • systems biology

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