Controlling gene expression with deep generative design of regulatory DNA
Artikel i vetenskaplig tidskrift, 2022
Design of de novo synthetic regulatory DNA is a promising avenue to control gene expression in biotechnology and medicine. Using mutagenesis typically requires screening sizable random DNA libraries, which limits the designs to span merely a short section of the promoter and restricts their control of gene expression. Here, we prototype a deep learning strategy based on generative adversarial networks (GAN) by learning directly from genomic and transcriptomic data. Our ExpressionGAN can traverse the entire regulatory sequence-expression landscape in a gene-specific manner, generating regulatory DNA with prespecified target mRNA levels spanning the whole gene regulatory structure including coding and adjacent non-coding regions. Despite high sequence divergence from natural DNA, in vivo measurements show that 57% of the highly-expressed synthetic sequences surpass the expression levels of highly-expressed natural controls. This demonstrates the applicability and relevance of deep generative design to expand our knowledge and control of gene expression regulation in any desired organism, condition or tissue.
regulatory DNA
generative adversarial networks (GAN)
Gene Expression
Författare
Jan Zrimec
Chalmers, Biologi och bioteknik, Systembiologi
National Institute of Biology Ljubljana
Xiaozhi Fu
Chalmers, Biologi och bioteknik, Systembiologi
Muhammad Azam Sheikh
Chalmers, Data- och informationsteknik, CSE Verksamhetsstöd
Christos Skrekas
Chalmers, Biologi och bioteknik, Systembiologi
Vykintas Jauniskis
Biomatter Designs
Nora Speicher
Chalmers, Data- och informationsteknik, Data Science och AI
Christoph Sebastian Börlin
Chalmers, Biologi och bioteknik, Systembiologi
Copenhagen N
Vilhelm Verendel
Chalmers, Data- och informationsteknik, Data Science och AI
Morteza Haghir Chehreghani
Chalmers, Data- och informationsteknik, Data Science och AI
Devdatt Dubhashi
Chalmers, Data- och informationsteknik, Data Science och AI
Verena Siewers
Chalmers, Biologi och bioteknik, Systembiologi
Florian David
Chalmers, Biologi och bioteknik, Systembiologi
Jens B Nielsen
Chalmers, Biologi och bioteknik, Systembiologi
Copenhagen N
Aleksej Zelezniak
Vilniaus universitetas
Faculty of Life Sciences & Medicine
Chalmers, Biologi och bioteknik, Systembiologi
Nature Communications
2041-1723 (ISSN) 20411723 (eISSN)
Vol. 13 1 5099-Ämneskategorier
Utvecklingsbiologi
Bioinformatik och systembiologi
Genetik
Styrkeområden
Hälsa och teknik
DOI
10.1038/s41467-022-32818-8
PubMed
36042233