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

Mer information

Senast uppdaterat

2023-10-27