Controlling gene expression with deep generative design of regulatory DNA
Journal article, 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
Author
Jan Zrimec
Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology
National Institute of Biology Ljubljana
Xiaozhi Fu
Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology
Muhammad Azam Sheikh
Chalmers, Computer Science and Engineering (Chalmers), CSE Verksamhetsstöd
Christos Skrekas
Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology
Vykintas Jauniskis
Biomatter Designs
Nora Speicher
Chalmers, Computer Science and Engineering (Chalmers), Data Science and AI
Christoph Sebastian Börlin
Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology
Copenhagen N
Vilhelm Verendel
Chalmers, Computer Science and Engineering (Chalmers), Data Science and AI
Morteza Haghir Chehreghani
Chalmers, Computer Science and Engineering (Chalmers), Data Science and AI
Devdatt Dubhashi
Chalmers, Computer Science and Engineering (Chalmers), Data Science and AI
Verena Siewers
Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology
Florian David
Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology
Jens B Nielsen
Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology
Copenhagen N
Aleksej Zelezniak
Vilnius University
Faculty of Life Sciences & Medicine
Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology
Nature Communications
2041-1723 (ISSN) 20411723 (eISSN)
Vol. 13 1 5099-Subject Categories
Developmental Biology
Bioinformatics and Systems Biology
Genetics
Areas of Advance
Health Engineering
DOI
10.1038/s41467-022-32818-8
PubMed
36042233