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Controlling gene ex...
Controlling gene expression with deep generative design of regulatory DNA
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- Zrimec, Jan, 1981 (author)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Fu, Xiaozhi, 1990 (author)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Sheikh, Muhammad Azam, 1979 (author)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Skrekas, Christos, 1990 (author)
- Chalmers tekniska högskola,Chalmers University of Technology
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Jauniskis, Vykintas (author)
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- Speicher, Nora, 1987 (author)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Börlin, Christoph Sebastian, 1989 (author)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Verendel, Vilhelm, 1980 (author)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Haghir Chehreghani, Morteza, 1982 (author)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Dubhashi, Devdatt, 1965 (author)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Siewers, Verena, 1976 (author)
- Chalmers tekniska högskola,Chalmers University of Technology
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- David, Florian, 1981 (author)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Nielsen, Jens B, 1962 (author)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Zelezniak, Aleksej, 1984 (author)
- Vilniaus universitetas,Vilnius University,Chalmers tekniska högskola,Chalmers University of Technology
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(creator_code:org_t)
- 2022-08-30
- 2022
- English.
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In: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723 .- 2041-1723. ; 13:1, s. 5099-
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Abstract
Subject headings
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- 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.
Subject headings
- NATURVETENSKAP -- Biologi -- Utvecklingsbiologi (hsv//swe)
- NATURAL SCIENCES -- Biological Sciences -- Developmental Biology (hsv//eng)
- NATURVETENSKAP -- Biologi -- Bioinformatik och systembiologi (hsv//swe)
- NATURAL SCIENCES -- Biological Sciences -- Bioinformatics and Systems Biology (hsv//eng)
- NATURVETENSKAP -- Biologi -- Genetik (hsv//swe)
- NATURAL SCIENCES -- Biological Sciences -- Genetics (hsv//eng)
Keyword
- regulatory DNA
- generative adversarial networks (GAN)
- Gene Expression
Publication and Content Type
- art (subject category)
- ref (subject category)
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- By the author/editor
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Zrimec, Jan, 198 ...
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Fu, Xiaozhi, 199 ...
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Sheikh, Muhammad ...
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Skrekas, Christo ...
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Jauniskis, Vykin ...
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Speicher, Nora, ...
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show more...
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Börlin, Christop ...
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Verendel, Vilhel ...
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Haghir Chehregha ...
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Dubhashi, Devdat ...
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Siewers, Verena, ...
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David, Florian, ...
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Nielsen, Jens B, ...
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Zelezniak, Aleks ...
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- About the subject
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- NATURAL SCIENCES
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NATURAL SCIENCES
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and Biological Scien ...
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and Developmental Bi ...
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- NATURAL SCIENCES
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NATURAL SCIENCES
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and Biological Scien ...
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and Bioinformatics a ...
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- NATURAL SCIENCES
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NATURAL SCIENCES
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and Biological Scien ...
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and Genetics
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Nature Communica ...
- By the university
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Chalmers University of Technology