Integrating Deep Learning and Synthetic Biology: A Co-Design Approach for Enhancing Gene Expression via N-Terminal Coding Sequences-Reference-Cited by-同舟云学术

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Integrating Deep Learning and Synthetic Biology: A Co-Design Approach for Enhancing Gene Expression via N-Terminal Coding Sequences

Published:2024-09-04 Issue: Volume: Page:
ISSN:2161-5063
Container-title:ACS Synthetic Biology
language:en
Short-container-title:ACS Synth. Biol.

Author:

Yan Zhanglu¹^ORCID,Chu Weiran²,Sheng Yuhua²,Tang Kaiwen¹,Wang Shida³,Liu Yanfeng²^ORCID,Wong Weng-Fai¹

Affiliation:

1. School of Computing, National University of Singapore, Singapore 117417, Singapore

2. Science Center for Future Foods, Jiangnan University, Wuxi 214122, PR China

3. Department of Mathematics, National University of Singapore, Singapore 119077, Singapore

Funder

Graduate Research and Innovation Projects of Jiangsu Province

National Key Research and Development Program of China

National Outstanding Youth Science Fund Project of National Natural Science Foundation of China

Natural Science Foundation of Jiangsu Province

National Natural Science Foundation of China

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acssynbio.4c00371

Reference25 articles.

1. Short tandem repeats bind transcription factors to tune eukaryotic gene expression

2. Regulation of gene expression by cis-acting long non-coding RNAs

3. Genome-wide gene expression tuning reveals diverse vulnerabilities of M. tuberculosis

4. An operator-based expression toolkit for Bacillus subtilis enables fine-tuning of gene expression and biosynthetic pathway regulation

5. CRISPR-assisted multi-dimensional regulation for fine-tuning gene expression in Bacillus subtilis

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