ArticlePloS one2013
Quantitative design of regulatory elements based on high-precision strength prediction using artificial neural network.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 58 citations in OpenAlex.
- Systematic representation and optimization enable the inverse design of cross-species regulatory sequences in bacteria.Nature communications · 2025Article
- Towards AI-designed genomes using a variational autoencoder.Proceedings. Biological sciences · 2024Article
- Transfer learning for cross-context prediction of protein expression from 5'UTR sequence.Nucleic acids research · 2024Article
- Machine Learning and Deep Learning in Synthetic Biology: Key Architectures, Applications, and Challenges.ACS omega · 2024Review
- Simulated Design-Build-Test-Learn Cycles for Consistent Comparison of Machine Learning Methods in Metabolic Engineering.ACS synthetic biology · 2023Article
- Synthetic Biology Meets Machine Learning.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Metabolomics and modelling approaches for systems metabolic engineering.Metabolic engineering communications · 2022Review
- Dynamic modulation of enzyme activity by synthetic CRISPR-Cas6 endonucleases.Nature chemical biology · 2022Article
- ProD: A Tool for Predictive Design of Tailored Promoters in Escherichia coli.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Modelling microbial communities: Harnessing consortia for biotechnological applications.Computational and structural biotechnology journal · 2021Review
- Insight to Gene Expression From Promoter Libraries With the Machine Learning Workflow Exp2Ipynb.Frontiers in bioinformatics · 2021Review
- Predictive design of sigma factor-specific promoters.Nature communications · 2020Article
- Promoter Architecture and Promoter Engineering inMetabolites · 2020Review
- Synthetic promoter design in Escherichia coli based on a deep generative network.Nucleic acids research · 2020Article
- Streamlining Natural Products Biomanufacturing With Omics and Machine Learning Driven Microbial Engineering.Frontiers in bioengineering and biotechnology · 2020Review
- Synbiological systems for complex natural products biosynthesis.Synthetic and systems biotechnology · 2016Review
- Synthetic promoter design for new microbial chassis.Biochemical Society transactions · 2016Review
- Characterization of regulatory sequences in alternative promoters of hypermethylated genes associated with tumor resistance to cisplatin.Molecular and clinical oncology · 2015Article
- Advances and computational tools towards predictable design in biological engineering.Computational and mathematical methods in medicine · 2014Review
- Expression of codon optimized genes in microbial systems: current industrial applications and perspectives.Frontiers in microbiology · 2014Review
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate and controllable regulatory elements such as promoters and ribosome binding sites (RBSs) are indispensable tools to quantitatively regulate gene expression for rational pathway engineering. Therefore, de novo designing regulatory elements is brought back to the forefront of synthetic biology research. Here we developed a quantitative design method for regulatory elements based on strength prediction using artificial neural network (ANN). One hundred mutated Trc promoter & RBS sequences, which were finely characterized with a strength distribution from 0 to 3.559 (relative to the strength of the original sequence which was defined as 1), were used for model training and test. A precise strength prediction model, NET90_19_576, was finally constructed with high regression correlation coefficients of 0.98 for both model training and test. Sixteen artificial elements were in silico designed using this model. All of them were proved to have good consistency between the measured strength and our desired strength. The functional reliability of the designed elements was validated in two different genetic contexts. The designed parts were successfully utilized to improve the expression of BmK1 peptide toxin and fine-tune deoxy-xylulose phosphate pathway in Escherichia coli. Our results demonstrate that the methodology based on ANN model can de novo and quantitatively design regulatory elements with desired strengths, which are of great importance for synthetic biology applications.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.