ArticleNucleic acids research2023
Design of synthetic promoters for cyanobacteria with generative deep-learning model.
Article in Nucleic acids research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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28 citing papers in PubMed.
- DESIGN: a framework for generative AI-enabled crop molecular design-driven breeding.Science China. Life sciences · 2026Review
- Attribution assignment for deep-generative sequence models enables interpretability analysis using positive-only data.PLoS computational biology · 2026Article
- PlantGFM: A Genomic Foundation Model for Discovery and Creation of Plant Genes.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A set of constitutive promoters with graded strengths for gene expression in diverse cyanobacterial strains.Applied and environmental microbiology · 2026Article
- Cyanobacteria as a Photosynthetic Chassis for Metabolic Pathway Engineering with Heterologous Gene Expression.Current issues in molecular biology · 2026Review
- De novo design of functional nucleic acids of aptamers.Nature computational science · 2026Article
- Context-aware synthetic promoter design using neural networks enables rewiring of eukaryotic transcriptional networks.NPJ systems biology and applications · 2026Article
- Design prokaryotic cis-regulatory elements using language model.Nucleic acids research · 2026Article
- Synthetic promoter design in plants: integration of computational and experimental approaches.Frontiers in plant science · 2026Review
- Synthetic tunable promoters for flexible control of multi-gene expression in mammalian cells.Journal of advanced research · 2025Article
- Deep Generative Model-Driven Design of Microbial Synthetic Promoters.Journal of microbiology and biotechnology · 2025Review
- iPro-MP: a BERT-based model to predict multiple prokaryotic promoters.Genome biology · 2025Article
- Deep learning guided programmable design of Escherichia coli core promoters from sequence architecture to strength control.Nucleic acids research · 2025Article
- De novo promoter design method based on deep generative and dynamic evolution algorithm.Nucleic acids research · 2025Article
- Combining diffusion and transformer models for enhanced promoter synthesis and strength prediction in deep learning.mSystems · 2025Article
- Microbial Technologies Enhanced by Artificial Intelligence for Healthcare Applications.Microbial biotechnology · 2025Review
- Cell-Free Systems to Mimic and Expand Metabolism.ACS synthetic biology · 2025Review
- Optimization of regulatory DNA with active learning.Computational and structural biotechnology journal · 2025Article
- Oligonucleotide library assisted sequence mining reveals promoter sequences with distinct temporal expression dynamics for applications inSynthetic biology (Oxford, England) · 2025Article
- Towards AI-designed genomes using a variational autoencoder.Proceedings. Biological sciences · 2024Article
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5 authors.
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Abstract
Deep generative models, which can approximate complex data distribution from large datasets, are widely used in biological dataset analysis. In particular, they can identify and unravel hidden traits encoded within a complicated nucleotide sequence, allowing us to design genetic parts with accuracy. Here, we provide a deep-learning based generic framework to design and evaluate synthetic promoters for cyanobacteria using generative models, which was in turn validated with cell-free transcription assay. We developed a deep generative model and a predictive model using a variational autoencoder and convolutional neural network, respectively. Using native promoter sequences of the model unicellular cyanobacterium Synechocystis sp. PCC 6803 as a training dataset, we generated 10 000 synthetic promoter sequences and predicted their strengths. By position weight matrix and k-mer analyses, we confirmed that our model captured a valid feature of cyanobacteria promoters from the dataset. Furthermore, critical subregion identification analysis consistently revealed the importance of the -10 box sequence motif in cyanobacteria promoters. Moreover, we validated that the generated promoter sequence can efficiently drive transcription via cell-free transcription assay. This approach, combining in silico and in vitro studies, will provide a foundation for the rapid design and validation of synthetic promoters, especially for non-model organisms.
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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.