We design de novo synthetic promoters with precisely tunable expression levels using deep generative models. Our AI platform generates novel promoter sequences for microbes and eukaryotes, accelerating your genetic engineering projects.
Traditional promoter engineering relies on labor-intensive mutagenesis and screening. Our AI platform uses deep generative models to design novel synthetic promoters with precisely controlled strength, dramatically reducing experimental burden.
Deep generative models (VAE, GAN, Diffusion) learn sequence patterns from natural promoters and generate novel functional promoters in hours rather than weeks of experimental screening.
Our platform generates promoters across a wide dynamic range. Conditional models allow targeting specific expression levels for precise metabolic engineering and circuit design applications.
We design promoters for multiple hosts including E. coli, yeast (S. cerevisiae), Bacillus, and mammalian cells. Our models learn species-specific regulatory patterns for optimal performance.
Generated promoters differ from natural sequences while maintaining functionality, avoiding regulatory conflicts and intellectual property issues in industrial applications.
From sequence generation to experimental validation, our platform integrates deep learning with synthetic biology workflows.
We employ state-of-the-art generative AI including VAEs, GANs, and Diffusion models trained on large promoter datasets to learn sequence-function relationships.
Convolutional and transformer-based neural networks predict promoter activity from sequence, enabling virtual screening of generated candidates before synthesis.
Comprehensive sequence analysis ensures generated promoters meet design specifications including k-mer distribution, motif preservation, and diversity requirements.
A streamlined process from specification to validated promoter sequences.
We analyze your target host's native promoter landscape to establish design constraints and learn species-specific patterns.
Deep generative models produce thousands of novel promoter candidates based on learned sequence-function relationships.
Predictive models rank candidates by predicted activity, filtering for targets within your desired expression range.
Final candidates undergo sequence quality checks and are delivered with experimental validation protocols.
Our synthetic promoter design services support diverse applications in synthetic biology and metabolic engineering.
Design promoters with precise expression levels to optimize metabolic flux through biosynthetic pathways and balance pathway burden.
Generate orthogonal promoters with minimal cross-talk for constructing complex genetic circuits and biosensors.
Engineer strong promoters for high-level recombinant protein expression in microbial and cell-free systems.
Our platform builds upon peer-reviewed research in deep learning-based synthetic promoter design.
Wang, Y., Wang, H., Wei, L., Li, S., Liu, L., & Wang, X. (2020). Synthetic promoter design in Escherichia coli based on a deep generative network. Nucleic Acids Research, 48(12), 6403-6412.
Nucleic Acids Research | PMID: 32424410 | DOI: 10.1093/nar/gkaa325Wang, H., Du, Q., Wang, Y., Xu, H., Wei, Z., & Wang, X. (2024). GPro: generative AI-empowered toolkit for promoter design. Bioinformatics, 40(3), btae123.
Bioinformatics | DOI: 10.1093/bioinformatics/btae123Seo, E., Sung, D., & Lee, J. W. (2025). Deep generative model–driven design of microbial synthetic promoters. Journal of Microbiology and Biotechnology, 35.
Journal of Microbiology and Biotechnology | DOI: 10.4014/jmb.2510.43004Höllerer, S., Papaxanthos, L., Gumpinger, A. C., et al. (2020). Large-scale DNA-based phenotypic recording and deep learning enable highly accurate sequence-function mapping. Nature Communications, 11(1), 3551.
Nature Communications | DOI: 10.1038/s41467-020-17222-4Common questions about our synthetic promoter design services.
We can design promoters spanning a wide dynamic range from very weak (background level) to very strong (constitutive high expression). Typical designs achieve 10^3 to 10^5 fold range in expression strength. For specific ranges, we can optimize using conditional generative models.
We support promoter design for major microbial hosts including E. coli, Bacillus subtilis, Pseudomonas putida, and yeast (Saccharomyces cerevisiae, Pichia pastoris). We can also design promoters for mammalian cell systems upon request.
We typically deliver 10-50 validated promoter candidates per project, spanning the requested expression range. Each design includes predicted activity scores, sequence quality metrics, and experimental validation protocols.
Yes. Our standard service includes in silico validation through strength prediction models. We also offer optional wet lab validation using fluorescent reporters or quantitative PCR to confirm expression levels before you proceed with downstream applications.
VAEs learn latent representations of promoter sequences for controlled generation. GANs (WGAN-GP) produce realistic sequences through adversarial training. Diffusion models iteratively denoise random inputs to generate high-quality promoters. Each has advantages; we select the best approach based on your specific requirements and target organism.
Contact our team to discuss your promoter design requirements. We'll provide a customized proposal for your project.
Tell us about your project and we'll get back within 24 hours.
