We design and optimize microbial cell factories using genome-scale metabolic modeling and machine learning. Our platform accelerates strain development for sustainable bioproduction of chemicals, fuels, and materials.
Traditional strain development relies on iterative trial-and-error, requiring extensive experimental screening. Our AI platform integrates genome-scale metabolic models with active learning to predict optimal genetic modifications before experimental validation.
Our computational pipeline screens thousands of genetic modifications virtually to identify high-yielding strain designs. Active learning workflows reduce experimental burden by prioritizing the most promising candidates.
We leverage enzyme-constrained metabolic models to capture proteome allocation constraints. This enables accurate prediction of metabolic fluxes and identification of targets across the entire metabolic network.
Balance product yield, growth rate, and strain robustness simultaneously. Our Pareto optimization identifies trade-offs and designs strains that maintain productivity under industrial fermentation conditions.
Optimize strains for process-relevant conditions including high substrate concentrations, varying oxygen availability, and product toxicity. Design strains that perform reliably in large-scale bioreactors.
From metabolic pathway analysis to industrial-scale strain validation, our platform covers the full strain development workflow.
Design heterologous biosynthetic pathways and optimize native metabolism using genome-scale models. We identify metabolic bottlenecks and predict flux distributions across the entire cellular network.
Build and analyze enzyme-constrained metabolic models to capture proteome limitations. These models enable accurate prediction of metabolic fluxes and identification of enzyme-level targets for strain improvement.
Combine machine learning with iterative experimental validation using active learning workflows. Our platform learns from each experimental round to efficiently explore the combinatorial space of genetic modifications.
Comprehensive strain engineering across diverse microbial platforms and applications
Enhance bioproduction pathways
Design and optimize metabolic pathways for sustainable production of chemicals, fuels, and materials. We integrate pathway design with genome-scale modeling to identify optimal flux distributions.
Improve industrial performance
Optimize chassis organisms including E. coli, S. cerevisiae, and non-model bacteria for enhanced production. We address metabolic burden, substrate utilization, and stress tolerance simultaneously.
Scale-up process integration
Integrate strain engineering with fermentation process optimization. We predict scale-up challenges and design strains that maintain productivity in industrial bioreactor conditions.
Industrial stress resilience
Design strains resilient to industrial stresses including high product titers, substrate inhibition, and process variations. Robust strains reduce process failures and improve overall economics.
Integrated computational design followed by experimental validation at each stage
We analyze your target compound, establish production metrics, define host constraints, and design screening assays for experimental validation.
Our AI models generate and rank metabolic engineering strategies using genome-scale modeling and machine learning to predict optimal genetic modifications.
Lead strain designs are constructed using precision genetic engineering and validated through high-throughput screening and analytical characterization.
Top-performing strains undergo fermentation optimization and scale-up assessment to ensure robust performance in industrial conditions.
Engineered strains for sustainable production across diverse chemical and material markets
Engineered microbial strains for sustainable production of ethanol, biodiesels, and advanced biofuels from renewable feedstocks including lignocellulosic biomass.
Microbial production of high-value chemicals including flavors, fragrances, pharmaceuticals, and agricultural compounds through precision metabolic engineering.
Engineered strains for production of biodegradable polymers including PLA, PHA, and other sustainable materials for packaging and industrial applications.
Microbial production of food-grade ingredients including amino acids, vitamins, organic acids, and natural colorants through engineered cell factories.
Production of pharmaceutical intermediates, antibiotics, and therapeutic compounds through engineered microbial platforms with optimized yields and purity.
Engineered microbial strains for production offeed additives including amino acids, enzymes, and vitamins that improve animal nutrition and gut health.
Our pipeline builds on peer-reviewed methods published in leading journals
Gong, X. et al. Advancing microbial production through artificial intelligence-aided biology. Biotechnol Adv 108, (2024). https://doi.org/10.1016/j.biotechadv.2024.108399
Comprehensive review of AI applications in metabolic engineering and strain development.Khamwachirapithak, P. et al. Optimizing Ethanol Production in Saccharomyces cerevisiae through Machine Learning-Guided Combinatorial Promoter Modifications. ACS Synth Biol 12, 2897-2908 (2023). https://doi.org/10.1021/acssynbio.3c00199
ML-guided combinatorial promoter engineering for enhanced ethanol production in yeast.Domenzain, I. et al. Computational biology predicts metabolic engineering targets for increased production of 103 valuable chemicals in yeast. PNAS 122 (2025). https://doi.org/10.1073/pnas.2417322122
Genome-scale metabolic modeling for identifying metabolic engineering targets in yeast.Mao, J. et al. Relieving metabolic burden to improve robustness and bioproduction by industrial microorganisms. Biotechnol Adv 74, 108401 (2024). https://doi.org/10.1016/j.biotechadv.2024.108401
Metabolic burden relief strategies for improved strain robustness and productivity.We work with established industrial hosts including E. coli, Saccharomyces cerevisiae, Pichia pastoris, Bacillus subtilis, and non-model organisms. Our AI platform adapts to host-specific metabolic networks and genetic tools.
Our AI platform uses genome-scale metabolic models combined with active learning to predict optimal genetic modifications. This approach reduces experimental burden by prioritizing high-yielding designs before laboratory validation.
We engineer strains for diverse chemical classes including alcohols, organic acids, amino acids, terpenoids, flavonoids, alkaloids, and polymer precursors. The Enginoma platform supports both native and heterologous biosynthetic pathways.
Yes. We integrate strain engineering with fermentation process development including media optimization, process parameter tuning, and scale-up assessment to ensure robust performance in industrial bioreactors.
We design strains for industrial-relevant conditions including product toxicity, substrate inhibition, and process variations. Our multi-objective optimization balances productivity with strain robustness to reduce process failures.
Our team combines genome-scale metabolic modeling with machine learning to design high-performing microbial strains for your target compound.
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