We enhance industrial microbial strains for robust bioproduction using machine learning and metabolic engineering. Our platform addresses metabolic burden, stress tolerance, and process-relevant performance.
Industrial microbial strains face multiple challenges during bioprocessing including product toxicity, substrate inhibition, and process variations. Our AI platform predicts and engineers solutions for strain robustness before experimental validation.
Balance heterologous expression with native metabolism to prevent growth inhibition. Our models predict optimal expression levels and genetic configurations that maximize productivity without compromising cell fitness.
Engineer strains resistant to industrial stresses including high product titers, osmotic pressure, oxidative stress, and temperature variations. Robust strains maintain productivity under challenging process conditions.
Design strains optimized for specific process conditions including feed strategies, temperature profiles, and oxygen requirements. Process-adapted strains reduce scale-up failures and improve overall economics.
Combine computational design with directed evolution to rapidly improve strain performance. Our platform guides ALE experiments to efficiently explore sequence space for beneficial mutations.
From strain characterization to industrial validation, our platform covers the complete strain optimization workflow.
Machine learning-guided exploration of strain design space using active learning workflows. Our platform efficiently identifies optimal genetic modifications with minimal experimental burden.
Genome-scale metabolic models predict metabolic burden and identify engineering targets for strain improvement. Enzyme-constrained models capture proteome allocation limitations.
Identify and engineer stress response pathways to enhance industrial robustness. Our platform targets transcription factors, chaperones, and regulatory networks for improved stress tolerance.
Comprehensive host strain optimization across diverse industrial applications
Balance expression and growth
Optimize metabolic flux allocation to relieve heterologous pathway burden. Our models predict expression level targets that maximize product yield without compromising cell growth and viability.
Resist toxic intermediates
Engineer strains tolerant to high product titers and toxic intermediates. We identify efflux transporters, metabolic bypass routes, and stress response pathways for enhanced tolerance.
Industrial condition adaptation
Optimize strains for specific process conditions including temperature profiles, pH ranges, and oxygen requirements. Process-adapted strains reduce variability and improve scalability.
Expand carbon source range
Enhance utilization of cost-effective feedstocks including lignocellulosic hydrolysates and industrial waste streams. We engineer transporter systems and metabolic pathways for diverse substrate utilization.
Integrated computational design followed by experimental validation at each stage
We characterize your production challenges including metabolic bottlenecks, stress factors, and process requirements. This defines optimization targets and acceptance criteria.
Our AI models predict optimal genetic modifications using genome-scale modeling and machine learning. Active learning workflows efficiently explore the combinatorial design space.
Lead designs are implemented using precision genetic engineering and validated through high-throughput screening and analytical characterization.
Top-performing strains undergo fermentation validation and scale-up assessment to ensure robust performance under industrial conditions.
Optimized strains for sustainable bioproduction across diverse markets
Ethanol, butanol, and advanced biofuel production with robust strains tolerant to high alcohol concentrations and process variations.
Therapeutic proteins and industrial enzymes with optimized expression systems and improved product quality attributes.
Lactic acid, succinic acid, and platform chemicals with acid-tolerant strains that reduce neutralization costs.
Lysine, glutamate, and specialty amino acids with producer strains optimized for high titer and yield.
Artemisinin precursors, flavors, and fragrances with strains engineered for complex terpenoid biosynthesis pathways.
Probiotics andfeed additives with gut-adapted strains optimized for survival and colonization in industrial applications.
Our pipeline builds on peer-reviewed methods published in leading journals
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.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.Pandi, A. et al. A versatile active learning workflow for optimization of genetic and metabolic networks. Nat Commun 13, 3872 (2022). https://doi.org/10.1038/s41467-022-31245-z
Active learning workflow for optimization of metabolic and genetic networks.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.We address metabolic burden, product toxicity, substrate inhibition, process variations, and scale-up challenges. Our platform is adaptable to specific production requirements and host organisms.
Our AI platform uses active learning to efficiently explore combinatorial design spaces. This reduces experimental burden by prioritizing high-performing designs before laboratory validation.
We work with E. coli, S. cerevisiae, Pichia pastoris, Bacillus subtilis, and other industrial microorganisms. Our platform adapts to organism-specific genetic tools and metabolic networks.
Yes. We can work with your existing production strains and engineering strategies. Our optimization focuses on addressing specific performance limitations while maintaining established genetic backgrounds.
We perform comprehensive characterization including growth profiling, stress tolerance testing, product titer analysis, and scale-up validation in relevant fermentation conditions.
Our team combines machine learning with metabolic engineering to address your specific strain challenges and improve industrial performance.
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