We optimize metabolic pathways for maximum yield, productivity, and industrial robustness. Enginoma combines multi-omics analysis, constraint-based modeling, and machine learning to identify and implement targeted improvements.
Traditional strain improvement relies on random mutagenesis and iterative screening, which is time-consuming and often plateaus. Our AI platform identifies precise genetic and process interventions to achieve step-change improvements in production performance.
Our constraint-based models and omics integration rapidly identify metabolic bottlenecks. We predict rate-limiting steps, precursor limitations, and competing pathways that limit production.
Machine learning models optimize gene expression levels, promoter strength, and enzyme loading. Our algorithms design multi-level interventions for balanced pathway flux and reduced metabolic burden.
We integrate bioreactor data with machine learning to optimize temperature, pH, dissolved oxygen, and feeding strategies. Our platform predicts scale-up challenges and designs robust production processes.
We engineer strains for industrial robustness including tolerance to product toxicity, substrate variability, and process fluctuations. Our platform designs dynamic regulation systems for consistent production.
From multi-omics analysis to fermentation optimization, our platform covers the full pathway improvement workflow.
We integrate transcriptomics, proteomics, and metabolomics data with genome-scale models to identify targets for improvement. Our platform reveals regulatory bottlenecks and metabolic imbalances.
We use flux balance analysis and related methods to predict optimal metabolic states. Our models identify gene knockout, knockdown, and overexpression targets for maximum yield.
We optimize fermentation conditions using machine learning models trained on process data. Our platform predicts optimal operating parameters and designs feeding strategies for maximum productivity.
A systematic approach from performance analysis to validated improvements.
We analyze your current strain's performance including titer, rate, and yield. Multi-omics profiling reveals the metabolic state and identifies initial improvement targets.
Our AI models identify genetic and process targets through constraint-based analysis, machine learning, and multi-omics integration. We prioritize interventions by predicted impact.
We design multi-level interventions including gene expression tuning, pathway balancing, and process parameter optimization. Our platform generates combinatorial strategies for maximum impact.
We validate improvements experimentally and optimize for scale-up. Our platform tracks key performance indicators and iterates to achieve production targets.
Our pathway optimization platform supports improvement of diverse bioprocesses across multiple industries.
Optimize pathways for ethanol, butanol, biodiesel precursors, and advanced biofuels. We improve yields and process economics for commercial viability.
Improve production of bioplastics, biofibers, and other sustainable materials. We optimize pathways for high-volume, low-cost manufacturing.
Optimize production of amino acids, vitamins, enzymes, and other food ingredients. We improve yields and product quality for food-grade applications.
Our platform builds upon peer-reviewed research in metabolic engineering and strain optimization.
Orth, J. D., Thiele, I., & Palsson, B. O. (2010). What is flux balance analysis? Nature Biotechnology, 28(3), 245-248.
Nature Biotechnology | DOI: 10.1038/nbt.1614Klamt, S., & von Kamp, A. (2022). Analyzing and Resolving Infeasibility in Flux Balance Analysis. Metabolites, 12(7), 585.
Metabolites | DOI: 10.3390/metabo12070585Feist, A. M., & Palsson, B. O. (2008). The growing scope of applications of genome-scale metabolic reconstructions using Escherichia coli. Nature Biotechnology, 26(6), 659-667.
Nature Biotechnology | DOI: 10.1038/nbt1401Fang, X., Lloyd, C. J., & Palsson, B. O. (2020). Reconstructing organisms in silico: progress and insights. Nature Reviews Microbiology, 18(12), 727-740.
Nature Reviews Microbiology | DOI: 10.1038/s41579-020-00440-4Common questions about our pathway optimization services.
Yield improvements depend on the pathway, host, and current performance. Our platform typically identifies strategies for significant yield improvements through multi-parameter optimization of gene expression, metabolic flux, and fermentation conditions.
We use constraint-based modeling and kinetic analysis to identify metabolic bottlenecks. Our algorithms predict rate-limiting steps and suggest targeted interventions including enzyme overexpression, knockdown, or addition of heterologous enzymes.
Yes. We integrate bioreactor data with machine learning models to optimize temperature, pH, dissolved oxygen, feeding strategies, and other fermentation parameters for maximum productivity.
We work with both existing production strains and design new optimized strains. Our platform can analyze your current strain performance and develop targeted improvement strategies.
We use genome-scale metabolic modeling, flux balance analysis, kinetic modeling, transcriptomics analysis, and machine learning-based process optimization to comprehensively improve pathway performance.
Contact our team to discuss your strain improvement requirements. We'll analyze your current performance and develop targeted optimization strategies.
Tell us about your project and we'll get back within 24 hours.
