Overview
We integrate artificial intelligence (AI) into the microbial engineering and bioproduction pipeline to accelerate decision-making, improve predictive accuracy, and optimize workflows from gene design to fermentation.
Our AI-Enabled Capabilities
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Smart Design of Genetic Constructs
AI models help predict promoter strength, codon usage impact, and regulatory interactions to guide rational strain design. -
High-Throughput Data Interpretation
Machine learning algorithms analyze large datasets from screening, expression profiling, and fermentation trials to identify high-performing clones. -
Predictive Process Optimization
AI-driven modeling enables accurate forecasting of protein yields based on experimental variables, reducing trial-and-error and improving scale-up efficiency. -
Real-Time Monitoring & Control
Integration of AI with sensors and process data allows automated adjustment of culture conditions to maintain optimal expression levels. -
Knowledge-Guided Learning Systems
Our platform continuously improves over time by learning from past results, creating a feedback loop for smarter experimental design.
Applications
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AI-assisted strain library screening
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Optimizing multi-gene expression systems
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Dynamic control of bioreactor conditions
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Reducing development time for new protein products
Why It Matters
Incorporating AI into synthetic biology enables faster innovation, higher success rates, and more scalable bioprocesses. We empower teams to make data-informed decisions with confidence, bringing advanced automation and intelligence into the heart of microbial engineering.