AI-Powered Strain Design
& Optimization
Engineer high-performance microbial strains for biomanufacturing. Predict optimal genetic modifications to maximize yield, titer, and productivity with AI foundation models.
The Challenge of Strain Engineering
Developing microbial cell factories for biomanufacturing is one of the most complex challenges in synthetic biology. Engineering a strain to produce a target compound at industrial titers requires navigating an astronomically large design space of genetic modifications, gene knockouts, promoter swaps, and pathway insertions. Traditional approaches relying on trial and error can take 5 to 10 years and tens of millions of dollars before a strain is production-ready.
The core difficulty lies in the interconnected nature of cellular metabolism. A single genetic change can cascade through hundreds of metabolic reactions, creating unpredictable effects on growth rate, product yield, and cellular fitness. Without the ability to model these complex interactions computationally, researchers are forced into expensive iterative cycles of build, test, and redesign.
Current computational tools address parts of this problem in isolation: flux balance analysis for steady-state metabolism, sequence models for gene expression, and statistical models for phenotype prediction. What is missing is an integrated AI framework that connects these layers to predict the full impact of genetic designs on strain performance.
How Avitai Accelerates Strain Design
Our foundation models work together to predict, optimize, and validate genetic designs before a single experiment is run.
Predict Modification Impact
Simulate the effect of gene knockouts, overexpressions, and pathway insertions on metabolic flux and product yield before committing to lab work.
Multi-Objective Optimization
Balance competing objectives such as product yield, growth rate, and genetic stability to find Pareto-optimal strain designs that work in practice.
Reduce DBTL Cycles
Cut Design-Build-Test-Learn iteration cycles by prioritizing the most promising genetic designs and eliminating dead ends computationally.
Scale Across Organisms
Transfer learned design principles across chassis organisms including E. coli, yeast, CHO cells, and non-model organisms used in industrial biotech.
Foundation Models for Strain Design
Three of our four foundation models work in concert to power strain engineering workflows.
Research Model
Mines scientific literature and biological databases to identify gene targets, regulatory elements, and metabolic strategies that have been validated in prior strain engineering studies. Surfaces knowledge that would take researchers months to compile manually.
Learn morePerturbation Model
Navigates the combinatorial design space to suggest minimal, high-impact genetic edits. Predicts how combinations of gene knockouts, insertions, and expression changes will affect target phenotypes while maintaining cellular viability.
Learn moreCentral Dogma Model
Predicts how genetic sequence changes translate to protein expression and function. Optimizes codon usage, promoter strength, and ribosome binding sites to achieve target expression levels for heterologous pathway enzymes.
Learn moreOpen-Source Tools for Strain Engineering
Build on our open-source JAX ecosystem to develop custom strain design workflows.
Differentiable bioinformatics pipelines for gradient-based sequence optimization
Physics-informed neural networks for modeling metabolic dynamics
Data pipelines for processing multi-omics strain characterization data
Knowledge extraction from strain engineering literature and databases
Accelerate Your Strain Development
See how Avitai Bio can reduce your strain engineering timelines from years to weeks.