Generative strain design platform
Build production strains in weeks, not years.
Generative AI models your organism, target molecule, and fermentation constraints, then ranks strain candidates for the next iteration cycle.
The constraint
Strain development bottlenecks cost fermentation teams years.
Industrial biomanufacturing relies on optimized microbial strains. Traditional random mutagenesis and empirical screening are costly, slow, and non-deterministic. A fermentation project may spend 18-24 months developing a strain before a viable production candidate appears, using most of a team's R&D capacity.
The bottleneck isn't scientific uncertainty. It's the absence of a systematic method for turning organism knowledge into specific, testable modification hypotheses ranked by predicted outcome.
"Each failed mutagenesis screen takes 6-8 weeks and a full team cycle. Most industrial projects run 4-6 screens before a production-viable strain is found."
The process
Guided iteration over random search.
Three phases. A single model that learns with every cycle.
Add genome sequence, fermentation data, and target molecule specifications. The platform creates a predictive model of your organism's metabolic constraints for your strain and production environment.
The generative engine ranks strain modification candidates using predicted pathway flux, tolerance windows, and yield optimization within your specific organism model.
Lab validation returns to the model. Candidates improve with each cycle. Most partners reach a production-ready strain in 2-3 cycles, generally within 8-10 weeks.
Generative strain optimization reduced what would have required two full mutagenesis screens to one guided iteration. We reached a production-ready candidate in under 10 weeks.
Pradeep S., Process Development Lead at a specialty fermentation ingredients company (early-access program participant)
Use cases
From flavors to biopolymers.
Guided strain optimization works across fermented ingredients.
Yeast and E. coli chassis for low-titer, diverse aromatic pathways need organism-specific flux rebalancing that conventional screening cannot address systematically.
StrainX Works creates organism-specific models to optimize flux for defined aromatic targets, cutting the screens needed to reach a viable production strain.
Optimizing titer and tolerance for organic acids means balancing competing goals: high yield requires stress tolerance that can conflict with growth kinetics.
Generative candidates receive simultaneous scoring against your titer targets and tolerance constraints, revealing modifications that balance both goals.
Directing metabolism toward high-value pigments and vitamins requires coordinated pathway edits whose interactions are hard to predict empirically.
Pathway-level flux modeling forecasts multi-gene interaction effects before lab validation, cutting wasted screens.
Biopolymer yield and physical properties depend on strain physiology, which can vary substantially across production organisms and fermentation conditions.
Organism-specific models capture production-environment constraints, generating candidates suited to your fermentation scale and media, not a generic chassis.
Pricing
Clear pricing for biotech teams.
No opaque enterprise contracts. Two tiers for active R&D teams, plus a custom track for larger programs.
- 3 concurrent strain programs
- 2 user seats
- Standard chassis models
- Ranked candidate reports per cycle
- CSV and FASTA formats
- Email help
- Unlimited concurrent programs
- 8 user seats
- Custom organism models
- SBML model import
- Full fermentation log integration
- Priority support, dedicated response
- Unlimited seats
- On-premise or private cloud
- Dedicated training for proprietary organisms
- API access for lab information systems
- Dedicated technical onboarding
Ready to speed up your strain development program?
Join the early-access program. Our team works directly with new partners on the first iteration cycle.