Strain design
Design the strain around pathway, host, and production goal.
TryptageniX AI
Biology data for agentic AI.
TryptageniX AI turns wet-lab cycles into training signal for agents that can design, test, and improve biological systems.
Strain designsFermentation outcomesSample analysisStructured data
- 01Strain design
- 02Fermentation
- 03Analyze
- 04Structure
- 05Agentic AI
- 06Next strain
∞Closed loop
The point
Wet-lab cycles train agents.
Each cycle connects strain design, fermentation, chemical analysis, structured data, and the next biological choice.
Closed loop
Design. Ferment. Analyze. Train.
The loop starts with a biological design, learns from measured outcomes, and carries that signal into the next strain choice.
Fermentation
Ferment the design and capture the process context behind the result.
Sample analysis
Read the chemistry so each cycle has measurable signal.
Structured data
Convert the cycle into data that agents can train on and reason over.
Agentic AI
Train agents on outcomes, context, and design rationale.
Next strain
Use the learned signal to choose the next biological design.
Data for agents
Training signal from the lab.
01Strain designs
02Fermentation outcomes
03Sample analysis
04Structured data
05Agent decisions
06Next-strain choices
Collaboration
Built for AI x bio teams.
AI labs building scientific agentsComputational biology teamsBiomanufacturing partners
TryptageniX AI
Partner with TryptageniX AI.
AI labs, computational biology teams, and biomanufacturing partners can work with CBT on data generated from real experimental cycles.
Platform inquiry