Design biology like an engineer.
Cellora uses AI to design programmable cellular systems from desired biological behavior.
Biology is programmable. We are building the compiler.
Cellora takes a specification of desired biological behavior and returns candidate biological architectures — components, regulation and interactions — that could produce it.
Cellora is not analyzing biology after the fact. It is proposing the architecture before the experiment exists.
Start with behavior. Work backward to biology.
The loop does not end at a prediction. Every experiment re-enters the model as constraint.
- step 01DEFINE
Desired cellular behavior.
- step 02DESIGN
Candidate genetic and signaling architecture.
- step 03SIMULATE
Model system behavior under different conditions.
- step 04RANK
Identify the most promising designs.
- step 05TEST
Experimental validation.
- step 06LEARN
Experimental results feed the next design cycle.
A biological system is more than a gene.
Change one component and the consequences propagate. Cellora models the interactions, not the parts in isolation.
Explore the design space before entering the lab.
Candidate architectures are generated, scored against biological constraints and clustered before any bench time is spent.
Biology changes state. So does the model.
Cellular behavior is conditional. Cellora carries environmental context and uncertainty through every predicted transition.
Designs are hypotheses. Experiments make them real.
Observed biology disagrees with the model constantly. That disagreement is the training signal.
- COMPUTATIONAL DESIGN01↓
- CANDIDATE SYSTEM02↓
- LAB EXPERIMENT03↓
- OBSERVED RESPONSE04↓
- MODEL UPDATE05↓
- NEXT DESIGN06↺
One design engine. Many biological systems.
The same design loop applies wherever cellular behavior has to be specified, engineered and tested.
design engine
- —programmable cellular systems
- —genetic circuits
- —engineered production pathways
- —cellular optimization
- —cellular detection systems
- —signal-responsive biology
- —cellular mechanisms
- —experimental hypothesis generation
- —biological response systems
- —signal interpretation
The design space is combinatorial.
A handful of biological components produces an intractable number of possible systems. Search has to happen computationally before it happens at the bench.
The laboratory becomes a feedback signal.
An AI-native biological R&D loop: design computationally, measure physically, and let the difference rewrite the model.
Living systems are programmable.
An AI design engine for living systems.
