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● Light intensity, wavelength, and duration
● Nutrient composition and timing
● CO₂ levels, humidity, and airflow
● Harvest timing based on real-time expression
kinetics
● Protein compartmentalization and downstream
bioactivity forecasts

All of these are tuned not by human guesswork, but by a
patented AI model trained on empirical data—linking
growth conditions to therapeutic output with remarkable
consistency.

It’s not a single therapeutic.
It’s not a novel antigen.
It’s not a custom capsule or excipient.

It’s the reproducible, scalable path from seed to
therapy.

This is the core of Zea’s platform: a repeatable,
defendable manufacturing intelligence that can take a
gene of interest and optimize its expression in a plant—
consistently, reliably, and at pharmaceutical standards.

Why This Matters

In a future where edible biologics become widespread, the
value won’t be in who owns the gene—it will be in who
can execute.

The gene might be public.
The plant might be generic.
But the process—the ability to take that raw input and

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