Podcast notes – Reshma Shetty (Ginkgo Bioworks founder) on Exponential View

Ginkgo Bioworks – automating biotech / synthetic bio

Podcast: Azeem Azhar’s Exponential View
Guest: Reshma Shetty – cofounder; compsci training; Ginkgo started as MIT grad student project
What if we can program cells the way we program computers?

Leverage software and automation

Started in a compsci + AI lab instead of a bio lab

Goal was not to discover but to ENGINEER biology

Engineering is about design – to make something, tangible discipline
Biology is fundamentally about discovery

We’re good at engineering at cell level, can do a little at organism level, but no idea how to do it at ecosystem level yet

McKinsey – Bioeconomy opportunity is multi-trillion

Ginkgo is a platform company helping other companies engineer cells
As platform, more customers = more benefits to all customers (more data, knowledge, programs)
Many partnerships – equity, cash, flexible depending on customer

First question is technical feasibility – can it be done?
Second is financial
Third is “caring” – not values neutral (eg, internal staff diversity)

Example – Motif food company (learning from Impossible Burger, which added heme protein to make their burger taste more like meat)
Motif are the food science experts, Ginkgo provides an “R&D engine” and innovation, they formulate and commercialize

Example – Sim Logic – metabolic disease, could we supplement patients’ metabolism with an engineered microbe?
A new therapeutic modality

Cell programming isn’t that different from other engineering disciplines:
DESIGN – BUILD – TEST – LEARN
Involves a lot of research into prior art, lessons from nature

Designing DNA sequences for each functionality, writing in DNA instead of code

Most folks on their team focus on architecture and “how to do it”
Then a specialized team that turns specs into DNA
They do many thousands of designs and test them all

Biological design today is a search problem
Very large search space – DNA combos are effectively unlimited
Nature has given a lot of clues

Many methods of design: Machine learning; Nature; Evolution; Simulation

Evolution is a powerful tool they can use
Generate a lot of diversity, and let the best cells replicate and win

No silver bullet tech – not even CRISPR

How to scale – similar process to brewing beer using engineered bacteria, fermentation -> harvesting -> purification

Ginkgo does design and process and how to scale
there are CMOs that can help manufacture (contract manufacturing orgs)

Best microbe isn’t always the most productive, but the one that can do it most reliably at scale
Microbes that can produce even with variation in environment and manufacturing conditions

Cells take time to grow – presents a fundamental limit to speed
Parallelizing helps

Miniaturization?
Helpful to reduce costs, and can be faster

Work with customers to share IP, re-use for future projects and across portfolio

Seeing more and more bio startups

IPO in Sept 2021 – surprised how well the story has resonated

Moonshot dream app?
Terraforming Mars (!)

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