🧵
Great clip from Voigt lab explaining technical barriers to creating *reliable* cell programs ("regulatory networks") with HUNDREDS of genes (like🦠found in nature)
$DNA has chipped away at some of these, though it can still only design reliable programs with ~5-10 genes
🧬
Great clip from Voigt lab explaining technical barriers to creating *reliable* cell programs ("regulatory networks") with HUNDREDS of genes (like🦠found in nature)
$DNA has chipped away at some of these, though it can still only design reliable programs with ~5-10 genes
🧬
^Barriers $DNA has chipped away at:
Design:
•Codebase has many well-characterized parts
•Software to string 1000's of design variations together
Build:
•@Gen9Bio tech for synthesizing big pieces of 🧬 ("challenging")
Test:
•Methods for debugging cell programs (reporters)
Design:
•Codebase has many well-characterized parts
•Software to string 1000's of design variations together
Build:
•@Gen9Bio tech for synthesizing big pieces of 🧬 ("challenging")
Test:
•Methods for debugging cell programs (reporters)
Circa 2016, Voigt lab at MIT said the limit for cell programs ("regulatory networks") in academia was ~2-4 genes
Compare this with naturally-occurring cell programs that might have hundreds of genes
Compare this with naturally-occurring cell programs that might have hundreds of genes
$DNA mgmt has said that its cell programs are limited to calling ~5-10 "functions" (genes)
$DNA should be able to do a lot with just 5-10 functions per program: specifically, ferment many useful/novel proteins that create new value or lower COGS for customers (see Aldevron $DHR)
$DNA should be able to do a lot with just 5-10 functions per program: specifically, ferment many useful/novel proteins that create new value or lower COGS for customers (see Aldevron $DHR)
Full video
The challenges in academic synbio described by Voigt above are similar to the ones in industry
Why would any company (startup or mature) try to replicate the infrastructure and approach at $DNA?
That’s why the #AWS analogy isn’t such a bad one
Why would any company (startup or mature) try to replicate the infrastructure and approach at $DNA?
That’s why the #AWS analogy isn’t such a bad one
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