Very happy to release our latest paper from @hsalis Lab in collaboration with @klavins Lab at UW on "Automated design of thousands of nonrepetitive parts for engineering stable genetic systems", now published in Nature Biotechnology! 1/18 nature.com/articles/s41587-0…
🧠 Why do smart scientists feel stupid when reading papers?
Because nobody teaches you HOW to read them efficiently.
This 3-pass system will change how you approach every paper: 🧵
🚨 New preprint 🚨
We introduce Generative Distribution Embeddings (GDEs) — a framework for learning representations of distributions, not just datapoints.
GDEs enable multiscale modeling and come with elegant statistical theory and some miraculous geometric results!
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New lab preprint! 🚀
Modeling complex data distributions is tough.
We designed GDEs, a new framework that tackles this head-on!
GDEs generalize across text, images & MANY bio apps (think virtual cells, spatial bio, viral genome tracking).
Thread 👇
If you have a solid strategy and a small amount of compute, you can go pretty far. If you have huge clusters of GPUs and no strategy, your only achievement will be burning capital.
This is a great paper from the @hsalis lab.
- Measure the decay rates of 50,000 mRNAs in bacteria.
- Use biophysical models ML to build models of mRNA stability.
- Profit.
And a good reminder of what's possible when one turns a biological problem into a sequencing problem!
I am pleased to announce our latest publication ‘Predicting synthetic mRNA stability using massively parallel kinetic measurements, biophysical modeling, and machine learning’ in @NatureComms
Designing synthetic mRNAs 🧬 for optimal stability and expression? Checkout our work led by @DanielCetnar on biophysics infused machine learning approaches for delineation of mRNA degradation kinetics ⚡ out now in @NatureCommsnature.com/articles/s41467-0…
We applied rational learn-by-design 🔢 methods to create a maximally informative library 📚, coupled that with high throughput, barcoded, massively parallel reporter assays to decrypt major design rules using Gradient Boosted Trees 🌳!
Engineered bacteria are great for making biofuels and other bioproducts, but you don’t want those changes escaping to the wild. Scientists @Harvard & @WUSTLmed engineered the E. coli genome to keep useful edits from spreading in an uncontrolled way: energy.gov/science/ber/artic…
ALT A bacterial virus inserting genetic material inside a bacterial cell during infection.
Image courtesy of Behnoush Hajian.
In our newest preprint,
• we explore the effects of synonymous genome recoding, and
• construct & troubleshoot a synthetic 57-codon E. coli genome using multi-omics, editing, and laboratory evolution.
1/n
After almost two years of optimization, SynOMICS's plasmid system is a fusion of @dbikard @MarraffiniLab's Cas9 & the pORTMAGE plasmids & ELSA/nonrepetitive parts by @hsalis@bioalgorithmist Alex Reis & co
I highly recommend using multiplexed sgRNAs from nature.com/articles/s41587-0…
Please RT: If there's an old unmaintained software that is prohibitively slow and is only one of its kind do you not benchmark your new tool against it? How to properly address reviewers and frame this issue in paper?
Our bacterial Promoter Calculator model has 348 parameters and predicts cont-val TX rates with R^2=0.80 (other factors constant).
Our bacterial RBS Calculator has 12 free parameters and predicts cont-val TL rates with R^2=0.75.
The bar is pretty high, but they don’t compare.
Do you need a billion parameters and millions of sequences for #proteindesign? Maybe not! See our paper: shorturl.at/novMO
We show that interpretable, non-autoregressive structure-based protein design can work!
Original thread: shorturl.at/cdgCP
Today we announced FDA clearance of our IND application for SENTI-202, a potential first-in-class, logic-gated treatment for acute myeloid leukemia. Learn more: bit.ly/3v4IMJY$SNTI#AML#CARNK