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Xiaojing Gao

@SynBioGaoLab

Assistant Professor @ Stanford ChemE
synthetic biology, biomolecular engineering
synbiogaolab.bsky.social
Stanford, CA gaolab.blog Joined June 2019
855 Following 3.2K Followers 437 Posts

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Xiaojing Gao @SynBioGaoLab
Selective cancer killing was among the key applications we envisioned for our RNA sensors. Now @mengzhang668 and @FloraHU8 have led a team to map the boundaries of “sense-and-ablate” operations and demonstrate it in two animal models—one step closer to that therapeutic aspiration
Xiaojing Gao @SynBioGaoLab Feb 2, 2022
As we work towards DNA-free circuits, we realized we could use some new RNA sensors in living cells. So we built RADAR biorxiv.org/content/10.110… Congrats to Eerik, @katznoa1, @NatalieSKolber, @claytoncalli, and thanks to our plant biologist neighbors, Diego, Will, and @Sattely_lab
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Xiaojing Gao @SynBioGaoLab
Numerous biotech innovations started with adapting a protein to its non-native context, which often requires substantial experimental optimizations. Here’s a breakdown by our lead, @Xiaowei0402, on a versatile, efficient computational framework to facilitate such adaptations.
Xiaowei Zhang Verified account @Xiaowei0402 Oct 20
We deimmunized a clinical CD19 CAR binder with experimentally detected HLA-I epitope depletion while retaining tumor killing, and rescued a secreted protease to function in the cytosol.

Both done with one unified design framework: ProVADA.

How it works and what we tested 🧵 (1/11)
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Xiaojing Gao @SynBioGaoLab
Congrats, Brian and team. It never ceases to amaze me what you guys can do with the language models!
Brian Hie Verified account @BrianHie Sep 22
As with biological design, AI can also accelerate biological discovery.

In new work led by @_David_Li and @garykbrixi with @mfgrp, we show how genome language models enable systematic discovery of genetic elements, revealing a new class of reverse-transcriptase mechanisms.
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Xiaojing Gao @SynBioGaoLab
Please RT: We're hiring! Computational postdoc for foundation model and sequence-structure-function studies of intrinsic disordered proteins through a consortium and unique large datasets. Ideal candidates have led ML/AI projects in protein design/analysis.