Antonio Vela Gartner

Synthetic Biology + Machine Learning 🧬🧠 @DarkMatterDNA @JefBoeke’s lab /MBA @QuanticSchool /MS Student @NYUDataScience /Colombiano🇨🇴

[Antonio Vela Gartner @antoniovela1101]

@UrnovFyodor Can you elaborate which risk factors you are taking about? The clear risk of gene editing for Mendelian disease is off-target edits with unknown effects. But isn’t it easier to develop methods that prevent this in embryos than in utero?

[Antonio Vela Gartner @antoniovela1101]

Super cool work by my good friends @julie_trolle and @sudpinglay!

[The Dark Matter Project @DarkMatterDNA]

https://t.co/CQFOYHYcgV
Highly multiplexed mammalian metabolic engineering with a shotgun approach
Congrats @julie_trolle @sudpinglay @JefBoeke et al!

[Antonio Vela Gartner @antoniovela1101]

@SoyChristian Obviamente si perdería alguno. Las respuestas anteriores no tienen en cuenta que:
1. Despues de tantos partidos con uno de ellos van a haber mejorado mucho
2. Después de tantos partidos les van a perder el respeto

[Antonio Vela Gartner @antoniovela1101]

@hsalis I don’t know… I thought Twist & others mostly use PCA. The difference to them is that for some experiments there was no T4 ligation involved. But as already commented this was done years ago by Gibson and others. I think the doing it in a pool is novel?

[Antonio Vela Gartner @antoniovela1101]

@SammiSison Very cool Sammi!

[Antonio Vela Gartner @antoniovela1101]

@nevillesanjana Very cool Neville! I bet this can help us make our mSwapIN method for BIG DNA delivery more efficient @DarkMatterDNA

[Antonio Vela Gartner @antoniovela1101]

@Patrick18287926 @ATinyGreenCell Any plans on organizing these alignments on groups of structural orthologs? Kind of like what OrthoDB does at sequence level?

[Antonio Vela Gartner @antoniovela1101]

@andrewjrod Great way to approach such a hard situation! If you are thinking or looking for some synthetic biology ideas or tools for development, I’m happy to chat

[Antonio Vela Gartner @antoniovela1101]

Come learn from our team!

[The Dark Matter Project @DarkMatterDNA]

🧬Sign up to our free online 2026 Genome Writing Workshop 🧬
https://t.co/eOpHo3xKCh

[Antonio Vela Gartner @antoniovela1101]

@mmbronstein It is. Is that a big no-no? :(

[Antonio Vela Gartner @antoniovela1101]

Aspiring cool ML kid. How am I doing @mmbronstein?

[Antonio Vela Gartner @antoniovela1101]

@ATinyGreenCell Have never used this but I would bet my money that this is just the result of one tired/overproductive scientist that annotated it a bit longer long time ago and never bother to fix it. It is likely that the coding sequence does not have a role in termination.

[Bo Xia @BoXia7]

1/ Plenty of genomics AI models out there but very few biological discoveries delivered?
Exciting new work from our lab shows how biologically grounded in silico screen with genomics AI can tackle fundamental questions in genome biology: How does the genome organize into topologically associating domains (TADs)?

TL;DR: Through AI-guided discovery + experimental validation, we discovered:
• ZNF654 – a vertebrate-specific protein that interacts with CTCF to uniquely define TAD boundaries
• JMJD6 – a conserved chromatin enzyme that stabilizes chromatin stripes at boundaries and enhancer-promoter sites

Kudos to the amazing Shawn Bai (@baijiangshan1) and Qingji Lyu for leading this effort.

Preprint👇

[Antonio Vela Gartner @antoniovela1101]

@BoXia7 Cool!

[Antonio Vela Gartner @antoniovela1101]

@NikoMcCarty Oh, that is a very fun idea!

[Dave Truong @DavidMTruong]

🚨 Preprint Alert!
The era of human genome writing begins…
We developed REWRITE for human iPSCs, enabling 100–209 kb rewriting of all 3 HLA genes - or anywhere.
➡️ On-demand HLA hiPSCs
➡️ Off-the-shelf cells
➡️ Extensible cell engineering

🔗 https://t.co/tIMjidaZgq
🧵⬇️

[Antonio Vela Gartner @antoniovela1101]

@pochekailov How do these behave for cleaning, and or, separating DNA of bigger size? 10kb? 50kb?

[Nadav Brandes @BrandesNadav]

Latest genomic AI models report near-perfect prediction of pathogenic variants (e.g. AUROC>0.97 for Evo2). We ran extensive independent evals and found these figures are true, but very misleading.

A breakdown of our new preprint: 🧵

[Antonio Vela Gartner @antoniovela1101]

@joe_pickrell Haven’t read beyond this post, but could be some cool genes to make another @DarkMatterDNA project @BroshRan