🧬🧪🔬Why are enhancers transcribed? How does it impact gene regulation? I’m excited to share our new paper in @ScienceMagazine showing that ncRNAs control the timing of gene activation in embryos. w/Mike Levine #ScienceResearch #RNA A few highlights 🧵👇 science.org/doi/10.1126/scie…

A call from one procrastinator to all others: sign up now for the @FASEB SRC on “Neuronal Experience-Regulated Transcription” Oct 5-7 in Melbourne, FL . I just did and already feel so much better 😎 And: there are still slots for ShortTalks - trainees, pls submit your abstracts!

Excited to share our latest paper

Science Fellow @zhou_jingtian builds single-cell and computational frameworks to probe the 3D genome. His work tells us more about how the epigenome and transcriptome interact to shape cell state and complex disease: arcinstitute.org/news/jingti…

There’s a big, under-appreciated reason why people may have very different experiences and opinions about using AI for work — are they using it for tasks they’re already an expert at, or tasks they can’t do themselves? The former leads to a *growth cycle* and the latter leads to a *dependence spiral*. When I use AI to do something I’m an expert at, like coding, I treat it as a tool. I can build quickly, maintaining an understanding of the code, knowing that if necessary, I can fix the code myself. It feels empowering. It frees up my time to think about the complex, judgment-oriented parts of software engineering that I can’t or won’t delegate to AI. That means my own skills improve rapidly, and I get to climb the ladder of complexity and develop higher-level skills, much more so than when I write the code myself. I feel in control. I can lock in and achieve a flow state — when AI is working, I’m reviewing, building understanding, and planning the next steps. I never get the feeling that the tool is about to replace me. This is the growth cycle. (Of course, the growth cycle is not automatic. I still need to exercise agency to use AI responsibly. But it’s the same challenge with any productivity-enhancing technology, and those who’ve navigated such transitions before are well-equipped to navigate it with AI as well.) On the other hand, if I use it for tasks I don’t understand and haven’t learned to perform myself, I have no choice but to treat it as a superintelligence. If something breaks, the best I can do is ask AI to fix it and hope for the best. I generally can’t evaluate the quality of the output myself. The only way to find out if it's any good is if and when the work is ultimately reviewed by an actual expert. The experience is confusing, unsettling and disempowering. And forget about flow state. By over-relying on AI, I risk losing whatever skill I had at the task in the first place, even if it boosts productivity in the short term. This is the dependence spiral. It’s no wonder that entry-level workers and students preparing to enter the workforce find themselves in a bind. To compete with the AI-enabled productivity of more seasoned workers, they must adopt AI themselves, but doing so risks the dependence spiral. I have some thoughts on solutions that I will share in later posts, but I think having a clear diagnosis of the problem is a useful first step.

Excited to share that Spatial Hi-C-RNA is now out in @CellCellPress! It brings the 3D genome into spatial multi-omics by co-mapping chromatin architecture and gene expression in the same tissue section. Congrats to the whole team! cell.com/cell/fulltext/S0092…

📢Thrilled to announce that the 2nd piece of work from my postdoc at @Arlottalab is out in @Nature! This has been a huge co-lead effort with my friend @IreneFaravelli 👩‍🔬👩‍🔬🤝 Link: nature.com/articles/s41586-0… 👇🧵

Only about a quarter of human diseases have an approved therapy. By some estimates it's a few percent. Most of those treatments slow a disease rather than stop it. Closing that gap is what the AI-cures-everything story promises. Build a system smart enough and the cures hidden in what we already know will fall out. I have worked at the intersection of machine learning and biology for three decades, and I believe #AI will eventually transform human health. The capabilities arriving now are extraordinary. But the promise rests on an assumption that is simply false: that we already understand human biology well enough for a clever enough reasoner to find the answers in it. We don't. More than 90% of drugs entering clinical trials fail, a number that has barely moved in decades. In the large majority of those failures the molecule was engineered just fine. The mechanism it targeted was wrong. We are doing a pretty good job at manufacturing keys, but they are generally for the wrong locks. And because nobody wants to fail in the clinic, the industry has retreated to the locks it already trusts: 38 targets now have more than 50 programs against each of them, while the number of novel targets advanced per year fell from roughly 100 in 2015 to about 30 in 2024. AI will not reason its way past this. Biology wasn't engineered. It is the product of billions of years of messy, stochastic evolution, and the variation that produced is too vast and too idiosyncratic to work out in the abstract. You have to measure it. Aimed at a biology this thinly sampled, AI will mostly help us generate failures faster. I founded @insitro because getting to the right locks requires a different kind of system. We generate multimodal human and cellular data at scale, use machine learning to find causal drivers of disease, and test those hypotheses experimentally. Virtual Human™ is built for causal discovery; TherML™ turns what it finds into the right therapeutic intervention. It is working: first-in-class programs internally and with partners, three #ALS targets that Virtual Human™ identified and @bmsnews nominated, and additional collaborations with @EliLillyandCo and @GileadSciences . Today we are launching Deep Phenotype: Scaled Biology, Deep Causality. Issue one is "Drug Discovery Has No Magic Wands," the first half of a two-part essay on the magical thinking currently running through our field and what I think it will actually take. After that you will hear from insitro's own scientists and engineers, people who work across computation and experiment because the problem requires both. Getting this right is hard, and we do not have all of it worked out. I hope you will follow along and think it through with us. DeepPhenotype.Substack.com