Owl Posting

Abhishaike Mahajan
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a podcast about the intersection of biology and computation. all episodes on https://www.owlposting.com/s/podcast! www.owlposting.com

Выпуски

  1. We don't know what most microbial genes do. Can genomic language models help? (Yunha Hwang, Ep #7)

    -4 ДН.

    We don't know what most microbial genes do. Can genomic language models help? (Yunha Hwang, Ep #7)

    Note: Thank you to rush.cloud and latch.bio for sponsoring this episode! Rush is augmenting drug discovery for all scientists with machine-driven superintelligence. LatchBio is building agentic scientific tooling that can analyze a wide range of scientific data, with an early focus on spatial biology. Clip on them in the episode. If you’re at all interested in sponsoring future episodes, reach out! *** This is an interview with Yunha Hwang, an assistant professor at MIT (and co-founder of the non-profit Tatta Bio). She is working on building and applying genomic language models to help annotate the function of the (mostly unknown) universe of microbial genomes. There are two reasons you should watch this episode. One, Yunha is working on an absurdly difficult and interesting problem: microbial genome function annotation. Even for E. coli, one of the most studied organisms on Earth, we don’t know what half to two-thirds of its genes actually do. For a random microbe from soil, that number jumps to 80-90%. Her lab is one of the leading groups working to apply deep learning to solving the problem, and last year, released a paper that increasingly feels foundational within it (with prior Owl Posting podcast guest Sergey Ovchinnikov an author on it!). We talk about that paper, its implications, and where the future of machine learning in metagenomics may go. And two, I was especially excited to film this so I could help bring some light to a platform that she and her team at Tatta Bio has developed: SeqHub. There’s been a lot of discussion online about AI co-scientists in the biology space, but I have increasingly felt a vague suspicion that people are trying to be too broad with them. It feels like the value of these tools are not with general scientific reasoning, but rather from deep integration with how a specific domain of research engages with their open problems. SeqHub feels like one of the few systems that mirrors this viewpoint, and while it isn’t something I can personally use—since its use-case is primarily in annotating and sharing microbial genomes, neither of which I work on!—I would still love for it to succeed. If you’re in the metagenomics space, you should try it out! Youtube: https://youtu.be/w6L9-ySnxZI?si=7RBusTAyy0Ums6Oh Spotify: https://open.spotify.com/episode/2EgnV9Y1Mm9JV5m9KAY6yL?si=J5ZmF2i3TtuT10D40jjgawApple Podcast: https://apple.co/4pu4TRBTranscript: https://www.owlposting.com/p/we-dont-know-what-most-microbial Timestamps: 00:02:07 – Introduction 00:02:23 – Why do microbial genomes matter 00:04:07 – Deep learning acceptance in metagenomics 00:05:25 – The case for genomic “context” over sequence matching 00:06:43 – OMG: the only ML-ready metagenomic dataset 00:09:27 – gLM2: A multimodal genomic language model 00:11:06 – What do you do with the output of genomic language models? 00:17:41 – How will OMG evolve? 00:20:26 – Why train on only microbial genomes, as opposed to all genomes? 00:22:58 – Do we need more sequences or more annotations? 00:23:54 – Is there a conserved microbial genome ‘language’? 00:28:11 – What non-obvious things can this genomic language model tell you? 00:33:08 – Semantic deduplication and evaluation 00:37:33 – How does benchmarking work for these types of models? 00:41:31 – Gaia: A genomic search engine 00:44:18 – Even ‘well-studied’ genomes are mostly unannotated 00:50:51 – Using agents on Gaia 00:54:53 – Will genomic language models reshape the tree of life? 00:59:18 – Current limitations of genomic language models 01:08:54 – Directed evolution as training data 01:12:35 – What is Tatta Bio? 01:19:02 – Building Google for genomic sequences (SeqHub) 01:25:46 – How to create communities around scientific OSS 01:29:06 – What’s the purpose in the centralization of the software? 01:35:37 – How will the way science is done change in 10 years? Get full access to Owl Posting at www.owlposting.com/subscribe

    1 ч. 43 мин.
  2. Bringing organ-scale cryopreservation into existence (Hunter Davis, Ep #6)

    24 НОЯБ.

    Bringing organ-scale cryopreservation into existence (Hunter Davis, Ep #6)

    Sponsor note: the supporter of this video is rush.cloud. If you are at all involved with doing preclinical drug discovery and would benefit from computational tools, you should check out their platform + beautiful website here: rush.cloud. If you’re at all interested in working together for future episodes, reach out! This is an interview with Hunter Davis, the CSO and co-founder (alongside Laura Deming) of Until Labs, which you may also know by its prior name, Cradle. They are a biotech startup devoted to organ-scale cryopreservation. They raised a $58M Series A back in September 2025, and are backed by Founders Fund (especially interesting!), Lux Ventures, and others. In this interview, we mainly talk about the engineering and scientific difficulties in the cryopreservation field, including some background details on their September 2024 progress report on neural slice rewarming, how they characterize tissue damage in their attempts to do kidney cryopreservation, the potential economics of future cryopreservation protocols, and lots more. One of the most interesting conversations I’ve had in a long time. If any of this work seems interesting, Until Labs is actively and aggressively hiring! Enjoy! Substack + Transcript: https://www.owlposting.com/p/bringing-organ-scale-cryopreservation Spotify: https://open.spotify.com/episode/23g2lR7dWl8NXUn893KMgv?si=5628cd0e56184130 Apple Podcasts: https://podcasts.apple.com/us/podcast/bringing-organ-scale-cryopreservation-into-existence/id1758545538?i=1000738128994 Youtube: https://youtu.be/xaqwPd3ujHg Timestamps: [00:01:50] Introduction [00:05:00] Why don’t we have reversible cryopreservation today? [00:07:05] Why is freezing necessary at all for preservation? [00:08:23] Let’s discuss cryoprotectant agents [00:14:09] Until Lab’s 2024 progress report on neural tissue cryopreservation [00:20:28] How do you measure cryopreserved tissue damage? [00:22:34] Translation across species [00:26:04] Why was the cryopreservation storage time so short in the progress report? [00:30:47] Nuances of loading cryoprotectants into tissue [00:37:03] Let’s discuss rewarming [00:43:02] What scientific problems amongst vitrification and rewarming keep you up at night? [00:45:58] Why are there so few cryoprotectants? [00:48:11] How can you improve rewarming capabilities? [00:53:03] What are the experimental costs of running cryopreservation studies? [00:57:49] What happens to the cryoprotectants and iron oxide nanoparticles after the organ has been thawed? [01:01:34] Cryopreservation and immune response [01:03:25] How do you filter through the cryopreservation literature [01:05:54] How much is molecular simulation used at Until Labs? [01:10:04] What are the (expected) economics of Until Labs? [01:14:49] How much does cryopreservation practically solve the organ shortage problem? [01:17:04] Synergy between xenotransplantation and cryopreservation [01:21:12] How much will the final cryopreservation protocol likely cost? [01:21:58] Who ends up paying for this? [01:23:28] What was it like to raise a Series A on such an unorthodox thesis? [01:27:49] What are common misconceptions people have about cryopreservation? [01:29:58] The beginnings of Until Labs [01:34:07] What expertise is hardest to recruit for? [01:39:27] What personality type do you most value when hiring? [01:44:17] Why work in cryopreservation as opposed to anything else? [01:46:26] Until Lab’s competitors [01:49:30] What would an alternative universe version of Hunter worked on? [01:51:33] What would you do with $100M? Get full access to Owl Posting at www.owlposting.com/subscribe

    1 ч. 55 мин.

  3. 10 НОЯБ.

    Can machine learning enable 100-plex cryo-EM structure determination? (Ellen Zhong, Ep #5)

    Sponsor note: I am extremely happy to announce my first commercial, service-oriented sponsor: rush.cloud. I’ve been doing these podcasts entirely through very kind philanthropic graces, which is very nice, but I’d ideally like to be helping someone when they sponsor me. And now I have that! So, if you are at all involved doing preclinical drug discovery and would benefit from computational tools, you should check out their platform + beautiful website here: rush.cloud. ****** Youtube: https://www.youtube.com/watch?v=W0m3Ltz_YqU Apple Podcasts: https://podcasts.apple.com/us/podcast/owl-posting/id1758545538?i=1000736122646 Spotify: https://open.spotify.com/episode/5l9RMbMwdgOrrZ6uLS656R?si=938af7d2b79440a1 Transcript: https://www.owlposting.com/p/can-machine-learning-enable-100-plex?open=false#%C2%A7transcript ****** Introduction: Ellen Zhong is perhaps one of the only people in the ML x bio field to have created an entirely new subfield of research during her PhD: the application of deep-learning to cryo-EM particle images. If you aren’t familiar with that field, I luckily have a 8,000~ word article covering it, which walks through a lot of Ellen’s papers. If you don’t have time to read something that grossly large, the general breakdown of the problem is as follows: cryo-EM can give you thousands of 2D views of a 3D protein from many different angles, from that data, can you discover what that 3D structure is? Ellen, who is a computer science professor at Princeton University, has spent her academic career investigating that question, and now has an entire lab at Princeton (E.Z. Lab) focused on that and related ones. Including, as the title mentions, the possibility of doing performing cryo-EM structure determination at ultra-high scales. In this podcast, we talk about her research, what she did during her recent sabbatical at Generate:Biomedicines, her recent interest in areas beyond cryo-EM (cryo-ET and NMR specifically), and more! Timestamps [00:00:00] Introduction [00:02:43]  What does it mean to apply ML to cryo-EM? [00:04:28] Ab initio reconstruction and conformational heterogeneity [00:15:41] Can we do multiplex cryo-EM structure determination? [00:22:19] Datasets in cryo-EM [00:26:25] Why isn’t there a foundation model for cryo-EM particle analysis? [00:33:07]  How much practical usage is there of these cryo-EM models amongst wet-lab cryo-EM researchers? [00:40:34] Where can things still improve? [00:46:57]  Has deep learning done something in cryo-EM that was previously impossible? [00:48:22] Ellen’s experience in the cryo-EM field [00:53:40] Deep learning in cryo-EM outside of structure determination [00:57:32] 3D volume reconstruction versus residue assignment in cryo-EM [01:00:26] What did Ellen do during her sabbatical at Generate Biomedicines? [01:07:07] Ellen’s research in cryo-ET [01:13:54] Ellen’s research in NMR [01:21:05] How did Ellen get into the cryo-EM field? [01:26:57] Why did Ellen go back to graduate school? [01:32:17]  What makes Ellen more confident about trusting an external cryo-EM paper? Get full access to Owl Posting at www.owlposting.com/subscribe

    1 ч. 40 мин.
  4. The DNA protection company (Alan Tomusiak, Ep #4)

    28 ИЮЛ.

    The DNA protection company (Alan Tomusiak, Ep #4)

    Note: Extremely grateful for Geltor (http://geltor.com/) for sponsoring this podcast, and for the founder of it (https://www.linkedin.com/in/alexanderlorestani) for reaching out to make to start with! Geltor produces designer proteins for beauty and wellness. The current in-vogue thing to do for most longevity companies is to go for cellular reprogramming. As in, fill a cell with the right transcription factors needed to reduce epigenetic noise, restore mitochondrial dysfunction, and so on. I’ve written about the promise there before, it’s definitely an exciting field. So, when I first met Alan— who told me that he was a longevity researcher — last October, I naively assumed he was also on the reprogramming train. But he told me that he was investigating something a bit different. His pitch was that, instead of reprogramming the cell to fix age-related damage, what if you just protected it from (genetic) insult first? It’s an obvious idea, but one that I’d never really deeply considered. He sold me on the concept, and I was very curious to hear what he’d do next to push it forwards. A few months after our chat, he spun up a company to pursue this line of thinking: Permanence Bio, which develops molecules that stabilize/protect the genome. They are just about eight months old, but there are already some exciting results coming out. I’m a sucker for people doing ‘contrarian research in consensus fields’, and I immediately knew I wanted to have Alan on the podcast. He graciously agreed and, during my trip to SF last month, we sat down and talked for a few hours. In this episode, we talk about why DNA protection is so important, what indications is it useful for, how to mentally conceptualize the idea of a molecule ‘stabilizing’ a genome, what it was like to raise money for a company pursuing such an out-of-distribution thesis, and lots more. Finally, Alan has a really great blog (something I mention in the video), and I wanted to attach a much longer article he’s written about the topic here. [00:00:00] Teaser clip [00:01:39] Introduction [00:07:32] What is Permanence working on? [00:11:48] What does DNA protection actually look like? [00:27:12] Why is DNA protection not focused on as much? [00:41:03] The utility of epigenetic clocks [00:46:47] Do you need multimechanism approaches for longevity? [00:51:58] Longevity outside of DNA protection [00:55:57] What's going on inside of Permanence? [01:05:54] How could Permanence fail? [01:09:03] How do you stay optimistic? [01:10:26] Why work on aging? [01:15:26] What are you bearish on? [01:19:12] Weirder types of aging beyond 110 [01:21:37] How did you decide on DNA protection and what else would you have done? [01:25:27] What was it like raising money? [01:31:48] What do you think of past cancer prevention trials? [01:34:12] What does good wet-lab talent look like? [01:37:02] What does your information diet look like? [01:40:06] What's it like going from research to being a CEO? [01:42:20] What happens after cancer prevention for Permanence? Get full access to Owl Posting at www.owlposting.com/subscribe

    1 ч. 43 мин.

  5. 25 АПР.

    What could Alphafold 4 look like? (Sergey Ovchinnikov, Ep #3)

    X: https://x.com/owl_posting Sergey's X: https://x.com/sokrypton Youtube: https://youtu.be/6_RFXNxy62c Spotify: https://open.spotify.com/episode/0wPs3rmp0zrfauqToozrcv?si=DCtRf-xQTPiVYwslo-b2rQ Apple Podcasts: https://podcasts.apple.com/us/podcast/what-could-alphafold-4-look-like-sergey-ovchinnikov-3/id1758545538?i=1000704927828 Transcript: https://www.owlposting.com/p/what-could-alphafold-4-look-like?open=false#%C2%A7transcript To those in the protein design space, Dr. Sergey Ovchinnikov is a very, very well-recognized name. A recent MIT professor (circa early 2024), he has played a part in a staggering number of recent innovations in the field: ColabFold, RFDiffusion, Bindcraft, automated design of soluble proxies of membrane proteins, elucidating what protein language models are learning, conformational sampling via Alphafold2, and many more. Of course, all these papers were group efforts, but Sergey's name comes up astonishingly frequently! And even beyond the research that have come from his lab in the last few years, the co-evolution work he did during his PhD/fellowship also laid some of the groundwork for the original Alphafold paper, being cited twice in it. As a result, Sergey’s work has gained a reputation for being something that is worth reading. But nobody has ever interviewed him before! Which was shocking for someone who was so pivotally important for the field. So, obviously, I wanted to be the first one to do it. After an initial call, I took a train down to Boston, booked a studio, and chatted with him for a few hours, asking every question I could think of. We talk about his own journey into biology research, some issues he has with Alphafold3, what Alphafold4-and-beyond models may look like, what research he’d want to spend a hundred million dollars on, and lots more. Take a look at the timestamps to get an overview! Final note: I’m extremely grateful to Asimov Press for helping fund the travel + studio time required for this episode! They are a non-profit publisher dedicated to thoughtful writing on biology and metascience, such as articles over synthetic blood and interviews with plant geneticists. I myself have published within them twice! I highly recommend checking out their essays at asimov.press, or reaching out to editors@asimov.com if you’re interested in contributing. Timestamps: [00:00:00] Highlight clips [00:01:10] Introduction + Sergey's background and how he got into the field [00:18:14] Is conservation all you need? [00:23:26] Ambiguous vs non-ambiguous regions in proteins [00:24:59] What will AlphaFold 4/5/6 look like? [00:36:19] Diffusion vs. inversion for protein design [00:44:52] A problem with Alphafold3 [00:53:41] MSA vs. single sequence models [01:06:52] How Sergey picks research problems [01:21:06] What are DNA models like Evo learning? [01:29:11] The problem with train/test splits in biology [01:49:07] What Sergey would do with $100 million Get full access to Owl Posting at www.owlposting.com/subscribe

    2 ч. 7 мин.

  6. 3 ФЕВР.

    How do you make a 250x better vaccine at 1/10 the cost? Develop it in India. (Soham Sankaran, Ep #2)

    This is an interview with Soham Sankaran, the founder and CEO of PopVax, an mRNA vaccine development startup. Curiously, PopVax is based in India, specifically Hyderabad. This should be a surprise to most people in the field: we never really hear of interesting biotech research being done in a place that isn’t [US, Europe, East Asia]. Yet, PopVax has been astonishingly successful, having a (in mouse) influenza vaccine that is 250x better than its competitors, multiple large research collaborations, and their first upcoming US based phase 1 clinical trial being fully sponsored and conducted by the NIH. It’s an extremely interesting success story from what feels like a very clear underdog. In this 2-hour podcast, we discuss everything from why so little biotech research gets done in India, a breakdown on what you care about in vaccine design (immunogens), how PopVax uses machine learning for precise immunogen design, how raising money for a vaccinology startup is going, and a lot more. Timestamps and transcripts are below. Just as in my last episode, I’ve included a ‘jargon explanation’ as a quick primer for some of the subjects discussed in the episode. Some final bits: the studio rental costs were kindly covered by Dylan Reid! Huge shout-out to him for making this episode possible. Also shout-out to Samarth Jajoo, Reha Mathur, and David Yang for some very helpful discussion about the Indian biotech scene. And, if you think PopVax is interesting, here is their Substack which has some articles on their results, their job section (they are actively hiring), and can be reached at contact@popvax.com. Timestamps 01:31 Introduction 02:38 Why is there such little biotech research in India? 17:43 Advantages of building a company in India 31:30 Policy prescriptions for India 35:39 Questions on vaccine design 50:55 What does PopVax do? 01:01:58 The role of machine learning in vaccine design 01:12:07 The (conservative) culture of vaccinology 01:26:57 Hiring in India 01:46:52 How fundraising for an Indian vaccine design startup is coming along 01:57:36 How is PopVax so good at designing vaccines? 02:02:07 Pet theories on immune mechanisms 02:09:07 mRNA beyond infectious diseases 02:12:38 What would you do with $100 million dollars? Get full access to Owl Posting at www.owlposting.com/subscribe

    2 ч. 15 мин.
  7. Can AI improve the current state of molecular simulation? (Corin & Ari Wagen, Ep #1)

    03.12.2024

    Can AI improve the current state of molecular simulation? (Corin & Ari Wagen, Ep #1)

    In my first (real) podcast episode, I talk with Corin and Ari Wagen, two brothers who I met through my writing. They are building something super cool: a molecular simulation company called Rowan (which recently got into the Nat Friedman AI grant program). We discuss neural network potentials (NNP’s), whether dynamics are useful at all, the role of computational chemistry in drug design, what the future of the field looks like for molecular simulation, and a lot more. If you work in molecular simulation, I recommend trying out their tool at rowansci.com. I’m not a chemist and cannot vouch for the tool personally, but I can vouch for how much I’d trust Corin and Ari to build something useful. Not a paid sponsorship, not anything I have an investment in, etc, etc, I just genuinely want their startup to succeed. If you're confused by this episode, check out the 'Jargon Explanation' on the Substack post: https://www.owlposting.com/i/152329408/jargon-explanation Transcript of this episode (contains links to all referenced organizations and papers) My Twitter My Substack (you should subscribe!) Timestamps: 00:00 Introduction 01:19 Divide between classical and quantum simulation 03:48 What are NNP's actually learning? 06:02 What will NNP's fail on? 08:08 Short range and long range interactions in NNP's 10:23 Emergent behavior in NNP's 16:58 Enhanced sampling 18:16 Cultural distinctions in NNP's for life-sciences and material sciences 21:13 Gap between simulation and real-life 36:18 Benchmarking in NNP's 41:49 Is molecular dynamics actually useful? 53:14 Solvent effects 55:17 Quantum effects in large biomolecules 57:03 The legacy of DESRES and Anton 01:02:27 Unique value add of simulation data 01:06:34 NNP's in material science 01:13:57 The road to building NNP's 01:21:13 Building the SolidWorks of molecular simulation 01:30:05 Simulation workflows 01:41:06 The role of computational chemistry 01:44:06 The future of NNP's 01:51:23 Selling to scientists 02:01:41 What would you spend 200 million on? Get full access to Owl Posting at www.owlposting.com/subscribe

    2 ч. 9 мин.
  • Выпусков: 7

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a podcast about the intersection of biology and computation. all episodes on https://www.owlposting.com/s/podcast! www.owlposting.com

Информация

  • Автор
    Abhishaike Mahajan
  • Годы выхода
    2024 - 2025
  • Выпуски
    7
  • Ограничения
    Без ненормативной лексики
  • Авторские права
    © Abhishaike
  • Сайт подкаста
    Owl Posting

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