Insights from recent episode analysis
Audience Interest
Podcast Focus
Publishing Consistency
Platform Reach
Insights are generated by CastFox AI using publicly available data, episode content, and proprietary models.
Total monthly reach
Estimated from 5 chart positions in 5 markets.
By chart position
- 🇮🇳IN · Nature#5910K to 30K
- 🇰🇷KR · Nature#1021K to 10K
- 🇭🇰HK · Nature#793K to 10K
- 🇨🇿CZ · Nature#106500 to 3K
- 🇮🇪IE · Nature#152500 to 3K
- Per-Episode Audience
Est. listeners per new episode within ~30 days
7.5K to 28K🎙 Weekly cadence·9 episodes·Last published 2mo ago - Monthly Reach
Unique listeners across all episodes (30 days)
15K to 56K🇮🇳54%🇰🇷18%🇭🇰18%+2 more - Active Followers
Loyal subscribers who consistently listen
4.5K to 17K
Market Insights
Platform Distribution
Reach across major podcast platforms, updated hourly
Total Followers
—
Total Plays
—
Total Reviews
—
* Data sourced directly from platform APIs and aggregated hourly across all major podcast directories.
On the show
Recent episodes
The printing press for biological data (Sterling Hooten)
Apr 20, 2026
2h 03m 08s
Neurotechnology? For Cancer? (Ben Woodington & Elise Jenkins)
Mar 2, 2026
1h 33m 20s
What if we could grow human tissue by recapitulating embryogenesis? (Matthew Osman & Fabio Boniolo)
Dec 17, 2025
2h 01m 18s
We don't know what most microbial genes do. Can genomic language models help? (Yunha Hwang, Ep #7)
Dec 8, 2025
1h 42m 42s
Bringing organ-scale cryopreservation into existence (Hunter Davis, Ep #6)
Nov 24, 2025
1h 54m 51s
Social Links & Contact
Official channels & resources
Official Website
Login
RSS Feed
Login
| Date | Episode | Description | Length | |
|---|---|---|---|---|
| 4/20/26 |  The printing press for biological data (Sterling Hooten) | Youtube: https://youtu.be/-rlJDGC2eC8Apple Podcasts: https://podcasts.apple.com/us/podcast/owl-posting/id1758545538?i=1000762410502Spotify: https://open.spotify.com/episode/1OtuQYwNhRhVSwHiHxPrmV?si=tD52iE5IR8i-3W6Q7Bhy8gSubstack/Transcript: https://www.owlposting.com/p/the-printing-press-for-biologicalAfter having written long-form essays over a weirdly diverse number of areas of the life-sciences, I am increasingly confident in my status as someone who knows a little about a lot of things. But every now and then, you meet someone who casually reveals to you an entire subfield who, up until your conversation with them, you’d never even thought of before. This happened to me when I met Sterling a few months back. We met in the elevator as we were both leaving an event, and by the time we’d reached the bottom floor, the conversation had become so interesting that we stood in the lobby for an hour as I pestered him with more and more questions.Sterling runs a company called Iku Bio. Iku ostensibly does something quite simple: it helps biologics manufacturers figure out what to feed their cells. This is called media optimization, and it is done in an astonishingly old-fashioned way. An engineer runs a handful of experiments in a benchtop bioreactor the size of a Fiji water bottle, waits days for analytical results, and repeats, maybe three or four times before timelines force them to stop searching.Sterling’s solution was to use printed circuit boards (PCBs)—the same green wafers inside your phone and your microwave—as the substrate for microfluidic bioreactors. Because PCBs are made via lithography, you get complexity for free. Because they’re already mass-manufactured at planetary scale, you inherit sixty years of cost optimization. And because they’re literally designed to carry electrical signals, you can embed sensors directly into the thing rather than cramming them in after the fact.The result is a device that costs $8 per experimental lane versus $20,000 for the nearest comparable microfluidic system. And there are many, many ways for to improve from here on out.This conversation covers the full stack: what cell culture media actually is and why it’s so much more than sugar water, why biologics manufacturing has more in common with semiconductor fabs than chemistry labs, how Sterling arrived at PCBs, and at the end of the talk, why he thinks a fair bit of lab automation is “philosophically a crime.”Timestamps* [00:00:48] Introduction* [00:01:26] What is Iku Bio?* [00:05:00] Media optimization as the biggest lever* [00:06:23] What actually is media?* [00:13:07] Fetal bovine serum and the move to synthetic media* [00:15:10] Walk me through a media optimization workflow* [00:18:49] Why biologics manufacturing is closer to semiconductors than chemistry* [00:21:50] Matching the phase three batch and generics* [00:24:12] The 200-dimensional search space* [00:37:02] Printed circuit boards as a medium for microfluidics, and the utility of lithography* [00:40:48] Anatomy of the Iku device* [00:57:09] What sensors are on the device today?* [01:01:36] How do you use the Iku device to perform media optimization?* [01:14:44] Does media optimization survive scale-up?* [01:24:32] $8/lane vs. $20,000/lane: the economic utility of Iku’s device* [01:32:05] Why PCB microfluidics didn’t exist 10 years ago* [01:39:24] Who is the customer?* [01:43:14] What is the ultimate goal of Iku?* [01:49:07] What does the validation evidence need to look like?* [01:52:14] What would you do with $100M equity-free?* [01:57:31] Lab automation is in a strange place right now Get full access to Owl Posting at www.owlposting.com/subscribe | 2h 03m 08s | |
| 3/2/26 |  Neurotechnology? For Cancer? (Ben Woodington & Elise Jenkins) | Youtube:https://youtu.be/JAxkqb-nBWsSpotify: https://open.spotify.com/episode/6BLZph2uGGUVphbNQ8NGPd?si=SVBSKJM8RdO4AhYzDa-ZfQApple Podcast: https://apple.co/3OU5Zse Transcript: https://www.owlposting.com/i/189602943/transcriptThis is an episode with Ben Woodington and Elise Jenkins, who are the cofounders of Coherence Neuro. The pitch for Coherence is as follows: a brain implant that treats cancer with electricity. When I first learned of the company in mid-2025, it was such an alien thesis that I instinctively wrote it off entirely. This surely isn’t clinically plausible at all, maybe it will be one day, but certainly not today.Then, while I was in San Francisco, I met up with Nicole, Coherence’s chief of staff. After that, I was far more convinced that there was something real here, especially after she told me that the electricity ←→ cancer thesis already has some merit: Optune, an FDA-approved medical device developed by Novocure. This has been on the market for over a decade, and uses externally delivered alternating electric fields to treat glioblastoma. And it works! If Optune is consistently used, glioblastoma patients can live up to twice as long compared to chemotherapy alone. How does it work? Simple: the alternating electrical fields prevent fast-dividing cells from replicating by interfering with the physical process of cell division (specifically, mitotic spindle formation).After this, Nicole connected me with Ben and Elise, the cofounders of the company. It was an incredible conversation. During it, I was informed that cancer cells behave eerily similar to neurons: hijacking neural pathways, attracting nerves into their microenvironment, and forming synaptic connections with surrounding tissue. Given this set of evidence, none of which felt particularly controversial, an easy logical leap is to ask the question: why can’t you throw neuromodulation at the tumor? Maybe not even just for treatment, but monitoring as well? Optune was a step in the right direction, yes, but surely it can be pushed even further.So Coherence was born, the only (neurotechnology x oncology) company in existence. Ben and Elise met during their PhD’s at Cambridge, spinning up the startup with the belief that a modality long assumed to be exclusively for neurological conditions like Parkinson’s, epilepsy, and chronic pain, may have a profound role to play in cancer. And perhaps even conditions outside of it.And during my last trip to San Francisco for JPM 2026, I had the honor to sit down with Ben and Elise to talk about it all.This conversation covers how Coherence’s first neurotech device (SOMA) works, the molecular reasons behind why neuromodulation affects cancer at all, what the biomarker readouts look like, the obvious Michael Levin comparison, and a lot more. Coincidentally, Ben helped me out a fair bit for my neurotechnology piece awhile back, and that article may be helpful reading material for this episode.Enjoy!Timestamps:00:00:00 Introduction00:01:42 How is SOMA different from Novocure’s Optune?00:08:57 Why does neuromodulation affect cancer at all?00:13:28 How was cancer-nervous system crosstalk first discovered?00:15:42 Anti-epileptics and beta blockers as accidental cancer drugs00:17:38 What is molecularly happening when you block cancer-neuron crosstalk?00:19:50 What is SOMA actually reading out as a biomarker?00:20:44 What does it mean that cancer is “very electric”?00:22:02 Can you derive universal biomarkers across patients?00:23:09 How is the device placed?00:24:45 How does the blocking stimulation regime work?00:26:43 Is it fair to say this is closed loop?00:29:05 Why not just spam the tumor with constant stimulation?00:32:31 Why MRI safety is non-negotiable for oncology devices00:33:35 Walk us through the patient journey from diagnosis to implantation00:36:13 The Michael Levin question: can you reprogram cancer back to normal?00:42:29 Efficacy, hospice settings, and the utility of the neuromodulation literature00:45:52 Why start with glioblastoma instead of an easier cancer?00:48:57 Regulatory strategy and the reimbursement threat00:55:37 How well does mouse-to-human translation work for neuromodulation?00:55:57 What do in silico models of neuromodulation look like?00:58:09 Why didn’t this exist 10 years ago?01:01:48 The founding story01:06:38 Why build your own device instead of using off-the-shelf arrays?01:08:35 Speaking with glioblastoma patients01:12:04 What was it like to raise money for this?01:13:56 Beyond cancer: TBI, lung disease, and the pan-disease argument01:17:40 Hiring at Coherence + what is the hardest type of talent to find01:23:17 What would you do with $100M equity-free?01:27:15 Are you a neurotech company or a cancer company? Get full access to Owl Posting at www.owlposting.com/subscribe | 1h 33m 20s | |
| 12/17/25 |  What if we could grow human tissue by recapitulating embryogenesis? (Matthew Osman & Fabio Boniolo) | This is an interview with Matthew Osman and Fabio Boniolo, the co-founders of Polyphron.The thesis behind Polyphron is equal parts nauseating and exciting in how ambitious it is: growing ex-vivo tissue to use in organ repair.And, truthfully, it felt so ambitious as to not be possible at all. When I had my first (of several) pre-podcast chats with Matt and Fabio to understand what they were doing, I expressed every ounce of skepticism I had about how this couldn’t possibly be viable. Everybody knows that complex tissue engineering is something akin to how fusion is viewed in physics; theoretically possible, but practically intractable in the near-term. What we can reliably grow outside of a human body are simple structures—bones, skin, cartilage—but anything beyond that is surely decades away.But after the hours of conversation I’ve had with the team, I’ve began to rethink my position. As Eryney Marrogi lines out in his Core Memory article over Polyphron (https://www.corememory.com/p/exclusive-cracking-the-only-engineering), there is an engineering system that has reliably produced viable human tissue for eons: embryogenesis.What if you could recapitulate this process? What if you could naturally get cells to arrange themselves into higher-order structures, by following the exact chemical guidelines that are laid out during embryo development? And, most excitedly, what if you didn’t need to understand any of these overwhelmingly complex development rules, but could outsource it all to a machine-learning system that understood what set of chemical perturbations are necessary at which timepoints?This does not exist today, but Polyphron has given early proof points that is possible. In their most recent finding, which we talk about on the podcast, their models have discovered a distinct set of chemical perturbations that force developing neurons to arrange themselves with a specific polarity: just shy of 90°, arranged like columns. This is obviously still a simple structure—still a difficult one to create, given that even an expert could not arrive to that level of polarity—but it represents proof that you can use computational methods to discover the chemical instructions that guide tissue self-assembly.We discuss this recent polarity result, what the machine-learning problems at Polyphron looks like, and the genuinely insane economics of the whole endeavour. The last of which is especially exciting; it is rare you hear biotech founders talk about ‘expanding the Total Addressable Market’, and actually believe them. But here, it is a genuine possibility if the Polyphron approach ends up working.Enjoy!Youtube: https://youtu.be/3DWTF5mNcUUSpotify: https://open.spotify.com/episode/3aZr5yTgwB4QzUV5ADN0y9?si=9aTLjRZDRHuSBvmckenO1QApple Podcasts: https://podcasts.apple.com/us/podcast/what-if-we-could-grow-human-tissue-by-recapitulating/id1758545538?i=1000741694661Substack/Transcript: https://www.owlposting.com/p/what-if-we-could-grow-human-tissueTimestamps:(00:00:00) Clips and ad roll(00:02:16) Introduction(00:02:37) Why replace tissue rather than the whole organ?(00:10:34) Why not do simple stem/progenitor cell injections?(00:13:51) Can organs repair themselves naturally?(00:18:21) What does “structure” actually mean in tissue engineering?(00:21:04) Why are skin and bone the only FDA-approved tissues today?(00:23:45) What exactly are tissue scaffolds?(00:27:52) Why are organoids a “dead end” for this field?(00:35:08) The argument for recapitulating developmental biology(00:40:28) Walk us through the Polyphron experimental loop(00:47:56) Can you simulate morphogenesis with only small molecules?(00:49:49) How large is the set of possible tissue scaffolds?(00:52:32) How reliable are developmental atlases?(00:56:45) What is the machine learning model actually optimizing for?(01:04:04) Polyphron’s first big tissue engineering result: polarity(01:15:33) What comes after polarity?(01:17:09) Why is vascularization the hardest problem of tissue engineering?(01:20:33) Why can’t you just wash angiogenesis factors over the tissue?(01:22:25) How does the graft integrate with the host’s blood supply?(01:25:45) How do you validate tissue function before implantation?(01:29:01) How do you design a clinical trial for a biological pacemaker?(01:37:01) The argument for being a pan-tissue company(01:41:57) What are the biggest scientific and economic risks?(01:45:23) Who are Polyphron’s competitors?(01:47:07) Expanding the TAM beyond transplant lists(01:52:28) Autologous vs. Allogeneic approaches(01:55:07) Is a 3-year timeline to the clinic realistic?(01:56:28) Cross-species translation(01:58:05) What would you do with $100M equity free?*********Note: Thank you to latch.bio for sponsoring this episode!LatchBio is building agentic scientific tooling that can analyze a wide range of scientific data, with an early focus on spatial biology. Check out their agent at agent.bio! Clip on them in the episode.If you’re at all interested in sponsoring future episodes, reach out! Get full access to Owl Posting at www.owlposting.com/subscribe | 2h 01m 18s | |
| 12/8/25 |  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-microbialTimestamps:00:02:07 – Introduction00:02:23 – Why do microbial genomes matter00:04:07 – Deep learning acceptance in metagenomics00:05:25 – The case for genomic “context” over sequence matching00:06:43 – OMG: the only ML-ready metagenomic dataset00:09:27 – gLM2: A multimodal genomic language model00:11:06 – 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 evaluation00:37:33 – How does benchmarking work for these types of models?00:41:31 – Gaia: A genomic search engine00:44:18 – Even ‘well-studied’ genomes are mostly unannotated00:50:51 – Using agents on Gaia00:54:53 – Will genomic language models reshape the tree of life?00:59:18 – Current limitations of genomic language models01:08:54 – Directed evolution as training data01:12:35 – What is Tatta Bio?01:19:02 – Building Google for genomic sequences (SeqHub)01:25:46 – How to create communities around scientific OSS01:29:06 – 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 | 1h 42m 42s | |
| 11/24/25 |  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-cryopreservationSpotify: https://open.spotify.com/episode/23g2lR7dWl8NXUn893KMgv?si=5628cd0e56184130Apple Podcasts: https://podcasts.apple.com/us/podcast/bringing-organ-scale-cryopreservation-into-existence/id1758545538?i=1000738128994Youtube: https://youtu.be/xaqwPd3ujHgTimestamps:[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 | 1h 54m 51s | |
| 11/10/25 |  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_YqUApple Podcasts: https://podcasts.apple.com/us/podcast/owl-posting/id1758545538?i=1000736122646Spotify: https://open.spotify.com/episode/5l9RMbMwdgOrrZ6uLS656R?si=938af7d2b79440a1Transcript: 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 | 1h 40m 18s | |
| 7/28/25 |  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 | 1h 43m 03s | |
| 4/25/25 |  What could Alphafold 4 look like? (Sergey Ovchinnikov, Ep #3) | X: https://x.com/owl_postingSergey's X: https://x.com/sokryptonYoutube: https://youtu.be/6_RFXNxy62cSpotify: https://open.spotify.com/episode/0wPs3rmp0zrfauqToozrcv?si=DCtRf-xQTPiVYwslo-b2rQApple Podcasts: https://podcasts.apple.com/us/podcast/what-could-alphafold-4-look-like-sergey-ovchinnikov-3/id1758545538?i=1000704927828Transcript: https://www.owlposting.com/p/what-could-alphafold-4-look-like?open=false#%C2%A7transcriptTo 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 | 2h 06m 39s | |
| 2/3/25 |  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.Timestamps01:31 Introduction02:38 Why is there such little biotech research in India?17:43 Advantages of building a company in India31:30 Policy prescriptions for India35:39 Questions on vaccine design50:55 What does PopVax do?01:01:58 The role of machine learning in vaccine design01:12:07 The (conservative) culture of vaccinology01:26:57 Hiring in India01:46:52 How fundraising for an Indian vaccine design startup is coming along01:57:36 How is PopVax so good at designing vaccines?02:02:07 Pet theories on immune mechanisms02:09:07 mRNA beyond infectious diseases02:12:38 What would you do with $100 million dollars? Get full access to Owl Posting at www.owlposting.com/subscribe | 2h 15m 07s | |
| 12/3/24 |  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-explanationTranscript of this episode (contains links to all referenced organizations and papers)My TwitterMy Substack (you should subscribe!)Timestamps:00:00 Introduction01:19 Divide between classical and quantum simulation03:48 What are NNP's actually learning?06:02 What will NNP's fail on?08:08 Short range and long range interactions in NNP's10:23 Emergent behavior in NNP's16:58 Enhanced sampling18:16 Cultural distinctions in NNP's for life-sciences and material sciences21:13 Gap between simulation and real-life36:18 Benchmarking in NNP's41:49 Is molecular dynamics actually useful?53:14 Solvent effects55:17 Quantum effects in large biomolecules57:03 The legacy of DESRES and Anton01:02:27 Unique value add of simulation data01:06:34 NNP's in material science01:13:57 The road to building NNP's01:21:13 Building the SolidWorks of molecular simulation01:30:05 Simulation workflows01:41:06 The role of computational chemistry01:44:06 The future of NNP's01:51:23 Selling to scientists02:01:41 What would you spend 200 million on? Get full access to Owl Posting at www.owlposting.com/subscribe | 2h 09m 20s |
Showing 10 of 10
Sponsor Intelligence
Sign in to see which brands sponsor this podcast, their ad offers, and promo codes.
Chart Positions
5 placements across 5 markets.
Chart Positions
5 placements across 5 markets.