Owl Posting

Youtube:https://youtu.be/JAxkqb-nBWs
Spotify: https://open.spotify.com/episode/6BLZph2uGGUVphbNQ8NGPd?si=SVBSKJM8RdO4AhYzDa-ZfQ
Apple Podcast: https://apple.co/3OU5Zse Transcript:
https://www.owlposting.com/i/189602943/transcript

This 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 Introduction
00:01:42 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 drugs
00:17:38 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 devices
00:33:35 Walk us through the patient journey from diagnosis to implantation
00:36:13 The Michael Levin question: can you reprogram cancer back to normal?
00:42:29 Efficacy, hospice settings, and the utility of the neuromodulation literature
00:45:52 Why start with glioblastoma instead of an easier cancer?
00:48:57 Regulatory strategy and the reimbursement threat
00:55:37 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 story
01:06:38 Why build your own device instead of using off-the-shelf arrays?
01:08:35 Speaking with glioblastoma patients
01:12:04 What was it like to raise money for this?
01:13:56 Beyond cancer: TBI, lung disease, and the pan-disease argument
01:17:40 Hiring at Coherence + what is the hardest type of talent to find
01:23:17 What would you do with $100M equity-free?
01:27:15 Are you a neurotech company or a cancer company?


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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/3DWTF5mNcUU

Spotify: https://open.spotify.com/episode/3aZr5yTgwB4QzUV5ADN0y9?si=9aTLjRZDRHuSBvmckenO1Q

Apple Podcasts: https://podcasts.apple.com/us/podcast/what-if-we-could-grow-human-tissue-by-recapitulating/id1758545538?i=1000741694661

Substack/Transcript: https://www.owlposting.com/p/what-if-we-could-grow-human-tissue
Timestamps:
(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?

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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

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!

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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?


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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?


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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.
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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
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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?


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