Episode

What if we could grow human tissue by recapitulating embryogenesis? (Matthew Osman & Fabio Boniolo)

Podcast
Owl Posting
Published
Dec 17, 2025
Duration seconds
7278
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not_requested
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https://www.owlposting.com/p/what-if-we-could-grow-human-tissue
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https://api.substack.com/feed/podcast/181817572/415f4aa4bebe105549fc8e8d4c20b3b9.mp3
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Markdown
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Summary

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…