The New Biology

Episódios

• The Bitter Lesson for Biology — Adam Green on Virtual Cells and Scaling Laws 12.06.2026 1h 29min

  Markov Biosciences, a startup in San Francisco, is betting that biology is about to have its GPT moment. In this episode, founder Adam Green explains the "bitter lesson" for biology, the idea borrowed from Richard Sutton that large unbiased datasets and the right training objective tend to outcompete models with hard-coded rules and human priors. Adam thinks, in particular, that the virtual cell field took a wrong turn by spending hundreds of millions of dollars collecting expensive perturbation data. Green’s counterargument is that the data needed to train useful virtual cells is not limiting, but rather compute (and the loss function) are. By treating single-cell RNA-seq as a ranking problem rather than raw counts (a century-old idea traceable to a 1927 psychophysics paper), they found that virtual cells pre-trained on plain observational data show clean scaling laws, getting monotonically better at predicting unseen perturbations as the models grow, and beating a state-of-the-art model built specifically for that task.00:00 - Cold open and introduction 01:58 - The first clinical prediction from a virtual cell05:38 - What is a "virtual cell," really? 08:01 - Single-cell RNA-seq biases and the urns analogy23:29 - The bitter lesson for biology30:55 - Geometric Plackett-Luce: the right loss function59:26 Trop2 deep dive1:11:16 - Top-down vs. bottom-up biology, mechinterp, and control as the goal Readings and mentions: Markus Covert — A Whole-Cell Computational Model Predicts Phenotype from GenotypeMarkov's ADC-predictions thread (Adam Green)Scannell et al. (2012), "Diagnosing the decline in pharmaceutical R&D efficiency" (Eroom's Law)Adam Green on the Bitter LessonAdam Green on RNA-seq issuesArc Institute — STATE model (Adduri et al., 2025)GPT-1: Radford et al. (2018), "Improving Language Understanding by Generative Pre-Training"Rich Sutton, "The Bitter Lesson" (2019)Yann LeCun's "cake" analogy (explainer)Markov paper — Generative ranking / Geometric Plackett–Luce (the GPL paper)Thurstone (1927), "A Law of Comparative Judgment"scBaseCount (Youngblut et al., 2025)CZ CELLxGENE Discover (data portal)X-Cell (Xaira Therapeutics), Wang et al. (2026)Adam Green / Markov, "A Future History of Biomedical Progress" (biocompute)Decoding TROP2 in breast cancer: significance, clinical implications, and therapeutic advancementsBunne et al. (2024), "How to build the virtual cell with artificial intelligence: Priorities and opportunities," CellNintil (2023), “Notes on end-to-end biology.”
• Magnet-Controlled Medicines — Andrew York & Maria Ingaramo 29.05.2026 2h 7min

  Nonfiction Laboratories is building a technology called “magnetogenetics” that promises to control proteins inside the body — such as antibodies or enzymes — using small magnets. In this episode, co-founder Maria Ingaramo and scientific advisor Andrew York explain how they engineered a protein, MagLOV, that responds strongly to magnetic fields, why most prior attempts have failed to replicate, and how the mechanism of magnetically-controlled proteins actually works. They also get into the “dream” use cases, like cancer drugs that activate only at the tumor, which might have a lower toxicity inside the body. This podcast is made possible by Astera Institute.Notes from our discussion: https://nikomc.com/essays/protein-magnets.html00:00 - Opening00:54 — Introduction01:35 — The dream05:38 — Why magnets vs. light or ultrasound10:05 — The physics17:48 — On the name "magnetogenetics"21:25 — Birds and cryptochromes27:09 — Why is the field filled with so much junk?29:51 — Adam Cohen's molecule33:24 — Markus Meister’s debunking38:06 — The experiment46:22 — Finding the LOV domain54:11 — Singlets, triplets, and cysteine56:54 — What the magnet is actually doing1:05:13 — The conformational-change red herring1:12:46 — The Quantum Biology Institute1:19:31 — Founding Nonfiction Labs1:24:38 — How to convince skeptical investors1:29:39 — What a magnetogenetic medicine might look like1:38:50 — First clinical indications1:45:12 — The regulatory path1:48:01 — What the field needs1:54:30 — Appendix: Whiteboard lecture
• Mark Budde - How to speed up wet-lab biology 08.05.2026 57min

  Plasmidsaurus took plasmid sequencing from $600 to $15 and turned a "boring" service company idea into a hugely successful company serving 70,000+ scientists. In this episode, CEO Mark Budde and Niko McCarty get into the bigger question: what does it take for companies to automate and scale wet-lab biology methods in the same way that Plasmidsaurus did for sequencing? They cover the early Oxford Nanopore bet, the obsession with speed, and why Mark won’t sell customer data to AI labs. This podcast is made possible by Astera Institute.