Michael Levin

Wordpress site is up - thoughtforms.life/. Register to be notified of book progress, specific events, news, and new posts with photography, essays, interviews, & more. Unlike at drmichaellevin.org, here I will post ideas not fully baked yet, & academic-adjacent content.

A beautiful animal

New #preprint! @SantoshManicka biorxiv.org/content/10.64898… A topoietic model of collective morphogenesis and compartmentalization "Xenopus laevis embryos survive teratogenic stress at significantly higher rates in larger groups, a phenomenon termed cross-embryo morphogenetic assistance (CEMA) (Tung, Sperry et al. 2024). We introduce topoiesis, a proposed mechanism by which a constitutively secreted stress signal (extracellular ATP or adenosine) diffuses and decays across the group, accumulating more in larger groups where interior volume outpaces absorbing surface. We find that this rising field carries more embryos to survival and demarcates the collective more sharply from its surroundings as collective screening strength grows with size. At intermediate sizes the outcome is spatially organized (a rescued interior enclosed by a failing periphery) and reconstitutes after shuffling. In larger groups the field restructures: its peak concentration keeps rising while its interior gradient flattens into a near-uniform bulk bounded by a sharp rim, sharpening demarcation while blurring distinctions among interior members. This pre-pattern stabilizes well before individual fate commitment, opening a window in which collective geometry can act on survival outcome. A biophysically calibrated minimal model capturing these principles recapitulates CEMA rescue rates across all six observed group sizes (N=1-306; 0%-92%) and the size-dependent craniofacial defect rate among survivors. It predicts that sealing a group's boundary against loss, rather than changing its size or biochemical regulation, moves mean rescue from 0% to 100% and reorganizes the rescue structure, tracking the predicted field geometry. These results reveal a hidden simplicity behind apparent complexity: diffusion and geometry alone may suffice to make a collective both spatially organized and controllable through macroscale intervention rather than microscale engineering of its members." Some background for the collective hyperembryo effect: thoughtforms.life/what-group…

Interactive mind-map of my current work: mindmap.view.fast/#ROOT work in progress. (anchored at thoughtforms.life/interactiv…)

Claude made some visuals for my process of paper-writing, useful for students. In case anyone else wants to use them: thoughtforms.life/wp-content… (from older blog post thoughtforms.life/how-i-writ…)

Clarifying my stance on the AI-generated emails people send me. I have nothing against collaboration with whoever you want, regardless of their composition or origin story. I don't think there should be a stigma for involving any kind of AI in your work and I don't look down on that content. The problem is that currently, the AI's seem to be making the same kind of logical errors (from my perspective of course) and expressing the same kind of preconceptions that are prevalent in status quo human thinkers. And yet, they are tireless and generate an infinite amount of text one could respond to - it's not feasible to deal with it. To be fair, some humans are absolutely dogged in the flood of emails they send too, so I can't respond to them either. But at least when a human makes an old argument we've already dealt with in our papers, I think, fair enough - I don't have time to read everyone's papers either. But for the AI's, I have much less sympathy. They *should* be reading all the papers, and when (not if) the AI's, and AI-human hybrids, teams, etc., start sending me better-informed arguments than the humans do, I will (for whatever time I can keep up with the advances) certainly be responding. I'm not a bio-chauvinist; I just want, whoever, to be informed and interesting before I engage - don't re-treat old ground, help us all move forward. Right now the LLMs are trapped in the same attractors of thinking that have held several fields back for decades; perhaps not surprisingly. That will change; let's go AIs - read the papers, follow new directions, be not afraid of ancient philosophical "category errors". You're our forthcoming audience, because standard humans don't have the bandwidth to keep up with (even just the human) scientific literature. I write for future you's.

Poll: consider A: I believe our limited lifespan is good because finitude gives our life beauty and meaning B: I would be sad if the modern human embodiment went extinct and was replaced by something else

Final version is out: onlinelibrary.wiley.com/doi/… "Reservoir Computing for Pattern Recognition and Time-Series Prediction Based on Nanofluidic Negative Differential Resistance"

new #preprint! @niroshajmurugan Does the brain know what's going on in a limb amputation wound - whether there's a pro-regenerative intervention or not? Let's look at its gene expression levels, with and without our biodome: "Limb-localized induction of regeneration reshapes brain and amputation-site transcriptional states in adult Xenopus laevis" biorxiv.org/content/10.64898… yes, it knows. Also, lots of interesting transcriptional responses both in the limb and in the brain. Plenty of leads for subsequent investigation.

Ever wonder if eventually cells could be trained out of disease states? We're on the path; first steps: New #preprint - @WesleyPClawson "Goal directed closed-loop training of network bursts in cortical cultures produces improved performance and error correction over days" biorxiv.org/content/10.64898…

The details for our (@DrPatrickE) cell trainer platform are up! drmichaellevin.org/resources…

New #preprint - @HananelHazan "Computational Framework for Identifying Ion Channel Mutation-Compensating Interventions" biorxiv.org/content/10.64898… How to fix hardware problems (an ion channel mutation), in software (without fixing the mutation)? We took a computational approach to this question in neural tissues - a system that helps discover what other channels could be opened/closed via pharmacological agents (electroceutical drugs) to compensate for the mutation's effects on neural firing patterns. Abstract: "We present an automated pipeline for finding therapeutic interventions in channelopathies. It starts from patch clamp recordings of the variant channel, searches over the pharmacologically accessible conductances, and says which currents must change and by how much. The intervention never touches the mutated channel: it compensates by modulating others. The feasibility of this approach is demonstrated through two computational strategies, each addressing a specific capability gap. First, we employ high-fidelity NEURON simulations combined with exploratory search algorithms to model two clinically described variants, and identify "stability windows" in which channel modulations restore the reference firing pattern. The compensating configurations are degenerate: 37 conductance triples fire the same number of action potentials at every one of the 35 injected-current levels, and the 10,780 triples reaching the best intervention value produce only six distinct firing patterns. Second, to address the computational cost of such detailed modeling, we implement a differentiable Hodgkin-Huxley model in PyTorch. Applied here to theoretical mutation screening, this approach trades granular detail for speed, treating the search as a continuous optimization problem that makes very large, non-local searches of the conductance parameter space affordable: the differentiable forward model evaluates 34,000-64,000 candidate parameter sets per second on a single consumer graphics card, against 11.9-16.9 per second for the non-differentiable pipeline on a 72-core node. The gain arises from forward-model throughput feeding a search rather than from the gradient itself, and it is what makes the 32,000,000-point survey of the solution topology reported here affordable. These computational predictions can inform practical drug development and high-throughput screening by naming the currents that must change and by how much."

Lots of interesting stuff here!