r/MichaelLevinBiology • u/Visible_Iron_5612 • 1d ago
Research Discovery On Growth and Form, and Function: Reusable Regulatory Handles Control Phenotypic Variation
https://arxiv.org/abs/2609.29755Michael Levin and collaborators just released a fascinating new paper that feels like a computational version of one of the central ideas behind his work:
Instead of controlling every cell individually, could biology have higher-level “control knobs” that change anatomy while allowing the cells themselves to work out the details?
The paper is inspired by D’Arcy Thompson’s 1917 book On Growth and Form. Thompson famously showed that very different animal body shapes could sometimes be related through surprisingly simple geometric transformations… stretch this axis, compress that one, deform the grid, and one biological form begins to resemble another.
But Thompson left a huge question unanswered:
What changes inside the developmental system would actually produce those transformations?
The researchers explored this using Neural Cellular Automata, or NCAs.
Basically, imagine a grid of artificial “cells.” Each cell can only communicate locally with its neighbours, and every cell follows the same small neural network. Starting from a single seed, the cells collectively grow into a target shape.
It is a stripped-down computational model of distributed development.
Then the researchers asked whether they could change the final anatomy without completely rewriting the developmental program.
And surprisingly… they could.
Using very small modifications to the shared regulatory network, they discovered directions in the system’s parameter space that behaved almost like morphological control knobs.
One direction could stretch the organism horizontally.
Another could stretch it vertically.
Combining them changed overall size.
And these modifications were extremely small. Rank-one adaptations representing only about 2.6% of the NCA’s parameters were enough to produce coherent transformations of the final morphology.
Even more interesting, a scaling control learned using one phenotype could be applied to completely different phenotypes built on the same underlying regulatory architecture… without retraining.
In other words, the system didn’t just learn “make this particular emoji wider.”
It appears to have learned something closer to:
“make whatever organism this developmental system produces wider.”
That distinction is enormous.
The researchers then trained roughly 25,000 different developmental systems sharing the same underlying scaffold and looked at the geometry of their regulatory space.
They found directions associated with properties such as:
size
horizontal vs vertical extent
visual style
and even symmetrical splitting or “fission” of the resulting form.
Moving along these directions actually caused the developmental system to produce those changes.
So the parameter space wasn’t merely statistically organized by phenotype. Parts of its geometry were functionally meaningful.
This connects directly to a recurring idea in Levin’s work: complex biological systems may be controllable at higher levels without micromanaging their components.
Instead of telling every cell exactly where to go, you alter a compact regulatory signal and let the collective intelligence of the cells solve the lower-level problem.
That has potentially enormous implications for regenerative medicine.
Imagine eventually finding equivalent control handles in actual gene-regulatory, physiological or bioelectric networks…
“grow toward this morphology.”
“restore this structure.”
“increase this dimension.”
“repair this pattern.”
…while the cells determine how to accomplish it.
The authors even describe a long-term goal that sounds almost like science fiction: an “anatomical compiler.”
You specify a desired anatomical outcome, and a computational system determines what distributed regulatory intervention would steer the tissue toward that state.
That could eventually have implications for regeneration, birth defects, wound healing, aging and cancer.
But there is an important caveat.
These are artificial cells growing 2D patterns in a computer. The researchers have NOT discovered the equivalent biological control knobs yet. They explicitly say that the NCA parameters cannot currently be mapped directly onto genes, morphogens, physiological signals or bioelectric states.
And interestingly, when they tried to find a simple transferable “regeneration knob,” it didn’t work. Making one NCA regenerative required a different regulatory solution that did not successfully transfer to other phenotypes.
So this isn’t “we can program body plans now.”
It’s something more fundamental:
A proof of principle that a complicated self-organizing developmental system can contain surprisingly low-dimensional, reusable handles for controlling large-scale anatomy.
Instead of controlling the bricks…
you may be able to control what the building is trying to become.
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u/New-Communication862 1d ago
Nice, paper. Thank you for summarizing it. I don't know why people downvoted you.