Breaking the Brain-Muscle Barrier: Scientists Discover Hidden Neural Network-Like Abilities of Self-Assembling Molecules

We tend to separate the brain and muscle – the brain does the thinking; the muscle does the doing. The brain takes in complex information about the world, makes decisions, while muscle merely executes. This distinction extends to our understanding of cellular processes, where certain molecules within cells are perceived as the ‘thinkers’, processing information from the chemical environment to determine necessary actions for survival, while others are viewed as the ‘muscle’, constructing the essential structures for the cell’s survival.

But a new study shows how the molecules that build structures, i.e, the muscle, can themselves do both the thinking and the doing. The study, by scientists at Maynooth University, the University of Chicago, and California Institute of Technology was published in the journal Nature.

“We show that a natural molecular process – nucleation – that has been studied as a `muscle’ for a long time can do complex calculations that rival a simple neural network,” said University of Chicago Associate Professor Arvind Murugan, one of the two senior co-authors on the paper. “It’s an ability hidden in plain sight that evolution can exploit in cells to do more with less; the `doing’ molecules can also do the `thinking.’”

Thinking using physics

Cells need to recognize the environment they are in and do different things to survive. For example, some combinations of molecules might indicate a time of stress that requires hunkering down while other combinations of molecules might indicate a time of plenty. But the difference between these molecular signals can be subtle – different environments might involve the same molecules but in different proportions.

Dr Constantine Evans, Research Fellow at the Hamilton Institute, Maynooth University, the lead author of the study, explained that it is a bit like walking into a house and smelling freshly baked cookies, versus smelling burning rubber. “Your brain would alter your behavior depending on you sensing different combinations of odorful chemicals. We set out to ask if just the physics of a molecular system can do the same, despite not having a brain of any kind,” he said.

The traditional view has been that cells might be able to sense and respond in this way using molecular circuits that conceptually resemble electronic circuits in your laptop; some molecules sense, other molecules make a decision on what to do, and finally `muscle’ molecules carry out an action (e.g., build a structure).

The alternative idea explored here is that all of these tasks – sensing, decision making, response – can be accomplished in one step by the physics inherent to the `muscle’ itself. The physics involved in this study is that of “phase transitions” – think of a glass of water freezing when it hits 0 °C; first, a little fragment of ice `nucleates’ and then grows out until the whole glass of water is frozen.

On the face of it, these initial steps in the act of “freezing” – nucleation – do not resemble `thinking’. But this work shows that the act of freezing can “recognize” subtly different chemical combinations – e.g., the smell of oatmeal raisin cookies vs chocolate chip – and build different molecular structures in response.

Robustness in experiments

The authors tested the robustness of nucleation-based decision-making using DNA nanotechnology, a field that Prof Erik Winfree helped pioneer. “The theory is general and should apply to any kind of molecule. But DNA lets us experimentally study nucleation in complex mixtures of thousands of kinds of molecules and systematically understand the impact of how many kinds of molecules there are and what kinds of interactions they have”, explained Erik.

The experiment revealed a few surprises – `muscle’-based decision making was surprisingly robust and scalable. Complications not modeled in theory, such as running out of molecules during the experiment, turned out to help rather than hurt. As a result, relatively simple experiments solved pattern recognition problems involving around a thousand kinds of molecules, nearly 10-fold larger than in earlier circuit-based approaches. In each case, the molecules came together to build different nanometer-scale structures in response to different chemical patterns – except the act of building the structure in itself made the decision on what to build.

The work points to a new view of computation that does not involve designing circuits but rather designing what physicists call a ‘phase diagram’; e.g., for water, a phase diagram might describe the temperature and pressure conditions in which liquid water will freeze or boil. Conventionally, phase diagrams are seen as describing `muscle’-like material properties. But this work shows that the phase diagram can also encode `thinking’ in addition to `doing’ when scaled up to complex systems with many different kinds of components.

“Physicists have traditionally studied things like a glass of water which has many molecules but all of them are identical. But a living cell is full of many different kinds of molecules that interact with each other in complex ways. This results in distinct emergent capabilities of multi-component systems,” said Dr Jackson O’Brien, who was involved in the study as a University of Chicago graduate student in physics. The theory in this work drew mathematical analogies between such multi-component systems and the theory of neural networks; the experiments pointed to how these multi-component systems might learn the right computational properties through a physical process, much like the brain learns to associate different smells with different actions.

While the experiments here involved DNA molecules in a test tube, the underlying concepts – nucleation in systems with many kinds of components – applies broadly to many other molecular and physical systems. The authors hope this work will spur work to uncover hidden `thinking’ abilities in other multi-component systems that currently appear to merely be `muscles.’

Reference: “Pattern recognition in the nucleation kinetics of non-equilibrium self-assembly” by Constantine Glen Evans, Jackson O’Brien, Erik Winfree and Arvind Murugan, 17 January 2024, Nature.
DOI: 10.1038/s41586-023-06890-z

Funding was provided by the National Science Foundation (USA), the Evans Foundation for Molecular Medicine, the European Research Council, Science Foundation Ireland, the University of Chicago Materials Research Science and Engineering Center, the Simons Foundation, and the Carver Mead New Adventures Fund.