Researchers have discovered ‘thinking’ capabilities in ‘action’ molecules, suggesting new computation models that do not involve circuits but instead rely on phase diagrams for more efficient compute power.
A traditional view of cells follows our understanding of the separation between the brain and muscles in that, within cells, it is thought that there are different molecules for sensing, decision-making and action that work together. However, researchers at Maynooth University, in collaboration with international colleagues, have published a new study which suggests that all these tasks can be accomplished by the action or ‘muscle’ molecule.
The researchers examined nucleation during self-assembly of multicomponent structures. Nucleation is the mechanism by which substances transition to another thermodynamic phase, for example when sugar is supersaturated in water, nucleation occurs allowing sugar molecules to stick together and form large crystal structures.
“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’.”
To test this theory of nucleation-based decision-making, the team used DNA nanotechnology (a field pioneered by Prof Erik Winfree, who is a co-author of the study) because DNA allows for the study of nucleation in complex mixtures of thousands of kinds of molecules in order to understand the impact of how many kinds of molecules there are and what kinds of interactions they have.
The theory in this work drew mathematical analogies between multicomponent systems and the theory of neural networks. The experiments pointed to how these multicomponent systems might learn the right computational properties through a physical process, much like the brain learns to associate different smells with different actions.
The experiments revealed that this muscle-based decision-making was surprisingly robust and scalable, with relatively simple experiments solving pattern recognition problems that were nearly 10 times larger than earlier circuit-based approaches. In each case, the molecules came together to build different nanometre-scale structures in response to different chemical patterns.
Dr Constantine Evans, research fellow at Maynooth University and lead author of the study, explained that it is like walking into a house and smelling freshly baked cookies, versus smelling burning rubber. “Your brain would alter your behaviour depending on you sensing different combinations of odourful 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.
This research suggests news ways of understanding computation that does not need circuits to perform tasks but instead can use phase diagrams.
In physics, phase diagrams show the limiting conditions for solid, liquid and gaseous phases of a single substance or of a mixture of substances while undergoing changes in pressure and temperature or in some other combination of variables, such as solubility and temperature. As the team explains: “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 multicomponent systems,” said Dr Jackson O’Brien from the University of Chicago.
“Whereas some biological systems may, like modern modular engineering, isolate information processing from the physical subsystems being controlled, other critical decision-making may be embedded within and inseparable from processes such as protein synthesis, metabolism, self-assembly and structural reconfiguration,” the researchers wrote.
“Understanding such physically entangled computation is necessary not only for understanding biology, but also for engineering autonomous molecular systems such as artificial cells, in which it is essential to pack as much capability as possible within limited space and energy budgets.”
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