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by Jordana Cepelewicz from QuantaMagazine Website
moves and responds to its environment with agility and seeming purpose, yet it has no neurons or muscles to coordinate its movements. New work shows that biomechanical interactions among the animal's cilia are sufficient
to
explain how it moves.
Biomechanical interactions, rather than neurons, control the movements of one of the simplest animals. The discovery offers a glimpse into how animal behavior worked before neurons evolved...
The animal beneath the lenses wasn't much to look at, resembling an amoeba more than anything else:
It moved on thousands of cilia that blanketed its underside to form the "sticky hairy plate" that inspired its Latin name, Trichoplax adhaerens.
This odd marine creature, classified as a placozoa, has practically an entire branch on the evolutionary tree of life to itself, as well as the smallest known genome in the animal kingdom.
But what intrigued Prakash most was the well-orchestrated grace, agility and efficiency with which the thousands to millions of cells in Trichoplax moved.
After all, such coordination usually requires neurons and muscles - and Trichoplax has neither...
Prakash later teamed up with Matthew Storm Bull, then his graduate student at Stanford University, to make this strange organism the star of an ambitious project aimed at understanding how neuromuscular systems might have evolved - and how early multicellular creatures managed to move, find food and reproduce before neurons existed.
In a trio of preprints totaling more than 100 pages - posted simultaneously on the arxiv.org server last year - he and Bull showed that the behavior of Trichoplax could be described entirely in the language of physics and dynamical systems.
Mechanical interactions that began at the level of a single cilium, and then multiplied over millions of cells and extended to higher levels of structure, fully explained the coordinated locomotion of the entire animal.
The organism doesn't "choose" what to do...
The researchers even showed that the cilia's dynamics exhibit properties that are commonly seen as distinctive hallmarks of neurons.
For years, a pair of scientists have studied how a simple multicellular animal called Trichoplax coordinates its complex behavior without neurons or muscles. Their work shows that mechanical interactions alone can explain how the organism moves,
seeks food and reproduces. for Quanta Magazine
The work not only demonstrates how simple mechanical interactions can generate incredible complexity, but also tells a compelling story about what might have predated the evolution of the nervous system.
The findings have already started to inspire the design of mechanical machines and robots, and perhaps even a new way of thinking about the role of nervous systems in animal behavior.
The Border Between Simple and Complex
Brains are overrated.
In the fields known as "soft robotics" and "active matter," research has demonstrated that the right mechanical dynamics can be all that's needed to accomplish complex tasks without centralized control.
In fact, single cells alone are capable of remarkable behaviors, and they can self-assemble into collective systems (such as slime molds or xenobots) that can achieve even more, all without the help of neurons or muscles.
But is that possible at the scale of an entire multicellular animal?
Trichoplax was a perfect case study:
Observing it,
It spins and moves across surfaces.
It clamps itself down over patches of algae to trap and consume them for food.
It reproduces asexually by rending itself in two.
Manu Prakash, a biophysicist at Stanford University, became famous for his work developing the Foldscope, an easily assembled $1 microscope. Now his research is illuminating how complex behaviors can emerge from simple systems of cells, and how those systems can serve
as inspirations for building better machines.
A middle ground between single cells and animals with muscles and nervous systems seemed like the perfect place for Prakash and Bull to ask their questions.
...a playground for testing hypotheses and a cradle of potential insight.
Prakash first built novel microscopes that could examine Trichoplax from below and from the side, and figured out how to track the high-speed movement of its cilia.
(This wasn't entirely new territory for him, as he was already famous for his work developing the Foldscope, an easily assembled microscope that costs less than $1 to build.)
He could then see and track millions of individual cilia, each appearing as a tiny spark in the microscope's field of view for a fraction of a second at a time.
He - and later Bull, who joined his lab six years ago - spent hours watching patterns in the orientation of those little footprints.
For these complex patterns to be possible, the scientists knew that the cilia must be engaging in some kind of long-distance communication.
And so they started putting together the pieces, until last year they finally decided they had their story.
Walking on Autopilot
Prakash and Bull started off expecting the cilia to glide over surfaces, with a thin layer of fluid separating animal and substrate.
After all, cilia are typically seen in the context of fluids:
But when the researchers looked through their microscopes, they saw that the cilia seemed to walk, not swim.
While some single-celled organisms have been known to use cilia to crawl, Wan noted, this kind of coordination had never been observed at this scale.
This magnified side view of Trichoplax crawling across a surface shows how the thousands of cilia on its underside move with a walking gait. The collective movements of the cilia are coordinated entirely by
mechanical interactions.
So Prakash, Bull and Laurel Kroo, a Stanford graduate student in mechanical engineering, set out to characterize the cilia's walking gait.
They traced the trajectory of the tip of each individual cilium over time, watching it sweep out circles and push against surfaces.
They defined three types of interactions:
In their models, the walking activity emerged naturally from the interplay between the internal driving forces of the cilia and the energy of their adhesion to the surface.
The right balance between those two parameters (calculated from experimental measurements of the cilia's orientation, height and beat frequency) resulted in regular locomotion, with each cilium sticking and then lifting away, like a leg.
The wrong balance produced the slipping or stalled phases.
Moreover, the walking could be modeled as an excitable system - a system in which, under certain conditions, signals spread and get amplified rather than progressively damping out and coming to a stop.
A neuron is a classic example of an excitable system:
The same phenomenon seemed to occur in the cilia.
In experiments and simulations, small perturbations in height, rather than voltage, led to relatively large changes in the activity of nearby cilia:
In fact, Prakash, Bull and Kroo's cilia models turned out to map very well onto established models for action potentials in neurons.
Sponberg agreed.
Cilia Flocking Like Birds
With this mathematical description in hand, Prakash and Bull looked at how each cilium pushed and pulled on its neighbors as it interacted with the surface - and how all that independent activity could coalesce into something synchronized and coherent.
They measured how the mechanical gait of each cilium led to small, local fluctuations in the height of the tissue.
They then wrote down equations for how this would tug at nearby cells to affect their behavior, even as the cilia on those cells cycled through movements of their own - like a network of springs tying together tiny oscillating motors.
When the researchers modeled "this dance between elasticity and activity," Prakash said, they saw that the mechanical interactions - of cilia pushing against a substrate and cells tugging at each other - transmitted information rapidly across the organism.
Stimulating one region led to waves of synchronized cilia orientation that moved through the tissue.
And the synchronized orientation patterns could be complex:
In other cases, the cilia reoriented in fractions of a second, first pointing one way and then another - flocking as a group of starlings or a school of fish might, and resulting in an agility that made it possible for the animal to sometimes change direction on a dime.
Prakash spends long hours collecting samples of Trichoplax
in Pacific Grove, California.
The agile flocking was particularly intriguing.
Flocking typically occurs in systems that act like fluids:
But that can't happen in Trichoplax, because its cilia are components of cells that have fixed positions.
The cilia move as "a solid flock," said Ricard Alert, a physicist at the Max Planck Institute for the Physics of Complex Systems in Germany.
Prakash and Bull also found in their simulations that the information transmission was selective:
Eventually, Prakash and Bull found that they could write down a set of mechanistic rules for when Trichoplax might spin in place or move about in lopsided circles, when it might follow a straight path or suddenly veer to the left, and when it might even use its own mechanics to rip itself into two separate organisms.
He speculates that the animal might be taking advantage of these spinning and crawling dynamics as part of a "run and tumble" strategy for finding food or other resources in its environment.
If further studies show that's true,
The mechanism would be bridging scales, not just from a molecular structure to a tissue to an organism, but to ecology as well.
In fact, for many researchers, that's a big part of what makes this work unique and compelling.
Usually, physics-based approaches to biological systems might describe activity at one or two scales of complexity, but not at the level of behavior for an entire animal.
As a doctoral student at Stanford University, Matthew Storm Bull studied and modeled the behavior of Trichoplax using the language of
physics and dynamical systems.
It's built on a foundation of excitability, it's constantly striking a careful balance between sensitivity and stability, and it's capable of complex collective behaviors.
That has implications for how neuroscientists think about the connection between neural activity and behavior more generally.
If mechanics alone can fully account for some simple behaviors, then neuroscientists may want to look more closely at how the nervous system takes advantage of an animal's biophysics to pull off complex behaviors in other situations.
A Step in Multicellularity
Alert agreed.
Solé sees Trichoplax as occupying a,
The animal seems to be starting to put in place,
Prakash, Bull and their collaborators are now looking at whether Trichoplax might be capable of other kinds of behaviors or even learning.
The researchers are also using some of the principles they uncovered to build what they call "perceptive machines" - robotic systems and smart materials that perform certain tasks without centralized control by exploiting their mechanical properties.
Students in Prakash's laboratory have already started to build working examples of those machines.
Kroo, for instance, has constructed a robotic swimming device driven by a viscoelastic material called active foam:
Prakash sees this as just the first chapter in what will likely be a decades-long saga...
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