Even a form of brainless slime mould can make decisions about its next venture. What can it teach humans?
Learn how slime mould is able to function without a brain in this resource for Year 8, 9 and 10 Biology students learning about cells and living things.
Word Count: 636
Ever met someone who seems to get around without a brain? Maybe they aren’t as dumb as they seem. A team of US biologists have shown that even a brainless slime mould, Physarum polycephalum, can compute where to grow next based on mechanical cues.
“People are becoming more interested in Physarum because it doesn’t have a brain but it can still perform a lot of the behaviours that we associate with thinking, like solving mazes, learning new things, and predicting events,” says Nirosha Murugan, first author on a paper describing the research, published in Advanced Materials.
“Figuring out how proto-intelligent life manages to do this type of computation gives us more insight into the underpinnings of animal cognition and behaviour, including our own.”
Slime moulds are single-celled organisms that can operate collectively. Physarym is made of a structure called a syncytium: a single membrane with many cell nuclei inside it, all floating in the same cytoplasm. This bizarre organism can grow up to a metre long and favours rotting leaves and trees in its natural environment.
The researchers put Physarum in the middle of petri dishes, filled with agar gel. They placed different numbers of glass discs at the edges of the dishes, either next to each other or stacked atop one another, theorising that the mould would be attracted to the heavier objects in the gel.
Their theory was correct: the moulds grew out in an even pattern initially, then extended branches towards the heavy glass. Interestingly, the mould moved towards one glass disc and three stacked glass discs at the same rate, despite the stacked discs being heavier. Even more interestingly, the slime did prefer to grow towards three discs sitting next to each other, instead of the single disc.
This indicated to the researchers that the mould was “calculating” where to move next, and using something other than weight alone to make its decisions. Computer modelling demonstrated that the different discs were putting different mechanical strains on the gel, and thus, the slime mould.
“Imagine that you are driving on the highway at night and looking for a town to stop at. You see two different arrangements of light on the horizon: a single bright point, and a cluster of less-bright points. While the single point is brighter, the cluster of points lights up a wider area that is more likely to indicate a town, and so you head there,” says Dr Richard Novak, a lead staff engineer at Harvard University, US, and co-author on the study.
“The patterns of light in this example are analogous to the patterns of mechanical strain produced by different arrangements of mass in our model. Our experiments confirmed that Physarum can physically sense them and make decisions based on patterns rather than simply on signal intensity.”
Deeper: Smart Slime?
The researchers then went looking for the proteins in the slime that allowed it to make these calculations. They spotted a protein that was similar to those that help detect when cells stretch into different shapes – the moulds primary form of movement – in other organisms. When this protein was incapacitated with a drug, the mould could no longer figure out the heavier or lighter areas, because there was no indication the slime stretched and changed shape.
“Our discovery of this slime mould’s use of biomechanics to probe and react to its surrounding environment underscores how early this ability evolved in living organisms, and how closely related intelligence, behaviour and morphogenesis are,” says Mike Levin, a researcher at Harvard and Tufts University, US, and co-author on the paper.
“With most animals, we can’t see what’s changing inside the brain as the animal makes decisions. Physarum offers a really exciting scientific opportunity because we can observe its decisions about where to move in real-time by watching how its [intercellular] behaviour changes,” says Murugan.
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