How to move in the desired direction without a brain or nervous system? Single-celled organisms seemingly accomplish this feat without any problem: for example, they can swim to food using small flagellar tails.
How these extremely simple creatures manage to do this hasn’t been entirely clear until now. However, a research team from TU Wien (Vienna) have now been able to simulate this process on a computer: they have calculated the physical interaction between a very simple model organism and its environment. This environment is a liquid of non-uniform chemical composition, it contains unevenly distributed food sources.
The simulated organism was endowed with the ability to process information about food in its environment in a very simple way. Using a machine learning algorithm, the information processing of the virtual being was then modified and optimized in many evolutionary steps. The result was a computer organism that moved in its search for food in a very similar fashion to its biological counterparts.
Chemotaxis: always go where the chemistry is right
“At first glance, it’s surprising that such a simple model can solve such a difficult task,” says Andras Zöttl, who led the research project, which was carried out in the “Theory of Soft Matter” group (led by Gerhard Kahl) at the Institute for Theoretical Physics at TU Wien. “Bacteria can use receptors to determine in which direction, for example, the concentration of oxygen or nutrients is increasing, and that information then triggers movement in the desired direction. This is called chemotaxis.”
The behavior of other multicellular organisms can be explained by the interconnection of nerve cells. But a single-celled organism does not have nerve cells – in this case, only extremely simple processing steps are possible in the cell. Until now, it was not clear how such a low degree of complexity could be enough to link simple sensory impressions – for example from chemical sensors – to targeted motor activity.
“To be able to explain this, you need a realistic physical model for the movement of these unicellular organisms,” says Andreas Zöttl. “We chose the simplest possible model that physically allows independent movement in a fluid in the first place. Our unicellular organism is made up of three masses connected by simplified muscles. The question now arises: can these muscles be coordinated in such a way that the whole organism moves in the desired direction? And above all: can this process be carried out in a simple way or does it require complicated control? “
A small network of signals and controls
“Even if the unicellular organism does not have a network of nerve cells – the logical steps that connect its ‘sensory impressions’ to its movement can be described mathematically in the same way as a neural network,” says Benedikt Hartl, who used his expertise in artificial intelligence to implement the model on the computer. In the unicellular organism, too, there are logical connections between different parts of the cell. Chemical signals are triggered and ultimately lead to a certain movement of the body.
“These elements and the way they influence each other have been simulated on a computer and adjusted with a genetic algorithm: generation after generation, the movement strategy of virtual single-celled organisms has been slightly modified”, reports Maximilian Hübl, who numerous calculations on this subject as part of his master’s thesis. The single-celled organisms that were most successful in directing their movement to where the desired chemicals were found were allowed to “reproduce”, while the less successful variants “died out.” In this way, after many generations, a control network has emerged – very similar to biological evolution – which allows a virtual single-celled organism to convert chemical perceptions into targeted movement in an extremely simple way and with very circuitry. basic.
Random oscillation movement – but with a concrete purpose
“You shouldn’t think of it as a highly developed animal that consciously senses something and then runs towards it,” says Andreas Zöttl. “It’s more like a random oscillating motion. But a motion that ultimately leads in the right direction on average. And that’s exactly what you observe with single-celled organisms in nature.”
Computer simulations and algorithmic concepts recently published in the famous journal PNAS prove that a minimal degree of complexity of the control network is in fact sufficient to implement relatively complex apparently complex movement patterns. If the physical conditions are properly taken into account, remarkably simple internal machinery suffices to reproduce in the model exactly the movements known to nature.