Tel Aviv University researchers have developed a computational model that explains how bacteria move in a swarm, which can be applied to man-made technologies, including computers, artificial intelligence, and robotics.
Ph.D. student Adi Shklarsh of TAU has discovered how bacteria collectively gather information about their environment and find an optimal path to growth even in the most complex terrains.
Studying the principles of bacteria navigation will allow researchers to design a new generation of smart robots that can form intelligent swarms, aid in the development of medical micro-robots used to diagnose or distribute medications in the body, or “de-code” systems used in social networks and throughout the Internet to gather information on consumer behaviours.
The assumption has been, Shklarsh said, that bacteria would be at a disadvantage compared to other swarming organisms.
But in a surprising discovery, the researchers found that computationally, bacteria actually have superior survival tactics, finding “food” and avoiding harm more easily than swarms such as amoeba or fish.
Many animal swarms, Shklarsh noted, can be harmed by “erroneous positive feedback,” a common side effect of navigating complex terrains. This occurs when a subgroup of the swarm, based on wrong information, leads the entire group in the wrong direction.
But bacteria communicate differently, through molecular, chemical and mechanical means, and can avoid this pitfall.
Based on confidence in their own information and decisions, “bacteria can adjust their interactions with their peers,” Prof. Ben-Jacob stated.
In the computer model developed by the TAU researchers, bacteria decreased their peers’ influence while navigating in a beneficial direction, but listened to each other when they sensed they were failing.
This is not only a superior way to operate, but a simple one as well. Such a model shows how a swarm can perform optimally with only simple computational abilities and short-term memory.
It’s also a principle that can be used to design new and more efficient technologies, said Shklarsh.
The research was recently published in PLoS Computational Biology.