Simulated interacting agents collectively navigate towards a target. Image: Tel Aviv University |
Much to humans’ chagrin, bacteria
have superior survival skills. Their decision-making processes and collective
behaviors allow them to thrive and even spread efficiently in difficult
environments.
Now researchers at Tel Aviv University have developed a
computational model that better explains how bacteria move in a swarm—and this
model can be applied to man-made technologies, including computers, artificial
intelligence, and robotics. PhD student Adi Shklarsh—with her supervisor Professor
Eshel Ben-Jacob of TAU’s Sackler School of Physics and Astronomy, Gil Ariel
from Bar Ilan University, and Elad Schneidman from the Weizmann Institute of
Science—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 microrobots 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 behaviors. The research was recently published in PLoS Computational Biology.
A dash of
bacterial self-confidence
Bacteria aren’t the only organisms that travel in swarms, says
Shklarsh. Fish, bees, and birds also exhibit collective navigation. But as
simple organisms with less sophisticated receptors, bacteria are not as
well-equipped to deal with large amounts of information or “noise” in
the complex environments they navigate, such as human tissue. The assumption
has been, she says, 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. Their secret? A liberal amount of self-confidence.
Many animal swarms, Shklarsh explains, 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,” Ben-Jacob
says. “When an individual bacterium finds a more beneficial path, it pays
less attention to the signals from the other cells. But at other times, upon
encountering challenging paths, the individual cell will increase its
interaction with the other cells and learn from its peers. Since each of the
cells adopts the same strategy, the group as a whole is able to find an optimal
trajectory in an extremely complex terrain.”
Benefitting
from short-term memory
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, says Shklarsh, It’s also a principle that can
be used to design new and more efficient technologies.
Robots are often
required to navigate complex environments, such as terrains in space, deep in
the sea, or the online world, and communicate their findings among themselves.
Currently, this is based on complex algorithms and data structures that use a
great deal of computer resources. Understanding the secrets of bacteria swarms,
Shklarsh concludes, can provide crucial hints towards the design of new
generation robots that are programmed to perform adjustable interactions
without taking up a great amount of data or memory.
