Run (and Tumble) to Dinner

Date:
February 16, 2022
Source:
Institute of Industrial Science, The University of Tokyo
Summary:
Researchers calculated the optimal search strategy for organisms
that employ run-and-tumble motion when looking for a food's
odor. They determined that the chemotaxis demonstrated by E. coli
closely resembles this system when accounting for the costs of
control and the noise of the environment. This work may lead to
new methods of designing chemical- seeking drones or nanobots.



FULL STORY ========================================================================== Researchers from The University of Tokyo calculated the optimal search
strategy for organisms that employ run-and-tumble motion when looking
for a food's odor.

They determined that the chemotaxis demonstrated by E. coli closely
resembles this system when accounting for the costs of control and the
noise of the environment. This work may lead to new methods of designing chemical-seeking drones or nanobots.


==========================================================================
The aroma of a favorite dessert can tempt almost anyone to follow the
scent. By moving in the direction of increasing smell, one can often
locate the desired confection. It turns out that even simple organisms,
like the single-celled E.

coli bacterium, can use a similar method to detect and move toward
food. Now, researchers have developed a theoretical model for the best
possible search strategy when searching for source of the scent, which
may help in the design of new drones or nanobots that can find their
own way to a chemical target.

Scientists from the Institute of Industrial Science, The University
of Tokyo have studied the odor-searching strategy used by organisms
ranging from bacteria to multicellular eukaryotes, which perform
"chemotaxis." Chemotaxis is the process of attraction in the direction
of a chemical gradient, and it takes several forms. E. coli bacteria
use the common approach called "run-and- tumble," in which periods of
forward swimming are interrupted by rotations that randomly change the direction of motion. Although linear control theory has become part of
the established practice of engineering, it does not suffice to handle
the nonlinearity and large noise seen in biological systems. A more
tailored theory is needed to better understand this phenomenon.

The research team used stochastic optimal control theory to find the best possible fully nonlinear sensing and control strategy of run-and-tumble
motion in environments with noisy chemical gradients. They modeled the
internal control using a partially observable Markov decision process. In
this framework, agents cannot directly observe the correct solution,
but they can update their beliefs by sensing their environment.

To make the model as realistic as possible, the researchers included
a control cost that represents the physical limitations of regulating
when tumbling occurs. "The correspondence between our optimal solution
and biochemical bacterial models demonstrates the applicability of our theoretical framework to the understanding of biological search systems,"
says first author Kento Nakamura. The primary way that organisms control
their motion and progressively move toward a target is by inhibiting
tumbling when sensing that the chemical concentration is increasing
along their current direction.

This work opens the way for new kinds of autonomous pathfinding algorithms
that can be employed to find specific targets, even if their exact
locations are unknown. "Understanding the internal control mechanisms of biological organisms would be helpful when designing biomimetic robots
that can take advantage of these systems," says senior author Tetsuya
J. Kobayashi.

========================================================================== Story Source: Materials provided by Institute_of_Industrial_Science,_The_University_of_Tokyo.

Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Kento Nakamura, Tetsuya J. Kobayashi. Optimal sensing and control
of run-
and-tumble chemotaxis. Physical Review Research, 2022; 4 (1) DOI:
10.1103/PhysRevResearch.4.013120 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/02/220216095844.htm

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