Caenorhabditis elegans (stock image).
Credit: © heitipaves / Fotolia
Biophysicists have used an automated method
to model a living system -- the dynamics of a worm perceiving and
escaping pain. The Proceedings of the National Academy of Sciences (PNAS) published the results, which worked with data from experiments on the C. elegans roundworm.
"Our method is one of the first to use machine-learning tools on
experimental data to derive simple, interpretable equations of motion
for a living system," says Ilya Nemenman, senior author of the paper and
a professor of physics and biology at Emory University. "We now have
proof of principle that it can be done. The next step is to see if we
can apply our method to a more complicated system."
The model makes accurate predictions about the dynamics of the worm
behavior, and these predictions are biologically interpretable and have
been experimentally verified.
Collaborators on the paper include first author Bryan Daniels, a
theorist from Arizona State University, and co-author William Ryu, an
experimentalist from the University of Toronto.
The researchers used an algorithm, developed in 2015 by Daniels and
Nemenman, that teaches a computer how to efficiently search for the laws
that underlie natural dynamical systems, including complex biological
ones. They dubbed the algorithm "Sir Isaac," after one of the most
famous scientists of all time -- Sir Isaac Newton. Their long-term goal
is to develop the algorithm into a "robot scientist," to automate and
speed up the scientific method of forming quantitative hypotheses, then
testing them by looking at data and experiments.
While Newton's Three Laws of Motion can be used to predict dynamics
for mechanical systems, the biophysicists want to develop similar
predictive dynamical approaches that can be applied to living systems.
For the PNAS paper, they focused on the decision-making involved when C. elegans responds to a sensory stimulus. The data on C. elegans
had been previously gathered by the Ryu lab, which develops methods to
measure and analyze behavioral responses of the roundworm at the
holistic level, from basic motor gestures to long-term behavioral
programs.
C. elegans is a well-established laboratory animal model system. Most C. elegans
have only 302 neurons, few muscles and a limited repertoire of motion. A
sequence of experiments involved interrupting the forward movement of
individual C. elegans with a laser strike to the head. When the
laser strikes a worm, it withdraws, briefly accelerating backwards and
eventually returning to forward motion, usually in a different
direction. Individual worms respond differently. Some, for instance,
immediately reverse direction upon laser stimulus, while others pause
briefly before responding. Another variable in the experiments is the
intensity of the laser: Worms respond faster to hotter and more rapidly
rising temperatures.
The researchers fed the Sir Isaac platform the motion data from the
first few seconds of the experiments -- before and shortly after the
laser strikes a worm and it initially reacts. From this limited data,
the algorithm was able to capture the average responses that matched the
experimental results and also to predict the motion of the worm well
beyond these initial few seconds, generalizing from the limited
knowledge. The prediction left only 10 percent of the variability in the
worm motion that can be attributed to the laser stimulus unexplained.
This was twice as good as the best prior models, which were not aided by
automated inference.
"Predicting a worm's decision about when and how to move in response
to a stimulus is a lot more complicated than just calculating how a ball
will move when you kick it," Nemenman says. "Our algorithm had to
account for the complexities of sensory processing in the worms, the
neural activity in response to the stimuli, followed by the activation
of muscles and the forces that the activated muscles generate. It summed
all this up into a simple and elegant mathematical description."
The model derived by Sir Isaac was well-matched to the biology of C. elegans,
providing interpretable results for both the sensory processing and the
motor response, hinting at the potential of artificial intelligence to
aid in discovery of accurate and interpretable models of more complex
systems.
"It's a big step from making predictions about the behavior of a worm
to that of a human," Nemenman says, "but we hope that the worm can
serve as a kind of sandbox for testing out methods of automated
inference, such that Sir Isaac might one day directly benefit human
health. Much of science is about guessing the laws that govern natural
systems and then verifying those guesses through experiments. If we can
figure out how to use modern machine learning tools to help with the
guessing, that could greatly speed up research breakthroughs."
Journal Reference:
- Bryan C. Daniels, William S. Ryu, Ilya Nemenman. Automated, predictive, and interpretable inference of Caenorhabditis elegans escape dynamics. Proceedings of the National Academy of Sciences, 2019; 201816531 DOI: 10.1073/pnas.1816531116
Courtesy: ScienceDaily
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