Biophysicists have used an automated method to model a living system—the dynamics of a roundworm perceiving and escaping pain.
“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 senior author Ilya Nemenman, 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 worm behavior that are biologically interpretable and experimentally verified.
The researchers used an algorithm, which Nemenman and first author Bryan Daniels, a theorist from Arizona State University, developed in 2015 and dubbed “Sir Isaac,” after one of the most famous scientists of all time—Sir Isaac Newton.
As a long-term goal, the scientists want to develop the algorithm into a “robot scientist,” to automate and speed up the scientific method of forming quantitative hypotheses, then look at data and experiments to test them.
While Newton’s Three Laws of Motion can predict dynamics for mechanical systems, the biophysicists want to develop similar predictive dynamical approaches that can apply to living systems.
Wiggle on or turn back?
For the paper, which appears in the Proceedings of the National Academy of Sciences, they focused on the decision-making involved when C. elegans responds to a sensory stimulus.
Coauthor William Ryu, an experimentalist from the University of Toronto, previously gathered the data on C. elegans. Ryu’s lab 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.
For the study, 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 attributable to the laser stimulus unexplained. This was twice as good as the best prior models, which didn’t use 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.”
Speed up the breakthroughs
The model Sir Isaac derived matched well 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.”
Source: Emory University