Scientists have developed a new method to simulate the complex movements of animals with exceptional accuracy. The research team set out to solve a long-standing challenge in biology—how to accurately model the intricate and seemingly unpredictable movements of living organisms. They focused on the nematode worm Caenorhabditis elegans, a model organism widely used in biological research. The findings, published in PNAS, help predict and understand animal behavior, with potential applications ranging from robotics to medical research.
"Unlike simple physical systems like a pendulum or a bead on a spring, animal behavior exists in a space between regular and random actions. Capturing that delicate balance is very tricky and that’s what makes our model unique—no one has ever presented a model of an animal this lifelike before,” explained Prof. Greg Stephens, leader of the Biological Physics Theory Unit at the Okinawa Institute of Science and Technology (OIST).
Accurately mimicking real worm movements
“An animal's actions are influenced by many factors, including its internal state, environmental experiences, developmental history, and genetic inheritance. Expressing these influences in a simple, predictive model is remarkable and somewhat counterintuitive. This complexity, and our ability to model it effectively, is noteworthy," explained Dr. Antonio Costa, lead author at the Paris Brain Institute at Sorbonne University.
Creating the model was a complex process involving several steps. The team started by recording high-resolution videos of worm movements. They used machine learning techniques to identify the worm's shape in every video frame. They then analyzed how these shapes changed over time, to obtain a deeper understanding of worm behavior. Finally, they determined how much past data was needed to make reliable predictions.
"We compared statistical properties of real animal behavior, such as movement speed and frequency of behavioral changes, with those generated by our simulations,” Dr. Costa added. “The close match between these data sets demonstrates the high accuracy of our model."
Computer-modeled body movements of a simulated worm (top) and a real worm (bottom). The graphs show body movement patterns over time for both worms (left). On the right, moving images show how closely the simulated worm matches the actual worm's motion.
Costa et al., 2024
The researchers used their mathematical model to create a series of worm body shapes over time. They then applied principles of how objects move in fluids to calculate how these changing shapes would propel the worm forward. The result is a realistic simulation of worm movement through its environment.
Costa et al., 2024
Implications for medicine and robotics
The implications of this research extend far beyond the study of worms. The team is already communicating with companies who use this nematode worm to test the effect of chemical compounds on behavior. They are also applying the model to other species, including zebrafish larvae, which are frequently used in drug discovery research. Additionally, the researchers are exploring applications in human medicine, particularly in the study of movement disorders like Parkinson's disease.
The potential impact on medical research is significant. Current diagnostic methods for movement disorders often rely on subjective observations made during brief clinical visits. These changes might be too subtle for direct observation, which is part of what makes diagnosing these medical conditions challenging. This new approach could provide more continuous, objective measurements of patient movements, even in home settings, leading to more precise diagnoses and personalized treatment strategies.
Beyond medicine, the model could have applications in fields such as robotics, where achieving natural-looking movement has been a persistent challenge. By better understanding how animals navigate their environments, engineers may be able to design more adaptable and efficient robotic systems.
As the team continues to refine and expand their modeling techniques, they anticipate that this approach will open new avenues for understanding the intricate relationships between environmental factors, genetics, and behavior across a wide range of species.
