A Morphologically Realistic Biomechanical Model of a Fly

NeuroMechFly, the primary correct “digital twin” of the fly Drosophila melanogaster, affords a extremely worthwhile testbed for research that advance biomechanics and biorobotics. This might assist pave the best way for fly-like robots, such because the one illustrated right here. Credit score: EPFL

A Digital Twin of Drosophila

“We used two kinds of data to build NeuroMechFly,” says Professor Pavan Ramdya on the Faculty of Life Sciences at Ecole Polytechnique Fédérale de Lausanne (EPFL). “First, we took a real fly and performed a CT scan to build a morphologically realistic biomechanical model. The second source of data was the real limb movements of the fly, obtained using pose estimation software that we’ve developed in the last couple of years that allow us to precisely track the movements of the animal.”

Ramdya’s group, working with the group of Professor Auke Ijspeert at EPFL’s Biorobotics Laboratory, is publishing a paper at present (Might 11, 2022) within the journal Nature Strategies showcasing the primary ever correct “digital twin” of the fly Drosophila melanogaster, dubbed “NeuroMechFly.”

Time flies

Drosophila is essentially the most generally used insect within the life sciences and a long-term focus of Ramdya’s personal analysis, who has been engaged on digitally monitoring and modeling this animal for years. In 2019, his group published DeepFly3D, a deep-learning primarily based motion-capture software program that makes use of a number of digicam views to quantify the actions of Drosophila in three-dimensional area.

Persevering with with deep-learning, in 2021 Ramdya’s team published LiftPose3D, a technique for reconstructing 3D animal poses from 2D pictures taken from a single digicam. These sorts of breakthroughs have supplied the exploding fields of neuroscience and animal-inspired robotics with instruments whose usefulness can’t be overstated.


A digital mannequin of Drosophila melanogaster known as NeuroMechFly. Credit score: Pavan Ramdya (EPFL)

In some ways, NeuroMechFly represents a end result of all these efforts. Constrained by morphological and kinematic information from these earlier research, the mannequin options impartial computational components that simulate completely different components of the insect’s physique. This consists of a biomechanical exoskeleton with articulating physique components, resembling head, legs, wings, stomach segments, proboscis, antennae, halteres (organs that assist the fly measure its personal orientation whereas flying), and neural community “controllers” with a motor output.

Why construct a digital twin of Drosophila?

“How do we know when we’ve understood a system?” says Ramdya. “One way is to be able to recreate it. We might try to build a robotic fly, but it’s much faster and easier to build a simulated animal. So one of the major motivations behind this work is to start building a model that integrates what we know about the fly’s nervous system and biomechanics to test if it is enough to explain its behavior.”

“When we do experiments, we are often motivated by hypotheses,” he provides. “Until now, we’ve relied upon intuition and logic to formulate hypotheses and predictions. But as neuroscience becomes increasingly complicated, we rely more on models that can bring together many intertwined components, play them out, and predict what might happen if you made a tweak here or there.”

The testbed

NeuroMechFly affords a extremely worthwhile testbed for research that advance biomechanics and biorobotics, however solely in as far as it precisely represents the actual animal in a digital surroundings. Verifying this was one of the researchers’ major considerations. “We performed validation experiments which demonstrate that we can closely replicate the behaviors of the real animal,” says Ramdya.

The researchers first made 3D measurements of actual strolling and grooming flies. They then replayed these behaviors utilizing NeuroMechFly’s biomechanical exoskeleton inside a physics-based simulation surroundings.

NeuroMechFly Research Team

Jonathan Arreguit, Victor Lobato Ríos, Auke Ijspeert, Pavan Ramdya, Shravan Tata Ramalingasetty, and Gizem Özdil. Credit score: Alain Herzog (EPFL)

As they present within the paper, the mannequin can really predict numerous motion parameters which can be in any other case unmeasured, such because the legs’ torques and phone response forces with the bottom. Lastly, they had been ready to make use of NeuroMechFly’s full neuromechanical capabilities to find neural community and muscle parameters that permit the fly to “run” in methods which can be optimized for each pace and stability.

“These case studies built our confidence in the model,” says Ramdya. “But we are most interested in when the simulation fails to replicate animal behavior, pointing out ways to improve the model.” Thus, NeuroMechFly represents a highly effective testbed for constructing an understanding of how behaviors emerge from interactions between complicated neuromechanical techniques and their bodily environment.

A neighborhood effort

Ramdya stresses that NeuroMechFly has been and can proceed to be a neighborhood undertaking. As such, the software program is open supply and freely obtainable for scientists to make use of and modify. “We built a tool, not just for us, but also for others. Therefore, we made it open source and modular, and provide guidelines on how to use and modify it.”

“More and more, progress in science depends on a community effort,” he provides. It’s vital for the neighborhood to make use of the mannequin and enhance it. However one of the issues NeuroMechFly already does is to lift the bar. Earlier than, as a result of fashions weren’t very sensible, we didn’t ask how they might be instantly knowledgeable by information. Right here we’ve proven how you are able to do that; you may take this mannequin, replay behaviors, and infer significant data. So this, I believe, is a large step ahead.”

Reference: “NeuroMechFly, a neuromechanical model of adult Drosophila melanogaster” by Victor Lobato Ríos, Shravan Tata Ramalingasetty, Pembe Gizem Özdil, Jonathan Arreguit, Auke Jan Ijspeert and Pavan Ramdya, 11 Might 2022, Nature Strategies.
DOI: 10.1038/s41592-022-01466-7

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