Drosophila melanogaster, the common fruit fly, is in some ways a simple creature. But in others, it’s so complex that, like any form of life, we just scratch the surface to understand it. Researchers have taken a big step with D. melanogaster by creating the most accurate digital twin yet — at least in how it moves and, to some extent, why.
NeuroMechFly, as the EPFL researchers call their new model, is a “morphologically realistic biomechanical model” based on careful scans and close observation of real flies. The result is a 3D model and motion system that, when prompted, does things like walk around or respond to certain basic stimuli, just like a real fly would.
To be clear, this is not a full cell-by-cell simulation, where we’ve seen some progress in recent years with much smaller microorganisms. It doesn’t simulate hunger, vision, or any sophisticated behavior – not even how it flies, just how it walks along a surface and grooms itself.
What’s so hard about that, you ask? Well, it’s one thing to approach this kind of movement or behavior and make a little 3D fly that moves more or less like a real one. It’s another thing to do this to some degree in a physically simulated environment, including a biologically accurate exoskeleton, muscles, and neural network analogous to the fly that controls them.
To create this very precise model, they started with a CT scan of a fly to create the morphologically realistic 3D mesh. Then they recorded a fly walking under very precisely controlled conditions and tracked its precise leg movements. They then had to model exactly how those movements corresponded to the physically simulated “articulated body parts, such as head, legs, wings, abdominal segments, proboscis, antennae, dumbbells,” the latter of which is a type of motion-sensing organ that aids in flight.
Image Credits: Pavan Ramdya (EPFL)
They showed that these worked by putting the precise movements of the observed fly into a simulation environment and replaying them with the simulated fly – the real movements mapped correctly to those of the model. They then showed that they could make new gaits and movements based on this, allowing the fly to run faster or more stable than they had observed.
Image Credits: Pavan Ramdya (EPFL)
Not that they improve nature, exactly, they just show that the simulation of the fly’s motion extended to other, more extreme examples. Their model was even robust against virtual projectiles… to some extent, as you can see in the animation above.
These case studies have increased our confidence in the model. But we’re most interested in when the simulation doesn’t mimic animal behavior and pointing out ways to improve the model,” said EPFL’s Pavan Ramdya, who leads the group that built the simulator (and others). Drosophilarelated models). If they see where their simulation fails, show them where there is work to be done.
“NeuroMechFly may advance our understanding of how behavior arises from interactions between complex neuromechanical systems and their physical environment,” the article summarizes published last week in Nature Methods† By better understanding how and why a fly moves the way it does, we can also better understand the systems that underlie it, giving us insights into other areas (fruit flies are among the most commonly used laboratory animals). And if for some reason we ever want to make an artificial fly, of course we first want to know how it works.
While NeuroMechFly is in some ways a huge advancement in digitally simulating life, it’s still (as its creators the first would acknowledge) incredibly limited, focusing solely on specific physical processes and not the many other aspects of life. the little body and mind that make a Drosophila a Drosophila† You can view the code and maybe contribute at: GitHub or Code Ocean†
