Guest Contributor
During the last century, biologists have extensively studied Drosophila melanogaster, the common household fruit fly. It’s become one of the most popular model organisms, but not because scientists have been determined to rid kitchen fruit bowls of these summertime nuisances. It’s because their biology is highly conserved and offers invaluable clues about how multicellular organisms all the way to humans function and evolve.
Their appeal lies in their simplicity: a small species with a well-studied genome that is highly fertile, inexpensive, and easy to observe over multiple generations. In many ways, fruit flies have become a convenient lens through which scientists can explore the complexities of life.
Now, in a paper published in the Proceedings of the National Academy of Sciences, researchers led by Andrea Liu, a theoretical physicist at the University of Pennsylvania, and Dan Kiehart and Christoph Schmidt from Duke University have accurately modeled a particular developmental process in Drosophila melanogaster wherein a tissue shrinks dramatically while driving the lateral epidermis—the tissue surrounding it—to stretch and close a gap in the developing embryo. This kind of tissue movement was expected to cause the cells to flow and exchange places, turning the tissue into a fluid. Surprisingly, the team found that it did not. Instead, the tissue stayed an elastic solid, and the approach used to determine how may prove fruitful for a new form of condensed matter physics.
“During the dorsal closure process, tissue, called amnioserosa, is shrinking like mad, and by all accounts it should turn into a fluid,” Liu says. “But it doesn’t. The cells stay locked in place with their neighbors, and we wanted to understand why.”
So how does this relate to cutting-edge physics? Liu and her colleagues saw a surprising connection to the work that earned last year’s Nobel Prize in Physics.
Tunable interactions
“It all harkens back to the Ising model,” Liu says, referring to a foundational model in statistical physics originally developed to “describe the behavior of magnetic spins that can point either up or down.” In its simplest form, the model shows how neighboring spins interact and influence each other to create large-scale magnetic properties, such as whether a material becomes magnetized.
John Hopfield, one of last year’s Nobel laureates, took this model a major step further by introducing tunable interactions between the spins, says Liu. In Hopfield’s version, the interactions between spins weren’t fixed but could adjust individually. He used this mathematical model to study how neurons in the brain might adjust their connections to learn and store memories.
“Hopfield, essentially, applied physics to neuroscience and created a subfield of the discipline, as well as the basis of neural networks,” Liu says about the seminal work that’s laid the foundation for artificial intelligence. “He showed that, by allowing the interactions between neurons to be individually adjustable, you could build a model of how the brain learns. So, we introduced tunable interactions among cells to see how a tissue of cells might remain rigid.”
Liu and her group have been pursuing the idea of tunable interactions in other systems as well, collaborating with Doug Durian from Penn’s Physics and Astronomy departments and Marc Miskin from the School of Engineering and Applied Science to build electrical networks that can perform computational tasks. Their circuits, made from networks with tunable interactions in the form of adjustable resistors, function as analog neural networks, learning tasks like classification on their own, without the need for digital processors. The same principles of tunable interactions that allow neurons to learn are applied in these mechanical networks, where resistors adjust their connections to solve problems in real time.
“The resistors tune themselves using a rule based on our theoretical work,” Liu says. “This allows the circuit to change in response to inputs or stimuli to learn a task by itself. It’s a completely different way to think about computation, and it ties into what we’re seeing with biological tissues.”
This story was written by Nathi Magubane. To read the full article, please visit Penn Today.