Fruit Flies Teach Us About the Physics of Life

During the past century, Drosophila melanogaster, commonly known as the fruit fly, has become one of the most widely used model organisms in scientific research. While fruit flies might seem like little more than a summertime nuisance hovering over ripening fruit, they hold immense value for researchers across a range of disciplines. Their appeal isn’t driven by the desire to rid kitchen fruit bowls of these pesky insects; rather, it stems from their simplicity and the profound insights their biology provides into the mechanisms governing more complex organisms, including humans.

Scientists have been drawn to D. melanogaster for several key reasons. Despite its small size, the fruit fly’s biology is remarkably conserved across species, offering deep evolutionary insights. It is easy and cost-effective to maintain in a lab, highly fertile, and reproduces quickly. This means researchers can observe multiple generations in a relatively short time, allowing them to conduct long-term studies of genetic and biological processes. Furthermore, scientists have sequenced its genome, making it a prime model for exploring the workings of genes, cells, and developmental processes.

A Breakthrough in Understanding Tissue Behavior

In a recent groundbreaking paper published in Proceedings of the National Academy of Sciences, a team of researchers led by Andrea Liu, a theoretical physicist at the University of Pennsylvania, and Dan Kiehart and Christoph Schmidt from Duke University, explored a fascinating biological phenomenon in D. melanogaster during its development. The study focused on a process called dorsal closure, a crucial event in the embryonic development of fruit flies that involves dramatic tissue shrinkage and mechanical interactions among cells that lead to the closing of a gap in the developing embryo.

Dorsal closure is vital for the proper development of the embryo, as it helps bring together the left and right sides of the body to form the fully encapsulated organism. This process involves the shrinking of the amnioserosa, a thin layer of epithelial cells that spans the dorsal side of the embryo. As this tissue contracts, it drives the surrounding epidermal cells to stretch, thereby closing the gap and sealing the body.

Researchers had long expected that such a dynamic process, involving tissue shrinkage and large-scale cell movements, would cause the tissue to transition into a liquid-like state. After all, tissue morphogenesis—a phenomenon in which tissues undergo shape changes during development—often involves cell movements where individual cells “flow” past each other, somewhat akin to how liquids behave.

However, this assumption was challenged by Liu’s research team, who demonstrated that, surprisingly, the amnioserosa tissue did not behave like a fluid during dorsal closure. Despite the significant deformation of the tissue as it shrank, the cells did not flow or change their neighbors. Instead, the cells remained locked into position relative to each other, preserving their integrity and forming a solid-like structure instead of transitioning into a fluid state. This paradoxical finding raised new questions about how tissues behave during developmental processes, and it suggested that there might be unknown physical principles governing tissue organization.

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The Connection to Physics

The implications of this discovery go beyond biology—this phenomenon ties directly to cutting-edge physics and advances in condensed matter physics. Condensed matter physics deals with how macroscopic properties of materials, such as solids and liquids, emerge from the behavior of microscopic particles (such as atoms and molecules). For Liu and her colleagues, the amnioserosa puzzle provided an unexpected link between developmental biology and the concepts of statistical physics.

Liu draws a comparison between the observed behavior of the fruit fly’s amnioserosa and the Ising model, one of the most foundational models in statistical physics. The Ising model originally described the magnetic properties of materials, modeling magnetic spins that could point up or down. When the spins of neighboring atoms align, they result in a larger-scale magnetic property (such as magnetization). Over the years, this model has been used to understand complex systems in fields like magnetism, chemistry, and biology.

A key feature of the Ising model is that the interactions between individual units—such as atoms or molecules—are typically fixed. However, John Hopfield, one of the recipients of last year’s Nobel Prize in Physics, introduced the concept of tunable interactions. Rather than interactions being fixed, Hopfield proposed a model in which the interactions between particles could be adjusted, much like how neural connections in the brain can change as learning occurs. Liu recognized a similar approach to how cells behave during the tissue movement in dorsal closure and applied this idea of “tunable interactions” to the behavior of cells in the amnioserosa.

A New Model for Biological Tissues

Liu and her colleagues introduced the concept of tunable interactions among the cells in the amnioserosa as a way to better understand how the tissue managed to retain its rigid structure despite undergoing extreme shrinkage. The theory postulates that the interactions between the cells in the tissue are not constant but can adjust to help maintain the overall structural integrity of the tissue as it undergoes deformation. This approach was inspired by the work in artificial intelligence and neural networks, where flexible interactions allow the system to adapt and “learn” as it processes information.

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While most traditional models in biology, such as the commonly used vertex model, treated cell interactions as fixed, Liu’s team realized that the flexibility in cell interactions could explain why the tissue remained solid instead of becoming liquid-like. They hypothesized that by allowing the cell boundaries—specifically the preferred perimeter of each cell—to shrink over time, the tissue would maintain its stability. The idea was simple: by modifying the preferred cell shape and size, the tissue could adapt to physical changes and still maintain its structural integrity.

With the help of advanced machine learning algorithms and computational models, the research team could precisely simulate the dynamics of cell behavior. They analyzed how individual cells within the tissue adjusted their shape and position during dorsal closure, and their results aligned with physical observations. The modified vertex model, where cell shapes and cell-cell interactions were tunable, could now explain why the cells didn’t “flow” as initially expected and instead maintained the structural rigidity required for proper closure.

Validation Through Experimental Data

To confirm their computational findings, the team relied on detailed experimental work in Kiehart’s laboratory, where they conducted laser ablation experiments to measure how the cells reacted when their junctions were cut. By precisely measuring how fast the tissue recoiled in response to these cuts, the researchers could quantify the tension along cell junctions, providing valuable insights into how cell mechanics might support the system’s rigid state. This laser-based technique confirmed the team’s predictions from the mathematical model, adding an extra layer of validation to their theoretical work.

Schmidt, co-author of the study, remarked that this achievement marked a significant milestone in understanding the dynamics of biological tissues. The ability to model such a complex biological phenomenon using a physics-based approach represents a powerful new tool for studying cellular mechanics. Their method of combining machine learning with high-quality biological data could lead to exciting new developments not only in tissue biology but also in fields like wound healing and developmental biology, where understanding the mechanical properties of tissues is crucial.

Applications Beyond Fruit Flies

Liu’s findings open new avenues in both condensed matter physics and biological research. Traditionally, condensed matter physics dealt with fixed interactions between particles, but the concept of tunable interactions introduces an entirely new category of systems that allows individual elements to adjust their behavior dynamically in response to external stimuli.

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“The tunable interactions we see in biology are fundamentally different from those seen in conventional condensed matter systems,” Liu notes. “In biological systems, interactions are dynamic, which means that scaling up these systems can produce emergent properties that are completely new. What we have learned from studying the fruit fly tissue could be applied to other living systems as well—such as the cytoskeleton in cells, which uses dynamic interactions to maintain structural integrity.”

Additionally, this concept of tunable interactions has applications beyond biology. Liu and her collaborators are also using similar ideas to build mechanical analogs, such as electrical circuits designed to perform tasks like pattern recognition or classification. These circuits, which feature adjustable components, function much like neural networks, where components “learn” how to adapt to inputs, potentially providing new ways of thinking about computation.

Conclusion: The Future of Biology and Physics

This innovative approach to understanding biological systems and tissue behavior also provides a blueprint for future scientific advancements in both biology and physics. By modeling the interactions between cells as tunable, Liu’s team has given us a fresh perspective on how complex biological processes, from tissue movement to cellular mechanics, can be understood using the principles of condensed matter physics.

The intersection of physics, developmental biology, and machine learning opens a new frontier for both fields. While it began with fruit flies, the implications of this work could ripple out to other areas of biology, providing insights into other crucial processes such as wound healing, tissue regeneration, and even the mechanics of diseases where tissue deformation is involved. It also pushes the boundaries of condensed matter physics, opening the door to more dynamic models for understanding how large systems of interacting particles—be they cells or atoms—behave and evolve over time. In essence, Liu and her collaborators have found a fascinating intersection between biology, physics, and computation, one that could transform how scientists approach complex systems and tackle age-old biological puzzles.

Reference: Indrajit Tah et al, A minimal vertex model explains how the amnioserosa avoids fluidization during Drosophila dorsal closure, Proceedings of the National Academy of Sciences (2024). DOI: 10.1073/pnas.2322732121

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