MIT Model Predicts Fruit Fly Cell Behavior with 90% Accuracy
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Engineers at MIT, led by Ming Guo, associate professor of mechanical engineering, have developed a deep-learning model capable of predicting, minute by minute, how individual cells will fold, divide, and rearrange during the earliest stages of fruit fly development. The model analyzes videos of developing embryos, each beginning as a cluster of approximately 5,000 cells, and tracks geometric properties such as cell position and neighboring cell contact. Results detailed in Nature Methods demonstrate the model predicts these cellular changes with 90 percent accuracy during the first hour of development—a critical period known as gastrulation—potentially enabling the identification of early indicators of diseases like asthma and cancer. Deep-Learning Model Predicts Fruit Fly Development A new deep-learning model can predict, minute by minute, how individual cells will fold, divide, and rearrange during the early development of fruit flies. The model achieved 90 percent accuracy in predicting changes to all 5,000 cells during the first hour of development – a phase called gastrulation. Researchers utilized high-quality videos of developing fruit fly embryos, labeled with details of cell edges and nuclei, to train and test the model’s predictive capabilities. The model uniquely combines two approaches – modeling cells as both moving points and shifting bubbles – represented as a “dual-graph” structure. This allows for a more detailed capture of geometric properties, including cell location, connections to neighbors, and whether a cell is folding or dividing. Beyond what will happen, the model can also predict when cellular changes will occur, like when one cell will detach from another, with notable precision. This approach has potential beyond fruit flies. Researchers believe the model could eventually predict cell-by-cell development in more complex species and even human tissues, contingent on access to similarly detailed high-quality video data.
The team emphasizes the model itself is ready for wider application, with data acquisition currently being the limiting factor in expanding its use to other multicellular systems and understanding early disease patterns. Model Approach: Dual-Graph Structure and Data The model employs a “dual-graph” structure to represent developing tissues, combining approaches that traditionally view cells as either moving points or shifting bubbles. This allows for a more comprehensive capture of geometric properties, including cell location, connections to neighbors, and instances of folding or division. By representing the embryo as both points and bubbles, researchers aim to highlight structural information and gain a deeper understanding of cell interactions during development. At the heart of the model is its ability to predict changes in individual cells over time. Trained on videos of fruit fly embryos – containing roughly 5,000 cells – the model achieved 90 percent accuracy in predicting how each cell would fold, shift, and rearrange during the first hour of development. Crucially, it wasn’t just whether changes would occur, but when, predicting events like cell detachment with minute-level precision. The researchers utilized high-quality videos of fruit fly gastrulation, recorded at single-cell resolution and labeled with cell edges and nuclei. These videos spanned one hour and provided detailed data necessary for training and testing the model.
The team believes this dual-graph approach is broadly applicable to other multicellular systems, though obtaining similarly detailed video data remains the primary limiting factor for wider application. Predictive Accuracy and Timing of Cell Changes The research team developed a deep-learning model to predict, minute by minute, how individual cells change during a fruit fly’s early development—specifically, during gastrulation, a roughly one-hour process. Utilizing a “dual-graph” structure, the model captures detailed geometric properties like cell location, connections to neighbors, and instances of folding or division. This approach combines modeling cells as both moving points and shifting bubbles, offering a more comprehensive representation of tissue behavior than previous methods. Testing the model on videos of developing fruit fly embryos – containing roughly 5,000 cells – revealed 90 percent accuracy in predicting cell changes. The model not only predicted if changes would occur, but also when, such as predicting the detachment of cells from one another within a minute or two. The high-quality videos used in the study provided submicron resolution of the 3D volume at a fast frame rate, along with labels for cell edges and nuclei, which were critical for training the model.
The team believes this approach is scalable to predict cell-by-cell development in other multicellular systems, even more complex species and human tissues. The primary limitation currently isn’t the model itself, but the availability of similarly high-quality video data of other developing tissues. With sufficient data, the model could be applied to predict the development of many more structures and potentially discern early patterns of disease like asthma, which exhibits different cell dynamics when imaged live. “This very initial phase is known as gastrulation, which takes place over roughly one hour, when individual cells are rearranging on a time scale of minutes.” Ming Guo Potential Applications to Other Species and Disease The research team hopes to extend the deep-learning model’s application to other species, specifically mentioning zebrafish and mice. The goal is to identify common patterns in cell development across different organisms. The model’s potential isn’t limited to simply predicting development; researchers believe it could also reveal early patterns related to disease, such as asthma, by capturing subtle differences in cell dynamics within tissues. The limiting factor for broader application is the availability of high-quality video data of developing tissues. The model demonstrated 90 percent accuracy in predicting the changes of approximately 5,000 cells within a fruit fly embryo during the first hour of development. This includes predicting when specific cellular events will occur, such as a cell detaching from a neighbor, with precision down to minutes. The research utilizes a “dual-graph” structure, combining methods of modeling tissues as both moving points and shifting bubbles to capture detailed geometric properties and cell connections. Researchers envision the model’s potential for understanding human tissues and organs, specifically mentioning the possibility of identifying early signs of asthma. Lung tissue affected by asthma exhibits different cell dynamics, and the model could capture these differences to improve diagnostics or drug screening.
The team emphasizes that, while the model is ready for broader application, securing high-quality video data of specific tissues remains a significant hurdle. Source: https://news.mit.edu/2025/deep-learning-model-predicts-how-fruit-flies-form-1215 Tags: