Researchers at the University of California, San Diego, have created two innovative methods to measure changes in mitochondrial networks within cells. These methods involve creating "virtual cells"—digital models that replicate the complex, dynamic processes of real cells. Both approaches use 4D lattice light-sheet microscopy, a cutting-edge imaging technique that records how mitochondria and other cell structures move in three dimensions over time. These studies, published in the journal Cell, aim to reduce reliance on traditional, time-consuming lab experiments and could significantly speed up drug discovery for diseases such as cancer, diabetes, Alzheimer’s, and mitochondrial disorders in children. One of the approaches involves a deep-learning AI model called MitoSpace. Researchers exposed cancer cells to 25 different compounds known to affect mitochondria, generating 40,000 single-cell 4D movies. Using these movies, they trained MitoSpace to identify patterns in mitochondrial behavior without human labeling. The model successfully grouped cells with similar responses and predicted a cell’s energy state based on mitochondrial shape and movement across 26 different drug conditions. It achieved 75% accuracy in identifying drug mechanisms, compared to 56% when using traditional 2D images. This AI model could help discover new treatments and repurpose existing drugs, as it can even sort cells by developmental stage without retraining. The second method builds a "digital twin" of a living cell using physics-based modeling. Researchers used 4D lattice light-sheet microscopy data to map the positions of mitochondria and the microtubules they travel on. They then incorporated motor proteins that move mitochondria according to known biological rates. By adjusting parameters, the model replicated the movement and behavior of mitochondria in real cells. When tested with a drug that disrupts microtubules, the digital twin accurately predicted how mitochondria would behave, matching real-world results. This approach could allow scientists to test drug effects, disease mutations, or cell engineering designs virtually, reducing the need for extensive lab work. Looking ahead, the researchers plan to integrate MitoSpace and digital twin models into a unified system. The AI would analyze large datasets to identify patterns, while digital twins would explore the physical causes behind those patterns. By incorporating other cell components, the team hopes to eventually build a complete virtual human cell. These advancements could transform cell biology by offering powerful new tools for understanding and manipulating cellular processes.