Crystal jellyfish have an eerie beauty: thanks to a natural protein, they emit a faint green glow. For decades, researchers have used that green fluorescent protein and similar molecules to light up the field of biology, tracking what's happening inside cells. Now these ubiquitous tools are getting a glow-up: their quantum properties are being harnessed to make them similar to the fundamental bits of quantum computing. 'These fluorescent proteins that everybody uses as a fluorescent label can actually be turned into a qubit,' says Peter Maurer, a quantum engineer at the University of Chicago in Illinois. The idea 'sounds very science fiction', says Maurer. But the physics isn't new, and the approach has already been shown to work in principle. Fluorescent-protein labels are currently one of the most important tools in biology laboratories around the world. They can monitor the location and activity of proteins, sense conditions inside a cell, check whether drug candidates are targeting the right spots and carry out a range of other tasks. But adding a quantum twist offers up fresh and exciting possibilities, say researchers....

Studying gene expression in a cancer patient's cells can help clinical biologists understand the cancer's origin and predict the success of different treatments. But cells are complex and contain many layers, so how the biologist conducts measurements affects which data they can obtain. For instance, measuring proteins in a cell could yield different information about the effects of cancer than measuring gene expression or cell morphology. Where in the cell the information comes from matters. But to capture complete information about the state of the cell, scientists often must conduct many measurements using different techniques and analyze them one at a time. Machine-learning methods can speed up the process, but existing methods lump all the information from each measurement modality together, making it difficult to figure out which data came from which part of the cell. To overcome this problem, researchers at the Broad Institute of MIT and Harvard and ETH Zurich/Paul Scherrer Institute (PSI) developed an artificial intelligence-driven framework that learns which information about a cell's state is shared across different measurement modalities and which information is unique to a particular measurement type....

James J. Collins, the Termeer Professor of Medical Engineering and Science at MIT and faculty co-lead of the Abdul Latif Jameel Clinic for Machine Learning in Health, is embarking on a multidisciplinary research project that applies synthetic biology and generative artificial intelligence to the growing global threat of antimicrobial resistance (AMR). The research project is sponsored by Jameel Research, part of the Abdul Latif Jameel International network. The initial three-year, $3 million research project in MIT's Department of Biological Engineering and Institute of Medical Engineering and Science focuses on developing and validating programmable antibacterials against key pathogens. AMR ' driven by the overuse and misuse of antibiotics ' has accelerated the rise of drug-resistant infections, while the development of new antibacterial tools has slowed. The impact is felt worldwide, especially in low- and middle-income countries, where limited diagnostic infrastructure causes delays or ineffective treatment....

The interior of a cell is densely packed with millions of molecules vibrating, jostling, and moving about. Sugar molecules fly through a cell at 250 miles per hour, ricocheting off of ribosomes, organelles, cytoskeletal fibers, and enzymes. Indeed, every protein in the cell is hit by about 1013 water molecules each second. This chaos makes biology seem hopelessly convoluted. With everything moving so quickly, how can we begin to understand biomolecules' As with other hard-to-intuit quantities in science, one could look up biological rates using resources like PubMed or BioNumbers, to discover facts like 'water flows through aquaporin at 100 million molecules per second,' or 'yeast transcribes RNA at 0.12 molecules per minute.' But knowing a number doesn't necessarily give one a feel for it. Are those rates' fast' How do they compare to protein folding' Or enzymatic activity' Or squeezing a muscle' We can answer this question with a quantitative metaphor, by visualizing the most important goings-on of a typical cell slowed down to speeds that are still accurate relative to one another, but matched to what we experience in the everyday world. The slowdown factor we pick should make it easy to understand the molecular machines that run our cells ' proteins. Ideally, we would scale the fastest functionally important protein event to match the shortest unit of human perception....