To determine which is faster, natural or artificial selection, one might select examples to compare: while wolves and wild jackals branched off from a common ancestor 3.5 million years ago (natural selection), arctic foxes were domesticated within a couple of decades (artificial selection). The oldest moth species appeared in the fossil record around 200 million years ago (natural selection), while the peppered moth changed from a light-colored to a primarily dark-colored one in just 47 years (artificial selection). But this becomes harder when we consider questions like: Is COVID well-adapted to human hosts' What animal or plant has had as much time to adapt to a new environment as COVID has to humans' Is the persistence of dominant genetic disorders best explained by a lack of purification time (i.e. that humans haven't lived in their current environment long enough to purify out harmful alleles that might have once been adaptive) or something else' Very quickly, two major issues arise. First, 'years' seems an inaccurate unit for expressing evolutionary rates because evolution considers changes between one generation and the next. In absolute terms, dog, moth, and COVID-19 generations occur over different spans of time; yet from the perspective of evolution, all three should probably be treated similarly....

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....