The discovery of microRNA wins the 2024 physiology Nobel Prize.
Victor Ambros, of the University of Massachusetts Chan Medical School in Worcester, and Gary Ruvkun, of Harvard Medical School, found that small snippets of RNA called microRNAs can help control production of proteins throughout the body. These minuscule RNAs may play an outsize role in health and disease. These microRNAs play important roles in cancer, pain and itchiness, eye diseases and in controlling the mix of microbes living in people’s colons. The discovery helps our basic understanding of all the things you’ve heard about how cells differentiate and become specialized. Much of the important work in cells (for example, making muscles contract, processing drugs, digesting food, transmitting signals to the brain) is done by proteins. Instructions for making those proteins are encoded for long-term storage in DNA. MicroRNAs fill a key role in the steps between reading those instructions and making the proteins.
More than 1,000 microRNAs are now known to regulate genes in people. Some microRNAs are evolutionarily ancient. Those old microRNAs tend to regulate basic biological processes that are fundamental to all plant and animal cells. But during evolution, new microRNAs have also appeared. The new ones tend to regulate processes that are specific to certain species or to particular branches of the evolutionary tree.
It’s a completely new physiological mechanism that no one expected. All started with curiosity research: they were looking at two worms that looked a bit funny and decided to understand why. Then, they discovered an entirely new mechanism for gene regulation.
The discovery of tools key to machine learning wins the 2024 physics Nobel
As artificial intelligence explodes in popularity, two of its pioneers have nabbed the 2024 Nobel Prize in physics.
The prize goes to John Hopfield and Geoffrey Hinton “for foundational discoveries and inventions that enable machine learning with artificial neural networks,” the Royal Swedish Academy of Sciences in Stockholm announced October 8. These computational tools, which seek to mimic the functioning of the human brain, underlie technologies like image recognition algorithms, large language models including ChatGPT, soccer-playing robots and more.
The prize surprised many, as these developments are typically associated with computer science rather than physics. But the Nobel committee noted that the techniques were based on physics methods. The techniques have underpinned a variety of scientific advancements. Neural networks have helped physicists grapple with large amounts of complex data, allowing important advances that include making images of black holes and devising materials for new technologies such as advanced batteries. Machine learning has made strides in the biological and medical fields too, with the promise of improving medical imaging and understanding protein folding.
Work on protein structure and design wins the 2024 chemistry Nobel
This Nobel Prize is a sort of bridge between the previous two. Efforts to unlock the mysteries of proteins, building blocks of life, have earned three scientists the 2024 Nobel Prize in chemistry. The prize goes to David Baker “for computational protein design,” and to Demis Hassabis and John Jumper “for protein structure prediction,” the Royal Swedish Academy of Sciences announced October 9 in a news conference in Stockholm.
As said before, proteins enable nearly every facet of life.
And just like the shape of a key determines which lock it can open, the shape of a protein influences its role in the body. In order to understand how proteins work, you need to know what they look like.
Researchers used their artificial intelligence chops to solve an even trickier problem. In fact the duo’s AI model, called AlphaFold, could predict protein structures from amino acid sequences with almost 60 percent accuracy, much higher than what had been previously achieved.
The AlphaFold team has now predicted structures for almost all the 200 million proteins scientists know today. The fact that neural networks were used by Hassabis and Jumper is another example of how computational and mathematical tools inform and underlie a lot of really exciting scientific work.
