Victoria Finizio ’27
Every October, scientists worldwide look to the Nobel Foundation for their declaration of what physicists, chemists and biologists have achieved in their fields. The Prizes often recognize more than one scientist after they have completed many years of research with large implications for treating disease and innovating new technology. The 2024 recipients of the Nobel Prizes in Physiology or Medicine, Physics and Chemistry were announced last week from Oct. 7 to 9. The recipients will be officially awarded Dec. 10 at the 2024 Nobel Prize Ceremony in Sweden.
The Nobel Prize in Physics will be awarded to John J. Hopfield and Geoffrey E. Hinton “for foundational discoveries and inventions that enable machine learning with artificial neural networks.” The idea of an “artificial neural network” is synonymous with artificial intelligence (AI). The work of Hopfield and Hinton has contributed to the design of machines that can simulate memory and learning. Artificial neural networks are composed of nodes that can strengthen or weaken as new information is taken in, just like how the brain makes connections between neurons.
In 1982, Hopfield developed a network that can store, recreate and recognize patterns. He used his physics background to create the Hopfield network, which uses a formula to determine the lowest energy state of an image, ultimately matching a distorted input to saved information. While Hopfield’s machine could recognize an image, Hinton started to work towards the interpretation of images by artificial neural networks. Hinton applied statistical physics to build on Hopfield’s work, allowing the AI to predict information based on knowledge of similar categories even if it has not been trained to recognize the specific pattern. Today, more complicated and advanced artificial neural networks have been developed for widespread use based on the same physical ideas and principles that Hopfield and Hinton first applied. The work of Hopfield and Hinton has significantly contributed to this “machine learning revolution.”
The Nobel Prize in Physiology or Medicine will be awarded to Victor Ambros and Gary Ruvkun “for the discovery of microRNA and its role in post-transcriptional gene regulation.” Most complex organisms, including humans, express a variety of specialized cells throughout the body. For example, the human body contains cells such as neurons, blood cells and muscle cells. Each cell contains an identical set of DNA. Gene regulation, which essentially picks out certain genes to be expressed for certain purposes or in certain locations, facilitates cell differentiation.
In 1993, Ambros and Ruvkun discovered microRNA when studying C. elegans, a short roundworm with specialized cells. They found that the gene called lin-4 produced a short, non-coding RNA molecule that inhibited the expression of the lin-14 gene. This discovery challenged the prior conception that transcription factors, proteins that also contribute to gene regulation, were the primary contributors to this biological process. For this reason, Ambros and Ruvkun’s findings were initially written off as unique to this species of roundworm and unimportant to other organisms. However, thousands of microRNAs have since been revealed in humans and are now accepted as universally important molecules. Ultimately, microRNA has allowed complex organisms to evolve, helps cells and tissues develop properly and is the key to understanding mutations that contribute to diseases like cancer. Ambros and Ruvkun’s prize-winning research has allowed scientists to improve their understanding of human physiology and the origin of several diseases and mutations.
The Nobel Prize in Chemistry will be awarded to David Baker “for computational protein design” along with Demis Hassabis and John M. Jumper “for protein structure prediction.” Proteins are composed of different combinations of 20 common amino acids that each have different chemical properties. Based on the chemical interactions between amino acids connected in a certain order, proteins fold into complex three-dimensional shapes, allowing them to control a multitude of the body’s processes from cell signaling and speeding up biochemical reactions to forming physical features like hair. Hassabis and Jumper have developed an AI program known as AlphaFold2 that can predict protein structures with impressive and unprecedented accuracy. The system is trained on known amino acid sequences and protein structures, allowing it to perform several rounds of analysis to determine the probable structure of a given amino acid chain.
While Hassabis and Jumper worked on this software, David Baker tackled the opposite challenge: predicting an amino acid sequence from a desired or given protein structure. He has successfully used his computer software, called Rosetta, to make several unique proteins with various applications. The possibilities are seemingly endless; these technologies will lead to the development of “new nanomaterials, targeted pharmaceuticals, more rapid development of vaccines, minimal sensors and a greener chemical industry,” according to the Nobel Foundation.
Ultimately, the science Nobel laureates represent some of the most impactful, groundbreaking research in the past several decades. Between leading the creation of artificial neural networks and adding to knowledge of biochemical processes, the laureates have made incredible scientific contributions that will push important developments in physics, medicine and chemistry further than ever before.
