Let's take a closer look at the awarded research works in the fields of physics, chemistry, medicine or physiology and economics, from the perspective of researchers from the University of Lodz. It should be noted that in the first three categories it is increasingly difficult to clearly assign achievements to one field – modern science is extremely interdisciplinary, combining different areas of knowledge in the search for breakthrough solutions to global challenges.
Nobel Prize in Physics: recognition for AI innovation
John Hopfield, a physicist by training, has been conducting research aimed at better understanding the functioning of the human brain using mathematical models since the 1970s. In 1982, he developed his groundbreaking model of the neural network, which – in today's terms – was a simple, single-layer network of neurons. What made it stand out? Hopfield introduced the feedback mechanism, i.e. a situation in which the output of one neuron influences the inputs of the others. This solution made it possible to create a model that could “learn” and remember patterns, which was an extraordinary achievement for the time. Thanks to this model, later called the "Hopfield network", it became possible to develop more complex neural network systems, which found application in artificial intelligence and other fields of technology.
A few years later, Geoff Hinton, who at that time had a PhD degree in Computer Science, extended these ideas and introduced a revolutionary method called "backpropagation” (Backward Propagation of Errors). Thanks to it, neural networks can effectively “learn” by correcting their errors at every stage of training. It is a technique that still forms the foundation of most modern artificial intelligence systems today. Hinton also researched the so-called Boltzmann machine, which was another important step towards developing modern AI systems.
The Nobel Prize for these two outstanding researchers is no coincidence – their work had a key impact on the development of the technology we now use every day. Interestingly, Hinton is also a winner of the Turing Award, a prestigious honour awarded in the field of computer science, making him an exceptional scientist, appreciated in the world of both physics and computer science.
The above text was prepared by the Dean of the Faculty of Physics and Applied Informatics, dr. hab. Tomasz Gwizdałła, Associate Professor at the University of Lodz, who emphasises that he is pleased with the fact that the award underlines the close connection between physics and informatics.
Both areas are crucial for my department. This distinction proves that modern science develops at the intersection of various disciplines. At the Faculty, we conduct research in the field of computer science on the development of the idea proposed by the Nobel Prize winners, and we work on the physical implementation of artificial synapses, which constitute the basis of hardware neural networks as part of the NCN project.
Nobel Prize in Chemistry: for the discovery of the structure of proteins, the basic tools of life
The 2024 Nobel Prize in Chemistry was awarded for two separate but thematically related achievements: Sir Demis Hassabis and John Jumper were awarded for predicting the structure of proteins, and Prof. David Baker for computational protein design.
Protein Folding – how nature creates the molecular foundations of life
All proteins, the basic components of living matter, are linear polymers composed of amino acids. Since the process of protein production (biosynthesis) in living organisms is regulated by the information contained in the genetic material, the set of possible amino acids incorporated into the resulting chain is strongly limited to about 20 different compounds. Nevertheless, because proteins are composed of several dozen, several hundred or even several thousand amino acid residues, the number of possible combinations is enormous. Despite this linear structure, proteins are not molecules in the shape of a long string – the chain of amino acid residues coils in space into complex structures called secondary and tertiary structures. As you can easily imagine, there are almost an infinite number of ways of such folding and, consequently, possible spatial structures of the same protein, and yet in nature only one or several of these structures will be appropriate for the biological role that a given protein is to play in cells, tissues and the organism.
In the process of protein biosynthesis, the stage of adopting the proper structure by newly formed molecules, called folding, is facilitated by a complex machinery of the so-called chaperone proteins, which guide the chain elements into the appropriate spatial arrangements. It is this ability of proteins to assume specific, very different shapes – sometimes similar for very different proteins, and sometimes drastically different in the case of proteins with a similar sequence – that has determined the universality of proteins as the basic molecular tools of life, whose evolution determines the diversity of the living world.
AlphaFold2: the result of a global effort and years of protein research
Biophysical methods for practical study of the spatial structure of finished proteins have existed for several decades. However, from the very beginning, the dream of researchers was the ability to predict structure, i.e. to determine the arrangement of subsequent amino acid residues in space only based on knowledge of their order (which has been easy to obtain for a long time because it is encoded in genes that we can sequence). Due to the importance of this issue – the ability to predict structure based on gene sequence facilitates drug design, determination of the function of proteins in physiological and pathological processes, biotechnological applications, etc. – thousands of researchers around the world have created methods, models and dedicated software for this purpose.
Since 1994, they have met every two years for an open competition called Critical Assessment of Structure Prediction (CASP), during which the effectiveness of various algorithms was compared. In 2020, the CASP competition was won with a bang by AlphaFold2, a program created by a team from DeepMind (owned by Google). What makes this achievement unique is, among other things, its unexpected nature: instead of careful, deep-knowledge-based predictive algorithms, AlphaFold2 uses deep machine learning. Thanks to this revolution, the problem of predicting protein structures has been considered largely solved, the AlphaFold2 program has been used to propose probable structures of all known proteins, and the people most responsible for this breakthrough – the founder of DeepMind and the originator of the most important principles of applied machine learning, Demis Hassabis, and the leader of the research team that created AlphaFold2, John Jumper – have just been awarded a Nobel Prize.
The youngest Nobel Prize winner in over 70 years
Interestingly, John Jumper is the youngest Nobel Prize winner in chemistry in over 70 years, and at the same time the first winner in a long time who has never headed a research group in the so-called academy, i.e. in scientific institutions, but has spent his entire professional career in the so-called industry, i.e. in commercial companies for which the development of knowledge is only a tool for economic success.
Nobel Prize winner from the computer games industry
Sir Demis, in turn, is the first Nobel Prize winner to have spent a large part of his career in the computer games industry, as a programmer at Lionhead studio and then as the founder of Elixir studio (although the games he designed did not enjoy particular success).
Reward for effort despite losing to machine learning
Prof. Baker also devoted his scientific career to the problem of predicting the structure of proteins, and his approaches to this problem were considered exceptionally original and potentially groundbreaking. The Rosetta algorithm, created by him in 1998, was one of the first algorithms to predict structure “from scratch” based solely on sequence information (without relying on similarity to other proteins with known structures), and has been developed over many years and has recorded many significant successes.
The Rosetta@home project, in which ordinary citizens could use their computers’ spare computing power to help servers predict the structure of important proteins, and the Foldit project, a computer game that involved making algorithmic decisions to improve the quality of predicted structures, are some of Prof. Baker’s unusual ideas for increasing Rosetta’s capabilities and throughput. Unfortunately, it turned out that all of these approaches lost to machine learning and AlphaFold2, whose performance and reliability clearly outperformed them. However, the Nobel Committee nevertheless decided to recognise the undoubted merits of Professor Baker and his enormous contribution to the field by awarding him the prize for something that Professor Baker himself probably considered to be a secondary and less important element of his scientific activity.
Designing proteins with given shapes
Designing protein structure is the exact opposite problem from prediction: the problem is to determine the amino acid sequence of an artificial protein that will assume a given, predetermined shape in space when produced in reality. This could have numerous biotechnological applications, e.g. for designing components of molecular machines or nanosensors, and the Rosetta algorithm has proven to be particularly well-suited to this purpose. The most convincing demonstration here is the design of new types of protein structure that have no analogy in nature (i.e. completely new three-dimensional geometric shapes) – Prof. Baker's team achieved this for the first time in 2003, designing a protein called Top7, which does not resemble anything that has arisen as a result of biological evolution. Although Top7 has no practical applications and was designed solely as a demonstration of its capabilities, it was this achievement that the Nobel Committee cited as key to their decision to award the prize.
While the Nobel Prize in Chemistry was for research on the fundamental molecules of life, i.e. proteins, biobanking also falls within this field of science, enabling the storage and study of biological materials for future discoveries. If you want to learn more about how biobanking supports the development of science and medicine, listen to our podcast on this topic!
The perversity of this year's Nobel Prize in Chemistry
It cannot be denied that, from a financial point of view, Prof. Baker came out better off than if his efforts at structure prediction had been recognized, since he received half the Nobel Prize (the other two laureates had to share the other half).
From the University of Lodz to the laboratory of a Nobel Prize winner's student
Interestingly, two weeks ago, the University of Lodz was visited by Prof. Łukasz Joachimiak from Dallas to encourage our students to participate in the BioLAB programme sponsored by the Fulbright Commission, which allows, among other things, to conduct research on designing new proteins in his laboratory – Prof. Joachimiak was a doctoral student of Prof. Baker.
Nobel Prize in Physiology or Medicine: for the discovery of microRNA and its role in post-transcriptional gene regulation
The Nobel Prize in Physiology or Medicine 2024 was awarded to Prof. Victor Ambrose and Gary Ruvkun for the discovery of microRNA and its role in post-transcriptional gene regulation.
Genetics Basics Review
Genes are sections of deoxyribonucleic acid (DNA), the carrier of genetic material, containing information about the structure of molecules that subsequently constitute cells, tissues and organisms. Most genes (in humans between 20 and 25 thousand) contain information about the sequence of amino acids in proteins, the most important components of living matter, which are its building blocks, enzymatic elements, etc. Genes, however, do not code for proteins directly – the sequential information about proteins contained in DNA is first transcribed into another nucleic acid, ribonucleic acid (RNA, in this case called mRNA), in the process of transcription. Only later are proteins synthesised using the information contained in the mRNA in the process of translation – both processes together are called expression of protein-coding genes. Since different cells and different situations require different sets of proteins in different amounts, the expression process is tightly regulated, and it has long been known that this regulation (switching on, off, modulation of intensity) occurs mainly at the level of transcription.
mRNA is not an abbreviation for microRNA
The groundbreaking research of this year's laureates has shown for the first time that there is also an equally important and complex regulatory mechanism at the stage between transcription and translation (therefore called post-transcriptional regulation). This mechanism is based on genes that do not contain information about the protein sequence (the so-called non-coding genes, i.e. not coding proteins), but are only transcribed into RNA (different from the above-mentioned mRNA, much shorter, therefore called microRNA). The resulting microRNA molecules then interact with selected mRNA molecules, using complex enzyme complexes to block their translation and thus, inhibit the expression of the relevant proteins. Because there are far fewer types of microRNAs than mRNAs, most microRNAs regulate the expression of a whole set of different proteins – this enables coordinated regulation of many proteins involved in the same life process.
Two trends in awarding Nobel Prizes
It is said that when selecting laureates, Nobel Committees try to balance two tendencies: to reward discoveries that are "hot", fresh and currently arouse great emotions among scientists and society (a good example is the last year's prize for mRNA vaccines); and to compensate for historical injustices, when a discovery initially went unnoticed and its authors were not recognised by the scientific community, but later turned out to be a groundbreaking discovery, laying the foundations for new scientific disciplines. This year’s Physiology or Medicine Prize is a classic example of the latter trend: the key publications of both laureates describing their discovery (Prof. Ambros describing the first microRNA, and Prof. Ruvkun describing its mechanism of action) were published in 1993 – and were met with general disinterest. The main reason was the fact that at the same time made the discovery possible: the researchers used a model organism in their experiments, the nematode Caenorhabditis elegans, which is a particularly valuable object of biological research due to its very simple structure. However, it is easy to accuse the phenomena discovered in an organism so evolutionarily distant from humans as a coincidental, unique oddity typical only of this creature, and this is how microRNA was treated for many years.
The nematode Caenorhabditis elegans as the “star” of the Nobel laboratories
It was not until the beginning of the 21st century that it turned out that the mechanism of gene expression regulation by microRNA is common and crucial for most organisms – including the same enzymatic machinery used, for example, in the phenomenon of RNA interference, used to inhibit gene expression by RNA introduced from outside, for which Prof. Andrew Fire and Prof. Craig Mello received the Nobel Prize in 2006, although their research was conducted almost a decade later than that of this year's laureates. The microRNA award is therefore a late but well-deserved recognition of both the researchers themselves and the research using Caenorhabditis as a model (this is an organism that is already exceptionally rich in Nobel history – counting the current winners, eight scientists have already received this award for research conducted on it).
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To receive a Nobel Prize, one must have studied with at least one previous Nobel Prize winner
This year's prize confirms yet another Nobel rule: in order to receive the prize, one must have studied with at least one previous Nobel laureate. Both laureates completed their preparation for independent research (the so-called post-doc) in the laboratory of Prof. R. Horwitz, the Nobel Prize winner in 2002 (there, by the way, they learned to work with Caenorhabditis); Prof. Ambros was also a doctoral student of Prof. D. Baltimore, the Nobel Prize winner in 1975, and Prof. Ruvkun worked in the laboratory of Prof. W. Gilbert, the Nobel Prize winner in 1980. Both professors, despite their advanced age, continue to conduct interesting research: Prof. Ruvkun is known, apart from microRNA, for groundbreaking research on the mechanisms of aging, also conducted using Caenorhabditis, and has recently been involved in the interesting SETG (Search for ExtraTerrestrial Genomes) initiative, which aims to search for life beyond Earth.
In turn, the research conducted by Prof. Ambros was recently described, for example, in his article published in “Postępy Biochemii”, the organ of the Polish Biochemical Society. There is a deeper reason for this – Professor Ambros is half-Polish, the son of a Vilnius high school graduate, Longin Ambros, who, after a dramatic escape from the Soviets and forced labour in Nazi Germany, joined the American army, combining the roles of a translator and a paratrooper.
Nobel Prize in Economics: for his research on how institutions are created and how they affect prosperity
The 2024 Nobel Prize in Economics was awarded for groundbreaking research on the role of institutions in shaping the economic well-being of countries. The winners are Daron Acemoglu and Simon Johnson from the Massachusetts Institute of Technology and James Robinson from the University of Chicago. Their work sheds new light on a question that has fascinated economists for years: Why do some countries achieve high levels of prosperity while others lag behind?
Learn more about how financial institutions in a globalised world contribute to the prosperity of entire nations. Listen to the podcast with Dr Ewa Feder-Sempach.
The research of the awarded scientists focuses on the quality of institutions – from their inclusiveness to their ability to support social and economic development. A key finding was that inclusive institutions that serve a broad range of citizens and are democratic and fair, promote long-term growth in prosperity. Exclusive institutions are the opposite. They favour small groups and can hinder the development of entire societies.
Resources are not enough – how do institutions shape economic success?
Award-winning research challenges traditional beliefs about the influence of natural resources or geographic location on the wealth of countries. They show that one of the key factors of economic success is the quality of institutions and their stable functioning over generations. Countries rich in raw materials, such as some African countries or Russia, often do not achieve high standards of living. In turn, countries with developed and stable institutions, such as Switzerland, Singapore or the Scandinavian countries, are characterised by a high standard of living.This year's Nobel Prize reminds us that economic development is not just a simple effect of acquiring natural resources or a favourable geographical location, but the conscious work of people in complex and effective institutions that create the foundations for equitable social development.
Source
- dr hab. Tomasz Gwizdałła, Associate Professor at the University of Lodz, Dean of the Faculty of Physics and Applied Informatics, University of Lodz, Head of the Department of Intelligent Systems at the Faculty of Physics and Applied Informatics of the University of Lodz
- dr hab. Łukasz Pułaski, Associate Professor at the University of Lodz, Department of Oncobiology and Epigenetics, Faculty of Biology and Environmental Protection
- Dr Ewa Feder-Sempach, Assistant Professor, Department of International Economics, Faculty of Economics and Sociology
Edit (including subheadings): Michał Gruda (Communications and PR Centre, University of Lodz)