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Hello, and welcome to Humanities Matter, brought to you by Brill. I'm Lee Jung-Grechault, and this week, we'll be looking at key issues in the field of humanities. Today we're speaking with Professor Klaus Meinser, who's worked also into mathematics, computational science, and even philosophy. He's the president of the European Academy of Science and Arts and the founder of the Munich Center for Technology in Society.
And today, he speaks to us about his book, Life Is Machine, How Do We Decipher the Corona Code? Dr. Meinser, thank you so much for being with us today. Yes, hello.
Dr. Meinser, the search for a vaccine against the coronavirus and new mutations is under intense pressure all over the world. What's the main point in your book, Life Is Machine, How Do We Decipher the Corona Code? Yeah, my first point is, virus can be understood as a kind of information processing machine that can be simulated on a computer.
In my book, I bring together bioinformatics, machine learning, AI research, and big data. Algorithms can help to this cipher and switch off Zarskovit 2. And my second point beyond that, I consider the evolutionary laws according to which viruses mutate. This could make it possible to anticipate coming pandemics and combat them as soon as they occur.
And all that is inspired, in my case, by the great British computer pioneer and the magician and mathematician Alan Turing. Alan Turing, who did this cipher, the code of the German Enigma machine that was a coding machine of the German army in the World War II. The enemy's message and information could be decoded, manipulated and interrupted. And the code breakers played a decisive, significant role in ending a global threat.
Even the control of a viral pandemic, such as Corona, Sarskovit 2, will also depend crucially on the decryption and manipulation of a code. Now then, tracking the Enigma code, Turing was inspired by the idea to simulate and imitate the Enigma machine on his computer in order to trace the codes used. Turing called it an imitation game and perhaps you remember the famous movie on Turing's life with that title. The key question is therefore, whether the idea of Turing can also help us to understand the information processing in Corona Sarskovit 2 and turn it off.
The machine this time is a virus and the code biochemical sequence. I love that metaphor with Turing as well, because so many of our world leaders have often described this fight against Corona as a war as well. Yes, nowadays we have to consider modern methods to quite a different Turing, of course, to model and imitate a virus in a computer. And that is the necessary step to later on to develop a vaccine against Corona.
Now, this could be done nowadays by modern machine learning and AI, artificial intelligence. And that is one of my main points. In this case, we use a simplified model of the human brain, a so-called neural network, which can be trained by data and learning algorithms to recognize complex patterns. Now already in 2016, a software called AlphaGo, which was developed by Google, by the way, had succeeded in training a neural network in such a way that it could defeat champions of the Asian board game go.
The network was able to recognize the patterns of distributed pieces, stones on the board and to improve them autonomously. And this was followed two years later, in 2018 by AlphaFold, also developed by this company and later on, similar software by other companies, AlphaFold in this case, which used the patterns of proton sequences to identify the complex proton foldings that determine all proton functions and thus the corresponding cellular functions of life. And therefore, the folding is so exciting and so important. Pattern recognition is a key to fighting in corona virus.
Now in more details, AlphaFold uses the patterns of proton sequences to identify the complex proton folds that determine all proton functions and thus corresponding cellular functions. Technically, AlphaFold is based on a multi-layered neural network in the sense of deep learning, which on the basis of inputted sequences of amino acids, the building blocks of a proton can predict suitable three-dimensional spatial shapes and folds of proton. This is done by the way by estimating the distances and angles between the amino acids, whose distribution is calculated by learning algorithms. Now, AlphaFold performs training with a proton databank, which stores three-dimensional spatial structures of large biomolecules, such as proton.
The output is predictions of suitable secondary, so-called secondary structures of proton, that means these spatial three-dimensional structures of proton. Now, and in the next step, we can develop or try to design vaccine. Bioinformatics tries to design vaccines in the computer. This includes, for example, membrane proteins of COVID-19.
The shape is well known to everyone nowadays, and these protein structures contain sites where new drugs and vaccine can dock to target COVID-19. In short, I suggest to create a digital twin of COVID-19 in the computer and to do digital experiments to learn more about the virus and to design vaccines. But what sounds so simple is extremely complex in biology. Proteins consists of thousands of thousands of molecules, the possible folds of proteins, which are used to determine their functions are exponentially diverse and cannot be fully computed.
Your large number of possibilities can be excluded from the outset of the computer model, which therefore no longer need to be tested in the laboratory. That is an advantage. However, candidates that have been identified as favorable still have to be tested and then discarded in order to select other candidates in the model, which then have to be tested again in the laboratory and so on and so on. So from a methodological point of view, we get a kind of spiral, a methodological spiral, machine learning and laboratory testing work together to approximate a favorable outcome.
So with respect to this spiral, we can sum up in short, we are up. This word vividly describes the research strategy perceived here in the end. However, there is no guarantee. There is no mathematical proof that the research spiral converges in every case to an optimal result.
So it seems like we're learning something new about COVID-19 and the coronavirus every single day. What do we know about the evolution of viruses? Yes, that is nowadays a brand new now are the crucial question. What do we know about the evolution of viruses?
By a human standard, evolution in plants and animals takes place over such a long time that people only noticed it very late in life. It talks in genius observation skills of Charles Darwin in the 19th century to notices. Since molecular biology and bioinformatics have opened up the micro world of genes and proteins, we humans observe a completely different rate of evolution every day in the laboratory. How can we react to this speed of viral evolution?
Every one nowadays, by the way, experiences viral evolution are unconsciously. Every year, when you and modified influencer vaccines have to be developed. This pandemic, like corona, the global threat to this invisible evolution in the micro world becomes apparent. Now, the first question which arises concerns the evolutionary origin of viruses.
The widely accepted theory explains viruses as nucleic acids that have evolved from RNA and DNA, molecules of host cells, and then have become independent. Now, what about the viability of viral evolution? How can it be explained? Computationally, the viability of viruses arises from copying errors in the replication of genetic information.
And that is in contrast to cellular life. Viruses generally have no correction and no repair procedures for copying errors. In organism, these procedures are vital since they can prevent the early deaths of the organism. In contrast, viruses are stable structures in which replications, due to copying errors, pay off as an increase in adaptability.
The far cycle of replication accelerates the adaptability and underlines the dangerousness of viruses. And therefore, for the future, it would be desirable if AI artificial intelligence could be used to simulate possible changes in viruses in advance. We should aim at a kind of toolbox for the rapid composition of vaccines with AI algorithms produced in advance. Here, a field of bioinformatics that is concerned with the decoding and simulation of evolutionary development is necessary.
It is not only the retrospective explanation of the past course of evolution as in the traditional Darwinian evolutionary biology. It is about evolutionary laws under changing constraints could develop in the future. On the basis of these predictions, an early warning system for future pandemics would be conceivable. And that is in the end, our goal, an early warning system.
And in this way, we can learn to live with the evolution of viruses. And that is a main message here. We cannot eliminate the viruses, but we have to learn to live with this kind of viral evolution in the future, with the help here of these early warning systems that can only be realized with AI machine learning, large databases, and supercomputers. But the exponentially growing possibilities of viral evolutionary trees cannot be fully mapped and computed with supercomputers, no matter how large they are.
It is conceivable, however, that at least for restricted classes of such viral evolutionary trees under certain constraints, genetically generated countermeasures can be predicted. And in this way, a kind of learning algorithms could be created in order to be able to react quickly if the worst comes to the worst. The entire business of science would itself be a learning system that is gradually expanded through new experiences. So in the end, we are meeting viral evolution, with an evolution of artificial intelligence.
But let me conclude here with some remarks concerning the societal and even ethical impact of this approach. This learning process seems like a kind of armament spiral of weapons and anti-weapons like in politics. But however, as in politics, it is also clear that we cannot get by with the armament of AI supported vaccination strategies alone. To solve the problem sustainably, we must also change the way we deal with nature.
Globalization must be made sustainable. More mindfulness in our nature is the fundamental condition for this. The ruthless invasion of the domains of Laura and Fauna also massively disrupts viral balances. Thinking in terms of systems biology and bioinformatics, with the close relationship of all forms of life on the level of proteins and ultimately of biological codes, one can only be appalled at the way humans treat nature.
It is in the end the holiness of nature with a sensitive position of us humans, which becomes obvious in the computer. And which demands a new way, and this is my urgent plea, a new way, a more sustainable way to treat nature. Really fascinating subject, looking at how we can use math to unlock this code. Dr.
Meinzer, thank you again so much for taking the time to speak with us. Yeah, thank you for your nice questions. Dr. Klaus Meinzer, he's author of Life as Machine, How We Decipher the Coronocode.
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