Dmitry Korkin: Computational Biology of Coronavirus | Lex Fridman Podcast #90 | Transcription

Transcription for the video titled "Dmitry Korkin: Computational Biology of Coronavirus | Lex Fridman Podcast #90".

1970-01-01T19:11:52.000Z

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Introduction

Intro (00:00)

The following is a conversation with Dmitry Korkin. He's a professor of bioinformatics and computational biology at WPI, Worcester Polytechnic Institute, where he specializes in bioinformatics of complex diseases, computational genomics, systems biology, and biomedical data analytics. I came across Dmitry's work when in February, his group used the viral genome of the COVID-19 to reconstruct the 3D structure of its major viral proteins. And their interaction with the human proteins. In effect, creating a structural genomics map of the coronavirus and making this data open and available to researchers everywhere. We talked about the biology of COVID-19, SARS and viruses in general, and how computational methods can help us understand their structure and function in order to develop antiviral drugs and vaccines. This conversation was recorded recently in the time of the coronavirus pandemic. For everyone feeling the medical, psychological, and financial burden of this crisis, I'm sending love your way. Stay strong, we're in this together, we'll beat this thing. This is the Artificial Intelligence Podcast.


Discussion On Viruses And Pandemics

The Romance of the RNA World (01:11)

If you enjoy it, subscribe on YouTube, review it with five stars and app of podcast, support it on Patreon, or simply connect with me on Twitter at Lex Friedman, spelled F-R-I-D-M-A-N. This show is presented by CashApp, the number one finance app in the App Store. When you get it, use code LexPodcast. CashApp, let's you send money to friends by Bitcoin and invest in the stock market with as little as $1. Since CashApp allows you to buy Bitcoin, let me mention that cryptocurrency in the context of the history of money is fascinating. I recommend A Sent of Money as a great book on this history. Debits and credits on ledgers started around 30,000 years ago. The US dollar created over 200 years ago. And Bitcoin, the first decentralized cryptocurrency, released just over 10 years ago. So given that history, cryptocurrency is still very much in its early days of development, but it's still aiming to, and just might redefine the nature of money. So again, if you get CashApp from the App Store Google Play and use the code LexPodcast, you get $10, and CashApp will also donate $10 to first, an organization that is helping to advance robotics and STEM education for young people around the world. And now here's my conversation with Dmitry Korkin. Define viruses, terrifying or fascinating?


In defense of viruses (02:36)

When I think about viruses, I think about them, I mean, I imagine them as those villains that do their work so perfectly well. That is impossible not to be fascinated with them. - So what do you imagine when you think about a virus? Do you imagine the individual, sort of these 100 nanometer particle things, or do you imagine the whole pandemic, like society level? The, when you say the efficiency of which they do their work, do you think of viruses as the millions that occupy human body or living organism, society level, like spreading as a pandemic, or do you think of the individual little guy? - Yes, this is, I think this is a unique concept that allows you to move from micro scale to the macro scale. All right, so the virus itself, I mean, it's not a learning organism. It's a machine, to me, it's a machine, but it is perfected to the way that it essentially has a limited number of functions, it needs to do necessary some functions. And essentially has enough information just to do those functions, as well as the ability to modify itself. So, you know, it's a machine, it's an intelligent machine. So, yeah, maybe on that point, you're in danger of reducing the power of this thing by calling it a machine, right? But you now mentioned that it's also possibly intelligent. It seems that there's these elements of brilliance that a virus has, of intelligence, of maximizing so many things about its behavior into an insured survival, and its success. So, do you see it as intelligent?


Intelligence of viruses (04:42)

So, you know, I think the, it's a different, I understand it differently than, you know, I think about, you know, intelligence of a human kind or intelligence of the, of the, you know, of the artificial intelligence mechanisms. I think the intelligence of a virus is in its simplicity. The ability to do so much with so little material and information. But also, I think it's interesting. It keeps me thinking, you know, it keeps me wondering whether or not it's also the, the, an example of the basic swarm intelligence, where, you know, essentially the viruses act as the whole and they're extremely efficient in that. So, what do you attribute the incredible simplicity and the efficiency to, is it the evolutionary process? To maybe another way to ask that, if you look at the next 100 years, are you more worried about the natural pandemics or the engineered pandemics? So, how hard is it to build a virus? Yes, it's, it's a very, very interesting question because obviously there's a lot of conversations about the, you know, whether we are capable of engineering a, you know, an even worse virus. I personally expect and am mostly concerned with the natural curing viruses, simply because we keep seeing that. We keep seeing new strains of influenza emerging, some of them becoming pandemic. We keep seeing new strains of coronaviruses emerging. This is a natural process and I think this is why it's so powerful. You know, if you ask me, you know, did, I've read papers about scientists trying to study the capacity of the modern, you know, by technology to alter the viruses.


Natural versus engineered pandemics (07:20)

But I hope that, that, you know, it won't be our main concern in the near future. What do you mean by hope? Well, you know, if you look back and look at the history of the, of the most dangerous viruses, right? So the first thing that comes into mind is a smallpox. So right now there is perhaps a handful of places where this, you know, the, the, the strains of this virus are stored, right? So this is essentially the effort of the whole society to limit the access to those viruses. You mean in a lab in a controlled environment in order to study and then smallpox is one of the viruses for which should be stated, there's a vaccine is developed. Yes, yes. And that's, you know, it's until 70s, it, I mean, in my opinion, it was perhaps the most dangerous thing that was there. Is that a very different virus than, than the influenza and the coronaviruses? It is. It is different in several aspects. Biologically, it's, you know, so-called double stranded DNA virus, but also in the way that it is much more contagious. So the R not for, so this is this is the... What's R not? R not is essentially an average number as person infected by the virus can spread to other people. So then the average number of people that he or she can, you know, spread it to.


Virus contagion at scale (09:41)

And, you know, the, there is still some, you know, discussion about the estimates of the current virus, you know, the estimations vary between, you know, 1.5 and 3. In case of smallpox, it was 5 to 7. And we're talking about the exponential growth, right? So that's, that's a very big difference. It's not the most contagious one. Measles, for example, it's, I think, 15 and up. So it's, it's, you know, but it's definitely, definitely more contagious that the seasonal flu than the current coronavirus or SARS for that matter. What makes a virus more contagious? I'm sure there's a lot of variables that come into play, but is it, is it that whole discussion of aerosol and like the size of droplets, if it's airborne or is there some other stuff that's more biology centered? I mean, there are a lot of components and there are biological components that there are also, you know, social components. The ability of the virus to, you know, so the, the ways in which the virus has spread is definitely one. The ability to virus to stay on the surfaces to survive. The ability of the virus to replicate fast. So, you know, once it's in the cell of whatever. Once it's inside the host. And interestingly enough, something that I think we didn't pay that much attention to is the incubation period, the, where, you know, hosts are symptomatic. And now it turns out that another thing that we, one really needs to take into account the percentage of the symptomatic population. Because those people still shed this virus and still are, you know, they still are contagious. So, there's an, the Iceland study, which I think is probably the most impressive size-wise. Shows 50% asymptomatic to this virus. I also recently learned the swine flu is like the just the number of people who got infected was in the billions. It was some crazy number. It was like, it was like 20% of the population, 30% of the population, something crazy like that. So, the lucky thing there is the fatality rate is low. But the fact that a virus can just take over an entire population so quickly. Yes. It's terrifying. I think, I mean, this is, you know, that's perhaps my favorite example of a butterfly effect. Because it's really, I mean, it's just even tinier than a butterfly. And look at, you know, and with, you know, if you think about it, right? So, it used to be in, in those bad species. And perhaps because of, you know, a couple of small changes in the viral genome, it first had, you know, become capable of jumping from bats to humans. And then it became capable of jumping from human to human. Right. So this is, this is, I mean, it's not even this size of a virus. It's the size of several, you know, several atoms or says, you know, a few atoms. And over sudden this change has such a major impact. So is that a mutation like on a single virus? Is that like, so if we talk about those, the, the flap of a butterfly wing, like, what's the first flap? Well, I think this is the, the mutations that make, that made this virus capable of jumping from bad species to human. Of course, there's, you know, the scientists are still trying to find, I mean, they're still even trying to find the, the, who was the first infected, right? The patient zero. The first human. The first human infected, right? I mean, the fact that there are coronaviruses, different strains of coronaviruses in various bad species. I mean, we know that. So, so we, you know, virologists, absurd them, they study them, they look at their, you know, genomic sequences, they are trying, of course, to understand what make this virus is to jump from, from bats to human. There was, you know, similar to that, and, you know, in influenza, there was, I think, a few years ago, there was this, you know, interesting story where several groups of scientists studying influenza virus essentially, you know, made experiments to show that this virus can jump from one species to another, you know, by changing, I think, just a couple of residues.


Mutating proteins to make pandemics (14:52)

And, and, and, of course, it was very controversial. I think there was a moratorium on this study for a while, but then the study was released. It was published. So that, so why was there moratorium? It's because it shows through engineering it, through modifying it, you can make it jump. Yes. Yes. I, I personally think it is important to study this. I mean, we should be informed, we should try to understand as much as possible in order to prevent it. But so then the engineering aspect there is, can't you then just start searching because there's so many strands of viruses out there. Can't you just search for the ones in bats that are the deadliest from the virologist perspective and then just try to engineer, try to see how to, but see, that's a, there's a nice aspect to it. The really nice thing about engineering viruses, it has the same problems, nuclear weapons is, it's hard for it to not to lead to mutual self destruction. So you can't control a virus. It can't be used as a weapon, right? Yeah, that's why I, in the beginning, I said, I'm hopeful because definitely, there are definitely regulations to be needed to be introduced. And I mean, as the scientific society is, we are in charge of making the right actions, making the right decisions. But I think we will benefit tremendously by understanding the mechanisms by which the virus can jump, by which the virus can become more, you know, more dangerous to humans. Because all these answers would, you know, eventually lead to designing better vaccines, hopefully universal vaccines, right? And that would be a triumph of the, of science.


Covid-19 Pandemic (17:56)

So what's the universal vaccine? So is that something that, well, how universal is universal? Well, I mean, you know, so what's the dream, I guess, because you kind of mentioned the dream of this. I would be extremely happy if, you know, we designed the vaccine that is able, I mean, I'll give you an example. So every year, we do a seasonal flu shot. The reason we do it is because, you know, we are in the arms race, you know, our vaccines are in the arms race with, with constantly changing virus. Right. Now, if the next pandemic, influenza pandemic will occur, most likely this vaccine would not save us. Right. Although it's, it's, you know, it's the same virus might be different strain. So if we're able to essentially design a vaccine against, you know, influenza a virus, no matter what's the strain, no matter which species did a jump from, that would be, I think that would be a huge, huge progress and advancement. You mentioned a smallpox until the 70s might have been something that you would be worried the most about. What about these days? Well, we're sitting here in the middle of a COVID-19 pandemic, but these days, nevertheless, what is your biggest worry virus wise? What are you keeping your eye on? It looks like, and, you know, based on the past several years of the, of the new viruses emerging, I think we're still dealing with different types of influence. I mean, so the H7N9 avian flu that was, that emerged, I think a couple of years ago in China, I think the, the, the mortality rate was incredible. I mean, it was, you know, I think about 30%. You know, so this is, this is huge. I mean, luckily for us, this strain was not pandemic, right?


Balance between pandemic and harm (20:17)

So it was jumping from birds to human, but I don't think it, it, it was actually transmittable between the humans. And, you know, this is actually a very interesting question, which scientists try to understand, right? So the balance, the delicate balance between the virus being very contagious, right? So efficient in spreading and virus to be very pathogenic, you know, causing, you know, harms, you know, and, and deaths to the host. So it looks like that the more pathogenic the virus is, the less contagious it is. Is that a property of biology or what is it? What is it? I don't have an answer to that. And I think this is, this is still an open question. But, you know, if you look at, you know, with the coronavirus, for example, if you look at, you know, the, the deadlier relative, MERS, MERS was never a pandemic virus. Right. But the, you know, the, again, the, the mortality rate from MERS is far above, you know, I think 20 or 30%. So, so whatever is making this all happen doesn't want us dead, because it's balancing out nicely. I mean, how do you explain that we're not dead yet? Like, because there's so many viruses and they're so good at what they do. Why do they keep us alive? I mean, we, we also have, you know, a lot of protection, right? So, the immune system and so, I mean, we do have, you know, ways to, to fight against those viruses. And I think with the, now we're much better equipped, right? So, with the discoveries of vaccines and, you know, there are vaccines against the viruses that maybe 200 years ago would wipe us out completely. But because of these vaccines, we are actually, we are capable of eradicating pretty much fully, as is the case with smallpox.


Attachment of protein to cells (22:50)

So, if we could, can we go to the basics a little bit of the biology, of the virus, how does the virus infect the body? So, I think there are some key steps that the virus needs to perform. And of course, the first one, the viral particle needs to get attached to the host cell. In the case of coronavirus, there is a lot of evidence that it actually interacts in the same way of the, as the SARS coronavirus. So, it gets attached to AC2 human receptor. And so, there is, I mean, as we speak, there is a growing number of papers suggesting it. Moreover, most recent, I think most recent results suggest that this virus attaches more efficiently to this human receptor than SARS. To suggest this, or back off, there is a family viruses, the coronaviruses, and SARS, whatever the heck, for that, this is the respiratory, whatever that stands for. So SARS actually stands for the disease that you get is the syndrome of acute respiratory. So SARS is the first strand, and there's MERS, MERS, and the family. And there is, yes, people, scientists actually know more than three strands. I mean, so there is the MHV strain, which is considered to be a canonical model, disease model in mice. And so there is a lot of work done on this virus because it's... But it hasn't jumped to humans yet? No, interesting. Yes, fascinating. So, and imagine AC2, so when you say, "attach, proteins are involved on both sides."


Attachment proteins (24:57)

Yes, so we have this infamous spike protein on the surface of the virion particle, and it does look like a spike. And I mean, that's essentially because of this protein, we call the coronavirus, coronavirus. So that what makes corona on top of the surface, so this protein, it actually acts... So it doesn't act alone. It actually makes a three copies, and it makes so-called trimer. So this trimer is essentially a functional unit, a single functional unit that starts interacting with the AC2 receptor. So this is, again, another protein that now sits on the surface of a human cell, a host cell, I would say. And that's essentially in that way, the virus anchors itself to the host cell because then it needs to actually... It needs to get inside. It fuses its membrane with the host membrane. It releases the key components. It releases its RNA, and then essentially hijacks the machinery of the cell because none of the viruses that we know of have ribosome, the machinery that allows us to print out proteins. So in order to print out proteins that are necessary for functioning of this virus, it actually needs to hijack the host ribosomes. So virus is an RNA wrapped in a bunch of proteins, one of which is this functional mechanism of a spike protein that does the attachment. So if you look at this virus, there are several basic components. So we start with the spike protein. This is not the only surface protein, the protein that lives on the surface of the viral particle. So there is also perhaps the protein with the highest number of copies is the membrane protein. So it essentially forms the envelope of the protein, of the viral particle, and essentially helps to maintain a certain curvature, helps to make a certain curvature. Then there is another protein called envelope protein or e-protein, and it actually occurs in far less quantities.


Surface proteins: a, structural protein and b, envelope protein (28:00)

And still there is ongoing research what exactly does this protein do. So these are sort of the three major surface proteins that make the viral envelope. And when we go inside, then we have another structural protein called nuclear protein. And the purpose of this protein is to protect the viral RNA. So it actually binds to the viral RNA, creates a capsid. And so the rest of the viral information is inside of this RNA. And if you compare the amount of the genes or proteins that are made of these genes, it's significantly higher than influenza virus. For example, influenza virus has around eight or nine proteins where this one has at least 29. Wow, that has to do with the length of the RNA strand. So it affects the length of the RNA strand. So because you essentially need to have the minimum amount of information to encode those genes. How many proteins did you say? 29. Yes. So this is something definitely interesting because, believe it or not, we've been studying coronaviruses for over two decades. We've yet to uncover all functionalities of these proteins. Could we maybe take a small tangent and can you say how one would try to figure out what a function of a particular protein is? So you've mentioned people are still trying to figure out what the function of the envelope protein might be or what's the process? So this is where the research that computational scientists do might be of help because in the past several decades, we actually have collected pretty decent amount of knowledge about different proteins in different viruses. So what we can actually try to do, and this could be our first lead to a possible function, is to see whether those, say we have this genome of the coronavirus, of the novel coronavirus, and we identify the potential proteins. Then in order to infer the function, what we can do, we can actually see whether those proteins are similar to those ones that we already know. In such a way, we can, for example, clearly identify some critical components that RNA polymerase or different types of proteases. These are the proteins that essentially clip the protein sequences. So this works in many cases. However, in some cases, you have truly novel proteins, and this is a much more difficult task. Now, as a small pause, when you say similar, like what if some parts are different and some parts are similar, like how do you disentangle that? You know, it's a big question. Of course, you know, what bioinformatics does, it does predictions.


Data input, algorithms, and predictions (31:51)

So those predictions, they have to be validated by experiments. Functional or structural predictions? Both. I mean, we do structural predictions, we do functional predictions, we do interactions predictions. So this is interesting. So you just generate a lot of predictions, like reasonable predictions, based on structural function, interaction, like you said.


Challenges And Future Of Protein Folding

Long-standing problems (32:16)

And then here you go. That's the power of bioinformatics is data grounded, good predictions of what should happen. So in a way, I see it, we're helping experimental scientists to streamline the discovery process. And experimental scientists, is that what a virologist is? So yeah, virologist, one of the experimental sciences that focus on viruses, they often work with other experimental scientists. For example, the molecular imaging scientists, right? So the viruses often can be viewed and reconstructed through electro-microscopy techniques. So but these are, you know, specialists that are not necessary virologists, they work with small particles, whether it's viruses or it's an organelle of a human cell, whether it's a, you know, complex molecular machinery. So the techniques that are used are very similar in sort of in its, in their essence. And so yeah, so, so typically, and in, we see it now, the research on, you know, that is emerging and that is needed often involves the collaborations between virologists, you know, biochemists, you know, people from pharmaceutical sciences, computational sciences. So we have to work, you know, together. So from my perspective, just to step back, sometimes I look at this stuff, just the, how much we understand about RNA DNA, how much we understand about protein, like your work, the amount of proteins that you're exploring, is it surprising to you that we were able, we descendants of apes were able to figure all of this out? Like how? So you're a computer scientist. So for me, from computer science perspective, I know how to write a Python program, things are clear, but biology is a giant mess. It feels like to me from an outsider's perspective. Is how surprising is it amazing is it that we were able to figure this stuff out? You know, if you look at the, you know, how computational science and computer science was evolving, right? I think it was just a matter of time that we would approach biology. So, so we started from, you know, applications to much more fundamental systems, physics, you know, and now we are, or, you know, small chemical compounds. All right, so now we are approaching the more complex biological systems. And I think it's a natural evolution of, you know, of the computer science of mathematics.


Order, pattern, and complexity (35:46)

So sure, that's the computer science. I just met even in higher level. So that to me is surprising that computer science can offer help in this messy world. But it just means it's incredible that the biologists and the chemists can figure all this out. Or is that just sound ridiculous to you that that of course they would. It just seems like a very complicated set of problems like the, the variety of the kinds of things that could be produced in the body. The just, just like you said, 29 approach. I mean, just getting a hand of it, a hang of it so quickly, it just seems impossible to me. I agree. I mean, it's, and I have to say we are, you know, in the very, very beginning of this journey. I mean, we, we've yet to, I mean, we've yet to comprehend, not even try to understand and figure out all the details, but we've yet to comprehend the complexity of the cell. We know that neuroscience is not even at the beginning of understanding the human mind. So where's biology set in terms of understanding the function, deeply understanding the function of viruses and cells. So there sometimes it's easy to say when you talk about function, what you really refer to is perhaps not a deep understanding, but more of a understanding sufficient to be able to mess with it using a antivir-, like mess with it chemically to prevent some of its function. Or do you understand the function? Well, I think we are much farther in terms of understanding of the complex genetic disorders, such as cancer, where you have layers of complexity. And we, you know, as in my laboratory, we're trying to contribute to that research, but we're also, you know, overwhelmed with how many different layers of complexity, different layers of mechanisms that can be hijacked by cancer simultaneously. And so, you know, I think biology in the past 20 years, again, from the perspective of the outsider, because I'm not a biologist, but I think it has advanced tremendously. And one thing that we're a computational scientist and data scientists are now becoming very, very helpful is in the fact, it's coming from the fact that we are now able to generate a lot of information about the cell, whether it's next-generation sequencing or transcriptomics, whether it's life imaging information, where it is, you know, complex interactions between proteins or between proteins and small molecules, such as drugs. We are becoming very efficient in generating this information. And now the next step is to become equally efficient in processing this information and extracting the key knowledge from that. That could then be validated with experiment. Yeah. Back. So maybe then going all the way back, we were talking, you said the first step is seeing if you can match the new proteins you found in the virus, again, something we've seen before to figure out its function. And then you also mentioned that, but there could be cases where it's a totally new protein. Is there something bioinformatics can offer when it's a totally new protein? This is where many of the methods, and you're probably aware of the case of machine learning, many of these methods rely on the previous knowledge. Right. So things that where we try to do from scratch are incredibly difficult, you know, something that we call a banishio. And this is, I mean, it's not just the function. I mean, you know, we've yet to have a robust method to predict the structures of these proteins in Abenishio, you know, by not using any templates of other related proteins. So protein is a chain of amino acids residues. Residues. Yeah. And then how somehow magically, maybe you can tell me, they seem to fold in incredibly weird and complicated 3D shapes. Yes. So, and that's where actually the idea of protein folding, or just not the idea, but the problem of figuring out how the concept, the concept, how they fold into those weird shapes comes in. So that's another side of computational work. So can you describe what protein folding from the computational side is, and maybe your thoughts on the folding at home efforts that a lot of people know that you can use your machine to do protein folding? So yeah, protein folding is, you know, one of those $1 million price challenges, right? So the reason for that is we've yet to understand precisely how the protein gets folded so efficiently to the point that in many cases where you, you know, where you try to unfold it due to the high temperature, it actually falls back into its original state. Right. So we know a lot about the mechanisms, right? But putting those mechanisms together and making sense, it's a computation of very expensive task. In general, do proteins fold, can they fold in arbitrary large number of ways, or do they usually fold in a very small number of ways? No, it's typically, I mean, we tend to think that, you know, there is a one sort of canonical fold for a protein, although there are many cases where the proteins, you know, upon this stabilization, it can be folded into a different conformation. And this is especially true when you look at sort of proteins that include more than one structural unit. So those structural units, we call them protein domains. Essentially, protein domain is a single unit that typically is evolutionary preserved, that typically carries out a single function and typically has a very distinct fold, like the structure, 3D structure organization.


Solving protein folding (42:55)

But turns out that if you look at human, an average protein in a human cell would have to have a bit of two or three such subunits, and how they are trying to fold into the sort of, you know, next level fold, right? So within subunits, they are folding, and then they fold into the larger 3D structure, right? And all that, there's some understanding of the basic mechanisms, but not to put together to be able to fold it. We're still, I mean, we're still struggling. I mean, we're getting pretty good about folding relatively small proteins up to 100 residues, but we're still far away from folding, you know, larger proteins. And some of them are notoriously difficult, for example, transmembrane proteins, proteins that sit in the membranes of the cell. They're incredibly important, but they are incredibly difficult to solve. And so basically, there's a lot of degrees of freedom, how it folds, and so it's a combinatorial problem, or just explodes, there's so many dimensions. Well, it is a combinatorial problem, but it doesn't mean that we cannot approach it from the not from the brute force approach. And so the machine learning approaches, you know, have been emerged, that try to tackle it. So folding at home, I don't know how familiar you are with it, but is that used machine learning or is it more brute force? No, so folding at home, it was a regionally, and I remember I was a, I mean, it was a long time ago. I was a postdoc, and we learned about this, you know, this game, because it was originally designed as the game. And we, you know, I took a look at it, and it's interesting because it's really, you know, it's very transparent, very intuitive. So, and from what I heard, I've yet to introduce it to my son, but you know, kids are actually getting very good at folding the proteins. And it was, you know, it came to me as the, as the, not as a surprise, but actually as the sort of manifest of, you know, our capacity to do this kind of, to solve these kind of problems, when a paper was published in one of these top journals with the co-authors being the actual players of this game. So, and what happened is, was that they managed to get better structures than the sciences themselves. So, so that, you know, that was very, I mean, it was kind of profound, you know, revelation that problems that are so challenging for a computational science may be not that challenging for a human brain. Well, that's a really good, that's a hopeful message always when there's a, the proof of existence, the existence proof that it's possible. That's really interesting. But, it seems, what are the best ways to do protein folding now? So, if you look at what DeepMind does with alpha fold, alpha fold. So, they kind of, is that's a learning approach? What's your sense? I mean, you're background is a machine learning, but is this a learnable problem? Is this still a brute force? Are we in the, Gary Kasparov deep blue days? Are we in the alpha go playing the game of go days of folding? Well, I think we are, we are advancing towards this direction. I mean, if you look, so there is sort of Olympic game for protein folders called CASP. And it's essentially it's, you know, it's a competition where different teams are given exactly the same protein sequences, and they try to predict their structures. Right? And of course, there are different sort of subtasks, but in the recent competition, alpha fold was among the top performing teams, if not the top performing team. So, there is definitely a benefit from the data that have been generated, you know, in the past several decades, the structural data. And certainly, you know, we are now at the capacity to summarize this data, to generalize this data, and to use those principles, you know, in order to predict protein structures. That's one of the really cool things here is there's, maybe you can comment on it. There seems to be these open data sets of protein. How did that, what, the protein data bank? The protein data bank. I mean, that's great. Is this a recent thing for just the coronavirus or is this? No, it's been for many, many years. I believe the first protein data bank was designed on flashcards. So, yes, it's so this, I mean, this is a great example of the community efforts of everyone contributing, because every time you solve a protein or a protein complex, this is where you submit it. And, you know, the scientists get access to it, scientists get to test it. And we, by informations, use this information to, you know, to make predictions. So, there's no, there's no culture like hoarding discoveries here. So, it's, I mean, you've, you've released a few or a bunch of proteins that were matching, whatever, we'll talk about details a little bit, but it's kind of amazing that that's the, it's kind of amazing how open the culture here is. It is. And I think this pandemic actually demonstrated the ability of scientific community to, you know, to solve this challenge collaboratively. And this is, I think, if anything, it actually moved us to a brand new level of collaborations of the efficiency in which people establish new collaborations in which people offer the help to each other, scientists offer the help to each other. And publish results, too. It's very interesting. We're now trying to figure out, as a few journals that are trying to sort of do the very accelerated review cycle, but so many preprints. So just posting a paper going out. I think it's fundamentally changing the way we think about papers. Yes. I mean, the way we think about knowledge, now, let's say, now, yes, because yes, I completely agree. I think now it's, the knowledge is becoming sort of the core value, not the paper or the journal where this knowledge is published. And I think this is, again, this we are living in the times where it becomes really crystallized, that the idea that the most important value is in the knowledge.


The future of knowledge sharing (51:32)

So maybe you can comment, like, what do you think the future of that knowledge sharing looks like? So you have this paper that will, I hope we get a chance to talk about a little bit, but it has a really nice abstract introduction related, like, it has all the usual, I mean, probably took a long time to put together. But is that going to remain, like, you could have communicated a lot of fundamental ideas here in much shorter amount that's less traditionally acceptable by the journal context. So, well, the first version that we posted, not even on a bio archive, because by archive back then, it was essentially overwhelmed with the number of submissions. So our submission, I think it took five or six days to just for it to be screened and put online. So we, essentially, we put the first print on our website and it started getting accessed right away. So this original preprint was in a much rougher shape than this paper. But we tried, I mean, we honestly tried to be as compact as possible with introducing the information that is necessary, that the two explain our results. So maybe you can dive right in, if it's okay. Sure. So it's a paper called Structural Genomics of SARS-CoV-2. How do you even pronounce SARS-CoV-2? COV-2. Yeah. By the way, COVID is such a terrible name, but it's stuck. Yes. SARS-CoV-2 indicates evolutionary conserved functional regions of viral proteins. So this is looking at all kinds of proteins that are part of this novel coronavirus and how they match up against the previous other kinds of coronaviruses. I mean, there's a lot of beautiful figures. I was wondering if you could, I mean, there's so many questions I could ask here, but maybe at the, how do you get started doing this paper? So how do you start to figure out the 3D structure of the novel virus? Yes. So there is actually a little story behind it. And so the story actually dated back in September of 2019. And you probably remember that back then we had another dangerous virus, triple E virus, is Eastern, a queen in encephalitis virus. And can you maybe linger on it? I have to admit, I was sadly completely unaware. So that was actually a virus outbreak that happened in New England only. The danger in this virus was that it actually targeted your brain. So the word deaths from this virus, it was, it was trans, you know, the main vector was mosquitoes. And obviously full time is, you know, the time where you have a lot of them in New England.


How you get started doing the protein structures (55:18)

And, you know, on one hand, people realized this is this is this is actually very dangerous thing. So it had an impact on the local economy. The schools were closed past six o'clock, no activities outside for the kids, because the kids were suffering quite tremendously from, you know, when infected from this virus. How do I not know about this was, it was impacted. It was in the news. I mean, it was not impacted to to high degree in in Boston, necessarily, but in the Metro West area. And actually spread around, I think, all the way to New Hampshire, Connecticut. And you mentioned affecting the brain. That's one other comment we should make. So you mentioned a AC two for the coronavirus. So these viruses kind of attached to something in the body. So it essentially attaches to the to these proteins. In those cells, in the body, where those proteins are expressed, where they actually have them in abundance. So sometimes that could be in the lungs, that could be in the brain, that could be so I think what they right now, from what I read, they have the epithelial cells inside. So the cells essentially inside the cells that are covering the surface.


SOARS - Cop2 receptors (56:57)

And also inside the nasal surfaces, the throat, the lung cells. And I believe, liver, a couple of other organs where they are actually expressed in abundance. That's for the AC two, for the AC two receptors. So, okay, so back to the story. Yes, in the fall. So now the this, you know, the impact of this virus is significant. However, it's a pre local problem to the point that, you know, this is something that we would call a neglected disease, because it's not big enough to make, you know, the drug design companies to design a new antiviral or a new vaccine. It's not big enough to generate a lot of grants from the nation of finding agencies. So, so does it mean we cannot do anything about it? And so what I did is I taught a bioinformatics class and is in Worcester Power Technic Institute. And we are very much a problem learning institution. So I thought that that would be a perfect, you know, perfect project. I'm going case study. So, so I asked it, you know, so, so I we essentially designed a study where we tried to use bioinformatics to to understand as much as possible about this virus. And a very substantial portion of the study was to understand the structures of the proteins, to understand how they interact with with each other. And with the with the host proteins, try to understand the evolution of this virus. So obviously, you know, a very important question, how, where it will evolve further, how, you know, how it happened here, you know. So, so we did all these, you know, projects. And now I'm trying to put them into a paper where all these undergraduate students will be co-authors. But essentially, the projects were finished right about mid December. And a couple of weeks later, I heard about this mysterious new virus that was discovered in, you know, was reported in Wuhan province. And immediately I thought that, well, we just did that. Can't we do the same thing with this virus? And so we started waiting for the genome to be released, because that's essentially the first piece of information that is critical. Once you have the genome sequence, you can start doing a lot using bioinformatics. When you say genome sequence, that's referring to the sequence of letters that make up the RNA. So the sequence that make up the entire information and code it in the protein, right? So, so that includes all 29 genes. What are genes? What's the encoding of information? So, genes is essentially is a basic functional unit that we can consider. So, so each gene in the virus would correspond to a protein that so gene by itself doesn't do it function. It needs to be converted or translated into the protein that will become the actual functional unit. Like you said, the printer. So we need the printer for that. We need the printer. Okay. So the first step is to figure out the genome, the sequence of things that will be then used for putting the protein. So, okay. So then the next step. So, once we have this, and so we use the existing information about SARS-CoS, the SARS genomics has been done in abundance. So we have different strains of SARS and actually other related coronaviruses, MERS, the bad coronavirus. And we started by identifying the potential genes because right now it's just a sequence, right? So it's a sequence that is roughly it's less than 30,000 nucleotide long. And just a raw sequence. It's a raw sequence. No other information really. And we now need to define the boundaries of the genes that would then be used to identify the protein and protein structures. How hard is that problem? I mean, it's pretty straightforward. So, you know, so, because we use the existing information about SARS proteins and SARS genes. So once again, you kind of, we are relying on the, yes. So, and then once we get there, this is where sort of the first more traditional bine fematic steps, the step begins. We're trying to use these protein sequences and get the 3D information about those proteins. So this is where we are relying heavily on the structure information specifically from the protein data bank that we are talking about. And here you're looking for similar proteins. Yes. So the concept that we are operating when we do this kind of modeling, it's called homology or template-based modeling. So essentially, using the concept that if you have two sequences that are similar in terms of the letters, the structures of these sequences are expected to be similar as well. And this is at the micro, at the very local scale and at the scale of the whole protein. The whole protein. I saw, actually, so, you know, so, of course, the devil is in details. And this is why we need actually, pre-sophisticated modeling tools to do so. Once we get the structures of the individual proteins, we try to see whether or not these proteins act alone or they have to be forming protein complexes in order to perform this function. And again, so this is sort of the next level of the modeling because now you need to understand how proteins interact. And it could be the case that the protein interacts with itself and makes sort of a multi-meric complex. The same protein just repeat it multiple times. And we have quite a few such proteins in SARS-CoV-2, specifically, spike protein needs three copies, two function. And the whole protein needs five copies to function. And there are some other multi-meric complexes. That's what you mean by interacting with itself and you see multiple copies. So how do you make a good guess whether something is going to interact? Well, again, so there are two approaches, right? So one is look at the previously solved complexes. Now we're looking not at the individual structures, but the structures of the whole complex. Complex is multiple proteins. So it's a bunch of proteins essentially glued together. And when you say glue, that's the interaction. That's the interaction. So the different forces, different sort of physical forces behind this. Sorry to keep asking dumb questions, but is it the interaction fundamentally structural or is it functional? Like in the way you're thinking about it. That's actually a very good way to ask this question because it turns out that the interaction is structural, but in the way it forms the structure, it actually also carries out the function. So interaction is often needed to carry out very specific function for protein.


Understanding The Structure And Function Of Proteins

Figuring out the structure before function (01:06:32)

But in terms of an error side, figuring out you're really starting at the structure before you figure out the function. So there's a beautiful figure two in the paper of all the different proteins that make up, able to figure out the makeup, the new novel coronavirus. What are we looking at? So these are like, that's through the step to the dimension when you try to guess at the possible proteins, that's what you're going to get. Is this blue cyan blobs? Yes. So those are the individual proteins for which we have at least some information from the previous studies. So there is advantage and disadvantage of using previous studies. The biggest, well, the disadvantage is that we may not necessarily have the coverage of all 29 proteins. However, the biggest advantage is that the accuracy in which we can model these proteins is very high, much higher compared to a Banisho methods that do not use any template information. So, but nevertheless, this figure also has, it's just beautiful. I love these pictures so much. It has the pink parts, the parts that are different. So you're highlighting, so the difference you find is on the 2D sequence and then you try to infer what that will look like on the 3D.


The pink marker difference on a 3D structure (01:08:11)

So the difference actually is on 1D sequence. 1D, 1D sorry, that's 2D right. So, and so this is one of these first questions that we tried to answer is that, well, if you take this new virus and you take the closest relatives, which are SARS and a couple of bad coronavirus strains, they are already the closest relatives that we are aware of. Now, what are the difference between this virus and its close relatives? And if you look typically when you take a sequence, those differences could be quite far away from each other. So what 3D structure makes those difference to do, very often they tend to cluster together. And over sudden, the differences that may look completely unrelated actually relate to each other and sometimes they are there because they attack the functional site.


Functional informatics (01:09:26)

So they are there because this is the functional site that is highly mutated. So that's a computational approach to figuring something out. When it comes together like that, that's kind of a nice clean indication that there's something this could be actually indicative of what's happening. Yes, I mean, so we need this information and the 3D structure gives us just a very intuitive way to look at this information and then start asking questions such as, so this place of this protein that is highly mutated, is it a functional part of the protein? So does this part of the protein interact with some other proteins or maybe with some other ligands, small molecules? So we will try now to functionally inform this 3D structure. So you have a bunch of these mutated parts of how many are there in the new novel coronavirus in compared to SARS? We're talking about hundreds of thousands, these pink regions. No, much less than that. And it's very interesting that if you look at that, so the first thing that you start seeing, you look at patterns.


Proteins unchanged from SARS (01:11:15)

And the first pattern that becomes obvious is that some of the proteins in the new coronavirus are pretty much intact. So they are pretty much exactly the same as SARS, as the bad coronavirus, whereas some others are heavily mutated. So it looks like that the evolution is not occurring uniformly across the entire viral genome, but actually target very specific proteins. What do you do with that, from the Sherlock Holmes perspective? Well, one of the most interesting findings we had was the fact that the viral, so the binding sites on the viral surfaces that get targeted by the known small molecules, they were pretty much not affected at all. And so that means that the same small drugs or small drug-like compounds can be efficient for the new coronavirus. So this all actually maps to the drug compounds too. So you're actually mapping out what old stuff is going to work on this thing, and then possibilities for new stuff to work by mapping out the things that have mutated. So we essentially know which parts behave differently, and which parts are likely to behave similar. And again, of course, all our predictions need to be validated by experiments. But hopefully that sort of helps us to delineate the regions of this virus that can be promising in terms of the drug discovery. You kind of mentioned this already, but maybe you can elaborate. So how different from the structural and functional perspective does the new coronavirus appear to be relative to SARS? We now are trying to understand the overall structural characteristics of this virus, because that's our next step, trying to model the viral particle of a single viral particle of this virus. So that means you have the individual proteins. Like you said, you have to figure out what their interaction is. Is that where this graph kind of interactome? So the interactome is essentially a prediction on the potential interactions. Some of them that we already decipher from the structural knowledge, but some of them that essentially are deciphered from the knowledge of the existing interactions that people previously obtained for SARS, for MERS, or other related viruses. So is there kind of interactomes? Am I pronouncing that correctly by the way?


Structuring and evolved relationships (01:14:54)

Yeah, is those already converged towards for SARS? I think there are a couple of papers that now investigate the sort of the large scale set of interactions between the new SARS and its host. And so I think that's an ongoing study.


Structure of the novel Corona (01:15:26)

The success of that, the result would be an interactome. So when you say, not trying to figure out the entire thing, so structure, so what this viral particle looks like. So as I said, the surface of it is an envelope, which is essentially a so-called lipid bilayer with proteins integrated into the surface. So an average particle is around 18 nanometers. So this particle can have about 50 to 100 spike proteins. So at least we suspect it, and based on the micrographs images, it's very comparable to MHV virus in mice and SARS virus. Micrographs are actual pictures of the actual virus. So these are models. They are actual images. What do they start for the tangents? So when you look on the internet, the models and the pictures are, and the models you have here are just gorgeous and beautiful. When you actually take pictures of them or the micrograph, what are we looking for?


Antiviral Drugs And Nano Particle Design

Nano Particle Design Part 1 (01:16:46)

Well, they typically are not perfect. So most of the images that you see now is the sphere with those spikes. You actually see the spikes? Yes, you do see the spikes. And now, our collaborators for Texas and the university Benjamin Newman, he actually in the recent paper about SARS he proposed, and there is some actually evidence behind it that the particle is not a sphere, but it actually is elongated, ellipsoid particles. So that's what we are trying to incorporate into our model. If you look at the actual micrographs, you see that those particles are not symmetric. So there are some of them, and of course it could be due to the treatment of the material. It could be due to some noise in the imaging. So there is a lot of uncertainty in all this. So it's okay structurally figuring out the entire part. By the way, sorry for the tangents, but why the term particle? Or is it just something that stuck? It's a single, we call it the virion. So virion particle is essentially a single virus. Single virus, but it just feels like this particle to me from the physics perspective feels like the most basic unit. Because there seems to be so much going on inside the virus. It doesn't feel like a particle to me. Yes, well, yeah, it's probably, I think it's, virion is a good way to call it. So, okay, so trying to figure out the entirety of the system. Yes. So, you know, so this is, so the virion has 5200 spikes, trimer spikes. It has roughly 200 to 400 membrane protein dimers. And those are arranged in the very nice lattice. So you can actually see sort of the, it's like a carpet of. On the surface again. Exactly, on the surface. And occasionally you also see this envelope protein inside. And that's the one we don't know what it does. Exactly, exactly. The one that forms the pentamer, this very nice pentameric ring. And so, you know, so this is what we're trying to, you know, we're trying to put now all our knowledge together and see whether we can actually generate this overall virion model with an idea to understand, you know, well, first of all, to understand how how it looks like, how far it is from those images that were generated. But I mean, the implications are, you know, there is a potential for the, you know, nanoparticle design that will mimic this virion particle. Is the process of nanoparticle design, meaning artificially designing something that looks similar? Yes. You know, so, so, so, the one that can potentially compete with the actual virion particles and therefore reduce the effect of the infection. So is this the idea of like, what is the vaccine? So vaccine, vaccine, so, yeah, so there are two ways of essentially treating, and in the case of vaccine is preventing the infection. So vaccine is, you know, a way to train our immune system. So our immune system becomes aware of this new danger and therefore is capable of generating the antibodies, then will essentially bind to the spike proteins, because that's the main target for the, you know, for the vaccines design and block its functioning. If you have the spike with the antibody on top, it can no longer interact with AC2 receptor. So the process of designing a vaccine then is you have to understand enough about the structure, the virus itself, to be able to create an artificial particle. Well, I mean, so, so, so, the nanoparticle is a very exciting and new research. So there are already established ways to, you know, to make vaccines and there are several different ones. Right? So, so there is one. Where essentially the virus gets through the cell culture multiple times. So it becomes essentially, you know, adjusted to the specific embryonic cell. And as a result, becomes less, you know, compatible with the, you know, host human cells. So, and therefore, it's sort of the the, the idea of the life vaccine, where the, the, the particles are there, but they are not so efficient, you know, so they cannot replicate, you know, as rapidly as, you know, before the vaccine. And they can be introduced to the immune system. The immune system will learn and the person who gets this vaccine won't, won't get, you know, sick or, you know, will have mild, you know, mild symptoms. So then there is sort of different types of the way to introduce the non-functional, non-functional parts of this virus or the virus where some of the information is stripped down, for example, the virus with no genetic material. So, so we, we, we, we, can't replicate, you know, exactly. So it cannot replicate. It cannot essentially perform most of its function. That's the thing. But what is the biggest hurdle to design the one of these to arrive at one of these? Is it the work that you're doing in the fundamental understanding of this new virus or is it in the, from our perspective, well, complicated world of experimental validation and sort of showing that this, like, going to the whole process of showing this is actually going to work with FDA approval, all that kind of stuff. I think it's both. I mean, you know, our understanding of the molecular mechanisms will allow us to, you know, to design, to have more efficient designs of the vaccines. However, the once you design a vaccine, it needs to be tested. But when you look at the 18 months and the different projections, it seems like an exceptionally from historically speaking, maybe you can correct me, but it's even 18 months seems like a very accelerated timeline. It is. It is. I mean, I remember reading about the, you know, in a book about some previous vaccines that it could take up to 10 years to design and, you know, properly test a vaccine before its mass production. So yeah, we, you know, everything is accelerated these days. I mean, for better for worse, but, but, you know, we definitely need that. Well, especially with the coronavirus, I mean, the scientific community is really stepping up and working together. The collaborative aspect is really interesting. You mentioned, so the vaccine is one and then there's antiviral drugs. So antiviral drugs. So where, you know, vaccines are typically needed to prevent the infection. Right. But once you have an infection, one, you know, so what we try to do, we try to stop it. So we try to stop virus from functioning. And so the antiviral drugs are designed to block some critical function of the of the proteins from the viral from the virus. So there are a number of interesting candidates. And I think, you know, if you ask me, I, you know, I think remdesivir is perhaps the most promising. It's it has been shown to be, you know, inefficient and effective antiviral for SARS. Originally, it was the antiviral drug developed for a completely different virus, I think, for a bull and bar, Marburg. At high levels, you know how it works. So it tries to mimic one of the nucleotides in RNA. And essentially that that stops the replication from so messes. I guess that's what any viral drugs mess with some aspect of this process. So, so, you know, so essentially we try to stop certain functions of the virus.


Anti-Viral Drugs Part 1 (01:27:15)

There are some other ones, you know, that are designed to inhibit the proteins, the thing that clips protein sequences. There is one that was originally designed for malaria, which is a bacterial, you know, bacterial disease. So this is so cool. So but that's exactly where your work steps in is you're figuring out the functional then the structure is different. So like providing candidates for where drugs can plug in.


Is the impact from an antiviral (01:27:54)

Exactly. Well, yes, because, you know, one thing that we don't know is whether or not, so let's say we have a perfect drug candidate that is efficient against SARS and against MERS. Now, is it going to be efficient against new SARS-CoV-2? We don't know that and there are multiple aspects that can affect this efficiency. So for instance, if the binding sites or the part of the protein where this ligand gets attached, if this site is mutated, then the ligand may not be attachable to this part any longer. And, you know, our work and the work of other binematics groups, you know, essentially are trying to understand whether or not that will be the case or and it looks like for the ligands that we looked at, the ligand binding sites are pretty much intact, which is very promising. So if we can just like zoom out for a second, what are you optimistic? So there's two, well, there's three possible ends to the coronavirus pandemic. So one is there's or drugs or vaccines get figured out very quickly, probably drugs first. The other is the pandemic runs its course for this wave, at least. And then the third is, you know, things go much worse in some in some dark, bad, very bad direction. Do you see, let's focus on the first two. Do you see the anti drugs or the work you're doing being relevant for us right now in stopping the pandemic or do you hope that the pandemic will run its course? So the social distancing, things like wearing masks, all those discussions that we were having will be the method with which we fight coronavirus in the short term. Or do you think that it'll have to be anti-viral drugs?


Think we need antiviral drugs? (01:30:21)

I think, I think antivirals would be, I would view that as the, at least the short term solution. I see more and more cases in the news of those new drug candidates being administered in hospitals. And I mean, this is right now the best what we have. But do we need it? I would do it to reopen the economy. I mean, we definitely need it. I cannot sort of speculate on how that will affect the reopening of the economy because we are, you know, we are kind of deep in into the pandemic. And it's not just the states. It's also, you know, worldwide, you know, of course, you know, there is also the possibility of the second wave as we, you know, as you mentioned, and this is why, you know, we need to be super careful. We need to follow all the precautions that the doctors tell us to do. Are you worried about the mutation of the virus? So it's, of course, a real possibility. Now, how to what extent this virus can mutate? It's an open question. I mean, we know that it is able to mutate to jump from one species to another and to become transmittable between humans. Right. So, will it, you know, so let's imagine that we have the new antiviral will this virus become eventually resistant to this antiviral? We don't know. I mean, this is what needs to be studied. This is such a beautiful and terrifying process that a virus, some viruses, may be able to mutate to respond to the, to mutate around the thing we've put before it. Can you explain that process? Like, how does that happen? It's just, is that just the way of evolution? I would say so. Yes. I mean, it's, it's the evolutionary mechanisms. There's nothing imprinted into this virus that makes it, you know, it, it just the way it, it evolves. And actually, it's the way it caught a wolf with its host. It's just amazing. It's, especially the evolutionary mechanisms, especially amazing given how simple the virus is.


How does a virus mutate to resist a drug? (01:33:21)

It's incredible that it's, I mean, it's beautiful. It's beautiful because it's the, one of the cleanest examples of evolution working. Well, I think I mean, the, one of the sort of the reasons for its simplicity is because it does not require all the necessary functions to be stored. Right. So, it actually can hijack the majority of the necessary function from the host cell. It's also, so, so, so, the ability to do so in my view reduces the complexity of this machine drastically. Although, if you look at the, you know, most recent discoveries, right, so the scientists discovered viruses that are as large as bacteria, right? So, this MIMI viruses and mama viruses, it actually, those discoveries made sciences to reconsider the origins of the virus, you know, and what are the mechanisms and how, you know, what are the mechanisms, the evolutionary mechanisms that leads to the appearance of the viruses. By the way, I mean, you did mention the viruses are, I think you mentioned that they're not living. Yes, they're not living organisms. So, let me ask that question again. Why do you think they're not living organisms? Well, because they, they are dependent, the majority of the functions of the virus are dependent on the, on the host. So, let me do the devil's advocate. Let me be the philosophical devil's advocate here and say, well, humans, which we would say are living need our host planet to survive. So, you can basically take every living organism that we think of as definitively living. It's always going to have some aspects of its host that it needs, of its environment. So, is that really the key aspect of why a virus is that dependence? Because it seems to be very good at doing so many things that we consider to be intelligent. It's just that dependence part. Well, I mean, it, yeah, it's, it's, no, difficult to answer in this way. I mean, I, I, the way I think about the virus is, you know, in order for it to function, it needs to have the critical component, the critical tools that it doesn't have. So, I mean, that's, that's, you know, in my way, you know, the, the, it's not autonomous, right? So, that's how I separate the, the idea of the living organism on a very high level.


Aging-based simulations zooming out from the individual level (01:36:46)

Yes. Between the living organism and, and you have some, we have, I mean, these are just terms and perhaps they don't mean much, but we have some kind of sense of what autonomous means and that humans are autonomous. You've also done excellent work in the epidemiological modeling, the simulation of these things. So, the zooming out outside of the body doing the aging based simulation. So, that's where you actually simulate individual human beings and then the spread of viruses from one to the other. How does, at a high level, aging based simulation work? All right. So, it's, it's also one of these iron, ironing of timing. Because I mean, we, we, we've worked on this project for the past five years. And the New Year's Eve, I got an email from my PhD student that, you know, the last experiments were completed. And, you know, three weeks after that, we get, we get this Diamond Princess story. And you made it each other with the same, you know, the same news saying like, so the Diamond Princess is a cruise ship. Yes. And what was the project that you were working on? So, so the project, I mean, it's, you know, the codename, it started with a bunch of undergraduates. The codename was Zombies on a Cruise Ship. So, they, they wanted to essentially model the, the, you know, zombie apocalypses on a cruise ship. And, and, you know, after having, you know, some fun, we then thought about the fact that, you know, if you look at the cruise ships, I mean, the infectious outbreak is, has been one of the biggest threat, you know, threats to the cruise ship economy. So, perhaps the most, you know, frequently occurring, is the Norwalk virus. And this is essentially one of these stomach flues that you have. And, you know, it, it can be quite devastating. You know, so there are occasionally there are cruise ships get, you know, they, they, they get canceled, they get returned to the, back to the, to the origin. And so we wanted to study, and this is very different from the traditional epidemiological studies where the scale is much larger. So, we wanted to study this in a confined environment, which is a cruise ship. It could be a school. It could be other, you know, other places such as, you know, the, this large, large company, where people are in interaction. And the benefit of this model is we can actually track that in the real time. So, we can actually see the whole course of the evolution, the whole course of the interaction between the infected, infected, infected, horse, and, you know, the horse and the patterns and etc. So, so agent-based system or multi-agent system to be precisely is a good way to approach this problem because we can introduce the behavior of the, of the passengers, of the crews. And what we did for the first time, that's where, you know, we introduced some knowledge is we introduce a pathogen agent explicitly. So, that allowed us to essentially model the behavior on the horse side as well on the pathogen side. And over sudden we can, we can have a flexible model that allows us to integrate all the key parameters about the infections. So, for example, the virus, right? So, the ways of, of transmitting the virus between the, the horse. How long does virus survive on the surface, the formite? What is, you know, how much of the viral particles does a horse shed when he or she is a symptomatic versus symptomatic? And you can encode all of that into this path. And just for people who don't know, so agent-based simulation, usually the agent represents a single human being. And then there's some graphs, like contact graphs that represent the interaction between those human beings. So, yes. So, essentially, you know, so, so agents are, you know, individual programs that are run and parallel. And we, we can provide instructions for these agents, how to interact with each other, how to exchange information, in this case, exchange the infection. But in this case, in your case, you've added a pathogen as an agent. I mean, that's kind of fascinating.


Agent-Based Simulations And International Collaborations

Biological viruses in agent-based simulations (01:42:45)

It's a, it's kind of a brilliant, like, a brilliant way to condense the parameters, to aggregate, to bring the parameters together that represent the pathogen, the virus. Yes. That's fascinating, actually. So, yeah, it was a, you know, we realized that, you know, by bringing in the virus, we can actually start modeling, I mean, we are not no longer bounded by very specific sort of aspects of the specific virus. So, we end up, we started with, you know, Norwalk virus and, of course, zombies. But we continue to modeling Ebola virus, outbreak, flu, SARS. And because I felt that we need to add a little bit more sort of excitement for our undergraduate students. So, we actually model the virus from the contagion movie. So, M-E-V-1. And, you know, unfortunately, that virus, and we, we try to extract as much information. Luckily, the, this movie was, the scientific consultant was Jan Deepkin, a virologist from Columbia University, who is actually, who provided, I think, he designed this virus for this movie based on NEPA virus. And I think with some ideas behind SARS of flu, like airborne viruses. And, you know, the, the movie surprisingly contained enough details for us to extract and to model it. I was hoping he would, like, publish a paper of how this virus works. Yeah, we, we are planning to publish. I would love it if you just, but it would be nice if, you know, the, the origin of the virus. But you're now actually being a scientist and studying the virus from that perspective. But the origin of the virus, you, you know, you know, the first time, actually, so this movie is assignment number one in my bioinformatics class that they, that they give. Yeah. Because it, it also tell, it tells you that, you know, bioinformatics can be of use because if, I don't know, you watched it, have you watched it? A long time ago. So, so there is, you know, approximately a week from the, you know, virus detection, we see a screenshot of a scientist looking at the structure of the surface protein. And this is where I tell my students that, you know, if you ask experimental biologists, they will tell you that it's impossible because it takes months, maybe years to get the crystal structure of this, you know, the structure that is represented. If you ask a bioinformatician, they tell you, sure, why not, you know, we'll just get it modeled. And, and, yes, but, but it was very interesting to, to see that there is actually, you know, and if you do its, you know, do screenshots, you actually see the phillogenetic tree, the evolutionary tree that relate this virus with other viruses. So it was a lot of scientific thought put into the movie. And one thing that I was actually, you know, it was interesting to, to learn is that the origin of this virus was, there were two, uh, animals that led to the, you know, the, the, you know, the zonotic, uh, origin of this virus were fruit bat and a pig. So, you know, so, so, so, so, this is, this doesn't feel like we're, this, this definitely feels like we're living in a simulation. Okay. Uh, but maybe a big picture, aging-based simulation now, larger scale, sort of not focused on a clucher, but larger scale are used now to drive some policy. So politicians using it to tell stories and narratives and try to figure out how, how to move forward under so much, so much uncertainty. But in your sense, are aging-based simulation useful for actually predicting the future?


Lockdowns: Unconsciously random? (01:47:25)

Or are they useful mostly for comparing relative comparison of different intervention methods? Well, I think both because, you know, in the case of, uh, new coronavirus, we, we essentially learning that the, uh, current intervention methods may not be efficient enough. One thing that one, um, important aspect that I find to be so critical and yet something that was overlooked, you know, during the past pandemics is the effect of the symptomatic period. This virus is different because it has such a long symptomatic, uh, period. And over sudden that creates a completely new game when trying to contain this virus. In terms of the dynamics of the infection. Exactly. Uh, do you also, I don't know how close you're tracking this, but, uh, do you also think that there's a different, like, uh, rate of infection from when you're asymptomatic, like that, that aspect, or does the virus not care? So, uh, there were a couple of works. Um, so one important parameter that tells us how, uh, contagious the, the person with asymptomatic versus asymptomatic is, uh, looking at the number of viral particles. This person sheds, you know, as a function of time. Um, so, so far what I saw is, uh, the study that, tells us that the, you know, the person during the asymptomatic period is already contagious. And it sheds, uh, the person sheds enough viruses to infect. Yeah, not at all. And I think there's too many excellent papers coming out, but I think I just saw some, maybe a nature paper that said the first week is when you're symptomatic or asymptomatic, you're the most contagious. So the highest level of, uh, the, like their, their plots are of in the 14 day period. They collected a bunch of subjects. And I think the first week is one of the most. Yeah, I think, I mean, I'm waiting, I'm waiting to see sort of more, uh, more populated studies, where I just say it was kind of numbers. Um, my, uh, one of my favorite studies was, again, very recent one where, uh, scientists determined that, um, tears are not contagious. So, so there's, you know, so there's no viral shading done through, through tears. So they found one moist thing that's not contagious. And I mean, there's a lot of, I'm personally been, because I'm on a survey paper somehow that's looking at masks. And there's been so much interesting debates and the efficacy of masks and there's a lot of work. And there's a lot of interesting work on, uh, whether this virus is airborne. I mean, it's a totally open question. There's, it's leaning one way right now, but it's a totally open question, whether it can travel in aerosols long distances. I mean, do you have a, do you think about the stuff? Do you track the stuff? Are you focused on them? Yeah, I mean, I'm from out of it. I mean, did this is, uh, this is a very important aspect for our epidemiology study. Um, I think the, I mean, and it's sort of a, a very simple, uh, sort of idea, but, uh, I agree with people who say that, uh, the mask, the masks work in both the, in both ways. So it not only protects you from the, you know, incoming viral particles, but also put the, you know, it, it, you know, makes the potentially, uh, contagious person not to spread the viral particles. Who is, when they're asymptomatic, may not even know that they're exactly, in fact, it seems to be there's evidence that they don't, surgical and certainly homemade masks, which is what's needed now actually because there's a huge shortage of they don't work as to protect you that well.


Masks: Airborne? (01:52:13)

They work much better to protect others. So it's, it's, it's a motivation for us to, um, all wear one. Yeah, exactly. Cause I mean, you know, very, you don't know where, you know, and, you know, about 30% as far as I remember, at least 30% of the asymptomatic cases are completely asymptomatic. Yeah, right. So you don't really cough. You don't, uh, I mean, you don't have any symptoms yet. You shed viruses. Do you think it's possible that we'll all wear masks? So I wore a mask at a grocery store and you just, you get looks. I mean, this was like a week ago. Maybe it's already changed because, uh, I think CDC or somebody's, I think the CDC has said that we should be wearing masks like LA, they're starting to happen, but do you, it just seems like something that this country will really struggle doing or no, I hope not. I mean, you know, it, it was interesting. I was looking through the, uh, through the old pictures during the Spanish flu and you could see that the, you know, pretty much everyone was wearing masks with some exceptions and there were like, you know, sort of iconic photograph of the, I think it was San Francisco, this, uh, tram who was refusing to let in a, you know, someone without the mask. So I think, well, you know, it's also, you know, it's related to the fact of, you know, how much we are scared, right? So how much do we treat this problem seriously? And, you know, my take on it is we should, because it is very serious. Yeah, I, I, from a psychology perspective, just worry about the entirety, the entire big mess of a psychology experiment that this is, whether masks will help it or hurt it, you know, masks have a way of distancing us from others by removing the emotional expression and all that kind of stuff. But at the same time, masks also signal that, uh, I care about your wellbeing. Exactly. So it's a really interesting trade off that's just, uh, yeah, it's, it's interesting, right? About distancing, aren't we distanced enough? Right. Exactly. Hey. And when we try to come closer together, when they do reopen the economy, that's going to be a long road of rebuilding trust and not, not all being huge germophobes. Let me ask sort of, you have a bit of a Russian accent, Russian or no, Russian accent? So, uh, were you born in Russia?


Favorite Memories of Russia (01:55:35)

Yes. And you're too kind. I have a pretty thick Russian accent. What are your favorite memories of Russia? So I, um, so I moved first to Canada and then to the United States back in '99. So by that time, I was 22. So, uh, you know, whatever, Russian accent I got back then, you know, it's, that was me for the rest of my life. Um, you know, it's, yeah. So I, you know, uh, by the time the Soviet Union collapsed, I was, you know, I was a kid, but sort of, you know, old enough to, to realize that there are changes and, uh, did you want to be a scientist back then? Oh, yes. Oh, yeah. I mean, my first, uh, the first sort of, uh, 10 years of my sort of, uh, you know, uh, juvenile life, I wanted to be a pilot of a passenger jet plane. Wow. So yes, it was like, you know, and I was getting ready, uh, you know, to, to go to a college to get the degree, but I've been always, uh, fascinated by science and, you know, so, uh, not just by math. Uh, of course, math was one of my favorite subjects, but, you know, biology, chemistry, physics, somehow I, I, you know, I liked those four subjects together and, um, guess also, so, so, essentially, after a certain period of time, I wanted to actually, back then it was a very popular, uh, um, sort of, uh, area of science called cybernetics. So it's sort of, it's not really computer science, but it's, it was like, you know, computational robotics. Yes. In this sense. And so I really wanted to, to do that. And, but then, you know, I, uh, you know, I, I realized that, you know, my biggest passion was in mathematics and, uh, later I, uh, you know, when, uh, you know, studying in, uh, Moscow State University, I also realized that I really want, uh, to apply the, the knowledge. So I really wanted to, to mix, you know, uh, the mathematical knowledge that I get with real life problems. And that could be, you mentioned chemistry and then, uh, now biology. And I sort of, um, doesn't make you sad. Maybe I'm wrong on this, but it seems like it's difficult to be in collaboration to do open big science in Russia. From my distant perspective in computer science, I don't, I'm not, we can go to conferences in Russia.


Difficulty in Joining Collaborative Research with Russia in the Scientific World (01:58:53)

I sadly don't have many collaborators in Russia. I don't know many people doing great AI work in Russia. Does it make, does that make you sad? Am I wrong in seeing it this way? Well, I mean, I am, I have to tell you I am, I am privileged to, uh, to have collaborators in mathematics in Russia. And I think this is the, the bine thematic school in Russia is very strong. We have in Moscow, uh, in Moscow, in the Wojciebersk, uh, in San Petersburg, uh, have great collaborators in, uh, Kazen. And, uh, so at least, uh, you know, in terms of, uh, you know, uh, my area of research, the strong people there. Yeah, strong people, a lot of great ideas, very open to collaborations. So I, I, I, perhaps, you know, it's my luck, but, uh, you know, I haven't experienced, you know, any difficulties in establishing collaborations that's been for mathematics, though. It could be by informatics too. And it could be, uh, yeah, it could be person by person related, but I just don't feel the warmth and love that I would, you know, you talk about the seminal people who are French in artificial intelligence. Friends welcomes them with open arms. In so many ways, I just don't feel the love from Russia. I, I do on the human beings, like people in general, like friends and, and just cool, interesting people, but from the scientific community, no conferences, no big conferences. And it's, uh, yeah, it's actually, you know, I, I'm trying to think, yeah, I, I cannot recall any, any big AI conferences in Russia. It has an effect on, uh, for me, I haven't sadly been back to Russia. So I, but my problem is, it's very difficult. So I'm now, I have to renounce citizenship. Always the right. I mean, I'm a citizen in the United States and it makes it very difficult. There's a mess now, right? So, I want to be able to travel like, you know, legitimately. Yeah. And, uh, it's not, it's not an obvious process. They don't make it super easy. I mean, that's part of that. Like, you know, it should be super easy for me to travel there. Well, you know, uh, hopefully there's unfortunate circumstances that we're in will actually promote the, the remote collaborations. Yes. And I think we, we've just, I think what we are experiencing right now is that you still can do science, you know, being current in your own homes. Yeah. Especially when it comes, I mean, you know, I, I certainly understand there is a very challenging time for experimental scientists. I mean, I have many collaborators who are, you know, who are affected by that, but for computational scientists. Yeah. We're really leaning into the remote communication. Nevertheless, I had to force you to talk to you in person because there's something that you just can't do in terms of conversation like this.


Yevrey and his research (02:02:14)

I don't know why, but in person is very much needed. So I really appreciate you doing it. Uh, you have a collection of science bobbleheads. Yes. Which look amazing. Which, which bobblehead is your favorite and which real world version, which scientist is your favorite. So yeah, by the way, I was trying to bring it in, but they are currently now in my, in my office, they sort of demonstrate the social distance. So they're nicely spaced away from each other. But, so, you know, it's interesting. So I've been, I've been collecting those bobbleheads for the past, maybe 12, 14 years and it, you know, interestingly enough, it started with the two bobbleheads of Watson and Creek. And, um, interestingly enough, my last bobblehead in this collection for now, and my favorite one, because I, I felt so good when I got it was the Rosalind Franklin. And, uh, so, so, you know, when I got, who is the full group? So I have Watson Creek, Newton, Einstein, Marie Curie, Tesla, uh, of course, Charles Darwin, sorry, Charles Darwin. And it wasn't a frankly. I am definitely missing quite a few of my, um, favorite scientists.


Miscellaneous Topics

The full list of the bobblehead collection (02:04:04)

And, but, uh, so, you know, if I were to add to this collection, so I would add, of course, Kolmogorov. That's, that's, that's, that's, you know, I've been always fascinated by his, well, his dedication to science, but also his dedication to, uh, educating young people, the next generation. So it's, it's, it's very inspiring. He's one of the, okay. Yeah. He's one of the Russia's great. Yes. Yeah. So he also, um, you know, the school, the high school that I attended was named after him, and he was a great, you know, so he founded the school, uh, school, um, and he actually thought there. Is this a Moscow? Yes. So, uh, but then I mean, um, you know, other people that I would definitely like to see in my collections, uh, was, uh, would be, um, Alan Turing would be John von Neumann. Yeah. You're, you're a little bit late on the computer scientists. Yes. Well, I mean, they don't, they don't make them. Because, you know, I, I still am amazed. They haven't made Alan Turing. Yeah. Yes. And, um, and I would also add the Linus Pauling.


Linus Pauling (02:05:35)

Linus Pauling. So who's Linus Pauling? So this is, this is, uh, to me, is one of the greatest chemists, um, and the person who actually discovered the secondary structure of proteins, who was very close to solving the DNA structure. And, um, you know, people argue, but some of them were pretty sure that if not for this, you know, uh, uh, uh, photograph 51 by Rosalyn Franklin that, you know, uh, what's an uncrew you got access to? Um, he would be, he would be the one who would solve it.


An uncrew photograph 51 (02:06:19)

Science is a funny race. It is. Let me ask the, the biggest and the most ridiculous question. So you've kind of studied the human body and, um, it's defenses and these enemies that are about, uh, from a biological perspective, a bioinformatics perspective, a computer science perspective. How is that made you see your own life? Sort of, uh, the meaning of it? Or just even seeing your, what it means to be human?


Philosophical Discussion

The meaning of life (02:06:51)

Well, it certainly makes me realizing how fragile the human life is. If you think about this little tiny thing can impact the life of the whole human kind to such extent. So, you know, it's, it's something to appreciate and to, you know, to remember that, that, you know, we are fragile. We have to bond together as a society. And, you know, it also gives me, um, sort of hope that what we do as scientists, um, is useful. Well, I don't think there's a better way to end it. It means you thank you so much for talking today. It was an honor. Appreciate it. Thank you very much. Thanks for listening to this conversation with me, your Corcan. And thank you to our presenting sponsor, CashApp. Please consider supporting the podcast by downloading CashApp and using code LexPodcast. If you enjoy this podcast, subscribe on YouTube, review it with five stars and Apple podcasts, supporting our Patreon, or simply connect with me on Twitter at Lex Friedman. And now let me leave you with some words from Edward Osborn Wilson, E.O. Wilson. The variety of genes on the planet in viruses, exceeds, or is likely to exceed, that in all of the rest of life combined. Thank you for listening and hope to see you next time. Bye.


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