Red Team Village - Back to the Future CompSci and System Biology
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Novogroder, Noa
Adlung, Lorenz Kurt
Which creature implemented code injection 1.5 billion years before any computer malware did? What is the decoding algorithm being used in each of our cells to run the program written in our genes? As computer scientists, we are pushing the edge to develop disruptive technologies for the future. In fact, we can learn from an industry that has been evolving since long before humankind existed: The evolution of biological systems. With our proposal we hope to show the incredible parallels between bacteria and computer malware, the complex algorithms implemented in each of our cells, and how each plays a pivotal role in furthering the research of the other. This lecture will take the audience on an educational journey through both disciplines. This will foster interdisciplinary collaboration and inspire innovative solutions to future challenges for instance in the context of synthetic biology (i.e. creating artificial life), or personalized medicine (i.e. machine learning to treat patients). We are made up of trillions of computational devices. The cells within our body are information-processing units, with memory, storage, cooling and communication devices. Hardware for executable programs was very successfully shaped during the evolution of uncountable biological entities. We are presenting a wormhole between the two parallel universes of computer science and systems biology. A leap through space and time will allow us to connect the evolution of life with recent advances in computer science. An intimate exchange between the computational and the biological spheres is a prerequisite for future generations to work together on aspects of gene editing, robotics and artificial intelligence. As an incentive, we will perform a small quiz during our lecture with attractive prizes. It is our firm belief that we are the right team to foster discussions on life-inspired computer (r)evolution.
 Okay, we're live. Take it away. Hi. Hi there. Yes. Yes, I guess. Let's see whether they can you are live your life. Okay, here we are. Hi. I'm Noah. This is Lawrence. And nice to meet you. We are here streaming from the wise minister to science in Israel. And together we are going to present the comparison between cybersecurity and biology. I hope you are ready. Well, I'm ready. So I guess it is yours. Okay. So nice to meet you. I will just present. Okay. Sorry. Okay. So our agenda for today. I have two targets with my presentation. First of all, I'll show you how beautiful and complex biology is. And the second part will be to make you jealous. So who am I? My name is Noah. I'm a master student at the wise minister of science. I'm a general scientist in that is doing research specifically in microbiome and also diabetes. And I'm a former cybersecurity researcher at checkpoint. So until a year ago, I was a reverse in checkpoint. My job was to reverse to as you all I'm sure that you all know about this job, 2000 in malware, and then to implement the defensive code into a checkpoint antivirus. So you know, I thought I'm pretty smart girl. After all, and I even did some fuzzing stuff. I found some vulnerabilities. And you know, I built myself as a hacker and as a researcher in checkpoint for many years. And then but I was always very curious about complex teams, not only malware, but always about biology. And a year ago, I decided to come here and to become a researcher in biology, specifically about the new, the new area in a microbiome. So I thought, you know, I'm so cool and I'm doing reverse engineering, like for so many years. So, you know, all of the bacteria and the COVID-19 and whatever like comes up, they would all like all of them gonna be like, you know, scared of me, because now there is a good reverse and a good hacker that is coming into the field. But then I found out that apparently it's not as I think that we all I think that it will be really easy for me to prove my point that it's not as easy as we can, as we thought, until it was not very far. And you can see like what happened when just one bacteria just did some evolution and become just a little bit more dangerous than before. It's like the COVID is doing evolution for many years. And like, I think it's really easy to see how does it affect the world right now. And also how I learned in the hardware, biology is way more complex than any malware that I ever did a reverse engineering. This is like actually one, just one slide of my class. What you see here is the complex system of what happens inside a cell when there is one ligand that is binding one of the receptors. So apparently, it wasn't the best idea for me. I'm joking, of course. I'm very curious and this area is very challenging for me. And I think that what we do here is just beautiful. And also one more thing that I want to point out during my lecture is that when I tell people about biology, they all think that I'm, you know, pipating and growing, growing microbes on a plate. But actually these days, computational biology is on the rise. And what we're doing, especially in the in white's man, is trying to take each of the biology systems we have in our body or in bacteria or anything else and build the model about it. This is like also one of my classes. And this is from Oriel Lónlav that we are doing collaboration with. So what he did here, he took the Altenard gland and tried to build actual equations of, I think, something, okay, and tried to build the equation in order to understand this is this thing here is the complex system of the glands. And this one and this one is some equations that are that are explaining why why does they are biggest type to develop in so many people these days. And now we'll pass the microphone to my friend that you explained to me in detail about some of the basic creature that all of us have in our body. Thank you very much indeed, Noor. I just want to point out that the person that actually introduced me into this world, it makes me a very stressed, very stressed, also like a very happy student, but also a very stressed and a very poor student is this guy. So when your mom tells you not to talk to stranger, maybe she has a point. Okay, so I guess I don't need any further ado. Well, if you can see the camera, you will see that in contrast to Noah, I will be sitting I'm a little older. So forgive me for that. So actually, when we were preparing this lecture, we came up first with this title, Back to the Future and me being this very modest, humble German dude that I am, I didn't have all these fancy animations that Noah showed to you. But actually, when I envisioned this presentation, I started with something lame looking like this. Okay, so again, we are from the Weizmann Institute of Science in Israel. And what I want to show you now in the next like 25 minutes is in essence, how computer science and biology not only go side by side exhibiting stunning parallels, but also that they in fact have an intersection that we can exploit nowadays to address current challenges that we are facing. But before I go into this, I actually want to fulfill another request that Noah kindly asked me, which is to explain a very central concept in biology, which by itself also has a counterpart in computer science. And I'm talking about genetic code. Okay, genetic code, our DNA is basically the language in which life is written. So that's sort of these chemical molecules that make up four letters, A, T, C, and G, they write a blueprint of life. But as such, they are not very useful because they have to be transcribed, they have to be transcribed from this DNA form, this genetic information into just another sort of code, which is RNA, which then is sort of translated to put into proteins. Protein molecules that make up all our cells and therefore our body as a central component. And speaking about code, I think, again, it's not very far fetched to reach from biology, so the science of life to computer science because you as computer scientists mostly out there in the audience, you will know what a good computer code is. And basically, I want to start by asking the question whether there is another analogy between computer science and biology. Are there any parallels in terms of computer viruses and biological viruses? Because, I mean, as you know, currently we are facing a global COVID-19 pandemic that originates from the deadly spread of the coronavirus SARS-CoV-2. And I want to raise the question whether there's anything, if you only know computer viruses because you're a cyber expert, that you can also learn about biological viruses such as this coronavirus. So let's start with the computer virus, right? With the computer virus that you probably heard about, the Melissa back in 1999, which based on a sequence, like a stretch of code that you can see on the left here, which basically with some letters just gives also the blueprint of a malware, basically, that causes a lot of damage. And I'm not sure whether you're surprised or not, but what you're going to see on the right now here are the first 3569 letters of the SARS-CoV-2, this coronavirus, the version Wuhan U1, the genome, the genetic information of this virus, which in total is about 30,000 letters long, that makes up all of this virus that now spreads on the face of the earth. And I'm saying genetic information here, but to be precise, this is not DNA that you're seeing, but actually it's an RNA virus. So why this is sort of the genetic information of this virus, it's not the same genetic information that we have in our body sets, but it's only this transcript that I introduced to you before. But looking at this just sequence or this source code of the virus, you wouldn't be able to judge that this is particularly harmful. Maybe on the left you could, Melissa, because you're an expert and you see what it actually does, but there is no chance, even the best molecular biologist looking at the code on the right would be able to tell, okay, this is a deadly virus. Okay, so, but still we desperately needed to know the sequence to detect this virus. But then the question is always having the sequence, having the source code, what does it do? What does the sequence translate it to? Okay, and starting again with the case of or the example of Melissa, basically what this virus did is it prompted always to send emails to your own Microsoft Outlook inbox or started new work files, which basically then overloaded and completely flooded servers like online servers and stuff. So you had like in, of course, I only made this up like an infinite inbox. And this is sort of how the source code of the computer virus translates into a 3D structure if you want to say so with a malfunction, so with a bad function. And actually the same thing biologists did then they asked the question, okay, given the sequence of the coronavirus, how does this translate into what I mentioned before this proteins, right, that make up the shell of the virus and this different parts of the virus that help it, help the virus to infect our body. And this is basically one part of this virus. So it's not the entire virus is just one tool actually what I said, like one element one part of a molecule that helps this virus to infect our body our body cells. And this is how this virus does harm right it infect cells and then eventually kills them. So this is how in biology a sequence or source code translates to to a structure and then a certain function. And then to sort of visualize how it looks like in total apart from, you know what it does the, the Melissa virus to to overload your inbox and the coronavirus to infect your body cells. And actually for for the Melissa virus then it took an artist to visualize how this overload looks like and this is how an artist apparently pictures how computer virus looks like, which as you can probably better judge and I do is just, you know, an orbit more or less arbitrary than a person. But in fact in biology again we can use this wonderful invention of microscopes to actually look at how the virus looks like. However, the conventional light microscopes are not sufficient enough here. So actually we need electron microscopes. However, if we use these very sophisticated a paralysis that costs like a million dollars each and require a lot of expertise to use them. And actually see a virus inside of cells so all these blue particles that in this forbers article are described as pepperoni, sorry, slices of sausage on your pizza. They are the actual virus. However, I also must say the truth is, if you use an electron microscope to observe the corona virus in cells. So this is actually the coronavirus of the first identified us patient. Okay, that was imaged here. This original picture from the electron microscope had no color. Okay, so this blue color is just put artificially in the end to visualize the virus particles. But here we are still talking about like, only the size of a meter divided by one billion. Okay, so it's in this order of magnitude. So it's very small. And you need sophisticated as I said microscopes to actually see it, but you can see it. Okay, you can understand how this sequence source code information that we that we saw how this translates to first individual aspects of this virus individual molecules and then the virus as such. And now, once it has done its terrible job in fact in our body cells. Okay, and this is this sort of, you know, there shows that to some extent there's really there are parallels between computer science and biology in this case, computer viruses and biological viruses such as their SARS cough to but is there, you know, besides, besides from, from this example is there, is there something we can do about this is there something we can use from computer science to help us fighting this terrible pandemic that we are facing in the US and all over the globe. Is there an intersection between computer science and biological aspects of this pandemic. This is a question no and I basically asked ourselves. And in the very beginning of this pandemic in March, in fact, we participated in hackathon that was initiated by in that collaboration with Microsoft, they called it hack for owner. Creativity will not be current time. And in the next five to 10 minutes, I would like to introduce to you this project that we initiated there to show you how computer science can actually facilitate very in a very concrete example, our fight against this virus. And the question that that we asked, basically is how can we use computers to combat COVID-19. And the particular question we were wondering about because this was of major concern in the beginning of this pandemic is how to help people who develop severe complications. Okay, because we, we understood that asymptomatic patients. Of course, they are very dangerous in terms of unobserved spreaders of this disease. But in case someone develops severe complications needs ventilation in the hospital. Then this might, you know, present a major challenge for the health system in Israel and all over the world. So how can we basically assess the uncertainty whether a certain COVID-19 patient will develop severe symptoms and require hospitalization and ventilation. This is the question we asked. And as you can see in this picture, again, we were pretty much using biology as well as computer science by these two mice that you can see here like a computer mouse and the actual mouse or, you know, at least a stuffed animal of the mouse. And so, in simple terms, our question was, given the fact that we have a certain number of COVID-19 patients that require because they develop severe complications, hospital beds, how to deal with them if the number of hospital beds is actually lower than the number of patients who require hospitalization. In other words, three exceeds, sorry, five exceeds three, right? So how to decide which of them to allocate to the hospital, how to estimate what's the fraction of people who develop severe complications and how to decide whom to offer with a certain priority these available resources. Okay. So this was sort of the question. And the reason why we deal with this was because at the Weizmann Institute, we are really at the forefront of this, not only that we have medical doctors on campus who see these patients on a day-to-day basis, but also the labs we're coming in from Professor Dr. Eran El-Dinav and Professor Dr. Ido Amit, they develop now technologies to not only detect these infections and stratify these patients, but also streamline this process, really linking the foundational research with the application in the clinics. Okay. So this is why we also felt dedicated to address this with our expertise, which comes more from the computational side here. So what we did in the beginning is, first of all, we asked, okay, what's the data we are dealing with, right? Because only if you have data, you can actually use computational routines to address all these issues. So we basically simulated based on existing data, an Israeli data set comprised of 10,000 cases, where we really used Israeli demographic information and health record data that was publicly available to a certain extent. And set up sort of a database where we can get an idea, okay, this is Israeli population, and now given all these demographic information and health record information, can we basically now sort of estimate what's the fraction of people who might develop severe COVID-19 complications. So first of all, as you can see, we're struggling, you know, entering all this data. But then what we obtained is basically a more or less realistic Israeli age distribution here. And also what seems to be important, and we will hear about diabetes and the metabolic syndrome later on in no as part, the distribution of the body mass index in the Israeli population, which is basically a measure for your body density. So it's your body size divided by your body height to the square. And it gives you an estimate of if you have a high BMI, you are considered obese, and then actually this also affects your risk of COVID-19 severe complications, but also where you get infected in the first place. It's not fully understood, it's just an observation. So anyways, we thought we need to include this information. So first of all, we needed to have some sort of realistic distribution of this kind of data. And then what we were basically sampling from this information we had back then when Israel was not really facing many, many cases at the time was this sort of distribution of people who got infected and then sort of were surviving through the disease and people who eventually died. And then you can see that even though the overall frequency of that people, of course, or of dying people is with the infection or upon infection, not necessarily caused by the infection, it's very low. But you can see that if you're older, the probability that you actually die after getting infected with SARS-CoV-2 is higher. So what we then did is we used this data set, this information that we compiled and trained the model to basically predict, given a certain patient with this health record and demographic information, what's the risk of developing severe complications. And then what you can always do is you can compare your prediction to the actually observed risk because you have information on this, right. And then we get a pretty good agreement between the predicted risk and the observed risk here in this preliminary data set. And then we can ask the question, okay, what kind of information are actually necessary to predict this risk in a reliable manner. And then just as a test case, proof of principle, we said, okay, age seems to be relevant and critical. So what happens if we are leaving out the age information? So what you are seeing here is basically a receiver operating characteristic curve, a rock plot. And basically if your line, so your specificity and sensitivity of this prediction performs pretty much randomly, then this line that you see is close to the diagonal line. So as you can see the orange line, if we leave out the age information, we cannot really predict that it's like randomly drawing, okay, this person will develop complications or this person won't. But if we just consider the age information as well in addition to all the other information, then this prediction becomes very sensitive and very specific. So it means that if we say a person develops, does develop complications, then the chance that this is a true positive indication is high. But also if we say this person won't develop complications, then also the chance is that this is a true negative. So it indeed won't develop complications. It's also sort of high. So of course the perfect prediction would be just going straight up and then straight to the right. But also with this in this toy model sort of or preliminary thing, the performance of this prediction was not so bad, I would say. So the way we envision this basically is once people get sick or are suspected of being infected, they have to see a doctor who then puts in all the information into a certain database and some device that collects this information. And then basically trains this model that is based on machine learning algorithms and all these health records information and then basically puts out back to the doctor a certain risk score. Right. So the application tells the doctor, okay, this person exhibits a certain probability to develop severe complications. So better save a hospital bed for this one. Or in the ideal case, just it can, this person can just be isolated at home. There is a very low chance that it's going to be severe. Just monitor it carefully, but most likely the hospital bed allocated to this patient can be given to somebody else with a higher risk. So what wait, what are we lacking here we I already presented you that this machine learning algorithm sort of works, but we need this device right we need this application. And this is also what we what we sort of developed like something that can work on your mobile phone and helps medical doctors again that we have in our group and we talk to them on a daily basis to just use it in a very handy way. If they see the patient they can check check in all this information in less than a minute. And then basically we can, we can, we can just let the machine learning algorithm do its job and see whether there's there's a certain risk. And of course this machine learning algorithm that I presented to you this model that we use there to make the predictions was just the first attempt that we had in the Sakaton, you know, very coarse grained first rough trial. Needless, needless to say you can also use more sophisticated methods where you can use all these different biological information on these patients to assign how important is this information to either say that there is a high risk or maybe just informative to say that there's a low risk or maybe a combination of both right so we can, of course, you want more data, we can try different models. And as I said, we are in day to day contact with the clinicians actually in charge actually doing this actually doing the analysis or talking to the patients, suspected infected people. And so this is what we envision that we, you know, we can't really not necessarily change the number of people who are susceptible and eventually will get infected. It's just a matter of time. It seems like. And maybe also, you know, the health system is sort of currently at its limits so we cannot really expand the number of hospital beds. But this app that we developed that by the way we we called virology and because it's like an intelligent solution to virology and the different inference of this virus induced complication health complications, we could now use to assign again, hospital beds to people who needed the most. So in the scope of this hackathon which of course is very preliminary and we are still sort of thinking about how to put this further in the scope of this hackathon this this question was solved. But as I already indicated in the scope of this hackathon and this is just covered 19 which at the moment seems to be a major part of our lives, but certainly it's not the only part of our life. So, can we make a more general observation when we look at computer science at by and biology. Or in other words, how can we combine powerful algorithms from computer science with biological data sets. And this is what what I've shown what I'm showing you here is basically the intersection of biological data this is how biological data nowadays sort of looks like. So, we combine this biology with this more like a conceptual or theoretical view of biology as a system. Okay, as a as a living system that you know just operate certain commands and has some input output functions. And the answer here is also the computer because only the computer will help us to process this data and to put it into these sort of models in terms of understanding how biological systems work. And I also have a discipline that I actually studied I did my PhD in this field which is called systems biology. Okay. And let me introduce to you the field of systems biology with one of the Godfathers of systems biology a guy who actually does also a lot of robotics and intelligent design and stuff Japanese guy who also invented robots that help people to to rescue from collapse buildings after earthquakes and stuff. He wrote a key to know his name, and he described systems biology as the integration of experimental and computational approaches to precisely characterize biological systems with quantitative data. Okay, so now we have quantitative data, which is machine readable from biology that we will use to understand in combination with experiments in the laboratory. How this biological system works simulating it at the computer using algorithms to to understand what's going on there. And what does go on there how does this look like. And you can imagine as this sort of simple diagram here, where basically on the x axis, you will have time. And on the y axis you will have the amount quantitative data okay, of a biological entity. Now, what does this mean so basically what you have in blue these blue dots these are your data points. And then with the computer you can basically simulate the behavior of the system and you have some mean simulation here the blue line. And then also you have this shaded region which is basically your your uncertainty. Okay, your confidence into what. So not only you get an understanding with the computer. How does the system change over time how do biological entities change over time. But also how certain are you that this happens right the virus can spread it like enormously. But maybe there's also a chance it doesn't spread or maybe there's a chance that it spreads like even more. And this is something we can quantify we can calculate. Sometimes we have a problem with communicating this but there are possibilities and mathematics to calculate these confidence intervals and these uncertainties. And the way this is done the way we describe our biological entity changes over time is in mathematics called ordinary differential equation. So this basically describes you how your biological entity be is changing so the D represents the change okay it's a change of B over the change of time t okay how does be change with time. And this is now depending on certain parameters p p one and p two. And the plus always means you know you get something more so depending on this parameter p one you get more of be if you already have be. So, if, if you have something that can sort of reproduce, like a virus can reproduce or we can reproduce you know you, you will get even more if you're more people to start with right. And then basically the reproduction rate is is your parameter P, and it's positive because you get more. But then this curve of be is not only going up it's not only just getting more with time right it's also going down. So there's also another parameter p two and there's a minus because it's getting less that tells me that that be is the change of be over time is getting negative it's there is something subtracted subtracted from it. And of course the more be we have the more likely it is that you know it distracts itself you know humanity is very good that distracting itself somehow unfortunately. But just by itself it won't go just up and down there, we needed some another component here that influences this and this other component is a. So for a we can actually write down a very similar equation so how does a change over time well it grows, but not only depending on a but also on be because you know it is it's distracted eats it for instance you know humans are very good at eating stuff. Also other living beings. But everything that lives also has to die so the more a we have the more likely is that it's also dying with another parameter before. And then you know we get we get more be but then a gets more to eat but then eats more and then be goes down again. So there's little a that can eat up be and be can we arrive and then it's going up and down so this is sort of how nowadays couple ordinary differential equations because there's two of them and they're depending on one another. We can describe the systems behavior and even these like weird oscillations like going up and down. And that's a very mathematical point of view. Very interesting observation of this system behavior. But now I talked about a and be a lot but saying be I don't necessarily mean a be of course be is also biological entity right the be that gives you honey and stuff. But it's not anything honestly we are talking about the human animal, the mirror in animal like mice. It could be the number of cells that are changing in our body. It could be bacteria that are also in our body also says it can be this virus right this is a picture of a virus. But it can also be just certain molecules to change over time. For instance here an antibody that is produced against against this virus. These are all options of biological entities that we can describe in biological systems. But to describe them actually we have to think on which level are we talking. Because biology happens on many different scales in space and in time right the human being is on the order of magnitude. Like, one to two meters let's say, you know, maybe basketball players are more like two meters and are people more like one meter. Never mind. But then we talked about a mouse that is certainly not a meter but just more like centimeters and then we have like cells that you have seen that are more like micrometer which is a meter divided by a million. And then we have in these cells we have small subunits that are even smaller bacteria also smaller than our body says. And here we are in the order of magnitude of a meter divided by a billion. Okay, so 10 to the minus nine a nanometer. And so and all at all these scales you know changes are happening all the time. That's how we are being alive because molecules are turned over cells are turned over our bodies are being turned over. Everything happens there and this not only happens like on very slow scale like billion three point six billion years this earth does exist and life does exist. But also in a matter of hours in the matter of days in a matter of minutes in a matter of seconds, you know, molecules change your confirmation in the order of microseconds again a second divided by one million so. And this all, you know, this is all part of this one biological system that we are trying to describe and there's no one out there that who can tell me that there's a way to handle this complexity without a computer. So we need computer science to address all this to understand biology at all these scales. And if you're curious for this. Don't you worry because there's a wonderful database at bio numbers.org initiated at Harvard Medical School with you guys in the US, where you can ask the question okay how big is the cell how many molecules are in a cell. Because human body cell is different from a bacterial body cell from a fruit fly from a warm. All these numbers are there with references you can look up the original scientific literature to see the evidence for these numbers. And it's just wonderful looking at, at all this information that is out there. And I don't need this information is out there but it's being continuously generated we are living in a time now in an era where biological data is not for the first time not a bottleneck anymore you don't need to spend three years on a PhD to gather one data point of information about one protein. And now sequence our entire genetic information DNA of a patient upon diagnosis after treatment and really understand how our medical treatments affect our biology on so many levels so we are living in an era of biological big data but speaking to you maybe of course I have to say that in fact there's only one true big data. And I really speak about data in biology that is out there on all of these scales that I've just introduced to you. So data is not a problem anymore, but we need concepts how to work with this data. And in systems biology there's two sort of complimentary approaches to deal with all this data. Again, data can be on the level of organisms it can be on the level of cells or it can be on the level of molecules. But the problem is you hardly ever have just one of each of these. So usually you have, right you have many human beings in a clinical study you have many different cells that make up our body. 30 trillion of them to be precise, or many molecules that do something right many virus particles or anything of that like. So you have two different ways to approach them either you do it sort of bottom up where you say okay let's just look at some of these different molecules where we know actually what they are doing how they interact with one another. And then we try to sort of describe this in a dynamic way so how they change over time so first one molecule interacts with another one and then these two interact with the third one. And then we understand the small part of like we start from the ground we understand the small part of it the small module and then we sort of subsequently try to to connect these modules. And as I said, they're based on a lot of knowledge what is going on we have some mechanistic understanding how these gear wheels are really fit into one another and then we can see how the system is changing over time. So there's another way to look at a biological system let's say a patient the covered 19 patient. Just look at them from the top okay you don't really know what's going on in this patient okay it's a black box this patient. But now you give a certain drug to this patient. This is your input into the system into your black box, and you see how the patient reacts to it, and this is your output your reaction, and then you do this not only with one but with the entire cohort of COVID 19 patients, and you try to see patterns and so whenever we give this to a certain patient where maybe we have all this health records. There's always a good outcome, or maybe not so good outcome and then you can start to try to understand this input output relationship meters to say for this you need a lot of data many patients, many drugs that you, that you potentially test. And then you can learn sort of associations okay smoking history is always connected to a bad outcome or as we said the high BMI is always connected to a bad outcome. And these are all these machine learning artificial intelligence methods. So these are sort of the two complementaries, not schools of thoughts but approaches, they are not mutually exclusive right because you can, while looking at these patients as a black box you can still start with some, for instance cells of the immune system that are attacked by the virus you can also start with some bottom up modeling there and then you can be input it all together. But to give you a precise example, I want to use the bottom up approach now as an example. Okay, so again where we look at how biological entities change over time. And actually these models, particularly also with these going ups and down these oscillations, they were introduced a long time ago for for foxes and rabbits. Biology is not just that and in fact, I wrote a book on rabbit free sort of biology. However, we can still use all this mathematics that was established to now bring this research back to the future using this mathematics to understand something about molecular biology. And the, the last example I want to give you before I hand over back to Noah is the example of a of a molecule that controls the survival of our red blood cells. And that's as complicated name signature and stucer and activator transcription start five. The only thing you need to know about this molecule is it can bind DNA. Okay, it can bind our genetic information and then exert certain tasks there. And if we have it, our red blood cells are surviving. And if this molecule is not at our genetic information doing its job, then red blood cells are dying. And so do we at some point. So this molecule basically shows us a life death decision, which starts with a simple very simple question. Does one plus one always give two, because if we have two of these molecules they are supposed to form this complex of two molecules. And this sounds simple, but actually biology is more complex than that because we not only have one family member of this molecule we have two of them the blue one and the red one. And also this can be modified it can be chemically modified to be to be switched. So the small p here is a chemical group that can be added to this molecule, and then it's switched from the off state by this edition of this chemical to the on state. We basically have four different molecules that potentially can do this job of binding genetic information and making sure our blood cells are surviving, which gives us a matrix of potential biological eight entities here. Right, not this kind of matrix but more like this kind of matrix. So from all these molecules we can form these potential complexes and now the problem is they are very small so we cannot just look at them to see which of them do actually exist and what do they do. So we have experimented data where we see how these complexes change over time. So what we see here in on the top is basically we see how many of this switched on molecules we have as compared to all possible molecules. And then we also see how the number of the blue molecules compared to all molecules change over time. And then here we basically see how these two ratios change with respect to one another. And then in these cells, we can measure this and then with a mathematical model we can basically describe, we can describe this data. As you can see again we have our, our simulation and also the confidence interval here. And now with the simulation we can basically look back and see okay, which kind of molecules from this match matrix to actually exist. I mean, which one of those we need to describe this data. And here is surprisingly a few of them we only need. Okay. And this is something we cannot just look at we really need to compute we needed the computer to find out okay what's the probability that these molecules exist. And this we also published it's not an a work but again a and b because these are two molecules. And with this maybe we better understand now how our red blood cells are surviving and be happy. And to wrap this all up. What what what I've shown to you is basically they are standing parallels between computer viruses and biological viruses. We can apply computational algorithms to address challenges that we are facing in the covered 19 pandemic. And systems biology helps us to answer complex biological questions with quantitative data, for instance, how molecular switches decide between life and death in our blood. And now I will give you some more teasers teasers how how this all relates. I'm just saying, if you feel like intrigued by this don't hesitate to reach out to us and we will be happy to, to also share more information and with having said that I hand over back to you. I'm going to explain again that that my name is not I'm a junior master scientist at the Wild Swan Institute of science here in Israel. And my background is from is my background is as most of you guys. I was a reverse engineering and cybersecurity expert at checkpoint for many years. And when I joined white man I had to close a huge gap in my in my knowledge in biology. And I was what was stunning for me was the parallel that I found so often in my material in the material that I studied in the courses and also for the lab with my with the knowledge that I have from the past. And I will just give you a few examples of the things to have in biology that will look very, very familiar for you. So, without any further introduction, let's start with cancer cells. Okay, so cancer cells, unlike most of the people think about them. And they're not just cancer that accumulate in our body this is more like super cells that in our body to have superpowers they evolve. They're better than the rest of the cells in the body like due to, due to a mutation from radiation from from UV light or from any other from smoking and from, or just by chance in the body. And then what's happening with these cells is that they become selfish, which means that instead of of working together for their like with the rest of the cells in the body. They just, they're selfish enough to try to control to control and to get all the resources for example to be to say they're actually sending signals to the body to build them to build veins in order to get more oxygen into the tumor in the tumor environment. And one, one stunning fact that I learned during during my courses is that not only that the cancer cells are selfish they also try to evade the immune system in order because one of the roles of the immune system is not only to eliminate pathogens but also eliminate cells that are selfish like cancer cell because cancer cell like we have them on a regular basis in our body in our body. And this is one of the main roles of the immune system to to detect them and and and to eliminate them. So, it's not only that the, that our immune system is working as an anti virus, but the thing here is that the cancers are fighting back. So, the picture here is represented interaction between cancer cells and different types of immune cells. Cancer cell are the one that shows in blue immune cells are in different colors and what they're trying to do is to send so you can you can think about the rest of the colorful cells as a different types of anti virus from many different companies and many or maybe a different model that we have in the one anti virus. And what the cancer cells trying to do is to actually reprogram or to send to send the signal to send signals to the different immune cells in order to in order to invade them. And this is something that was you know when I heard about it was really familiar for me like I diverse many models that try to evade my VM to evade my to evade my my my item I already my already bg, all the antivirus in general. And so, for me it sounds pretty familiar so apparently we, we, it wasn't our idea to evade the to evade people that to evade the entities that are trying to detect us. I can promise you that cancels are doing it in a in a much more efficient way. For example, one, one very, one very stunning example that I have is that every cell is supposed to present to present a molecule on his surface which is the name of the molecule is an mhc. And this molecule and this molecule is actually like is, it's like the info that an executable can have, which is just which describes what what what the cells are are producing what are the proteins that are running inside of it. And this comes to show what they actually do is they just, they just don't show they just don't present is the mhc, this mhc molecules so the so it's really hard to be in for the immune cells to understand what actually going is going on inside of them. And, and here you can see something that we call a hot tumor. The tumor is immunologically hot. It means that the immune system is operation here. However, when cancer progress it that it learn how to deactivate the new cells and make tumors and you know, it's called like look at this tumor and you can see here many different many different of immune cells that are trying to detect and to like you know trying to get that actually can understand what's going on inside. But after after a while what we can see in tumors that are actually as successful with debating the immune system is that they become immunologically cold tumor and you can see that like most of the immune most of the immune cell they just gave up and like you know went to like they said okay whatever like I have other stuff to do and they just you know left the tumor there alone. And this is why yeah like so many. This is why you get the tumor in your body. And here is just here what you can see here is just a network of the different. This is just you know this is just a brief, a brief knowledge that we have so far about how all the different all the different ways that cancer healthy that cancer has in order to try to invite immune system and generally cells can communicate with each other using the vessels signal transaction across that lead to different outcomes. And what consciousness do they upregulate the spot was with the which he that nervous normal function of immune cell they don't they don't they don't even just tell the like the specific you know just leave me alone. They also ask him to spend their the rumor and tell her and tell the rest of them you know this guy, it's okay like it's okay you can just leave him alone. And this is something that was really fascinating for me to learn. And here is an example where a cancer cell over a specific pdl one a miracle that suppresses the cell important cell type involved in cancer cell elimination. On the right you can also see a reprogramming immune cell called tumor associated macrophage, which helps to spread the and inactivating signal instead of fighting cancer. Okay, so you can see that it's not only telling him listen I'm okay. It also asked him, you also have to these cells, you can also tell the rest of the guys that I'm fine and like to leave me alone. And this was amazing. I don't know like too many models that are actually so so powerful and sophisticated and it was amazing for me to learn. And my next example which is actually one of my life, like one of my favorite is bacteria fudge. So what is a bacteria fudge here you can see I like, let's, let's take a look at this very beautiful gift of like, okay, so just explain first of all my character five is like my favorite creature so far. And what it's actually doing there is a creature that we all know it's an awful bacteria, but there is also another tiny tiny picture and what the bacteria is actually trying to do is to inject its DNA into the bacteria and then the bacteria instead of running their own genome it will run the bacteria genome like and does it sounds familiar to anyone else and so currently also with this we didn't come up. And this was amazing for me to understand like you can actually see it. This is actually a bacteria. Okay, and here you can see a bacteria that are attaching into it and injecting their genome into the bacteria. And here you can see this is a very sad start because the bacteria like you know, sometimes they understand that they are like, you know, they are doomed. This is just a gift. And so we just explained a bit about about about the progress of it so what what you see here is that there is attachment of the bacteria into the bacteria bacteria is like, you know, the bacteria that you know that we all have in our body. And this is the bacteriophage and then and the which has a very, very nice and interesting picture and structure. And so what it's doing is actually attaching into the bacteria. It's injection it's injecting in the its own genome into the bacteria inside surface and then the bacteria instead of running like you see this is the genome of the of the of the bacteria instead of running like its own code it's running the bacteriophage code. And then, like the whole purpose of it is that there were there are more and more bacteria like being from the inside of the area. And then the, the poor bacteria you know just just full of bacteria until they release each other until they get like the it's too much for it and they go out and infect even more and more bacteria that are inside of this environment. And, and another thing, and that I'm pretty sure that most of you heard about if you are interested in biology at all is something that is called CRISPR. And I don't know that CRISPR is being used these days in order to kind of engineering the babies of future and stuff like this but what was fascinating from it to learn is that actually CRISPR the whole purpose of it is this is a defense mechanism of the bacteria in order to detect bacteria in order to detect phages. So what you see here is that, like this, this thing, evil thing is the bacteria. And what it's trying to detect, and this is something that we actually stole from them is what it's trying to detect is the sequence that he also had that he, that he was that he saw before of phages that try to attack it. And the way that it's, and the way that it's doing this is just you know he has a sequence that he remember, and then he just tried to do pairing, like to compare the new, the new DNA that he's seen in the life of the cell, and then the sequence that he already know that is belong to the phages. So, yeah. And of course, I think it's very it's, it's very obvious that this, this cast nine system is very similar to what we know as the Md5 or the hashes of the malvulous that we have been said many different antiviruses. And the very cool thing is that it's not only that the bacterias detecting phages in like using the non sequence is that the phages are actually invading this defensive system by changing the genome, the same way that malvulous are changing the Md5, you know, it's enough to change just one bit, just one bit. Phages are doing the same in order to, to, like, you know, be evolution in order to be able to, to inject their code into new bacterias. And so this mechanism that we actually start from bacteria, we will, we are trying these days to use it for genetic engineering. But just to know, like, we, it wasn't our idea we stole it from a very, actually smart creature apparently. And, and I would just say, since our time is up, I would just say briefly what's like the role of our specific lab. So what you see is so my, so my research, my research is specifically about the microbiome. And so the microbiome is what we're trying to do as you, as you all, and none of you heard about it, but we are all full of bacteria inside of inside of our gut and we are trying in our lab to, to understand, and maybe in the future also to manipulate the different different diseases of our gut and all the networks that are inside of it. And first of all, we try to find the connection between them and different diseases like the diabetes and the less and obesity. So this is a very new and interesting field. And you should know that you have you all have bacteria is all over your hands, your eyes and specific inside your gut. And it's not just that they are there happily like not doing anything they're actually sometimes very crucial. And then in order to keep the home of status. And so this is what's just a brief thing about the microbiome, just a brief introduction to the microbiome. And a few things that we do that we found out so far in our lab is the connection between the bacteria that are inside the gut and the very well known disease that are inside the al s because the bacteria that they can they can send signals from the gut into the into the brain. And there is a there is a very clear connection between the dysbiosis that we'll see in the gut and the, and the, and the from of this disease. And we can see many different, many different types of disease that are linked to, to this versus in the macro bottom, and another very cool and another very cool subject we are looking into in our lab is diabetes. And just one thing that I, I learned and it's very it was really interesting and and like this is one of my favorite challenges in this field is that when you have any kind of disease it's not like that, for example, if it is if you have too much sugar in your blood, the problem in the problem with it is like, wouldn't be what just about the fact that you have too much sugar in your veins, like many different diseases. They're like, there is a hip corruption, let's say, let's say, in our body but unfortunately, unlike in the new operation systems in our body, like if there is no such thing as the different processes that have different that are different classes of memory so if there is, so if there is a hip corruption in one process, or in one part in one in one system in our body, it affects the rest of it as well. So, for example, diabetes one of the problems that we see that we see is that they're, that we lose the most status of, of, of also the, the CA2 inside our, inside each of ourselves. And actually we can, we can see that the blood is become becoming acidic. So this is just one thing. And so, I would just say briefly like that I'm really proud, like really, really proud that taking part of my lab like this is the very nice person that you see here is my supervisor, I have a professor and also a supervisor and he recently used me as a, as a lab rat. What you see here is an insulin, is an insulin sensor that was attached to my shoulder for around three weeks. But of course, very proud, like, I wish for myself and for everyone always to be in the side of like to help, to help patients get like a better cure. And this is something that I'm really happy about like that I'm actually what I'm doing here it's actually like helping people, helping people that are sick. But yes, like I also, we also, all of us actually do stuff like this in the lab. Also, the reason that this guy is just like this is because like during the corona, during the corona, our all up became, became, of course we, it's like we are not, we are not doing anything that is linked to, to, to corona. We didn't do anything that is about it like before, but during the, during this break, this breakdown of like.
 Okay, we're live. Take it away. Hi. Hi there. Yes. Yes, I guess. Let's see whether they can  you are live your life. Okay, here we are. Hi. I'm Noah. This is Lawrence. And nice to meet you. We  are here streaming from the wise minister to science in Israel. And together we are going to  present the comparison between cybersecurity and biology. I hope you are ready. Well, I'm ready.  So I guess it is yours. Okay. So nice to meet you. I will just present. Okay. Sorry. Okay. So our agenda  for today. I have two targets with my presentation. First of all, I'll show you how beautiful and  complex biology is. And the second part will be to make you jealous. So who am I? My name is Noah.  I'm a master student at the wise minister of science. I'm a general scientist in  that is doing research specifically in microbiome and also diabetes. And I'm a former cybersecurity  researcher at checkpoint. So until a year ago, I was a reverse in checkpoint. My job was to reverse  to as you all I'm sure that you all know about this job, 2000 in malware, and then to implement the  defensive code into a checkpoint antivirus. So you know, I thought I'm pretty smart girl. After  all, and I even did some fuzzing stuff. I found some vulnerabilities. And you know, I built myself  as a hacker and as a researcher in checkpoint for many years. And then  but I was always very curious about complex teams, not only malware, but always about biology.  And a year ago, I decided to come here and to become a researcher in biology, specifically  about the new, the new area in a microbiome. So I thought, you know, I'm so cool and I'm doing  reverse engineering, like for so many years. So, you know, all of the bacteria and the COVID-19  and whatever like comes up, they would all like all of them gonna be like, you know, scared of me,  because now there is a good reverse and a good hacker that is coming into the field.  But then I found out that apparently it's not as I think that we all I think that it will be  really easy for me to prove my point that it's not as easy as we can, as we thought, until  it was not very far. And you can see like what happened when just one bacteria just  did some evolution and become just a little bit more dangerous than before. It's like the  COVID is doing evolution for many years. And like, I think it's really easy to see how  does it affect the world right now. And also how I learned in the hardware, biology is way more  complex than any malware that I ever did a reverse engineering. This is like actually one,  just one slide of my class. What you see here is the complex system of what happens inside a cell  when there is one ligand that is binding one of the receptors. So apparently, it wasn't the best  idea for me. I'm joking, of course. I'm very curious and this area is very challenging for me.  And I think that what we do here is just beautiful. And also one more thing that I want to point out  during my lecture is that when I tell people about biology, they all think that I'm, you know,  pipating and growing, growing microbes on a plate. But actually these days,  computational biology is on the rise. And what we're doing, especially in the in white's man,  is trying to take each of the biology systems we have in our body or in bacteria or anything else  and build the model about it. This is like also one of my classes. And this is from  Oriel Lónlav that we are doing collaboration with. So what he did here, he took the Altenard gland  and tried to build actual equations of, I think, something, okay, and tried to build the equation  in order to understand this is this thing here is the complex system of the glands. And this one  and this one is some equations that are that are explaining why why does they are biggest type to  develop in so many people these days. And now we'll pass the microphone to my friend that  you explained to me in detail about some of the basic creature that all of us have in our body.  Thank you very much indeed, Noor. I just want to point out that the person that actually introduced  me into this world, it makes me a very stressed, very stressed, also like a very happy student,  but also a very stressed and a very poor student is this guy. So when your mom tells you not to  talk to stranger, maybe she has a point. Okay, so I guess I don't need any further ado. Well,  if you can see the camera, you will see that in contrast to Noah, I will be sitting I'm a little  older. So forgive me for that. So actually, when we were preparing this lecture, we came up first  with this title, Back to the Future and me being this very modest, humble German dude that I am,  I didn't have all these fancy animations that Noah showed to you. But actually, when I envisioned  this presentation, I started with something lame looking like this. Okay, so again, we are from the  Weizmann Institute of Science in Israel. And what I want to show you now in the next like 25 minutes  is in essence, how computer science and biology not only go side by side exhibiting stunning  parallels, but also that they in fact have an intersection that we can exploit nowadays to  address current challenges that we are facing. But before I go into this, I actually want to  fulfill another request that Noah kindly asked me, which is to explain a very central concept  in biology, which by itself also has a counterpart in computer science. And I'm talking about genetic  code. Okay, genetic code, our DNA is basically the language in which life is written. So that's  sort of these chemical molecules that make up four letters, A, T, C, and G, they write a blueprint of life. But as such, they are not very useful because they have to be transcribed, they have to be transcribed from this DNA form, this genetic information into just another sort of code, which is RNA, which then is sort of translated to put into proteins.  Protein molecules that make up all our cells and therefore our body as a central component. And speaking about code, I think, again, it's not very far fetched to reach from biology, so the science of life to computer science because you as computer scientists mostly out there in the audience, you will know what a good computer code is.  And basically, I want to start by asking the question whether there is another analogy between computer science and biology. Are there any parallels in terms of computer viruses and biological viruses? Because, I mean, as you know, currently we are facing a global  COVID-19 pandemic that originates from the deadly spread of the coronavirus SARS-CoV-2. And I want to raise the question whether there's anything, if you only know computer viruses because you're a cyber expert, that you can also learn about biological viruses such as this coronavirus.  So let's start with the computer virus, right? With the computer virus that you probably heard about, the Melissa back in 1999, which based on a sequence, like a stretch of code that you can see on the left here, which basically with some letters just gives also the blueprint of a malware, basically, that causes a lot of damage.  And I'm not sure whether you're surprised or not, but what you're going to see on the right now here are the first 3569 letters of the SARS-CoV-2, this coronavirus, the version Wuhan U1, the genome, the genetic information of this virus, which in total is about 30,000 letters long, that makes up all of this virus that now spreads on the face of the earth.  And I'm saying genetic information here, but to be precise, this is not DNA that you're seeing, but actually it's an RNA virus. So why this is sort of the genetic information of this virus, it's not the same genetic information that we have in our body sets, but it's only this transcript that I introduced to you before.  But looking at this just sequence or this source code of the virus, you wouldn't be able to judge that this is particularly harmful. Maybe on the left you could, Melissa, because you're an expert and you see what it actually does, but there is no chance, even the best molecular biologist looking at the code on the right would be able to tell, okay, this is a deadly virus.  Okay, so, but still we desperately needed to know the sequence to detect this virus. But then the question is always having the sequence, having the source code, what does it do? What does the sequence translate it to?  Okay, and starting again with the case of or the example of Melissa, basically what this virus did is it prompted always to send emails to your own Microsoft Outlook inbox or started new work files, which basically then overloaded and completely flooded servers like online servers and stuff.  So you had like in, of course, I only made this up like an infinite inbox.  And this is sort of how the source code of the computer virus translates into a 3D structure if you want to say so with a malfunction, so with a bad function.  And actually the same thing biologists did then they asked the question, okay, given the sequence of the coronavirus, how does this translate into what I mentioned before this proteins, right, that make up the shell of the virus and this different parts of the virus that help it, help the virus to infect our body.  And this is basically one part of this virus. So it's not the entire virus is just one tool actually what I said, like one element one part of a molecule that helps this virus to infect our body our body cells.  And this is how this virus does harm right it infect cells and then eventually kills them. So this is how in biology a sequence or source code translates to to a structure and then a certain function.  And then to sort of visualize how it looks like in total apart from, you know what it does the, the Melissa virus to to overload your inbox and the coronavirus to infect your body cells.  And actually for for the Melissa virus then it took an artist to visualize how this overload looks like and this is how an artist apparently pictures how computer virus looks like, which as you can probably better judge and I do is just, you know, an orbit more or less arbitrary  than a person. But in fact in biology again we can use this wonderful invention of microscopes to actually look at how the virus looks like. However, the conventional light microscopes are not sufficient enough here.  So actually we need electron microscopes. However, if we use these very sophisticated a paralysis that costs like a million dollars each and require a lot of expertise to use them.  And actually see a virus inside of cells so all these blue particles that in this forbers article are described as pepperoni, sorry, slices of sausage on your pizza.  They are the actual virus. However, I also must say the truth is, if you use an electron microscope to observe the corona virus in cells.  So this is actually the coronavirus of the first identified us patient. Okay, that was imaged here.  This original picture from the electron microscope had no color. Okay, so this blue color is just put artificially in the end to visualize the virus particles.  But here we are still talking about like, only the size of a meter divided by one billion. Okay, so it's in this order of magnitude. So it's very small.  And you need sophisticated as I said microscopes to actually see it, but you can see it. Okay, you can understand how this sequence source code information that we that we saw how this translates to first individual aspects of this virus individual molecules and then the virus as such.  And now, once it has done its terrible job in fact in our body cells. Okay, and this is this sort of, you know, there shows that to some extent there's really there are parallels between computer science and biology in this case, computer viruses and biological viruses such as their SARS cough to  but is there, you know, besides, besides from, from this example is there, is there something we can do about this is there something we can use from computer science to help us fighting this terrible pandemic that we are facing in the US and all over the globe.  Is there an intersection between computer science and biological aspects of this pandemic. This is a question no and I basically asked ourselves. And in the very beginning of this pandemic in March, in fact, we participated in hackathon that was initiated by in  that collaboration with Microsoft, they called it hack for owner. Creativity will not be current time. And in the next five to 10 minutes, I would like to introduce to you this project that we initiated there to show you how computer science can actually facilitate very in a very concrete  example, our fight against this virus. And the question that that we asked, basically is how can we use computers to combat COVID-19. And the particular question we were wondering about because this was of major concern in the beginning of this  pandemic is how to help people who develop severe complications. Okay, because we, we understood that asymptomatic patients. Of course, they are very dangerous in terms of unobserved spreaders of this disease.  But in case someone develops severe complications needs ventilation in the hospital. Then this might, you know, present a major challenge for the health system in Israel and all over the world.  So how can we basically assess the uncertainty whether a certain COVID-19 patient will develop severe symptoms and require hospitalization and ventilation.  This is the question we asked. And as you can see in this picture, again, we were pretty much using biology as well as computer science by these two mice that you can see here like a computer mouse and the actual mouse or, you know, at least a stuffed animal of the  mouse. And so, in simple terms, our question was, given the fact that we have a certain number of COVID-19 patients that require because they develop severe complications, hospital beds, how to deal with them if the number of hospital  beds is actually lower than the number of patients who require hospitalization. In other words, three exceeds, sorry, five exceeds three, right? So how to decide which of them to allocate to the hospital, how to estimate what's the fraction of people who develop severe  complications and how to decide whom to offer with a certain priority these available resources. Okay. So this was sort of the question. And the reason why we deal with this was because at the Weizmann Institute, we are really at the forefront of this, not only that we have medical doctors on campus who see these patients on a day-to-day basis, but also the labs we're  coming in from Professor Dr. Eran El-Dinav and Professor Dr. Ido Amit, they develop now technologies to not only detect these infections and stratify these patients, but also streamline this process, really linking the foundational research with the application in the clinics.  Okay. So this is why we also felt dedicated to address this with our expertise, which comes more from the computational side here. So what we did in the beginning is, first of all, we asked, okay, what's the data we are dealing with, right?  Because only if you have data, you can actually use computational routines to address all these issues. So we basically simulated based on existing data, an Israeli data set comprised of 10,000 cases, where we really used Israeli demographic information and health record data that was publicly available to a certain extent.  And set up sort of a database where we can get an idea, okay, this is Israeli population, and now given all these demographic information and health record information, can we basically now sort of estimate what's the fraction of people who might develop severe COVID-19 complications.  So first of all, as you can see, we're struggling, you know, entering all this data. But then what we obtained is basically a more or less realistic Israeli age distribution here. And also what seems to be important, and we will hear about diabetes and the metabolic syndrome later on in no as part, the distribution of the body mass index in the Israeli population, which is basically a measure for your body density.  So it's your body size divided by your body height to the square. And it gives you an estimate of if you have a high BMI, you are considered obese, and then actually this also affects your risk of COVID-19 severe complications, but also where you get infected in the first place.  It's not fully understood, it's just an observation. So anyways, we thought we need to include this information. So first of all, we needed to have some sort of realistic distribution of this kind of data.  And then what we were basically sampling from this information we had back then when Israel was not really facing many, many cases at the time was this sort of distribution of people who got infected and then sort of were surviving through the disease and people who eventually died.  And then you can see that even though the overall frequency of that people, of course, or of dying people is with the infection or upon infection, not necessarily caused by the infection, it's very low.  But you can see that if you're older, the probability that you actually die after getting infected with SARS-CoV-2 is higher.  So what we then did is we used this data set, this information that we compiled and trained the model to basically predict, given a certain patient with this health record and demographic information,  what's the risk of developing severe complications. And then what you can always do is you can compare your prediction to the actually observed risk because you have information on this, right.  And then we get a pretty good agreement between the predicted risk and the observed risk here in this preliminary data set.  And then we can ask the question, okay, what kind of information are actually necessary to predict this risk in a reliable manner. And then just as a test case, proof of principle, we said, okay, age seems to be relevant and critical.  So what happens if we are leaving out the age information? So what you are seeing here is basically a receiver operating characteristic curve, a rock plot.  And basically if your line, so your specificity and sensitivity of this prediction performs pretty much randomly, then this line that you see is close to the diagonal line.  So as you can see the orange line, if we leave out the age information, we cannot really predict that it's like randomly drawing, okay, this person will develop complications or this person won't.  But if we just consider the age information as well in addition to all the other information, then this prediction becomes very sensitive and very specific.  So it means that if we say a person develops, does develop complications, then the chance that this is a true positive indication is high.  But also if we say this person won't develop complications, then also the chance is that this is a true negative. So it indeed won't develop complications.  It's also sort of high. So of course the perfect prediction would be just going straight up and then straight to the right.  But also with this in this toy model sort of or preliminary thing, the performance of this prediction was not so bad, I would say.  So the way we envision this basically is once people get sick or are suspected of being infected, they have to see a doctor who then puts in all the information into a certain database and some device that collects this information.  And then basically trains this model that is based on machine learning algorithms and all these health records information and then basically puts out back to the doctor a certain risk score.  Right. So the application tells the doctor, okay, this person exhibits a certain probability to develop severe complications.  So better save a hospital bed for this one. Or in the ideal case, just it can, this person can just be isolated at home.  There is a very low chance that it's going to be severe. Just monitor it carefully, but most likely the hospital bed allocated to this patient can be given to somebody else with a higher risk.  So what wait, what are we lacking here we I already presented you that this machine learning algorithm sort of works, but we need this device right we need this application.  And this is also what we what we sort of developed like something that can work on your mobile phone and helps medical doctors again that we have in our group and we talk to them on a daily basis to just use it in a very handy way.  If they see the patient they can check check in all this information in less than a minute.  And then basically we can, we can, we can just let the machine learning algorithm do its job and see whether there's there's a certain risk.  And of course this machine learning algorithm that I presented to you this model that we use there to make the predictions was just the first attempt that we had in the Sakaton, you know, very coarse grained first rough trial.  Needless, needless to say you can also use more sophisticated methods where you can use all these different biological information on these patients to assign how important is this information to either say that there is a high risk or maybe just informative to say that there's a low risk or maybe a combination of both right so we can, of course, you  want more data, we can try different models. And as I said, we are in day to day contact with the clinicians actually in charge actually doing this actually doing the analysis or talking to the patients, suspected infected people.  And so this is what we envision that we, you know, we can't really not necessarily change the number of people who are susceptible and eventually will get infected. It's just a matter of time. It seems like.  And maybe also, you know, the health system is sort of currently at its limits so we cannot really expand the number of hospital beds. But this app that we developed that by the way we we called virology and because it's like an intelligent solution to virology and  the different inference of this virus induced complication health complications, we could now use to assign again, hospital beds to people who needed the most. So in the scope of this hackathon which of course is very preliminary and we are still sort of thinking  about how to put this further in the scope of this hackathon this this question was solved. But as I already indicated in the scope of this hackathon and this is just covered 19 which at the moment seems to be a major part of our lives, but certainly it's not the only part of our  life. So, can we make a more general observation when we look at computer science at by and biology. Or in other words, how can we combine powerful algorithms from computer science with biological data sets.  And this is what what I've shown what I'm showing you here is basically the intersection of biological data this is how biological data nowadays sort of looks like.  So, we combine this biology with this more like a conceptual or theoretical view of biology as a system. Okay, as a as a living system that you know just operate certain commands and has some input output functions.  And the answer here is also the computer because only the computer will help us to process this data and to put it into these sort of models in terms of understanding how biological systems work.  And I also have a discipline that I actually studied I did my PhD in this field which is called systems biology. Okay.  And let me introduce to you the field of systems biology with one of the Godfathers of systems biology a guy who actually does also a lot of robotics and intelligent design and stuff Japanese guy who also invented robots that help people to to rescue  from collapse buildings after earthquakes and stuff.  He wrote a key to know his name, and he described systems biology as the integration of experimental and computational approaches to precisely characterize biological systems with quantitative data.  Okay, so now we have quantitative data, which is machine readable from biology that we will use to understand in combination with experiments in the laboratory.  How this biological system works simulating it at the computer using algorithms to to understand what's going on there. And what does go on there how does this look like.  And you can imagine as this sort of simple diagram here, where basically on the x axis, you will have time. And on the y axis you will have the amount quantitative data okay, of a biological entity.  Now, what does this mean so basically what you have in blue these blue dots these are your data points. And then with the computer you can basically simulate the behavior of the system and you have some mean simulation here the blue line.  And then also you have this shaded region which is basically your your uncertainty. Okay, your confidence into what. So not only you get an understanding with the computer. How does the system change over time how do biological entities change over time.  But also how certain are you that this happens right the virus can spread it like enormously. But maybe there's also a chance it doesn't spread or maybe there's a chance that it spreads like even more.  And this is something we can quantify we can calculate. Sometimes we have a problem with communicating this but there are possibilities and mathematics to calculate these confidence intervals and these uncertainties.  And the way this is done the way we describe our biological entity changes over time is in mathematics called ordinary differential equation.  So this basically describes you how your biological entity be is changing so the D represents the change okay it's a change of B over the change of time t okay how does be change with time.  And this is now depending on certain parameters p p one and p two.  And the plus always means you know you get something more so depending on this parameter p one you get more of be if you already have be.  So, if, if you have something that can sort of reproduce, like a virus can reproduce or we can reproduce you know you, you will get even more if you're more people to start with right.  And then basically the reproduction rate is is your parameter P, and it's positive because you get more.  But then this curve of be is not only going up it's not only just getting more with time right it's also going down.  So there's also another parameter p two and there's a minus because it's getting less that tells me that that be is the change of be over time is getting negative it's there is something subtracted subtracted from it.  And of course the more be we have the more likely it is that you know it distracts itself you know humanity is very good that distracting itself somehow unfortunately.  But just by itself it won't go just up and down there, we needed some another component here that influences this and this other component is a.  So for a we can actually write down a very similar equation so how does a change over time well it grows, but not only depending on a but also on be because you know it is it's distracted eats it for instance you know humans are very good at eating stuff.  Also other living beings.  But everything that lives also has to die so the more a we have the more likely is that it's also dying with another parameter before. And then you know we get we get more be but then a gets more to eat but then eats more and then be goes down again.  So there's little a that can eat up be and be can we arrive and then it's going up and down so this is sort of how nowadays couple ordinary differential equations because there's two of them and they're depending on one another.  We can describe the systems behavior and even these like weird oscillations like going up and down.  And that's a very mathematical point of view. Very interesting observation of this system behavior. But now I talked about a and be a lot but saying be I don't necessarily mean a be of course be is also biological entity right the be that gives you honey and stuff.  But it's not anything honestly we are talking about the human animal, the mirror in animal like mice. It could be the number of cells that are changing in our body. It could be bacteria that are also in our body also says it can be this virus right this is a picture of a virus.  But it can also be just certain molecules to change over time. For instance here an antibody that is produced against against this virus.  These are all options of biological entities that we can describe in biological systems.  But to describe them actually we have to think on which level are we talking.  Because biology happens on many different scales in space and in time right the human being is on the order of magnitude.  Like, one to two meters let's say, you know, maybe basketball players are more like two meters and are people more like one meter. Never mind.  But then we talked about a mouse that is certainly not a meter but just more like centimeters and then we have like cells that you have seen that are more like micrometer which is a meter divided by a million.  And then we have in these cells we have small subunits that are even smaller bacteria also smaller than our body says.  And here we are in the order of magnitude of a meter divided by a billion.  Okay, so 10 to the minus nine a nanometer. And so and all at all these scales you know changes are happening all the time. That's how we are being alive because molecules are turned over cells are turned over our bodies are being turned over.  Everything happens there and this not only happens like on very slow scale like billion three point six billion years this earth does exist and life does exist.  But also in a matter of hours in the matter of days in a matter of minutes in a matter of seconds, you know, molecules change your confirmation in the order of microseconds again a second divided by one million so.  And this all, you know, this is all part of this one biological system that we are trying to describe and there's no one out there that who can tell me that there's a way to handle this complexity without a computer.  So we need computer science to address all this to understand biology at all these scales.  And if you're curious for this. Don't you worry because there's a wonderful database at bio numbers.org initiated at Harvard Medical School with you guys in the US, where you can ask the question okay how big is the cell how many molecules are in a cell.  Because human body cell is different from a bacterial body cell from a fruit fly from a warm. All these numbers are there with references you can look up the original scientific literature to see the evidence for these numbers.  And it's just wonderful looking at, at all this information that is out there.  And I don't need this information is out there but it's being continuously generated we are living in a time now in an era where biological data is not for the first time not a bottleneck anymore you don't need to spend three years on a PhD to gather one data point of information about one protein.  And now sequence our entire genetic information DNA of a patient upon diagnosis after treatment and really understand how our medical treatments affect our biology on so many levels so we are living in an era of biological big data but speaking to you maybe of course I have to say that in fact there's only one true big data.  And I really speak about data in biology that is out there on all of these scales that I've just introduced to you.  So data is not a problem anymore, but we need concepts how to work with this data.  And in systems biology there's two sort of complimentary approaches to deal with all this data.  Again, data can be on the level of organisms it can be on the level of cells or it can be on the level of molecules.  But the problem is you hardly ever have just one of each of these. So usually you have, right you have many human beings in a clinical study you have many different cells that make up our body.  30 trillion of them to be precise, or many molecules that do something right many virus particles or anything of that like.  So you have two different ways to approach them either you do it sort of bottom up where you say okay let's just look at some of these different molecules where we know actually what they are doing how they interact with one another.  And then we try to sort of describe this in a dynamic way so how they change over time so first one molecule interacts with another one and then these two interact with the third one.  And then we understand the small part of like we start from the ground we understand the small part of it the small module and then we sort of subsequently try to to connect these modules.  And as I said, they're based on a lot of knowledge what is going on we have some mechanistic understanding how these gear wheels are really fit into one another and then we can see how the system is changing over time.  So there's another way to look at a biological system let's say a patient the covered 19 patient. Just look at them from the top okay you don't really know what's going on in this patient okay it's a black box this patient.  But now you give a certain drug to this patient. This is your input into the system into your black box, and you see how the patient reacts to it, and this is your output your reaction, and then you do this not only with one but with the entire cohort of COVID 19 patients, and you try to see patterns  and so whenever we give this to a certain patient where maybe we have all this health records.  There's always a good outcome, or maybe not so good outcome and then you can start to try to understand this input output relationship meters to say for this you need a lot of data many patients, many drugs that you, that you potentially test.  And then you can learn sort of associations okay smoking history is always connected to a bad outcome or as we said the high BMI is always connected to a bad outcome. And these are all these machine learning artificial intelligence methods.  So these are sort of the two complementaries, not schools of thoughts but approaches, they are not mutually exclusive right because you can, while looking at these patients as a black box you can still start with some, for instance cells of the immune system that are attacked by the virus you can also start with some bottom up  modeling there and then you can be input it all together.  But to give you a precise example, I want to use the bottom up approach now as an example. Okay, so again where we look at how biological entities change over time.  And actually these models, particularly also with these going ups and down these oscillations, they were introduced a long time ago for for foxes and rabbits.  Biology is not just that and in fact, I wrote a book on rabbit free sort of biology. However, we can still use all this mathematics that was established to now bring this research back to the future using this mathematics to understand something about molecular biology.  And the, the last example I want to give you before I hand over back to Noah is the example of a of a molecule that controls the survival of our red blood cells.  And that's as complicated name signature and stucer and activator transcription start five. The only thing you need to know about this molecule is it can bind DNA. Okay, it can bind our genetic information and then exert certain tasks there.  And if we have it, our red blood cells are surviving. And if this molecule is not at our genetic information doing its job, then red blood cells are dying. And so do we at some point.  So this molecule basically shows us a life death decision, which starts with a simple very simple question. Does one plus one always give two, because if we have two of these molecules they are supposed to form this complex of two molecules.  And this sounds simple, but actually biology is more complex than that because we not only have one family member of this molecule we have two of them the blue one and the red one.  And also this can be modified it can be chemically modified to be to be switched. So the small p here is a chemical group that can be added to this molecule, and then it's switched from the off state by this edition of this chemical to the on state.  We basically have four different molecules that potentially can do this job of binding genetic information and making sure our blood cells are surviving, which gives us a matrix of potential biological eight entities here.  Right, not this kind of matrix but more like this kind of matrix.  So from all these molecules we can form these potential complexes and now the problem is they are very small so we cannot just look at them to see which of them do actually exist and what do they do.  So we have experimented data where we see how these complexes change over time. So what we see here in on the top is basically we see how many of this switched on molecules we have as compared to all possible molecules.  And then we also see how the number of the blue molecules compared to all molecules change over time. And then here we basically see how these two ratios change with respect to one another.  And then in these cells, we can measure this and then with a mathematical model we can basically describe, we can describe this data.  As you can see again we have our, our simulation and also the confidence interval here.  And now with the simulation we can basically look back and see okay, which kind of molecules from this match matrix to actually exist. I mean, which one of those we need to describe this data.  And here is surprisingly a few of them we only need. Okay. And this is something we cannot just look at we really need to compute we needed the computer to find out okay what's the probability that these molecules exist.  And this we also published it's not an a work but again a and b because these are two molecules. And with this maybe we better understand now how our red blood cells are surviving and be happy.  And to wrap this all up. What what what I've shown to you is basically they are standing parallels between computer viruses and biological viruses.  We can apply computational algorithms to address challenges that we are facing in the covered 19 pandemic.  And systems biology helps us to answer complex biological questions with quantitative data, for instance, how molecular switches decide between life and death in our blood.  And now I will give you some more teasers teasers how how this all relates. I'm just saying, if you feel like intrigued by this don't hesitate to reach out to us and we will be happy to, to also share more information and with having said that I hand over back to  you.  I'm going to explain again that that my name is not I'm a junior master scientist at the Wild Swan Institute of science here in Israel. And my background is from is my background is as most of you guys.  I was a reverse engineering and cybersecurity expert at checkpoint for many years. And when I joined white man I had to close a huge gap in my in my knowledge in biology.  And I was what was stunning for me was the parallel that I found so often in my material in the material that I studied in the courses and also for the lab with my with the knowledge that I have from the past.  And I will just give you a few examples of the things to have in biology that will look very, very familiar for you. So, without any further introduction, let's start with cancer cells.  Okay, so cancer cells, unlike most of the people think about them. And they're not just cancer that accumulate in our body this is more like super cells that in our body to have superpowers they evolve.  They're better than the rest of the cells in the body like due to, due to a mutation from radiation from from UV light or from any other from smoking and from, or just by chance in the body.  And then what's happening with these cells is that they become selfish, which means that instead of of working together for their like with the rest of the cells in the body. They just, they're selfish enough to try to control to control and to get all the  resources for example to be to say they're actually sending signals to the body to build them to build veins in order to get more oxygen into the tumor in the tumor environment. And one, one stunning fact that I learned during during my courses is that not only that the  cancer cells are selfish they also try to evade the immune system in order because one of the roles of the immune system is not only to eliminate pathogens but also eliminate cells that are selfish like cancer cell because cancer cell like we have them on a regular basis in our body in our body.  And this is one of the main roles of the immune system to to detect them and and and to eliminate them. So, it's not only that the, that our immune system is working as an anti virus, but the thing here is that the cancers are fighting back.  So, the picture here is represented interaction between cancer cells and different types of immune cells. Cancer cell are the one that shows in blue immune cells are in different colors and what they're trying to do is to send so you can you can think about the rest of the  colorful cells as a different types of anti virus from many different companies and many or maybe a different model that we have in the one anti virus. And what the cancer cells trying to do is to actually reprogram or to send to send the signal to send signals to the different  immune cells in order to in order to invade them. And this is something that was you know when I heard about it was really familiar for me like I diverse many models that try to evade my VM to evade my to evade my my my item I already my already bg, all the antivirus in general.  And so, for me it sounds pretty familiar so apparently we, we, it wasn't our idea to evade the to evade people that to evade the entities that are trying to detect us.  I can promise you that cancels are doing it in a in a much more efficient way. For example, one, one very, one very stunning example that I have is that every cell is supposed to present to present a molecule on his surface which is the name of the molecule is an mhc.  And this molecule and this molecule is actually like is, it's like the info that an executable can have, which is just which describes what what what the cells are are producing what are the proteins that are running inside of it.  And this comes to show what they actually do is they just, they just don't show they just don't present is the mhc, this mhc molecules so the so it's really hard to be in for the immune cells to understand what actually going is going on inside of them.  And, and here you can see something that we call a hot tumor.  The tumor is immunologically hot. It means that the immune system is operation here. However, when cancer progress it that it learn how to deactivate the new cells and make tumors and you know, it's called like look at this tumor and you can see here many different many different of  immune cells that are trying to detect and to like you know trying to get that actually can understand what's going on inside. But after after a while what we can see in tumors that are actually as successful with debating the immune system is that they become  immunologically cold tumor and you can see that like most of the immune most of the immune cell they just gave up and like you know went to like they said okay whatever like I have other stuff to do and they just you know left the tumor there alone.  And this is why yeah like so many. This is why you get the tumor in your body. And here is just here what you can see here is just a network of the different. This is just you know this is just a brief, a brief knowledge that we have so far about  how all the different all the different ways that cancer healthy that cancer has in order to try to invite immune system and generally cells can communicate with each other using the vessels signal transaction across that lead to different outcomes.  And what consciousness do they upregulate the spot was with the which he that nervous normal function of immune cell they don't they don't they don't even just tell the like the specific you know just leave me alone.  They also ask him to spend their the rumor and tell her and tell the rest of them you know this guy, it's okay like it's okay you can just leave him alone. And this is something that was really fascinating for me to learn.  And here is an example where a cancer cell over a specific pdl one a miracle that suppresses the cell important cell type involved in cancer cell elimination. On the right you can also see a reprogramming immune cell called tumor associated  macrophage, which helps to spread the and inactivating signal instead of fighting cancer. Okay, so you can see that it's not only telling him listen I'm okay. It also asked him, you also have to these cells, you can also tell the rest of the guys that I'm fine and like to leave me alone.  And this was amazing. I don't know like too many models that are actually so so powerful and sophisticated and it was amazing for me to learn. And my next example which is actually one of my life, like one of my favorite is bacteria fudge.  So what is a bacteria fudge here you can see I like, let's, let's take a look at this very beautiful gift of like, okay, so just explain first of all my character five is like my favorite creature so far.  And what it's actually doing there is a creature that we all know it's an awful bacteria, but there is also another tiny tiny picture and what the bacteria is actually trying to do is to inject its DNA into the bacteria and then the bacteria instead of  running their own genome it will run the bacteria genome like and does it sounds familiar to anyone else and so currently also with this we didn't come up.  And this was amazing for me to understand like you can actually see it. This is actually a bacteria. Okay, and here you can see a bacteria that are attaching into it and injecting their genome into the bacteria.  And here you can see this is a very sad start because the bacteria like you know, sometimes they understand that they are like, you know, they are doomed.  This is just a gift. And so we just explained a bit about about about the progress of it so what what you see here is that there is attachment of the bacteria into the bacteria bacteria is like, you know, the bacteria that you know that we all have in our body.  And this is the bacteriophage and then and the which has a very, very nice and interesting picture and structure. And so what it's doing is actually attaching into the bacteria.  It's injection it's injecting in the its own genome into the bacteria inside surface and then the bacteria instead of running like you see this is the genome of the of the of the bacteria instead of running like its own code it's running the  bacteriophage code. And then, like the whole purpose of it is that there were there are more and more bacteria like being from the inside of the area. And then the, the poor bacteria you know just just full of bacteria until they release each other until they get like the it's too much for it and they  go out and infect even more and more bacteria that are inside of this environment. And, and another thing, and that I'm pretty sure that most of you heard about if you are interested in biology at all is something that is called CRISPR.  And I don't know that CRISPR is being used these days in order to kind of engineering the babies of future and stuff like this but what was fascinating from it to learn is that actually CRISPR the whole purpose of it is this is a defense mechanism of the bacteria in order to detect bacteria in order to  detect phages. So what you see here is that, like this, this thing, evil thing is the bacteria. And what it's trying to detect, and this is something that we actually stole from them is what it's trying to detect is the sequence that he also had that he, that he was  that he saw before of phages that try to attack it. And the way that it's, and the way that it's doing this is just you know he has a sequence that he remember, and then he just tried to do pairing, like to compare the new, the new DNA that he's seen  in the life of the cell, and then the sequence that he already know that is belong to the phages. So, yeah.  And of course, I think it's very it's, it's very obvious that this, this cast nine system is very similar to what we know as the Md5 or the hashes of the malvulous that we have been said many different antiviruses.  And the very cool thing is that it's not only that the bacterias detecting phages in like using the non sequence is that the phages are actually invading this defensive system by changing the genome, the same way that malvulous are changing  the Md5, you know, it's enough to change just one bit, just one bit. Phages are doing the same in order to, to, like, you know, be evolution in order to be able to, to inject their code into new bacterias.  And so this mechanism that we actually start from bacteria, we will, we are trying these days to use it for genetic engineering. But just to know, like, we, it wasn't our idea we stole it from a very, actually smart creature apparently.  And, and I would just say, since our time is up, I would just say briefly what's like the role of our specific lab. So what you see is so my, so my research, my research is specifically about the microbiome.  And so the microbiome is what we're trying to do as you, as you all, and none of you heard about it, but we are all full of bacteria inside of inside of our gut and we are trying in our lab to, to understand, and maybe in the future also to manipulate the different  different diseases of our gut and all the networks that are inside of it. And first of all, we try to find the connection between them and different diseases like the diabetes and the less and obesity.  So this is a very new and interesting field. And you should know that you have you all have bacteria is all over your hands, your eyes and specific inside your gut. And it's not just that they are there happily like not doing anything they're actually sometimes very crucial.  And then in order to keep the home of status. And so this is what's just a brief thing about the microbiome, just a brief introduction to the microbiome.  And a few things that we do that we found out so far in our lab is the connection between the bacteria that are inside the gut and the very well known disease that are inside the al s because the bacteria that they can they can send signals from the gut into the into the brain.  And there is a there is a very clear connection between the dysbiosis that we'll see in the gut and the, and the, and the from of this disease.  And we can see many different, many different types of disease that are linked to, to this versus in the macro bottom, and another very cool and another very cool subject we are looking into in our lab is diabetes.  And just one thing that I, I learned and it's very it was really interesting and and like this is one of my favorite challenges in this field is that when you have any kind of disease it's not like that, for example, if it is if you have too much sugar in your blood,  the problem in the problem with it is like, wouldn't be what just about the fact that you have too much sugar in your veins, like many different diseases. They're like, there is a hip corruption, let's say, let's say, in our body but unfortunately, unlike in the new operation systems in our body,  like if there is no such thing as the different processes that have different that are different classes of memory so if there is, so if there is a hip corruption in one process, or in one part in one in one system in our body, it affects the rest of it as well.  So, for example, diabetes one of the problems that we see that we see is that they're, that we lose the most status of, of, of also the, the CA2 inside our, inside each of ourselves.  And actually we can, we can see that the blood is become becoming acidic. So this is just one thing. And so, I would just say briefly like that I'm really proud, like really, really proud that taking part of my lab like this is the very nice person that you see here is my supervisor,  I have a professor and also a supervisor and he recently used me as a, as a lab rat. What you see here is an insulin, is an insulin sensor that was attached to my shoulder for around three weeks.  But of course, very proud, like, I wish for myself and for everyone always to be in the side of like to help, to help patients get like a better cure. And this is something that I'm really happy about like that I'm actually what I'm doing here it's actually like helping people, helping people that are sick.  But yes, like I also, we also, all of us actually do stuff like this in the lab. Also, the reason that this guy is just like this is because like during the corona, during the corona, our all up became, became, of course we, it's like we are not, we are not doing anything that is linked to, to, to corona.  We didn't do anything that is about it like before, but during the, during this break, this breakdown of like.
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