 Okay, we're live take it away. Hi. Hi there. Yes. Yes, I guess so let's see whether they can you are live your life. We are live. Okay, so here we are. Hi, I'm Noah. This is Lawrence. And nice to meet you. We are here streaming from the wise minister of science in Israel. And then together we are going to present the comparison between cyber security and biology. I hope you're 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 junior scientist in Iran in Nambulada that is doing research specifically in microbiome and also diabetes. And I'm a former cyber security researcher at the checkpoint. And so until a year ago, I was I was a reverser in checkpoint. My job was to reverse it to as you all I'm sure that you all know about this job, 2000 engineering malware, and then to implement the defensive code into a checkpoint antivirus. And so, you know, I thought I'm pretty, I'm pretty smart girl after all. And I even did some buzzing stuff. I found some vulnerabilities. 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 also about biology. And a year ago, I decided to come here and to become a researcher in biology, specifically about the new area in 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 bacterias and the COVID-19 and whatever like comes up, they would all like all of them going to be like, you know, scared of me, because now there is a good reversion and good hyper 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 not very far. You can see like what happened when just one bacteria just began just did some some evolution and become just a little bit more dangerous than before. It's like the COVID is doing an evolution for many years and I think it's really easy to see how does it affect the world right now. And also how I learned in the hard way, biology is way more complex than any malware that I ever did a reverse engineering. This is like actually 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 microbes on a plate but actually these days computational biology is on the rise and what we're doing especially in in the in Weitzman is trying to take each of the biology systems that 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 Uria-Lonelab that we are doing collaboration with. So what he did here is he took the alginal gland and tried and tried to build actual equations of okay and tried to build equation in order to understand this is this thing here is that is the complex system of the glands and this one and this one is a some equation that are that are explaining why does diabetes type 2 develop in so many people these days. And now we'll pass the microphone to my friend that we'll explain to you in detail about some of the basic creature that all of us have in our body. Thank you very much indeed, Noah. I just want to point out that the person that actually introduced me into this world and make 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 order so forgive me for that. Um so actually when we were preparing this lecture and we came up first you know with this with this title back to the future and me being this you know 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 um 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 ATC and G they write a blueprint of life um 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 of life to to computer science because you as computer scientists mostly out there in the audience you will know what what a good computer code is and basically I want to start by by asking the question whether there is another analogy between computer science and biology are there any parallels in in terms of computer viruses and biological viruses because I mean as you know currently we are facing a global uh COVID-19 pandemic that originates from the uh 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 you're a cyber expert that you can also learn about biological viruses such as the this coronavirus so let's start with a computer virus right with a computer virus that you probably heard about the melissa in back in 1999 which based on 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 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 this virus that now spreads on the face of the earth and um I'm saying genetic information here but to be precise this is not uh DNA that you are seeing but actually it's an RNA virus so um while this is sort of the the genetic information of this virus it's it's not um this same genetic information that we have in our body sets but it's only this transcript that I introduced to you before but you know looking at this just sequence or this source code of the virus you know you you wouldn't be able to judge that this is particularly harmful maybe on the on the left you could you know melissa um because you're you're an expert and you see um what it actually does um 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 to detect this virus um but then the question is always having the sequence having the source code what does it do what does what is the sequence translated to okay and starting again with the with the case of or the example of melissa um basically what this virus did is it prompted always to send uh emails to your own microsoft outlaw inbox or or started new uh word files which basically then overloaded and completely flooded um servers like online servers and stuff so you had like an of course I only made this up like an infinite inbox um 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 the shell of the virus and and um this different parts of the virus that help it help the virus to infect our body um and this is basically one part of this virus so it's not the entire virus it's 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 um and this is how this virus does harm right it infects cells and then eventually kills them um so this is how in biology a sequence or a source code uh 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 um actually for for the melissa virus then it took a an artist to visualize how this overload looks like and this is how an artist apparently pictures how a computer virus looks like um which as you can probably better judge than I do um is just you know an orbit more or less arbitrary representation um but in fact in biology again we can use um this wonderful invention of microscopes to actually look at how how the virus looks like um however the conventional light microscopes are not sufficient enough here so actually we we need electron microscopes however if we use these very sophisticated apparatuses that cost like a million dollars each and require a lot of expertise to use them then we can actually see a virus inside of uh cells so all these blue particles that in this forebors uh article are described as uh pepperoni uh sorry um uh slices of 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 coronavirus uh in cells so this is actually the coronavirus of the first identified us patient okay that was uh imaged here um this original picture from the electron microscope had no color okay so this blue color is just pulled up artificially in the end to visualize the virus particles um but here we are still talking about like um only um uh the size of a meter divided by one billion okay so it's uh in this order of magnitude so it's very small um 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 um first individual aspects of of this virus individual molecules and then the virus as such now once it's it has done its terrible job infecting 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-CoV-2 um but is there you know besides besides from from this example is there is there something we can do about it is there something we can use from computer science um to help us fighting this terrible pandemic that we are facing in the US and all over the globe um is there an intersection between computer science and biological aspects of of this pandemic um this is a question Noah and I have basically asked ourselves and in the very beginning of this pandemic uh in March in fact we um participated in uh hackathon that was uh initiated by uh in-depth in collaboration with Microsoft they called it hack corona uh creativity will not be quarantined and in the next five to ten minutes I would like to introduce to you this project that we initiated there to show you how computer science can actually facilitate a very in a very concrete example our fight against this virus and the the question that that we asked basically is um how can we use computers to 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 uh unobserved spreaders of this disease but in case someone develops severe complications needs ventilation in the hospital um then this might um you know present a major challenge for the health system in Israel and all over the world um so how can we basically assess the uncertainty whether a certain COVID-19 patient will develop severe uh symptoms and require um hospitalization and ventilation um this is the question we ask and as you can see in this picture again we were pretty much uh 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 a mouse um and so in simple terms our question was given the fact that we have a certain number of COVID-19 patients um 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 to allocate to to the hospital how to estimate what's the fraction of 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 working in from Professor Dr. Dr. Eran Elinav and Professor Dr. Edo 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 so this is why we also felt you know dedicated to address this with our expertise which comes more from the from the computational side here so what we did in the beginning is first of all we asked okay what's what's the data we are dealing with right because only if you have data you can actually use computational routines um to to address all these issues so we we basically simulated based on 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 um and set up set up a sort of a database where we can get an idea okay this is Israeli population what and now given on 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 uh 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 the distribution of the body mass index in the Israeli population which is basically a measure for your your body density so it's it's your body size divided by your body height uh to the square and it gives you an estimate of um if if you have IDMI you are considered obese and then actually this also affects your risk of COVID-19 um severe complications but also whether you get infected in the first place but 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 um people who got infected and then sort of were 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 with the infection or upon infection not necessarily caused by the infection it's very low but you can see that the if you're older the the the probability that you actually die after getting infected with SARS-CoV-2 is higher um so what we what we then did is we used this data set this information that we that we um compiled and trained trained the model to basically predict given a certain patient with with this health record and demographic information um 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 that because you have information on this right um and then we get a pretty good agreement between the predicted risk and the observed risk here in this you know preliminary data set and then we can ask the question okay what kind of information are actually necessary to to predict this risk in a reliable manner and then just as a test case uh proof of principle we said okay age seems to be relevant and and and critical so what happens if if we if we are leaving out the age information so and what you are seeing here is basically a receiver operating characteristic a curve a rock plot and basically if if you're aligned so you are your specificity and sensitivity um of this prediction performs pretty much randomly then this line that you see is is close to the diagonal line so as you can see the orange line if we leave out the age information we we cannot really predict that it's like randomly drawing okay this person will will develop complications or this person want but if we just consider the age information as well in addition to all the other information then um 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 is um high but also if we say this person won't develop complications then also the the chance is that this is a true negative so it indeed won't develop complications it's 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 was um not so bad I would say so the way we we envision this basically is um once people get sick or are suspected of being infected they have to see a doctor who then um puts in all the information into a certain database and some device that collects this information and then basically trains this this model that is based on machine learning algorithms um and all these health record information and then basically puts out back to the doctor um a certain risks or right so it the application tells the doctor okay this person exhibits a certain probability to to develop severe complications so better save a hospital bed for this one or in the ideal case um just it can this person can just be isolated at home there's 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 um 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 um and this is also what we what we sort of develop like uh 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 um to just use it in a very handy way if they see the patient they can check checking all this information in less than a minute um and then basically we can we can um we we can just let the the machine learning algorithm do its job and 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 hackathon you know very coarse grained first rough trial um needless needless to say you can also use um more sophisticated methods where you can use all these uh different um biological information on these patients to assign how important is this um information to either say that there is a high risk or maybe it's just informative to say that there's a low risk or maybe a combination of both right so we can of course you always want more data we can try different models um and as I said we are in day-to-day contact with the clinicians actually in charge actually doing this actually doing the an amnesis or talking to the patients uh put suspected uh infected people um so this is what what we envisioned that we you know we can't really not necessarily uh change the number of uh people who are susceptible and eventually will get infected it's just a matter of time it seems like um 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 because it's like an intelligent solution to virology and inference of this uh virus induced complication health complications we could now use to 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 um thinking of how to how to put this further uh in the 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 COVID-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 um can we make a more general observation when we look at computer science and biology um or in other words how can we uh combine powerful algorithms from computer science with biological data sets and this is what what I've shown what I'm showing you here it's basically the intersection of biological data this is how biological data nowadays sort of looks like um how can 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 operates 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 this is 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 people from from collapsed buildings after earthquakes and stuff uh Hiroaki Kitano is his name and he describes 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 um 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 well 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 um 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 but then also you have this shaded region which is basically your your uncertainty okay your confidence interval so not only you get an understanding with the computer how does the system change over time how do biological entities change over time um 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 to calculate these confidence intervals and these uncertainties um the way this is done the way we describe how a biological entity changes over time is in mathematics called ordinary differential equation so this basically describes you how your biological entity b is changing so the d represents the change okay it's a change of b over the change of time t okay how does v change with time and this is now depending on certain parameters p p1 and p2 and the plus always means you know you get something more so depending on this parameter p1 you get more of b if you already have b 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 are 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 b 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 p2 and there's a minus because it's getting less that tells me that that b is the change of b over time is getting negative it's there is something subtracted subtracted from it and of course the more b we have the more likely it is that you know it distracts itself you know humanity is very good at distracting itself somehow unfortunately um 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 b because you know it it's it's distracted it eats it for instance you know humans are very good at eating stuff also other living beings um but everything that lives also has to die so the 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 b but then a gets more to eat but then eats more and then b goes down again so there's little a that can eat up b and b can we arrive and then it's going up and down so this is sort of how now with these coupled 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 pretty also from a mathematical point of view a very interesting observation of this system behavior but now I talked about a and b a lot but saying b I don't necessarily mean a b of course b is also a biological entity right the b that gives you honey and stuff but b could be anything honestly we are talking about the human animal the marine 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 that change over time for instance here an antibody that is produced against against this virus so 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 other people are more like one meter never mind but then we we talked about amounts 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 even smaller bacteria also smaller than our body cells and here we are then in the order of magnitude of a meter divided by a billion okay so 10 to the minus nine a nanometer and and so and 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 um but also in a matter of hours in a matter of days in a matter of minutes in a matter of seconds you know molecules change your conformation in the order of of microseconds again a second divided by one million so and and this all you know this is all part of this one biological system that we are trying to describe and there is no one out there that who can tell me that there's a way to handle this complexity without a computer okay so we need computer science to address all this to understand biology at all these scales and if you're curious um for this uh don't you worry because there's a wonderful database at bio numbers.org um initiated at Harvard Medical School um with you guys in the U.S. where you can ask the question okay how big is the cell how many molecules are in a cell how does a human body cell is different from a bacterial body cell from a fruit fly from a worm 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 then not only 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 the bottleneck anymore you don't need to spend three years of a phd to gather one data point of information about one protein no we can now sequence uh our entire genetic information dna of a patient upon diagnosis after treatment and really understand how our medical treatments affect um 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 um but here I really speak about um data in biology that is out there on all of these uh scales that I've just introduced to you so data is not the problem anymore but we need concepts how to work with this data and in systems biology there's two sort of complementary approaches to deal with all this data so 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 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 like 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 um 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 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 uh understanding how these gear wheels are really fit into one another and then we can see how the system is changing over time but then there's another way to look at a biological system let's say a patient the COVID-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 okay 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 a good outcome and then you can start to try to understand this input output relationship means to say for this you need a lot of data many patients many drugs that you potentially test but 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 okay so these are sort of the two complementary 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 again put 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 but biology is not just that and in fact I wrote a book on rabbit free sort of biology however we can still use all these mathematics that was established to now bring this research back to the future using these mathematics to understand something about molecular biology and the exact the last example I want to give you before I hand over back to Noah is the example of a molecule that controls the survival of our red blood cells it has this complicated name signal transducer and activator of 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 this 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 addition of this chemical to the on state and so 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 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 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 but we do have experimental 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 okay in in 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 the answer 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 we better understand now how our red blood cells are surviving and and we have so to wrap this all up what what what I've shown to you is basically there are stunning parallels between computer viruses and biological viruses we can apply computational algorithms to address challenges that we are facing in the COVID-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 no 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 Lola. Hi and welcome back for anyone that lost my that joined us after the after my introduction I would just explain again that that my name is no I'm a junior master scientist at the wild swan institute of science here in Israel and my background is from in my background is as most of you guys I was a reverse engineering and the cybersecurity expert at checkpoint for many years and when I joined Weitzman I had to close a huge gap in my in my knowledge in biology and I was what was stunning for me was the parallels 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 so I will just give you a few examples of the things we have in biology that will look very very familiar for you so without any further introduction let's start with the cancer cells okay so cancer cells and unlike most of the people think about them and they are not just the cancer that accumulate in our body this is more like super cells in our body that have superpowers they evolve they evolve the better than the rest of the cells in the body like due to a mutation from a radiation from from a UV light or from any other from smoking and from or just by chance in 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 the like with the rest of the cells in the body they just they are they're selfish enough to try to control to control and to get all the resources for example to be to they're actually sending signals to the 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 because one of the roles of the immune system is not only to eliminate pathogens but also eliminate cells that are selfish like cancer cells because cancer cells like we have them on a regular basis in our 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 the 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 are the one that shows in blue immune cells are in different colors and what they are trying to do is to send so you can you can think about the rest of the the rest of the colorful cells as different types of anti-virus from many different companies and or many or maybe a different model that we have in a one anti-virus and what the cancer cell is 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 invade them and this is something that I was you know when I heard about it 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 IDAM I already my already dbg all the antivirus in general and so for me it sounds pretty familiar so apparently we we it wasn't our idea to evade to evade people that to evade the entities that are trying to detect us and I can promise you that cancers 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 and it's the name of the molecule is an MHC and this molecule and this molecule is actually like his it's like the info that an executable can have which is which describes what what what these cells are are producing what are the proteins that are running inside of it and cancer cell what they actually do is they just they just don't show they just don't present this MHC molecule so the so it's really hard for the immune cells to understand what actually is going on inside of them and here you can see something that we call a hot tumor if a tumor is immunologically hot it means that the immune system is operation here however when cancer progress it learned how to deactivate the immune cells and make tumors an immunological call like look at this tumor and you and we can see here many many different of many different of immune cells that are trying to detect and to like you know trying to get that actually to understand what's going on this side but after after a while what we can see in tumors that are actually successful with debating the immune system is that they become an immunologically cold tumor and you can see that like most of the 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 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 notice that we have so far about about how all the different all the different ways that cancer healthy that cancer has in order to try to invade the immune system and generally cells can communicate with each other using diverse signals transaction networks that lead to different outcomes and what cancer cells do they upregulate the spot was with lucid he but normal normal function of immune cell they don't they don't they don't even just tell the like the specific immune cell you know just leave me alone they also ask him to spend the the rumor and tell him and tell the rest of the immune cells 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 and here is an example where a cancer cell overexposing pdl1 a molecule that suppresses T cell important cell type involving cancer cell illumination on the right you can also see a reprogramming immune cell called tumor associated macrophage which helps to spread the 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 and you also have to these cells and you can also tell the rest of the guy 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 powerful and sophisticated and it was amazing for me to learn and and my next example which is actually one of my like one of my favorite is bacteriophage okay so what is a bacteriophage here you can see i like let's let's take a look at this very beautiful gift of a of a like okay so just explain first of all my bacteriophage is like my favorite creature so far and what it's actually doing there is a creature that we all know it's name of bacteria but there is also another tiny tiny picture and the and what the bacteriophage 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 bacteriophage genome like and does does it sound familiar to anyone else and so currently also with this we didn't come up and 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 bacteriophage that are attaching into it and injecting their the genome into the bacteria and there 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 and this is just a gift and so we'll just explain a bit about about about the progress of it so what what you see here is that there is a attachment of the bacteriophage into the bacteria bacteria is like you know the bacteria is that you 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 injecting it's injecting in its own genome into the bacteria inside surface and then the bacteria instead of running like you see this is the gene 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 are more and more bacteriophages that are like being from the inside of the bacteria and then the the poor bacteria you know just just full of of bacteriophage until they release each other until they get like the it's it's too much for it and they just 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 so we all know that CRISPR is being used these days in order to a kind of engineering the babies of the future and stuff like this but what was fascinating for me 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 pink 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 that he that he was detected 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 try to do pairing like to compare the new the new DNA that is seen sort of fits of the cell and and then and the sequence that they already know that is belong to the phages and so yeah so of course I think it's very it's a it's very obvious that this this Cas9 system is very similar to what we know as the MD5 or the hashes of the malware that we have been said many different antiviruses and and another very cool thing is that it's not only that the bacteria is detecting phages like using the non-sequence is that the phages are actually invading this defensive system by changing the genome the same way that malware are changing their mv5 you know it's it's enough to change just one a bit just one bit phages are doing the same in order to uh to uh like you know via evolution in order to be enabled to uh to inject their code into new bacteria and so this mechanism that we actually store from bacteria we will uh the we are trying these days to use it for genetic engineering um but just you know like it we it wasn't our idea we stole it from a very actually smart creature apparently and and I would just um since our time is up I would just say briefly what's like the role of our of our specific lab so what you see so my so my research my research is specifically about the microbiome what is the microbiome is what we are trying to do as you as you all uh none of you heard about it but we are all full of bacteria as inside of inside of our gut and we are trying in our lab uh to uh to understand and maybe in the future also to manipulate the different bacteria that are inside of our gut and all the networks that are inside of it uh first of all we try to find the connection between them and different diseases like uh the diabetes and the ls and the obesity and and so this is a very new and interesting field and you should know that uh you have you all have bacterias all over your hands your eyes and specifically inside your gut and it's not just that they are there happily like not doing anything they're actually sometimes very crucial in a very in order to keep the home of status and so this is what's just a brief and think about the microbiome just a brief introduction to the microbiome and just few things that we 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 ls because the 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 um in the form of this disease and here you can see many different many different types of diseases that are linked to uh to dysbiosis in the microbiome 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 where 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 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 uh there are 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 there like if there is no such thing as the different processes that have different uh that have different uh classes of memory so if there is so if there is hip corruption in one process or in one part you know in one in one system in our body it affects the rest of it as well so for example diabetes is one of the problems that we see that we see is that that there we we lose the homeostasis of of of also the the ca2 inside our inside each of ourselves and actually we can we can see that the blood is becoming acidic um so this is just one thing and uh so I would just say briefly like that I'm really proud like really really proud that I'm taking part of my lab like this is the 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 uh what you see here is a insulin is an insulin sensor that uh that was attached to my shoulder for around three weeks um but um of course very proud like uh I wish for myself and for everyone always to be in the side of like to help uh to help patients get like a better cure um 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 that helping people that are sick um 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 lab became became they of course we it's like we are not we are not doing anything that is linked to uh to uh to to corona uh we didn't we didn't do anything that that is about it like before but during the during this break this breakdown of like