WEBVTT 00:00.000 --> 00:01.280 Okay, we're live. 00:01.280 --> 00:02.260 Take it away. 00:03.800 --> 00:04.580 Hi. 00:04.580 --> 00:05.840 Hi there. 00:06.620 --> 00:07.760 Can they see us? 00:07.760 --> 00:09.260 Yes, I guess so. 00:09.260 --> 00:10.480 Let's see whether they can... 00:10.480 --> 00:11.220 You are live. 00:11.220 --> 00:11.920 You're live. 00:11.920 --> 00:12.660 We are live. 00:12.660 --> 00:14.460 Okay, so here we are. 00:14.460 --> 00:14.460 Hi everyone . 00:14.460 --> 00:15.180 Hi. 00:15.380 --> 00:16.460 I'm Noa. 00:16.460 --> 00:17.820 This is Lawrence. 00:17.860 --> 00:19.000 Nice to meet you. 00:19.000 --> 00:23.360 We are here streaming from the Weissman Institute of Science in Israel. 00:23.540 --> 00:31.160 And together we are going to present the comparison between cybersecurity and biology. 00:31.160 --> 00:32.340 I hope you are ready. 00:32.800 --> 00:35.180 Well, I'm ready, so I guess the stage is yours, Noa. 00:35.180 --> 00:35.980 Okay. 00:37.940 --> 00:39.820 So, nice to meet you. 00:39.820 --> 00:43.200 I will just present... 00:43.200 --> 00:43.920 Okay. 00:44.480 --> 00:45.360 Sorry. 00:45.720 --> 00:51.020 Okay, so our agenda for today... I have two targets with my presentation. 00:51.020 --> 00:54.320 First of all, I'll show you how beautiful and complex biology is. 00:54.320 --> 00:57.300 And the second part will be to make you jealous. 00:57.600 --> 00:59.120 So, who am I? 00:59.120 --> 01:00.580 My name is Noa. 01:00.640 --> 01:03.320 I'm a master student at the Weissman Institute of Science. 01:03.320 --> 01:11.820 I'm a junior scientist in a lab that is doing research specifically in microbiome, but also diabetes. 01:12.040 --> 01:16.800 And I'm a former cybersecurity researcher at Checkpoint. 01:18.480 --> 01:24.820 So, until a year ago, I was a reverser in Checkpoint. 01:24.820 --> 01:26.380 My job was to reverse... 01:27.500 --> 01:39.360 I'm sure that you all know about this job, to reverse engineering malware, and then to implement the defensive coding into Checkpoint antivirus. 01:39.960 --> 01:44.100 So, you know, I thought I'm a pretty smart girl, after all. 01:44.160 --> 01:46.740 I even did some fuzzing stuff. 01:46.740 --> 01:48.300 I found some vulnerabilities. 01:50.260 --> 01:57.500 You know, I built myself up as a hacker , as a researcher in Checkpoint for many years. 01:57.860 --> 01:57.860 And 02:01.040 --> 02:01.740 then... 02:01.740 --> 02:09.140 But I was always very curious about complex themes , not only malwares, but also about biology. 02:09.760 --> 02:20.760 And a year ago, I decided to come here and to become a researcher in biology, specifically about the new area in microbiome. 02:20.860 --> 02:27.400 So I thought, you know, I'm so cool, and I'm doing reverse engineering for so many years. 02:27.460 --> 02:42.780 So, you know, all of the bacteria and the COVID-19 and whatever comes up, all of them are going to be scared of me, because now there is a good reverser and a good hacker that is coming into the field. 02:42.880 --> 02:47.440 But then I found out that apparently it's not as... 02:47.440 --> 02:49.540 I think that we all... 02:49.540 --> 02:55.440 I think that it will be really easy for me to prove my point that it's not as easy as we can... 02:56.080 --> 03:00.120 as we thought, until not very far. 03:00.700 --> 03:13.560 You can see, like, what happened when just one bacteria just did some evolution and become just a little bit more dangerous than before. 03:13.560 --> 03:25.660 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. 03:26.720 --> 03:35.380 And also how I learned in a hard way biology is way more complex than any malware that I ever did a reverse engineering in. 03:35.380 --> 03:37.740 This is actually one... 03:37.740 --> 03:40.480 just one slide of my class. 03:40.480 --> 03:51.360 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. 03:52.420 --> 03:56.300 So, apparently it wasn't the best idea for me. 03:56.560 --> 03:58.300 I'm joking, of course. 03:58.360 --> 04:03.320 I'm very curious and this area is very challenging for me. 04:03.760 --> 04:08.680 And I think that what we do here is just beautiful. 04:10.860 --> 04:39.340 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 palpating and growing microbes on a plate but actually these days computational biology is on the rise and what we are doing especially in Weizmann is trying to take each of the biology systems that we have in our body or in bacteria or anything else and build a model about it. 04:39.340 --> 04:45.420 This is also one of my classes and this is from Uri Alonlav that we are doing a collaboration with. 04:45.420 --> 05:20.020 So what he did here, he took the aldenal gland and tried to build actual equations I think I did something okay, and tried to build equations in order to understand that this thing here is the complex system of the glands and this one is some equations that are explaining why does diabetes type 2 develop in so many people these days. 05:20.800 --> 05:28.720 And now I will pass the microphone to my friend that will explain to you in detail about some of the basic creatures that all of us have in our body. 05:29.880 --> 05:32.140 Thank you very much indeed Noor. 05:32.760 --> 05:49.420 By the way, I just want to point out that the person that actually introduced me into this world and made me very stressed also a very happy student but also a very stressed and a very poor student is this guy. 05:49.420 --> 05:53.360 So when your mom tells you not to talk to strangers, maybe she has a point. 05:54.440 --> 05:57.040 Okay, so I guess I don't need any further ado. 05:57.040 --> 06:00.720 Well, if you can see the camera, you will see that in contrast to Noor, I will be sitting. 06:00.720 --> 06:03.780 I'm a little older so forgive me for that. 06:04.680 --> 06:11.240 So actually when we were preparing this lecture we we 06:14.520 --> 06:25.820 we came up first with this title Back to the Future and me being this very modest, humble German dude that I am. 06:25.820 --> 06:30.020 I didn't have all these fancy animations that Noor showed to you. 06:30.020 --> 06:35.720 But actually when I envisioned this presentation I started with something lame looking like this. 06:35.720 --> 07:05.380 So again, we are from the Advisory Institute of Science in Israel and what I want to show you now in the next 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. 07:05.460 --> 07:26.800 But before I go into this I actually want to fulfill another request that Noor 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. 07:26.800 --> 07:27.620 Okay? 07:27.620 --> 07:33.760 Genetic code, our DNA is basically the language in which life is written. 07:33.760 --> 07:41.300 So that's sort of these chemical molecules that make up four letters A, T, C and G. 07:41.300 --> 07:49.980 They write a blueprint of life but as such they are not very useful because they have to be transcribed . 07:49.980 --> 08:11.040 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 so put into proteins, protein molecules that make up all our cells and therefore our body as a central component. 08:11.420 --> 08:30.460 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. 08:31.860 --> 08:39.920 And basically I want to start by asking the question whether there is another analogy between computer science and biology. 08:39.920 --> 09:03.620 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, 09:03.620 --> 09:13.460 if you only know computer viruses because you're a cyber expert, that you can also learn about biological viruses such as this coronavirus. 09:13.740 --> 09:15.940 So let's start with a computer virus, right? 09:15.940 --> 09:45.320 With a 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 3,569 09:45.320 --> 10:20.800 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 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 while this is sort of the genetic information of this virus, 10:20.800 --> 10:29.700 it's not the same genetic information that we have in our body sense but it's only this transcript that I introduced to you before. 10:30.080 --> 10:37.920 But looking at this sequence or this source code of the virus, you wouldn't be able to judge that this is particularly harmful. 10:37.920 --> 10:55.960 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. 10:56.740 --> 11:08.000 But still, we desperately needed to know this sequence to detect this virus but then the question is always having this sequence, having this source code, what does it do? 11:08.000 --> 11:10.520 What is this sequence translated to? 11:10.740 --> 11:36.800 And starting again with 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 Word files which basically then overloaded and completely flooded servers, like online servers and stuff. 11:36.800 --> 11:53.960 So you had, 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. 11:54.540 --> 12:14.620 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 these proteins, right, that make up the shell of the virus and these different parts of the virus that help the virus to infect our body. 12:14.840 --> 12:28.640 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. 12:29.380 --> 12:35.200 And this is how this virus does harm, right, it infects cells and then eventually kills them. 12:35.900 --> 12:43.520 So this is how in biology a sequence or a source code translates to a structure and then a certain function. 12:44.420 --> 13:10.500 And then to sort of visualize how it looks like in total, apart from what it does, the Melissa virus to overload your inbox and the coronavirus to infect your body cells, actually 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 a computer virus looks like, 13:10.500 --> 13:18.920 which as you can probably better judge than I do is just more or less arbitrary representation. 13:19.440 --> 13:28.520 But in fact, in biology, again, we can use this wonderful invention of microscopes to actually look at how the virus looks like. 13:28.620 --> 13:34.880 However, conventional light microscopes are not sufficient enough here, so actually we need electron microscopes. 13:34.880 --> 13:47.880 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 cells. 13:47.880 --> 14:01.080 So all these blue particles that in this Forbes article are described as pepperoni, sorry, slices of sausage on your pizza, they are the actual virus. 14:01.080 --> 14:22.480 However, I also must say the truth is if you use an electron microscope to observe the coronavirus in cells, so this is actually the coronavirus of the first identified US patient that was imaged here, this original picture from the electron microscope had no color. 14:22.720 --> 14:29.700 So this blue color is just put up artificially in the end to visualize the virus particles. 14:30.980 --> 14:41.040 But here we are still talking about only the size of a meter divided by one billion. 14:41.040 --> 14:43.400 So it's in this order of magnitude. 14:43.400 --> 14:45.080 So it's very small. 14:46.300 --> 14:50.360 And you need sophisticated, as I said, microscopes to actually see it, but you can see it. 14:50.360 --> 15:04.020 You can understand how this sequence source code information that we saw, how this translates to first individual aspects of this virus, individual molecules, and then the virus as such. 15:04.020 --> 15:09.300 Now, once it has done its terrible job infecting our body cells. 15:09.640 --> 15:24.540 And this sort of shows that to some extent there are parallels between computer science and biology, in this case computer viruses and biological viruses such as SARS-CoV-2. 15:26.200 --> 15:35.600 But is there, you know, besides from this example, is there something we can do about this? 15:35.600 --> 15:47.120 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? 15:47.220 --> 15:52.320 Is there an intersection between computer science and biological aspects of this pandemic? 15:52.420 --> 15:56.420 This is a question Noah and I basically asked ourselves. 15:56.420 --> 16:10.080 And in the very beginning of this pandemic in March, in fact, we participated in a hackathon that was initiated by InDev in collaboration with Microsoft. 16:10.080 --> 16:12.000 They called it HackCorona. 16:12.440 --> 16:15.180 Creativity will not be quarantined. 16:15.180 --> 16:32.440 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 in a very concrete example, our fight against this virus. 16:33.340 --> 16:44.280 And the question that we asked basically is how can we use computers to combat COVID-19? 16:44.280 --> 16:55.260 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. 16:55.260 --> 17:04.580 Because we understood that asymptomatic patients, of course, they are very dangerous in terms of unobserved spreaders of this disease. 17:04.820 --> 17:20.040 But in case someone develops severe complications, needs ventilation in the hospital, then this might present a major challenge for the health system in Israel and all over the world. 17:20.680 --> 17:32.650 So how can we basically assess the uncertainty whether a certain COVID-19 patient will develop severe symptoms and require hospitalization and ventilation? 17:33.820 --> 17:35.760 This is the question we asked. 17:35.760 --> 17:48.860 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 an actual mouse, or at least a stuffed animal of a mouse. 17:50.540 --> 18:13.980 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? 18:13.980 --> 18:18.220 In other words, five exceeds three, right? 18:18.220 --> 18:23.880 So how to decide which of them to allocate to the hospital? 18:23.880 --> 18:33.880 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? 18:33.880 --> 18:36.060 This was the question. 18:36.060 --> 18:52.880 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 Prof. 18:52.960 --> 18:53.480 Dr. 18:53.480 --> 18:53.800 Dr. 18:53.800 --> 18:55.040 Eran Elinav and Prof. 18:55.040 --> 18:55.400 Dr. 18:55.400 --> 19:13.760 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. 19:14.100 --> 19:14.780 Okay? 19:14.780 --> 19:25.240 So this is why we also felt dedicated to address this with our expertise, which comes more from the computational side here. 19:25.240 --> 19:31.780 So what we did in the beginning is, first of all, we asked, okay, what's the data we are dealing with? 19:31.780 --> 19:39.980 Because only if you have data, you can actually use computational routines to address all these issues. 19:40.640 --> 20:06.440 So we basically simulated based on existing data, an Israeli dataset 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, 20:06.440 --> 20:20.460 okay, this is Israeli population, and now given all this demographic information and health record information, can we basically now estimate what's the fraction of people who might develop severe COVID-19 complications? 20:20.460 --> 20:38.740 So first of all, as you can see, we are struggling 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 Noah's part, 20:38.740 --> 20:59.160 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... 20:59.640 --> 21:10.120 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 whether you get infected in the first place. 21:10.120 --> 21:21.960 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. 21:22.560 --> 21:41.220 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, 21:41.220 --> 21:58.640 and people who eventually died, and then you can see that even though the overall frequency of dead people, of course, or of dying people is with the infection or upon infection, not necessarily caused by the infection, is very low, but you can see that if you're older, 21:58.640 --> 22:05.660 the probability that you actually die after getting infected with SARS-CoV-2 is higher. 22:07.180 --> 22:29.040 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, 22:29.040 --> 22:36.660 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? 22:36.820 --> 22:44.780 And then we get a pretty good agreement between the predicted risk and the observed risk here in this preliminary data set. 22:45.100 --> 22:52.660 And then we can ask the question, okay, what kind of information is actually necessary to predict this risk in a reliable manner? 22:52.860 --> 23:08.880 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? 23:08.880 --> 23:28.640 And 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. 23:28.640 --> 23:44.680 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, 23:44.680 --> 23:49.380 then this prediction becomes very sensitive and very specific. 23:49.380 --> 24:00.880 So it means that if we say a person does develop complications, then the chance that this is a true positive indication is high. 24:00.880 --> 24:11.180 But also if we say this person won't develop complications, then also the chances that this is a true negative, so it indeed won't develop complications, it's also sort of high. 24:11.180 --> 24:27.380 So of course the perfect prediction would be just going straight up and then straight to the right, but also in this toy model or preliminary thing, the performance of this prediction was not so bad, I would say. 24:27.660 --> 24:58.880 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 record information and then basically puts out back to the doctor a certain risk score. 24:58.880 --> 25:17.600 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 this person can just be isolated at home, there is a very low chance that it's going to be severe, 25:17.600 --> 25:27.300 just monitor it carefully, but most likely the hospital bed allocated to this patient can be given to somebody else with a higher risk. 25:28.860 --> 25:32.580 So, wait, what are we lacking here? 25:32.580 --> 25:37.820 I already presented to you that this machine learning algorithm sort of works, but we need this device, right? 25:37.820 --> 25:39.060 We need this application. 25:39.600 --> 25:52.500 And this is also 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. 25:52.500 --> 26:10.140 If they see the patient, they can check in all this information in less than a minute , and then basically we can just let the machine learning algorithm do its job and see whether there's a certain risk. 26:10.660 --> 26:23.880 And, of course, this machine learning algorithm that I presented to you, this model that we used there to make the predictions, was just the first attempt that we had in the Sakaton, you know, very coarse-grained first rough trial. 26:25.320 --> 26:46.900 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 it's just informative to say that there's a low risk, 26:46.900 --> 26:48.420 or maybe a combination of both, right? 26:48.420 --> 26:51.600 So we can, of course, you always want more data. 26:51.660 --> 27:04.500 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 anamnesis or talking to the patients, suspected infected people. 27:05.720 --> 27:16.640 So this is what we envisioned, you know, we can't really not necessarily change the number of people who are susceptible and eventually will get infected. 27:16.640 --> 27:19.860 It's just a matter of time, it seems like. 27:21.160 --> 27:28.800 And maybe also, you know, the health system is sort of currently at its limit, so we cannot really expand the number of hospital beds. 27:29.020 --> 27:50.060 But this app that we developed, that by the way we called Virologens, because it's like an intelligent solution to virology and inference of this virus-induced health complications, we could now use to assign again hospital beds to people who need it the most. 27:50.060 --> 28:01.920 So in the scope of this hackathon, which of course is very preliminary, and we are still sort of thinking of how to put this further, in the scope of this hackathon, this question was solved. 28:02.440 --> 28:20.700 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 can we make a more general observation when we look at computer science and biology? 28:22.040 --> 28:31.200 Or in other words, how can we combine powerful algorithms from computer science with biological data sets? 28:31.200 --> 28:41.000 And this is 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. 28:42.060 --> 28:50.720 How can we combine this biology with this more like a conceptual or theoretical view of biology as a system? 28:51.080 --> 28:58.540 As a living system that just operates certain commands and has some input-output functions. 28:58.840 --> 29:09.520 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. 29:09.660 --> 29:16.880 And this is a discipline that I actually studied, I did my PhD in this field, which is called systems biology. 29:17.760 --> 29:41.160 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, a Japanese guy who also invented robots that help people to rescue people from collapsed buildings after earthquakes and stuff. 29:42.360 --> 29:55.020 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. 29:55.100 --> 30:15.640 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 understand what's going on there. 30:15.700 --> 30:17.480 And what does go on there? 30:17.480 --> 30:19.040 How does this look like? 30:19.560 --> 30:34.160 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. 30:34.560 --> 30:38.000 Now, what does this mean? 30:38.000 --> 30:50.700 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, 30:50.700 --> 30:54.220 which is basically your uncertainty, okay, your confidence interval. 30:54.520 --> 31:06.080 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? 31:06.080 --> 31:14.980 The virus can spread, 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. 31:15.240 --> 31:18.040 And this is something we can quantify, we can calculate. 31:18.580 --> 31:27.120 Sometimes we have a problem with communicating this, but there are possibilities in mathematics to calculate these confidence intervals and these uncertainties. 31:28.120 --> 31:37.780 The way this is done, the way we describe how a biological entity changes over time is, in mathematics, called ordinary differential equation. 31:37.920 --> 31:48.700 So this basically describes to you how your biological entity B is changing, so the D represents the change, okay, it's the change of B over the change of time T, okay? 31:48.700 --> 31:51.000 How does B change with time? 31:51.200 --> 31:56.140 And this is now depending on certain parameters, P, P1 and P2. 31:56.340 --> 32:04.200 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. 32:04.340 --> 32:16.140 So if you have something that can sort of reproduce, like a virus can reproduce or we can reproduce, you know, you will get even more if you have more people to start with, right? 32:16.200 --> 32:21.920 And then basically the reproduction rate is your parameter P, and it's positive because you get more. 32:22.440 --> 32:26.900 But then this curve of B is not only going up, it's not only just getting more with time, right? 32:26.900 --> 32:28.180 It's also going down. 32:28.500 --> 32:38.400 So there's also another parameter, P2, and there's a minus because it's getting less, that tells me that B is... the change of B over time is getting negative. 32:38.700 --> 32:41.420 There's something subtracted from it. 32:41.620 --> 32:45.100 And of course, the more B we have, the more likely it is that, you know, it destructs itself. 32:45.100 --> 32:48.580 You know, humanity is very good at destructing itself somehow, unfortunately. 32:49.560 --> 32:52.380 But just by itself, it won't go just up and down there. 32:52.380 --> 32:55.560 We need another component here that influences this. 32:55.560 --> 32:57.280 And this other component is A. 32:57.740 --> 33:01.460 So for A, we can actually write down a very similar equation. 33:01.460 --> 33:03.120 So how does A change over time? 33:03.120 --> 33:09.720 Well, it grows, but not only depending on A but also on B, because, you know, it destructs it, it eats it, for instance. 33:09.720 --> 33:14.780 You know, humans are very good at eating stuff, and also other living beings. 33:15.940 --> 33:17.800 But everything that lives also has to die. 33:17.800 --> 33:23.460 So the more A we have, the more likely it is that it's also dying with another parameter, P4. 33:23.760 --> 33:30.880 And then, you know, we get more B, but then A gets more to eat, but then eats more, and then B goes down again. 33:31.140 --> 33:35.520 So there's little A that can eat up B and B can re-arrive, and then it's going up and down. 33:35.520 --> 33:47.580 So this is sort of how nowadays coupled ordinary differential equations, because there's two of them and they are depending on one another, we can describe the system's behavior and even these weird oscillations, like going up and down. 33:48.320 --> 33:53.600 Also from a mathematical point of view, a very interesting observation of this system behavior. 33:54.360 --> 34:00.420 But now I talked about A and B a lot, but saying B, I don't necessarily mean a bee. 34:00.420 --> 34:02.740 Of course, a bee is also a biological entity, right? 34:02.740 --> 34:04.440 The bee that gives you honey and stuff. 34:04.580 --> 34:06.280 But B could be anything, honestly. 34:06.280 --> 34:10.420 We are talking about the human animal, the marine animal like mice. 34:10.420 --> 34:13.960 It could be the number of cells that are changing in our body. 34:14.040 --> 34:17.240 It could be bacteria that are also in our body, also cells. 34:17.240 --> 34:18.800 It can be this virus, right? 34:18.800 --> 34:20.500 This is a picture of a virus. 34:20.800 --> 34:23.960 But it can also be just certain molecules that change over time. 34:23.960 --> 34:28.440 For instance, here an antibody that is produced against this virus. 34:29.940 --> 34:36.160 So these are all options of biological entities that we can describe in biological systems. 34:36.160 --> 34:42.260 But to describe them actually we have to think on which level are we talking. 34:43.300 --> 34:48.000 Because biology happens on many different scales, in space and in time, right? 34:48.000 --> 34:57.420 The human being is on the order of magnitude like one to two meters, let's say. 34:58.760 --> 35:03.900 Maybe basketball players are more like two meters and other people more like one meter. 35:03.900 --> 35:04.800 Never mind. 35:05.700 --> 35:11.720 But then we talked about a mouse that is certainly not a meter but more like centimeters. 35:11.760 --> 35:17.720 And then we have cells that you have seen that are more like micrometer which is a meter divided by a million. 35:18.740 --> 35:26.420 And then in these cells we have small subunits that are even smaller. 35:26.420 --> 35:28.780 Bacteria are also smaller than our body cells. 35:29.460 --> 35:32.160 And here we are then in the order of magnitude of 35:35.780 --> 35:38.580 a meter divided by a billion, okay? 35:38.580 --> 35:41.800 So ten to the minus nine, a nanometer. 35:43.820 --> 35:47.740 so... and all these scales, you know, changes are happening all the time. 35:47.740 --> 35:48.980 That's how we are being alive. 35:48.980 --> 35:53.900 Because molecules are turned over, cells are turned over, our bodies are being turned over. 35:54.040 --> 35:55.080 Everything happens there. 35:55.080 --> 36:04.340 And this not only happens on a very slow scale, like billions 3.6 billion years, this earth does exist and life does exist. 36:05.140 --> 36:14.760 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 their conformation in the order of microseconds. 36:14.760 --> 36:16.840 Again, a second divided by one million. 36:16.840 --> 36:29.160 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 who can tell me that there's a way to handle this complexity without a computer, okay? 36:29.160 --> 36:34.660 So we need computer science to address all this, to understand biology at all these scales. 36:35.080 --> 36:48.320 And if you're curious for this, don't you worry because there's a wonderful database at bionumbers.org initiated at Harvard Medical School with you guys in the US where you can ask the question, okay, how big is a cell? 36:48.320 --> 36:50.080 How many molecules are in a cell? 36:50.080 --> 36:57.020 How does a human body cell is different from a bacterial body cell, from a fruit fly, from a worm ? 36:57.060 --> 36:59.300 All these numbers are there with references. 36:59.300 --> 37:03.740 You can look up the original scientific literature to see the evidence for these numbers. 37:03.740 --> 37:09.220 And it's just wonderful looking at all this information that is out there. 37:09.440 --> 37:14.500 And not only this information is out there, but it's being continuously generated. 37:14.500 --> 37:20.400 We are living in a time now, in an era where biological data is not, for the first time, not a bottleneck anymore. 37:20.400 --> 37:28.000 You don't need to spend three years of a PhD to gather one data point of information about one protein. 37:28.260 --> 37:44.680 No, we can 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. 37:44.680 --> 37:49.140 So we are living in an era of biological big data. 37:49.140 --> 37:54.460 But speaking to you, maybe of course I have to say that in fact there's only one true big data. 37:54.800 --> 38:05.360 But here I really speak about data and biology that is out there on all of these scales that I've just introduced to you. 38:05.640 --> 38:11.000 So data is not a problem anymore, but we need concepts how to work with this data. 38:11.780 --> 38:18.360 And in systems biology, there's two sort of complementary approaches to deal with all this data. 38:18.940 --> 38:25.220 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. 38:25.740 --> 38:29.740 But the problem is you hardly ever have just one of each of these. 38:29.740 --> 38:42.120 So usually 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? 38:42.120 --> 38:45.860 Many virus particles or anything of that like. 38:45.980 --> 38:48.320 So we have two different ways to approach them. 38:48.320 --> 39:00.160 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. 39:00.820 --> 39:07.580 And then we try to sort of describe this in a dynamic way, so how they change over time. 39:07.580 --> 39:12.880 So first one molecule interacts with another one, and then these two interact with the third one. 39:13.100 --> 39:23.700 And then we understand the small part, like we start from the ground, we understand the small part of it, the small module, and then we sort of subsequently try to connect these modules. 39:24.360 --> 39:36.960 And as I said, there, based on a lot of knowledge, what is going on, we have some mechanistic understanding how these gear wheels really fit into one another, and then we can see how this system is changing over time. 39:37.940 --> 39:44.620 But then there's another way to look at a biological system, let's say a patient, a COVID-19 patient. 39:44.680 --> 39:46.680 Just look at them from the top, okay? 39:46.680 --> 39:49.040 You don't really know what's going on in this patient, okay? 39:49.040 --> 39:50.920 It's a black box, this patient. 39:51.040 --> 39:58.640 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. 39:58.640 --> 40:00.160 And this is your output, your reaction. 40:00.160 --> 40:07.340 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? 40:07.340 --> 40:14.200 Whenever we give this to a certain patient, where maybe we have all these health records, there's always a good outcome. 40:14.200 --> 40:15.460 Or maybe not a good outcome. 40:15.460 --> 40:19.580 And then you can start to try to understand this input- output relationship. 40:19.580 --> 40:25.480 Needless to say, for this you need a lot of data, many patients, many drugs that you potentially test. 40:25.800 --> 40:28.280 But then you can learn associations. 40:28.280 --> 40:36.140 Okay, smoking history is always connected to a bad outcome, or as we said, a high BMI is always connected to a bad outcome. 40:36.220 --> 40:39.740 And these are all these machine learning, artificial intelligence methods. 40:40.920 --> 40:48.340 Okay, so these are sort of the two complementaries, not schools of thoughts, but approaches that are not mutually exclusive, right? 40:48.340 --> 41:00.340 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. 41:01.440 --> 41:08.560 But to give you a precise example, I want to use the bottom-up approach now as an example, okay? 41:08.560 --> 41:13.000 So again, where we look at how biological entities change over time. 41:13.240 --> 41:30.480 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. 41:30.480 --> 41:38.860 And in fact, I wrote a book on rabbit -free biology . 41:38.940 --> 41:50.160 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. 41:50.240 --> 42:04.340 And 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. 42:04.680 --> 42:10.840 It has this complicated name , signal transducer and activator of transcription, STAT5. 42:11.900 --> 42:16.980 The only thing you need to know about this molecule is it can bind DNA , okay? 42:16.980 --> 42:22.720 It can bind our genetic information and then exert certain tasks there. 42:22.900 --> 42:34.320 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. 42:34.320 --> 42:36.280 And so do we at some point. 42:36.280 --> 42:44.760 So this molecule basically shows us a life-death decision, which starts with a simple, very simple question. 42:44.760 --> 42:47.020 Does 1 plus 1 always give 2? 42:47.020 --> 42:51.280 Because if we have two of these molecules, they are supposed to form this complex of two molecules. 42:51.940 --> 43:01.520 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. 43:01.900 --> 43:03.640 And also this can be modified. 43:03.640 --> 43:06.520 It can be chemically modified to be switched. 43:06.820 --> 43:16.700 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. 43:17.640 --> 43:31.960 And so we basically have four different molecules that potentially can do this job of finding genetic information and making sure our blood cells are surviving, which gives us a matrix of potential biological entities here. 43:32.300 --> 43:35.440 Not this kind of matrix, but more like this kind of matrix. 43:35.820 --> 43:38.900 So from all these molecules, we can form these potential complexes. 43:38.900 --> 43:45.320 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. 43:46.240 --> 43:52.280 But we do have experimental data where we see how these complexes change over time. 43:52.520 --> 44:00.780 So what we see here on the top is basically we see how many of the switched on molecules we have as compared to all possible molecules. 44:01.120 --> 44:06.120 And then we also see how the number of the blue molecules compared to all molecules change over time. 44:06.120 --> 44:15.140 And then here we basically see how these two ratios change with respect to one another in these cells. 44:15.380 --> 44:21.200 We can measure this, and then with a mathematical model, we can basically describe this data. 44:21.280 --> 44:26.560 As you can see, again, we have our simulation and also the confidence interval here. 44:26.940 --> 44:34.000 And now with the simulation, we can basically look back and see which kind of molecules from this matrix do actually exist. 44:34.000 --> 44:37.480 I mean, which one of those we need to describe this data. 44:37.680 --> 44:40.660 And the answer is surprisingly few of them we only need. 44:41.240 --> 44:43.420 And this is something we cannot just look at. 44:43.420 --> 44:49.020 We really needed the computer to find out what's the probability that these molecules exist. 44:49.300 --> 44:50.660 And this we also published. 44:50.660 --> 44:54.500 It's not an A work, but again, A and B because these are two molecules. 44:54.580 --> 45:00.300 And with this, maybe we better understand now how our red blood cells are surviving and be happy. 45:00.980 --> 45:09.000 To wrap this all up, what I've shown to you is basically there are standing parallels between computer viruses and biological viruses. 45:09.060 --> 45:15.260 We can apply computational algorithms to address challenges that we are facing in the COVID-19 pandemic. 45:15.600 --> 45:25.320 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. 45:25.480 --> 45:31.320 And Noah will give you some more teasers how this all relates. 45:31.320 --> 45:37.120 I'm just saying, if you feel intrigued by this, don't hesitate to reach out to us. 45:37.120 --> 45:40.900 We will be happy to also share more information. 45:40.900 --> 45:43.560 Having said that, I hand over back to Noah. 45:49.430 --> 45:51.450 Hi, and welcome back. 45:51.450 --> 46:01.050 For anyone that joined us after my introduction, I will just explain again that my name is Noah. 46:01.050 --> 46:06.270 I'm a Junior Master Scientist at the Weizmann Institute of Science here in Israel. 46:06.270 --> 46:09.190 And my background is from... 46:10.370 --> 46:19.510 My background is as most of you guys, I was a reverse engineering and cybersecurity expert at Checkpoint for many years. 46:19.510 --> 46:27.230 And when I joined Weizmann, I had to close a huge gap in my knowledge in biology. 46:27.970 --> 46:29.410 And I was... 46:29.410 --> 46:42.230 What was stunning for me was the parallels that I found so often in my in the material that I studied in the courses and also for the lab with the knowledge that I have from the past. 46:42.290 --> 46:52.310 So, I will just give you a few examples of the things we have in biology that will look very familiar for you. 46:52.490 --> 47:01.110 So, without any further introduction, let's start with cancer cells. 47:01.110 --> 47:09.650 Okay, so cancer cells, unlike most of the people think about them, they are not just cancer that accumulate in our body. 47:09.650 --> 47:15.350 This is more like cells in our body that have superpowers. 47:15.350 --> 47:32.150 They evolve better than the rest of the cells in the body due to a mutation from a radiation, from a UV light, or from any other, from smoking, or just by chance in the body. 47:32.150 --> 47:36.010 And then, what's happening with these cells is that they become selfish. 47:36.010 --> 48:00.790 Which means that instead of working together with the rest of the cells in the body, they are selfish enough to try to control and to get all of the resources, for example, they are actually sending signals to the body to build veins in order to get more oxygen into the tumor environment. 48:01.150 --> 48:23.650 And one stunning fact that I learned 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, 48:23.650 --> 48:24.490 like cancer cells. 48:24.490 --> 48:37.190 Because cancer cells, like we have them on a regular basis in our body, and this is one of the main roles of the immune system, to detect them and to eliminate them. 48:37.470 --> 48:47.030 So, it's not only that our immune system is working as an antivirus, but the thing here is that the cancer cells are fighting back. 48:47.410 --> 48:53.030 So, the picture here is representing the interaction between cancer cells and different types of immune cells. 48:53.190 --> 48:55.750 Cancer cells are the one that shows in blue. 48:55.750 --> 49:16.910 Immune cells are in different colors, and what they are trying to do is to send... so, you can think about the rest of the colorful cells as different types of antivirus from many different companies, or maybe different models that we have in one antivirus. 49:16.910 --> 49:29.470 And what the cancer cell is trying to do is to actually reprogram or to send signals to different immune cells in order to evade them. 49:29.930 --> 49:34.350 And this is something that was, you know, when I heard about it, it was really familiar for me. 49:34.350 --> 49:45.950 Like, there were many models that tried to evade my VM, to evade my AIDA, my OLADPG, or the antivirus in general. 49:46.130 --> 49:48.870 And so, for me, it sounds pretty familiar. 49:48.870 --> 49:59.830 So, apparently, it wasn't our idea to evade people, to evade entities that are trying to detect us. 49:59.830 --> 50:06.190 And I can promise you that cancer cells are doing it in a much more efficient way. 50:06.270 --> 50:23.590 For example, one very stunning example that I have is that every cell is supposed to present a molecule on its surface, which the name of the molecule is an MHC. 50:23.610 --> 50:40.050 And this molecule is actually like the info that an executable can have, which describes what these cells are producing, what are the proteins that are running inside of it. 50:40.070 --> 50:54.150 And cancer cells, what they actually do is they just don't show, they just don't present these MHC molecules, so it's really hard for the immune cells to understand what actually is going on inside of them. 50:55.530 --> 51:02.710 And here you can see something that we call a hot tumor. 51:02.710 --> 51:08.250 If a tumor is immunologically hot, it means that the immune system is operating here. 51:08.310 --> 51:16.110 However, when cancer progresses, it learns how to deactivate immune cells and make tumors immunologically cold. 51:16.110 --> 51:29.730 Like, look at this tumor, and we can see here many different of immune cells that are trying to detect and trying to get attached to it and to understand what's going on inside. 51:29.810 --> 51:40.270 But after a while , what we can see in tumors that are actually successful with evading the immune system is that they become immunologically cold tumors. 51:40.270 --> 51:52.350 You can see that most of the immune cells just gave up and they said, okay, whatever, I have other stuff to do, and they just left the tumor there alone. 51:52.690 --> 51:59.150 This is why you get a tumor in your body. 51:59.610 --> 52:23.490 And here is just what you can see here is just a network of different... this is just a brief knowledge that we have so far about all the different ways that cancer has in order to try to evade the immune system. 52:23.590 --> 52:31.690 Generally, cells can communicate with each other using diverse signal transaction networks that lead to different outcomes. 52:31.690 --> 52:38.850 And what cancer cells do, they upregulate the pathways which inhibit normal function of immune cells. 52:38.850 --> 52:47.470 They don't even just tell the specific immune cell, you know, just leave me alone. 52:47.470 --> 52:56.450 They also ask him to spread the rumor and tell the rest of the immune cells, you know, this guy, it's okay, you can just leave him alone. 52:56.910 --> 53:00.530 And this is something that was really fascinating for me to learn. 53:01.650 --> 53:10.170 And here is an example where cancers are overexposing PD-L1, a molecule that suppresses T-cell. 53:10.170 --> 53:13.290 Important cell type involved in cancer cell elimination. 53:13.570 --> 53:24.870 On the right, you can also see a reprogrammed immune cell called tumor-associated macrophage, which helps to spread the inactivating signal instead of fighting cancer. 53:24.870 --> 53:36.110 Okay, so you can see that it's not only telling him, listen, I'm okay, it also asks him, it also asks these cells, you can also tell the rest of the guys that I'm fine and to leave me alone. 53:36.190 --> 53:37.830 And this was amazing. 53:37.830 --> 53:46.070 I don't know too many models that are actually so powerful and sophisticated and it was amazing for me to learn. 53:46.870 --> 53:52.490 And my next example, which is actually one of my favorites, is bacteriophage. 53:52.830 --> 53:55.190 Okay, so what is a bacteriophage? 53:55.190 --> 54:08.370 Here you can see, let's take a look at this very beautiful gif of like, okay, so I'll just explain. 54:08.370 --> 54:12.090 First of all, bacteriophage is like my favorite creature so far. 54:12.570 --> 54:17.570 And what it's actually doing, there is a creature that we all know, it's known for bacteria. 54:17.710 --> 54:37.630 But there is also another tiny picture and what 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. 54:37.630 --> 54:40.050 Does it sound familiar to anyone else? 54:40.050 --> 54:42.630 Apparently also with this we didn't come up. 54:43.110 --> 54:45.290 And this was amazing for me to understand. 54:45.290 --> 54:46.790 You can actually see it. 54:46.790 --> 54:59.150 This is actually a bacteria, and here you can see a bacteriophage that are attaching into it and injecting their genome into the bacteria. 54:59.270 --> 55:07.310 And here you can see this is a very sad story because the bacteria, sometimes they understand that they are doomed. 55:07.450 --> 55:08.930 But this is just a gif. 55:09.270 --> 55:13.750 So I will just explain a bit about the progress of it. 55:13.750 --> 55:18.210 So what you see here is that there is attachment of the bacteriophage into the bacteria. 55:18.230 --> 55:24.070 Bacteria is the bacteria that you know, that we all have in our body. 55:24.070 --> 55:33.050 And this is the bacteriophage which has a very, very nice and interesting picture and structure. 55:33.070 --> 55:36.970 So what it's doing is actually attaching into the bacteria. 55:37.030 --> 55:44.850 It's injecting its own genome into the bacteria's inside surface. 55:44.850 --> 55:50.150 And then the bacteria, instead of running, like you see, this is the genome of the bacteria. 55:50.150 --> 55:54.530 Instead of running its own code, it's running the bacteriophage code. 55:54.690 --> 56:05.030 And then the whole purpose of it is that there are more and more bacteriophages that are being formed inside of the bacteria. 56:05.190 --> 56:12.870 And then the poor bacteria, you know, are just full of bacteriophages until they release each other. 56:12.870 --> 56:16.310 Until they get, like, it's too much for it. 56:16.310 --> 56:22.930 And they just go out and infect even more and more bacteria that are inside of this environment. 56:23.970 --> 56:35.710 And another thing 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. 56:35.710 --> 56:43.070 So we all know that CRISPR is being used these days in order to kind of engineering the babies of the future and stuff like this. 56:43.070 --> 56:58.050 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. 56:58.050 --> 57:04.870 So what you see here is that this pink evil thing is the bacteria. 57:05.110 --> 57:22.550 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 saw before of phages that try to attack it. 57:22.550 --> 57:44.810 And the way that it's doing this is just, you know, he has a sequence that he remembers and then he just tries to do pairing, like to compare the new DNA that he sees inside of the cell and the sequence that he already knows that this belongs to the phages. 57:46.350 --> 58:09.790 So, of course, I think it's very obvious that this Cas9 system is very similar to what we know as MD5, or the hashes of the models that we have inside many different antiviruses. 58:11.070 --> 58:26.370 And another very cool thing is that it's not only that the bacteria is detecting phages, like using the known sequence, is that the phages are actually invading this defensive system by changing their genome. 58:26.370 --> 58:32.610 The same way that malwares are changing their MD5, you know, it's enough to change just one bit. 58:32.690 --> 58:44.190 Phages are doing the same in order to like, you know, via evolution in order to being able to inject their code into new bacterias. 58:44.950 --> 59:03.950 And so this mechanism that we actually stole from bacterias, we will we are trying these days to use it for genetic engineering, but just so you know, it wasn't our idea, we stole it from a very actually smart creature, apparently. 59:04.510 --> 59:14.670 And I will just since our time is up, I will just say briefly what the role of our specific lab. 59:14.670 --> 59:21.010 So what you see is so my research is specifically about the microbiome. 59:21.010 --> 59:43.010 What is the microbiome is what we are trying to do as you all I don't know if you heard about it, but we are all full of bacterias inside of our gut and we are trying in our lab to understand and maybe in the future also to manipulate the different bacterias that are inside of our gut and all the networks that are inside of it. 59:43.050 --> 59:51.310 First of all we try to find the connection between them and different diseases like diabetes and ALS and obesity. 59:52.690 --> 01:00:15.230 And so this is a very new and interesting field and you should know that 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 not doing anything, they are actually sometimes very crucial in order to keep the homeostasis. 01:00:16.310 --> 01:00:23.910 So this was just a brief thing about the microbiome, just a brief introduction to the microbiome. 01:00:23.970 --> 01:00:51.070 Just a few things that we found out so far in our lab is the connection between the bacterias that are inside the gut and the very well known disease that are inside the ALS because the bacterias, they can send signals from the gut into the brain and there is a very clear connection between the dysbiosis that we will see in the gut and the form of this disease. 01:00:51.430 --> 01:00:59.690 Here you can see many different types of diseases that are linked to dysbiosis in the microbiome. 01:00:59.690 --> 01:01:06.150 And another very cool subject we are looking into in our lab is diabetes. 01:01:07.190 --> 01:01:12.210 Just one thing that I learned and it was really interesting and 01:01:15.310 --> 01:01:32.990 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 diabetes, if you have too much sugar in your blood, the problem with it wouldn't be just about the fact that you have too much sugar in your veins. 01:01:33.390 --> 01:01:53.370 Many different diseases, they are like, there is a hip corruption, let's say, in our body, but unfortunately unlike in new operation systems, in our body there is no such thing as different processes that have different classes of memory. 01:01:53.370 --> 01:02:02.830 So if there is hip corruption in one process, in one system in our body, it affects the rest of it as well. 01:02:02.910 --> 01:02:22.430 For example, in diabetes, one of the problems that we see is that we lose the homeostasis of also the CA2 inside our cells. 01:02:22.470 --> 01:02:27.490 And actually we can see that the blood is becoming acidic. 01:02:28.110 --> 01:02:30.090 So this is just one thing. 01:02:31.550 --> 01:02:40.110 so, I would just say briefly that I'm really proud really, really proud that I'm taking part of my lab. 01:02:40.110 --> 01:02:44.770 The very nice person that you see here is my supervisor. 01:02:44.770 --> 01:02:50.690 I have a professor who is also a supervisor, and he recently used me as a lab rat. 01:02:50.690 --> 01:02:58.770 What you see here is an insulin sensor that was attached to my shoulder for around three weeks. 01:02:59.030 --> 01:03:01.610 But I'm, of course, very proud. 01:03:02.150 --> 01:03:11.350 I wish for myself and for everyone always to be in the side to help patients get a better cure. 01:03:11.850 --> 01:03:19.730 And this is something that I'm really happy about, that what I'm doing here is actually helping people that are sick. 01:03:19.730 --> 01:03:26.450 But yes, also, all of us actually do stuff like this in the lab. 01:03:26.450 --> 01:03:43.750 Also, the reason that this guy is dressed like this is because during the corona, our whole lab became of course, we are not doing anything that is linked to corona. 01:03:43.830 --> 01:03:52.970 We didn't do anything about it like before, but during this breakdown of...