Scientist Stories: Uri Alon, The Mathematical Essence of Aging

Hey everyone, good morning, good afternoon, good evening depending where you are, and welcome to today's talk, theoretically speaking, lecture from the Simons Institute on the mathematical essence of aging. And just to say a word even before, I've seen a part of this talk before and it really made me change my habits. We'll see how that reverses aging or slows it down. But joking aside, let me just tell you some serious stuff, and that is, first of all, you'll have the opportunity to ask questions during the webinar, and Uli actually encourages it, and I think he's going to actually force us to ask questions. And to do so, just type your question into the Q&A at the bottom of the control panel, and he will address as many of the questions as he can during the lecture and afterwards. By the way, my name is Shafi Golda, so I'm the— I am the director of the institute. And the Simons Institute, just a few words about it. It was established in 2012 with a grant from the Simons Foundation. Our mission is to bring together the world's researchers in theoretical computer science and related fields to come and study unsolved problems together and define models and investigate the nature and limits of computation. And we pride ourselves at trying to use the tools and the perspective of theoretical computer science in other related fields, and as such are always fascinated by progress in other fields that has, in a sense, some computational aspect, computational perspective. I think that the talk today is a wonderful example of that. We— today's talk is going to be given by a distinguished speaker, Uri Alon. Uli Löhn is a professor of systems biology at Weizmann Institute. He has incredibly creative and highly cited research which has looked at gene expression, network motifs, the design and principles of biological networks, and all using both computational biology and traditional wet laboratory biology and so forth. And Uli actually comes from a background of physics and mathematics, and he got his PhD in physics at Weizmann. And then he became a biologist or a systems biologist. But I think that he brings a unique perspective into biology. And he, in fact, I think includes both biologists and physicists working together to understand the principles of molecular systems that guide the decision of our bodies. And Uli has many featured articles and videos on YouTube. Such as, I think, Sunday at the Lab and How to Give a Good Talk. And he's been awarded many awards, the Breakthrough in Life Science Award on his network motif, and the 2017 Jacques Solvay Chair in Physics, he holds it. I, and I think the 2014 Nakason Prize by the Human Frontier Science Project. I am extremely happy to have Uli join us. Not only a great speaker and a great scientist, he's also a great playback artist, which is how I met him first. So I'm looking forward to it, and I'm going to pass the baton to you.

Thank you, Shlafi. It's a pleasure to be here. Hi everyone, and I'm speaking to you from Israel, from the Galilee, and which is a kind of rolling hills and also has some some of Israel's only flowing streams. We were at a really flowing stream today with my kids. It was exhilarating to see some water after being locked up in Corona times, COVID-19, and to get my mind off the news and off the exponential rise and into some nature was very, very relaxing. And then, yeah, I realized we're all in one boat. We're just talking to Shafi about what it's like in your area, and the whole world is in one boat in this case. And we're also— there's another thing we're in one boat is we're all going to get old and die. And not all get old, but we'll all die, I think. And aging is part of our life and always has been. And when I was studying physics and also biology, I always thought that physicists, there's nothing really interesting for us about aging except for our own aging processes, personal as a human being, because I thought that aging was a kind of a falling apart of the body, the kind of chaotic loss of function, decline. But my opinion changed about that. There's fascinating things to talk about aging. In fact, it's one of the great open questions in biology. And also there's tremendous advances on understanding the core processes of aging. And this picture that I put up here is one picture that helped me really get it. This was from Van Dorsen's lab, Jan Van Dorsen, an experiment from 2011 and a really even more decisive one from 2016 about rejuvenation. So these are two mice and they are two years old. The one on the left, this one looks like a typical two-year-old mouse. You see the hunchback, the fur. Mice live about two, two and a half years. Look at the crumpled ears, look at the cataract. You can see this mouse is hardly running on the wheel, maybe shivering, not very resilient there. This is the other mouse, is also 2 years old. They're the same age. In fact, they're genetically identical. They're twins born from the same mom, suckling from the same mom. But something was done to this mouse starting in middle age that slow down the aging process, and I mean systemically. So this mouse has nice shiny fur and nice shiny ears and no cataract and very perky and runs on the wheel quite a lot. Cancer is delayed and diabetes is delayed, all these things. And on average, this mice with this treatment live about 25% longer. But the important thing here in my talk is not about living longer. It's having less of this, more healthy aging, how to delay age-related diseases by addressing the core process of aging. So we know that doctors treat, if you have heart disease, they treat heart disease, diabetes, they treat diabetes, dementia, osteoarthritis, they treat disease by disease. Can we treat the core process of aging? That was done here in these mice. And therefore in one fell swoop delay all aging-related diseases. So how can we understand that, and how can mathematics play a role in understanding this process? I hope I got you interested. It's a good time to start typing questions in the Q&A. I'm going to check the Q&A. I don't see any questions yet, but I'm going to start talking now, and soon I'm going to stop, and I'm not going to continue until there's a question. So So to rescue everyone, start typing your question. I'm sure you have questions already. Oh, there's one already. Look, see, it just— did the mice look the same a year previous? So the answer is yes. And in fact, the treatment started about a year previous. So they started— they looked the same, you can say, a year previous. Oh, there's another question. I love it. Thank you. It makes me feel more connected. So what was done to mouse 2? I'm going to tell you. Hold your horses. What is intervention slowing aging? I'm going to tell you. What aging slowed or reversed? Was aging slowed or reversed? So in many cases, you can say it was slowed, but some treatments given to very old mice can reverse some aspects. I'll tell you about that. How long did mouse 2 live? So I don't know about this individual, but on average, this population got the treatment and lived 25% longer. Now I'm going to stop with the questions. I'm always I'm joyous, thank you, and we continue and keep typing. I'm going to check my questions soon. Okay, so I'm going to try to now advance the slide. I don't want to be stuck on just on this slide. Oh, okay, so I want to recognize Omer Karim, a PhD student from math background who did the theory work, and my collaborators Valerie Kozhanovskaya and Amit Agarwal. Valerie is a aging, a researcher of aging. I don't want to say aging researcher in my department, one floor down, and a mate who's a PhD student. So, you know, when we talk about aging, it's studied from multiple points of view. If you talk, ask sociologists about aging, they'll say the most important thing about aging is if you are— the lower your socioeconomic index, the faster you age, the more diseases you have, and the sooner you die. In fact, if you're The difference between the top 90% and the bottom 10% in income across cultures is 10 years of life, even if you factor out healthcare. So that's important. But if you now go away from, so also loneliness, isolation, et cetera. Now that we're in corona time, isolating people in isolation because of, how do you call it in America, house safety, I don't remember how you call it, but you may have to stay at home, can make it very bad for some aged people because of loneliness and isolation. I talk about that, but I want to talk about biology, and maybe we can connect it to sociology. Inside biology, it's also studied from two completely different disciplines. One is the theory of molecular damage, what kind of damage in our body happens and causes aging. And there's a lot of damage theories: exhaustion of stem cells, exhaustion of mitochondria, the cell's energy factories, loss of protein stability, epigenetic alterations, DNA damage. All these kind of— each one of these that stopping the body from repairing DNA damage makes you age faster. There's actually diseases like that called progeria. Also stopping the body from repairing proteins make you age faster. So a lot of these damages are clearly causal for aging. And so that's one way to think about aging. And then there's a completely other way, which has been with us for centuries, which is demographics. For example, what is the risk of dying at age 60, at age 61, at age 62, age 63? This is called demographics. And we see these demographic laws that I'll talk about. And what I want to do in this talk is to connect these two levels which don't talk to each other generally, the demographics and the molecular theory of aging, and come up with what is the core process of aging or one of them that we can address. Yeah, so if we start with demographics, I can tell you some facts. For example, if you take identically twin mice and raise them in the same conditions, they die at different times. So the survival fraction, the fraction that survived to a given age, is basically the same variation as between two unrelated mice. Also people, if you take genetically identical twins, they die at different times, almost like just random people from the population. And the heredity of longevity is about 20% or less. It's not that much. So it's not only the genes and it's not only the environment. There's something else stochastic, like a clock that's running at different times in different individuals that's beyond the environment and beyond their genes. That's one striking thing about about aging. I'm looking at the Q&A. In what other animals does this work? There's clinical trials in human beings I'll tell you about. By the way, also disclosure, I'm not in any company. I'm not going to try to sell you anything. I don't have advice about your health. I'm just going to tell you about some mathematics. How does this relate to the old blood, new blood transfusion work? It doesn't relate. And in fact, that work is kind of shady in my opinion. Why not try primates? Okay. Continuing on in the talk, one thing that we know from demographics for about 150 years already, thanks to the work of Gompertz, who was a mathematician, British mathematician. Um, because he was Jewish, he couldn't get work as a mathematician. He worked in an insurance company calculating the odds, like what's your probability of dying at a given age, and he discovered it's exponential. This graph is amazing. On the x-axis is your age: 20, 40, 60, 80. On the y-axis is your probability to die in a given year. So when you're just born, it's kind of a, you know, 1 in a few thousand, and then in teenage it's very low, and then starts going up, and it goes up exponentially. So it doubles about every 8 years. That's the Gompertz law. So every 8 years your risk of dying in a given year rises, and then it kind of slows down at age 120. Above age 100, people think it's about 50% chance to die in a given year. So when somebody reaches 120 and it's their birthday, you can say, "Have a nice day." Let's see, Q&A. Will the delayed aging process also delay the physical developing or intelligence gaining process? So if we delay aging, usually we think about doing that starting at old age. So we can probably, by that time, physical and intellectual development is done, even though the spiritual and wisdom development, I hope, will go on. Okay, let's continue. And now I'm going to jump to the biology, from the demographics to biology. I'll tell you already what we believe, we, I mean, a large part of the field of biology of aging think is the culprit, or is the biological entity that accumulates with age that is causal for aging. And when I say accumulates with age, if you look at this graph, there's something so different between being 20 and being 80. I mean, I want to also say that 20-year-olds in terms of health, most of them are really like replicas. They're like mass-produced poster, whereas an 80-year-old, they're all so different. Some of them are so— some of them have the health of a 50-year-old, some of them have the health of a, you know, 90-year-old, that some of them can't walk, some of them are doing marathons. So the variation increases with age in almost every trait, and so every 80-year-old's health is like a unique work of art. A unique combination. So here's what we think is accumulating with age that makes the decade of 20 to 30 so different from the decade of 70 to 80. They're kind of a state of our cells. They're called senescent cells, and senescent cells are cells that are damaged. So when a normal cell becomes damaged, it tries to repair the damage. If it can't repair the damage, it either commits suicide, that's called apoptosis, or it becomes a zombie cell, senescent cell. Think of it like a zombie cell. It's large, metabolically very active, and it causes, it stops dividing. It takes a few days for a cell to become a senescent cell, and it is very active. And these cells actually have a very important role when you're young. They have a role when we are injured. So suppose you have an injury somewhere and you have some damaged cells. If all the cells there committed suicide and died, you would have a hole in your tissue. So instead, the cells become senescent cells and they kind of hold the fort. And they also secrete chemicals to call in the immune system to repair. That's called inflammation. So senescent cells make inflammation, swelling, redness, heat, and pain around the area by secreting signals that cause inflammation. The immune system comes in, they're like garbage trucks that clear the senescent cells. And when the senescent cells are cleared, the tissue regenerates. That's why senescence, we have senescent cells. They play a crucial role in wound healing. Okay. They secrete what's called pro-inflammatory signals, as I said. And for the experts, IL-6, IL-1, TNF, these kind of pro-inflammatory signals. They inhibit local regeneration. So tissue doesn't regenerate until senescent cells are removed by those garbage trucks, the macrophages and the NK cells of the immune system. But listen to this. They're great when we're young, but we're not designed to be old. So this is the thing about aging. Aging, as far as we know right now, most biologists think it's not a program that says, okay, at age 60 you'll have gray hair, at age 70 you'll have no hair and cataracts, as a program to get rid of the old professors to make room for new faculty. That's not what looks like happening. Aging instead is this. It's evolution, natural selection trying to make us very fit when we're young so we can make babies. To pass on our genes. But after the typical time span where the animal is not eaten by predators, all this investment in being fit when you're young comes at the expense of not having repair when you're old. So, it's basically what happens is that things go out of whack when you're old. And one of the things that go out of whack is that senescent cells accumulate with age in humans and in mice and different tissues. And actually, they accumulate almost exponentially with age. So think about this. These cells that were so useful for wound healing are now accumulating all over our body with age. And in this talk, I'll tell you why. And so our whole body is full of inflammation. That's called inflammaging. These cells are secreting these inflammatory signals all over our body. They're also causing this body to stop regenerating just like they did as if there's an injury. So they're doing what they think is right 'cause they think we're still young and they're causing inflammation and reducing regeneration all over the body. And that is a huge factor in most aging-related diseases. And this is what is the difference between the two mice I told you about. These two mice are genetically engineered so that you can kill their senescent cells. But for experts, I'll say the senescent cells express a protein called p16, which stops the cell cycle. There's also a few other non-senescent cells that do that, but let's ignore that for a second. And these genetically modified mice have a poison attack to the p16 promoter, that only works when you add a drug. You give them some kind of drug, and then when you give them that drug, the senescent cells die. So these mice, there's a way to kill senescent cells. And the difference between these two mice is that the mice, the shiny mouse, since one year of age was given this treatment and had the senescent cells killed every once in a while. And the one mouse on the left just got a placebo. So they're genetically identical twin mice, and one had senescent cell removal from middle age and on. So that answers the question I was asked by 4 different people: what is the intervention that slowed aging, etc. Um, do the treated mice still look younger when they eventually die, or do they catch up with untreated mice? Samuel asked, do the mice catch up? So eventually there's other things beyond senescent cells. Senescent cells is not the only thing. There's additional forms of damage. So this delays aging, but eventually these mice die. They usually die of lymphomas. Mice usually die of lymphomas. Will COVID— telomeres. Telomeres is another theory of aging, and shortened telomeres lead to senescent cells. There's a question. I'm just reading out the questions. Will COVID teach us something new about aging? Because we need a paradigm to understand why older people are so much more vulnerable. Exactly. And indeed, in almost all infectious diseases, it's the older people who are more likely to die from this. And in many infectious diseases, actually also very young, but not in COVID-19. So it's very interesting. And I do, I do believe that COVID-19 will again focus us— focus on the vulnerability of aged people. Is the immune system a crucial element? Indeed it is. And I'll tell you Y is suppressed under stress. It is true. So that's why we think that people in low socioeconomic status usually have chronic stress or often have chronic stress, which is not being stressed about something at your work or something like that. It's being stressed, let's say, by taking care of a parent with dementia or something, something you can't get out of. This kind of stress increases cortisol levels, decreases the immune system, and it's the immune system that removes senescent cells. So that's one link between aging and stress. All right, I'm gonna continue with the talk. So thanks for the questions. Keep them coming. I'm gonna try to answer them. I'm grateful for your questions. So, senescent cells are really in a perfect position to understand aging because almost every kind of damage, cell damage, leads to senescence, like telomere shortening, all kinds of DNA damages there. So, all these different damage theories of aging are okay. So, if you screw up DNA repair, DNA damage will cause more senescence, more aging, etc. So, they kind of sum up all these different damages, and they lead to serious systems-level effects like chronic inflammation and stem cell exhaustion, which lead to many diseases. So they're at a perfect place to understand aging and also to treat aging because, as I showed you, if you remove senescent cells, if you remove senescent cells, it not only makes the mice shiny, and their eyes bright, etc. There are mouse models of diseases. So there's mouse models which have things that look like glaucoma, idiopathic pulmonary fibrosis, atherosclerosis, liver cirrhosis, all these osis. I hope we don't hear of these osis, okay? But if we do, it's good to have these mouse models. Type 2 diabetes, muscle loss, ulcer, and in every one of these diseases removing senescent cells helped either reverse or ameliorate the disease. I want to add to this list Alzheimer's disease mouse models. They're not perfect, but they're— and the list is going daily. And there's different ways to remove senescent cells. There's what's called senolytic drugs. These senolytic drugs, what they do is they latch on to something that's different between senescent cells and all the other cells of our body. For instance, senescent cells stop their own suicide programs because they don't die. So if you interfere with that, they die more. And so some of those senolytic drugs do that. There's several classes of senolytic drugs. And as I said, since 2019, there's already some clinical trials for idiopathic pulmonary fibrosis, and osteoarthritis in human beings with senolytics based on this mouse work. We still don't know what the results are, but it's definitely a promising direction. If we eliminate senescent cells, there is a question, and it rescues the disease process involved in aging, but the mouse that has been treated only enjoys a 25% increase in longevity And could this mean that there are separate systems involved in the process of aging versus longevity? So, I think the reason that these mice don't live longer than 25% is that senescence are only part of the answer. They are— I think there's a lot of damaged cells that somehow manage not to become senescent, but they can still disturb our physiology. So, even if you get rid of senescent cells, you can enjoy delaying a lot of diseases, but there'll still be decline eventually. And so we need to find the next layer in the onion. And Akash just asked, what's the difference between clock and environment in this context? Are they related? I'm not sure I understand that, so maybe you can clarify. What about the decreased likelihood or trend of death over 100? I'll address that. I'll address that too. OK. So let's— I'm going to continue. OK. So what I know that I want to talk a little bit about the role of mathematics. As always, in my opinion, biology mathematics is a kind of scaffold that helps us ask new questions, answer them in powerful ways. They should go from assumptions to solutions in a powerful way. And in the end you come up with an answer that can be tested experimentally and can be explained without the mathematics. And if you can't do that, experimentally test it and explain it without the mathematics, you haven't done your job. So I'm going to try to— my goal is that everyone in this webinar is going to understand what I'm talking about without the mathematics, and that'll test my ability to explain to you to see if I really understand it. Um, and we started by asking OK, senescent cells accumulate with age, and that's the reason we think that drives a lot of the aging process. And so it wasn't known when we started, do they just accumulate? Does it just, like, stay there forever, more and more made, more and more and more, and accumulate like that? Or are they actually made, destroyed, made, destroyed, made very quickly? What happens is that something like their removal rate goes down with age or their production rate goes up with age to shift the balance. And so nothing was known about that, or very little. What is the half-life of senescent cells? What's going on with aging? What explains the vast difference between individuals with age? So it must be some kind of stochastic process. Stochastic means random process. And there mathematics can really help because you can't— and start thinking about this without the precision of stochastic ordinary differential equations and things like that. And we had some data to work on, so we weren't just groping in the dark. We used a beautiful experiment done by Sharpless, Bird, et al., in 2013, and they made mice which are genetically engineered. And these mice, they're engineered so that their senescent cells emit photons. Their senescent cells make light, and you can photograph these mice and count or measure how much senescent cells they have. And so you get on the x-axis is age in weeks. They had to stop this experiment around late middle age because the way they did this construct interfered with some part of the biology of these mice, and they had at old age they had some problems. But At young age, it's okay. And this is on the y-axis, it's a light, and each point here is a different mouse. So you can see that there's just few senescent cells at young age and a lot of senescent cells in some of the mouse in old age. But you can see the variation is huge in the old mice. You know, some of them have few senescent cells just like young mice. I hope you can see that. I hope you can see my cursor. Can one of the panelists tell me, can you see my little arrow here?

Yes.

Oh, great. So, so you see the average goes up, the variation becomes huge in old mice. Some of them have a lot of, a lot of synapses, some of them have just a little. Okay. And, and we got from these researchers a very precious thing connecting the dots. So the mice aren't killed in this experiment, You can see this green mouse, you can see their senescent cells over time. So it's what's called longitudinal, or this purple mouse is here. So you can see them go up and down, and this data has noise in it, but from beyond the noise, you can use it to analyze and answer questions about the production and removal of senescent cells. The mathematics says that the rate of change of senescent cells is production minus removal plus noise. —and this is kind of the essence of it—and we searched through a lot of mathematical models. This is a very beautiful thing you can do with mathematics. You can say, I'm going to make as many models as I can think of that are consistent with the biology, and ask, do they describe this data? And we came up with a minimal model—this is the one that's simplest—that the simplest model that can describe this data within, that's in a satisfactory way, is written here. And we have by now tested this model in many different ways experimentally and theoretically, and we are gaining confidence that it is in the right direction to explain the accumulation of senescent cells. In this model, there is a production that rises with age a removal that is inhibited by senescent cells themselves— I'm going to explain it— and a simple kind of white noise term. So this model, what we get from this data is that senescent cells are removed in a way that is inhibited by senescent cells themselves. And the simplest way to understand that is you have those garbage trucks coming to remove them. And the more senescent cells you have, the more those garbage trucks are overloaded. So when you're young, you're like a young village with a little bit of garbage, and you're given, let's say, 100 garbage trucks. Those are the immune cells, and they're working fine. But we're not designed to be old. When you're old, you have so much senescent cells that the same— but you still have 100 garbage trucks because evolution works by giving you immune system for when you're young, not when you're old. And they're overloaded. They're overloaded, and you're and that makes the senescent cells just pile up. And also if you're injured or something, you have many more senescent cells. So this kind of model— I'm just going to flash a few slides that say this model describes the data in a satisfactory way. So the black dots here are data. X-axis is age, y-axis senescent cells. You get not only the average, also the distributions of senescent cells between different mice, which are widening with age. And the standard deviation, etc. You have something very important, which is that when you're old, if you have more senescent cells than average, you stay that way for a long, long time. So that means senescent cells stay there, or if you're lucky enough to have a few senescent cells, you're below average, that's to say you're healthy for a long period of time. When you're young, things balance out very quickly and you go up above average. And that is again because of those garbage trucks. When they're overloaded, if you have some extra senescent cells, they just stay there for a long time. So there's more persistence, and that kind of explains the variation in senescent cells between individuals. Everything slows down, the removal slows down. So if you're unlucky enough to have a lot of senescent cells, you stay that way for a long time. It's fast dynamics when you're young and persistent or slow dynamics when you're old. Okay, now that model was interesting enough for us to actually do an experiment and to test it. The model was predicting not only that senescent cells turn over quickly, it told us exactly how quickly. It told us that in young mice, senescent cells are removed every 5 days. In old mice, because of the slowdown of removal, they're removed every month or so. So they stick around for much longer, and that's part of the reason that they accumulate. And that was interesting enough to actually do an experiment. And the experiment which we did with Valery Kryzhanovskyi was aimed to measure the half-life of senescent cells. And this is the way it's done. You give the mice a bad drug that produces senescent cells. It's called bleomycin in the lung, so makes senescent cells. And then you see how long it takes, what happens with time. So if there's no removal of senescent cells, they just stay there, the extra senescent cells. If there's slow removal, they're removed slowly. If there's fast removal, they're removed quickly. We use kind of a state-of-the-art method to measure the senescent cells, which is to take out the lungs and put up a kind of a marker of senescent cells and then dissociate the whole cells and move them through a machine that takes a picture of each individual cell in different colors. That's an imaging flow cytometer for experts. So we can count the fraction of cells that are senescent in the lung. And we found that in young mice, the extra senescent cells vanish with a half-life of about 5 days.. And in old mice, which shows that they're in a rapid balance of production and removal, but in old mice they stick around for a long time and have a half-life of about a month. So, and that was very accurately predicted by the model, and that gave us a lot of confidence that indeed senescent cells are turned over quickly in the young and have a long half-life in the old because We think they inhibit their own removal rate. And I'm just going to now ask Sif about the questions and let you ponder what I'm saying. Do various diseases' mortality rates have similar age dependence? Indeed, a lot of diseases also go up exponentially with age. I'll talk about that at the end of the talk.. And we think that the same kind of model can explain age-related disease incidence. Are senescent cells pre-apoptosis? Is signaling responsible for difference between the cells? Senescent cells stop cell division and they don't commit cell death. They're anti-apoptosis, I would say. Doesn't killing senescent cells have an adverse effect in repairing injuries? I think it does, so I wouldn't advise taking those drugs once they exist. After heart surgery or something like that when you're healing. But they don't appear to cause adverse effects in the mice unless they're injured. So that's interesting. It could— mutated mice could die from a wound. So I suppose if you're injured and you remove senescence, keep removing senescent cells, that's not a good idea. But if you're injured, you can just stop removing senescent cells and you'll be fine. But I'm so glad these questions show that you're right on You're with me, or helps me see that I'm clear. I hope I'm clear. So mouse with senescent cells was more at risk when injured because you need senescent cells for wound repair? Yeah, these questions are around the same point. The thing is, when you remove senescent cells because of this half-life, you can stop removing them when the mouse is injured. They will appear there for the injury, for the repair process, and then once there's repair, you can continue removing senescent cells. I would counsel that. Shouldn't having more senescent cells induce stronger immune system? What is the relationship between autophagy and senescent cells? How does this relate to the immune system? So autophagy is a kind of repair process in the cells that addresses damaged proteins. If you mess up with autophagy, you get increased aging, and you also get senescence. So autophagy is a kind of anti-senescence. If you have more autophagy, cells are able to repair themselves. If you have less autophagy, cells become more senescent. And that's, I think, one of the links between autophagy and aging. And the immune system is the system that removes senescent cells. So they know when cells are damaged and they can remove senescent cells, especially NK cells and macrophages. Does the age-restricted mouse exhibit behavior that is different from actual young mice? Eats more, moves less. You know, that's something to be studied. If you remove senescent cells, is the mouse 100% like young mice? I know that removing senescent cells can't reverse some aging phenotypes, like damage to muscle fibers. It kind of seems like an aging phenotype that can't be removed when you remove senescent cells. But other things apparently can. A lot of a lot of decline can. So it's partially you can become younger, but I have seen experiments done by Valerie Kryzhanovskyi, my colleague, where you take an old mouse and inject senolytic drugs that remove senescent cells. And within a couple of days, the mouse starts running 10 hours a day on the wheel. That's a mouse that was very old and didn't run. So that was very striking. And that lasted for a month. I was very impressed to see that. But there's a lot of work to do about what exactly is reversible, what isn't. And of course, also when you translate to human beings, it could be completely— a lot of times things don't translate from mice to human beings. So it's a young field. And certainly having more senescent cells induce stronger immune response in older people. I think one thing that happens in old people with a lot of senescent cells is the immune system shifts towards the part that removes senescent cells. That's called the myeloid shift. More macrophages and less T cells and B cells. I believe that's a— I would guess that's a response of the immune system to the senescent cells. And that doesn't help us a lot with things like COVID-19, which needs T cells and B cells. Okay. I'm gonna now stop with the questions for a second. And we'll have a vast amount of time, I think 30 minutes, for more questions. I want to now link— go back to the question I asked: can this understanding of senescent cells help explain Gompertz's law, the exponential rise in death probability? And to do that, we need to link something cellular, senescent cells, to death. And cells and death, there's a vast gap between the two. So how can we go from senescent cells to death? It's not clear yet, but there's a lot of research about that. How an individual mouse with a given amount of senescent cells, does it die younger or older? That link needs a lot of mice experiments which are being done. But here we use mathematics again. So we know that senescent cells are involved in many aging-related diseases, but it's not only that you die from diseases, often you die of diseases. And here's the mathematics. You can explore simple-minded hypotheses, which are of course just an approximation to probably a more complicated reality. And let's play with the following. On the x-axis is your age, and y-axis is how many senescent cells you have. And here are two individuals, the blue and the orange, go up, down, up, down. And let's imagine that you die when senescent cells cross a threshold. There's a level of senescent cells that's incompatible with life, x critical, let's call it. And naturally, that's just a simplified assumption, You can also test other assumptions, just the probability of dying increases with senescent cells in a kind of a functional, monotonically increasing function. But this threshold one has the elegance that you can actually analytically solve the equation I showed you before. This is what's called in mathematics a first passage time. So you ask, when does a stochastic process like senescent cells first cross a threshold? It's a famous problem. First passage time problem with a long history in chemical physics, etc. And you can solve it for the equation that I showed, and you basically get the Gompertz law. You get an exponentially increasing rate of death. Here the red line is the simulation, or the— if you want, the theory. We added to it something called extrinsic mortality. So between age 20 and 40, notice here, most deaths aren't caused from aging-related reasons. They're caused by accidents, suicide, homicide, especially in men. If you factor those out, this exponential line you just take those out, the risk of death goes down exponentially down to here. So another decade of exponential. So we added here the extrinsic mortality. But the amazing thing is that this has no free parameters. You take the same parameters you fit to the senescent cell production, removal, etc., and then just add a threshold and you get this exponential rate. Not only the exponential, which you can now connect to basic molecular parameters like the noise and the threshold and the rate of production of senescent cells, etc. You also get this slowing down. The question was asked, what happens at age 100? So this also comes out of this model, and it has to do with, um, removal being so slow that now the amount of senescent cells is governed by noise. And when it's governed by noise, it becomes what's called a random walk process. And then the crossing of this threshold becomes basically a constant probability per unit time. What I just said is for experts who understand stochastic process or math random walks. It's basically a luck game when at that age, of course you have the genes to make sure that you haven't died from other causes, but it's not only genes. It's also luck that plays a role here as far as we see things. It has a major, major role. So we can understand the slowdown in old age is from the same model. Now we come to diseases. This is the last part. I'm just gonna make sure I haven't missed any questions here. I apologize if I missed questions. The adverse effects of some of the drugs. So, you know, there's different classes. Some of them have side effects because they stop, let's say, some of them have side effects. Some of them don't have serious side effects as far as safety trials in humans have shown. Those with strong side effects can be used, let's say, for osteoarthritis by injecting them into the joints because there they don't go into the rest of the body and they have local effects. But it's early days, and it's possible that they'll have adverse effects we don't know about apart from their role in wound healing. So it'll be interesting to see. Of course, the thing in biology is you discover something, you believe it's gonna cure big diseases, and usually there's maybe an early success, but then in the end, it's not as optimistic as you thought in the beginning. We're right now in the very optimistic phase because we still don't know the results of big clinical trials. On the other hand, killing senescent cells is much easier than killing cancer cells. Cancer cells mutate, and, you know, they're so clever and they're so similar to our cells. They're really a dangerous foe. Senescent cells don't divide. They don't mutate. They just sit there. And so killing them should be easier. Also, strengthening the immune system to kill them is— there's just been a paper by Lowell's lab about using, like, immune therapy against cancer by designing T cells to kill cancer cells, doing the same for senescent cells. So they design T cells to kill senescent cells, take out your own T cells, engineer them to recognize the protein on senescent cells. So there's at least two strategies: senolytic drugs and immune therapy. And I believe there will be something that works. That's my gut feeling. Early days. Yeah, the mice don't have adverse phenotypes. The mice only have benefits as far as— there's maybe 50 reports by different labs now, so they all say that. Why can macrophages clear out senescent cells during wound healing but not once they start to accumulate? The point, I think, is that Senescent cells become so numerous that they overwhelm the body's ability to remove them. And they also probably, and that's a numbers game as far as I can tell. So why does their production rate increase with age? That's because as we age, we have more and more mutations and stem cells get mutated, producing damaged cells all over our body. And that rate rises linearly with age because the amount of stem cell divisions is constant, and so you have a stem cell mutant here, here, here, here, here, here, and above it is a column of senescent cells. So the production rate increases linearly with age. Your 100 garbage trucks, all the macrophages, get overwhelmed, and then it goes up like this because removal stuff. Then with the noise, it's really interesting to think about. If senescent cells release some sort of not super targeted chemical that inhibits regeneration, does this mean that sufficient quantity of senescent cells can inhibit regeneration of a lot of healthy cells in the area? How important is this to the aging process? So, this question, I think, very deep. Senescent cells indeed release factors that slow down regeneration in their own area and also systemically. And this is, in my opinion, very important for the aging process and at the core of some aging-related diseases, especially degenerative diseases where removal of cells needs to be more than proliferation of cells. But if proliferate— if proliferation needs to be more than removal. But if you stop proliferation, it goes below removal, a tissue crashes. That's our explanation for idiopathic pulmonary fibrosis and for osteoarthritis and a lot of diseases like that. So I think it's very important. Um, so, so many questions, 64 questions. I don't think I can answer all the questions right now. What do you think about the Horvath clock? What did the mouse 2 tend to die of? Are there trends in diseases? Yeah, actually, that's not clear yet. What do treated mice die of? I think it's mostly lymphomas in mice because of some strange reason they're very prone to lymphoma. Shouldn't having more senescent cells induce stronger immune response? Okay, we already did that. Okay. What about NAD+ studies show that supplementation is dramatic anti-aging effect? So I think a lot of anti-aging studies address factors that connect also to senescence. They have their own effects, but also they connect to senescent cells. For example, calorie restriction, mTOR inhibitors, all these increase repair in cells, and the repair prevents senescence. And so there's a lot of connection between different anti-aging interventions in mice and senescent cells. Of course, Senses are the only thing. Why production is running linearly in time? I think I answered that because of stem cell mutations. Surgically having an old mouse share its blood supply with the young one seems to reverse aging. Has been this shown to— yeah, so Andrew Clark says if you take an old mouse and connect circulation to a young mouse and now you have Siamese twin mice, that's called parabiosis. They can stay like that for a few months. The old mice rejuvenates and the young mice slightly ages. And recently, there's a paper measuring senescent cells, and indeed the old mouse has senescent cells reduced. And the way we explain this is that the garbage trucks from the young mice go through the circulation to the old mouse and help that big city with a lot of garbage remove garbage. I hope I'm being clear. So we think what happens is that the saturated removal capacity of the old mice is alleviated by making an alliance by having shared circulation with the young mouse. So that could be explained actually perfectly by the same model quantitatively. So we think that's an explanation. We don't think it's only a bloodborne factor. We think it's the immune system that's moving. That's our unpublished manuscript, and if anyone's interested, I can send you that. We want to try to submit that. It's possible that an ancient civilization slowed this chemical process enough to be 80 years and look 20 years. It is possible. But yeah, I wonder what the fossil— like, if there would be like a layer with, uh, with a lot of— like, we can— the layer in the— okay, I look at the rocks in the Golan, you can see the, you know, the volcanic, and then there's like sediment. And so I wonder what that layer, that ancient civilization, would look like. A lot of like jogging 80-year-olds. Can the exponential increase be explained by the hierarchical nature of biological circuits? It can, but I like— and of course, I love networks and hierarchical circuits, but I more love this very simple stochastic equation that captures so much with so little. So that's my favorite explanation right now. OK. Let's talk about incidence of diseases. There was a question. On the x-axis is the age, and the y-axis is the chance to get the disease. It's called incidence. So the— this is, for example, or condition. This is hypertension. This is log scale, by the way. Here, diabetes. Here we have heart attack. This is IPF. This is lung failure, the black line. Here we have kidney failure. Disease after disease. I mean, not every disease is aging-related. For instance, type 1 diabetes you get at age 11. Thyroiditis you get at age, you know, teenage. Depression is age-related, but you also can get it at young ages. But many diseases are age-related diseases. We looked at the Israel medical record. We found 800 Disease codes which rise by a factor of 30 between when you're 20 and you're 80, and they all have the same general exponential rise. You see the exponential rise with very similar slope, 6 to 8% per year. Not the same slope as death probability, but similar slope. And they have this decline in old age. It's not a plateau. It's actually decline at slow ages. Decline at slow ages, long ages. What is going on here? This again hints at a shared process that times the diseases. Of course, which disease you get does matter. It does matter what your genes are. It does matter what your environment is. If you have the genes and the lack of exercise and nutrition, you'll get diabetes. Whereas people with other genes or another behavior won't. So it does depend very much, but you still have this exponential rise and decay at old ages. And we found that we can explain all of these curves using the same kind of model, as I said before, and link it to senescent cells. I'm not saying senescent cells are the only cause, but they're sufficient to give you this kind of curve. Now, there's been theories in the past about, let's say, cancer incidence. Cancer also has similar— most cancers— that you have multiple hits, like 2 or 3 mutations at the same time, and they give you a power law behavior. But there was never a theory that connects all these very different diseases, and we think senescent cells can do that. And basically what it says is that you have a physiological process that crosses a threshold, like let's say proliferation and removal of the cells. Once cells are removed more than they can divide, the organ crashes. Or in cancer, once cancer cells divide more than they're removed by the immune system, the cancer takes off. And if senescent cells affect those rates, there'll be a threshold senescent cells for the onset of the disease. And as I said before, that's a first-pass-through-time problem. You get this exponential rise. Why the drop in old ages? It's a very simple epidemiological principle. Not everyone is susceptible to diabetes. At a certain age, everyone who is susceptible will already have gotten it. So that's why you have a decline at old ages. The susceptible population is a fraction of the population, and everyone who is susceptible has already gotten it at a certain age. So age 90, a lot of diseases are actually on the decline in terms of incidence. And as I said before, if you just take one threshold for each disease, the dashed lines are models, and the susceptibility, what's the fraction you get, you can explain a lot of these curves satisfactorily. And then what you could do is you can predict what happens when you take those drugs that remove senescent cells or immune therapy. So then you can do a model for disease, age on the x-axis, Disease goes up, insulin goes up and down, right? It's very important for these drugs to, because they might have side effects, to design treatments with the lowest possible dose and the most infrequent treatment. You don't want the treatments to be far away from each other to minimize side effects. And now you can, these models you can calculate. So here's a very modest treatment. You take the drug, starting at age 60 that removes senescent cells. It removes only a quarter of the senescent cells, and you take it once every 2 months. Why do we know 2 months? Because we know the half-life of senescent cells, at least in mice, and we have reason to believe in humans that it's similar. So we know we don't need to remove them every day because we know that it takes them 2 months to accumulate again. We know that from the model. And this modest treatment makes the incidence plummet, and you still age, but your incidence at age 80 is like the incidence of a person at age 60 or something. At 85, like, this is a 25-year rejuvenation in terms of diseases. And then you can make— plot the dose versus the time interval and see how many years rejuvenation. A strong dose taken very, very frequently will give you a high rejuvenation, and Etc. So you can argue or suppose with these— test with these mathematical models what's the optimal treatment. You can start at old age and take it infrequently and kill only a fraction of the cells and believe that senescent cells only affect a fraction of the disease-causing potential and still get a sizable rejuvenation in terms of years. And that's exciting for us to pursue. And also, um, I want to say that this way of thinking creates novel explanations for mysterious diseases like idiopathic pulmonary fibrosis, osteoarthritis. We can explain what exactly the senescent cells are doing to basic physiological parameters and provide new explanations for mysterious diseases. But I think it's a good time to stop and take your questions. So We found out that one way to think about aging is that there's a core process that runs randomly. Its production increases with age. It saturates its own removal, and that coupled with noise creates stochastic dynamics that makes people much more different from each other with age. It creates a first passage time where you have exponential rise in risk of disease in death. It can explain slowdown in old ages and decline in disease incidence. It can explain parabiosis, what happens when you link up two mice. And it can explain a lot of other factors in aging that I can answer if you want. But we think it's a minimal description that explains a lot, and of course there's other things too. Turnover slows down in old mice, leading to large and persistent variation in senescent cells between individuals. Which can explain the Gompertz law of variation, mortality, and disease. These are the linked-up mice, by the way. And, um, I believe we can, um, okay, there's 67 questions. Hal Cox says, thank you, Uri, I hope your talk can be posted. Blessings on the stream of the Galilee, may they continue to run. What a beautiful comment. Thank you. Nimrod Rubinstein, many of the parabiosis studies are replicated with plasma transfer. So immune cells cannot be the only explanation, right? What's your opinion on these studies? So what Nimrod is saying is that if you connect two mice so they share circulation, you get some effects. And then if you take plasma, that is to say only blood without immune cells, and transfer it from one mouse to the other, you get replication of those parabiosis studies. Now, I might be missing a paper, and maybe Nimrod, maybe you're an expert and you can teach me something new. But as far as I know, those plasma transfers have been done for brief periods of time. And as far as I know, they don't really replicate the full effects of parabiosis. And please correct me if I'm wrong. So there's a long search for the plasma factors. But as far as I know, there's not a satisfactory replication with plasma transfer. It could be out of date, so Nirav, please correct me. Maria: Does the model consider the effects of different removal rates in time, such that the increase in senescence is due to reaching a passage to remove it, maybe due to a depletion of the garbage trucks? Thank you. So what Maria is saying, maybe if I understand correctly, the garbage trucks are becoming damaged. Our immune system is going down with age, and that would also give you an increase in senescence. So that's, um, very likely because we know that our immune system gets worse with age. But the main part that gets worse with age are those T cells and B cells, the adaptive immune system. The part that removes senescent cells, NK cells and macrophages, doesn't seem to get much worse with age. It In fact, it stays kind of constant with age. Maybe macrophages increase. They do look more exhausted, which is what you would expect if they're working very hard. So, we think that right now our best hypothesis is that you don't have to invoke the garbage trucks themselves becoming worse with age, only that they become saturated or inhibited. But I'm sure that there's also a component of them being worse with age, which will only compound the effects. That's the way we look at it right now. But that's a question for the frontier of research right now. If susceptibility— Arag is asking, if susceptibility of disease incidence is from increase in senescent cells production increases with age, does it mean that folks who are not susceptible for disease do not senescent cell production with age? So, the question is about what do I mean by susceptible with disease. So, I think Everyone has increasing production in senescent cells with age. Some of us are luckier or not in the stochastic process, and at age 80, we might have more or less than the average based on genetics and environment, but mostly on luck. That's the way to look at it. Now, susceptibility is something else. For instance, it's easy to understand smokers. If you're smoking, you're much more susceptible to lung cancer. Yeah, so by the age of 80, if you are smoking and not have lung cancer, the incidence, you're in the down curve, you probably don't have the genes for lung cancer or something like that. So the susceptibility is a lot to do with genetics and environment. You're born with the genes that'll give you this. So most people, luckily, are not susceptible to, let's say, pancreatic cancer, and they will never get it. And some people are, and Maybe pancreatic cancer is not such a great example because let's talk about osteoarthritis even. It looks like there's only a fraction of people that will ever get osteoarthritis, and that's completed by a certain age, or also diabetes or hypertension, etc. That's the leading epidemiological role. It's not the senescent cells, it's the fragility of a certain organ system to the effects of senescent cells. Let's call it like that. "Does the neighboring cells become cancerous if senescent cells are removed?" Janet is alluding to a role in senescent cells of preventing cancer. It's thought that a lot of cancer cells become senescent and they stop dividing, but it doesn't look like it. It doesn't look like removing senescent cells increases the risk of cancer, as far as I know. Nimrod says, "I meant more generally, rejuvenation." Nimrod, do you know if plasma exchange actually causes rejuvenation? Because I haven't seen convincing data about that. So if you know about that, please tell me. I mean, there's some kind of companies that are doing research that's— I would be careful to over-interpret. So, but academic labs that are Credible. I haven't seen that, but if there's a reference, please tell me. What— of course, it could be not up to date. Why do big dogs age so much faster than small dogs? Somehow body size relates. Great question. Why do big dogs age so much faster than small dogs? So I was pondering this question, and I know that part of the mutations in the breeding process of big dogs and small dogs are in the same pathway as that targeted by anti-aging intervention. So one of those pathways is the IGF-1, the growth pathway. So if you want a big dog, you make the cells activate their growth pathway. When cells activate the growth pathway, there's a trade-off. They make less repair. So growth and repair are in a trade-off. You grow more resources to growth, less resources to repair. So when you grow faster, you have less repair and therefore more damage, more senescence, faster aging. Drugs that— calorie restriction, for example, makes you grow less and have more repair, and therefore Calorie restriction in many organisms slows down aging. Also, metformin, the anti-diabetes drug, is a good candidate for slowing down the same IGF-1 pathway and apparently slows aging because of increased repair, less senescence. At least that's one. So I think I love the small dog question. Are the middle-aged mice reproductive? Why wouldn't natural selection have selected for production of more and better garbage trucks? So Why should natural selection give us— just increase the number of garbage trucks as we age, right? Why do we need to stick with 100 garbage trucks? Why can't we have 1,000 garbage trucks as we age? Mice are reproductive, right? So natural selection should work. So if we look at mice, why do mice live 2 years It's because in nature, their average time before they are killed by a predator is about 1 year. And that's what sets their half-life, so their lifespan. So we think it's called life history theory. And that's— it's like you have a car, and if you know that it's going to be stolen within a month, you don't go to the garage and repair it and buy extra doodads. So it's going to be stolen, right? So the same thing with creatures. On the other hand, if you have a naked mole rat, it's like about the same size as a mouse or a rat. Lives underground. It lives for 40 years because it's never predated. It's protected. So the natural killing rate in nature is what sets— makes evolution set lifespan. What does that mean, evolution set lifespan? It's basically how much you devote to growth and reproduction versus repair. So mice, they're reproductive. They live fast, die young, leave a good-looking corpse, and leave a lot of babies. That's their strategy. They're going to be eaten in a year. So they do a lot of— they do very little repair. That's why it's so easy for biologists to give mice cancer. Anything, you know, it's so easy to give mice cancer. They have very little repair. Human beings, elephants, whales, they have a lot more repair, so we can make it to age 100. And apparently the reason different mammals evolved to have different lifespans is by playing with this trade-off of reproduction and growth versus repair. In different ways. And I hope I answered your question, Kenneth. Akash, when we look at senescent cells, what kind of cells are we looking at most of the time? Is the situation of senescence different for a neuron? Thank you. Senescent cells a lot of times are cells that do tend to divide. So we're looking at epithelial cells of the gut, we're looking at skin cells, we're looking at lung cells, liver cells, senescent cells. Neurons don't divide anyway. They don't really become senescent. I mean, they have different aging, but their glia, the helper cells around them, do become senescent. I want to say that from our mathematical analysis, age-related diseases of the mind— Parkinson's, Alzheimer's disease— they have a different story. They have a much faster, like, the 20% increase per year or something like that. That's not explained by the senescent cells in the body, and we're thinking maybe there's a senescent cell compartment in the brain, like the senescent glia or something like that, the cells that maintain the neurons that have their own dynamics and can explain slightly different dynamics for the age-related diseases in the main. That's just a hunch right now. But there's senescence all over the body. So wherever there's stem cells, you'll see senescent cells near them. In fat cells, there's a lot of senescent cells, etc. The treatment you mentioned seems to affect only kappa. Can the generation rate be affected? So this is Marina showing great attention to the equation that I showed. The treatments I mentioned affect only kappa, which is halfway point for saturation. I would say the treatments, what they do is they add an additional removal term that's very strong, like beta, you can say, or something like that. Equations, and you can model them that way. But maybe adding more immune cells could affect kappa, something like that. James asks, do animals in these studies observably age at different rates, or do they just randomly die after different times? There's a lack of experiments on the aging rate of animals treated in this way. There is this Van Der Zande experiment. They're very difficult experiments to do. They do, but I'd say there's more to do about that. And especially what I'd like to see is connecting the time of death to the amount of senescent cells in an individual. That's one unknown. Okay, I'm going to go down. Bernhard, I mean the mouse with killed senescent cells was more at risk when injured because you need senescent cells for wound repair. Okay, so Bernard, the way I look at it is that you kill the senescent cells that are already there. If now the mouse gets injured, new senescent cells will form. You don't want to kill them after injury. You don't want to kill senescent cells. I would advise against that. But after the injury is healed, you can continue killing senescent cells. So once you kill senescent cells, you kill the senescent cells. Most of the senescent cells you kill are ones that are made from the damaged from the mutated stem cells, not from a wound per se, as far as I can tell. Jeff, is calorie restriction, which also extends life in mice, related to this? I think it is because, as I said before, calorie restriction affects the IGF-1 pathway. It shifts the cells away from growth and towards repair. And the way I look at it is that it reduces the probability that a damaged cell becomes senescent because the cell can repair itself. And therefore reduces actually the rate of production of senescent cells. And that fits when we look at calorie restriction in fruit flies and in mice, and we use these models. That's the parameter affected, is the rate of production of senescent cells. This is so intense, you know, these questions, they're coming. There's so many questions, 71 questions through text. We have to finish soon, I think. Can I ask one of the panel members to tell us how much time we have?

Yeah, we have time till 12:30. So we have another 10 minutes.

10 minutes? Okay. Okay. Ismael, have any of those treatments been applied to humans? So as I said, there's a couple of trials now, one for idiopathic pulmonary fibrosis, terrible disease that lungs stop working and you die within a year or two. So removing senescent cells helps that mice with the same model for that disease. And now there's a trial with senolytic drugs and also for osteoarthritis, maybe more. "Can fasting kill these cells?" I don't think fasting kills those cells. I think fasting increases repair and reduces the rate of production of senescent cells. That's the way we think. Sarah, what do you think about the Horvath clock? So Horvath clock is a big advance where you can look at the DNA of an organism and look at certain places and see how they're modified and get a good reading of the age of the organism. I believe that those changes are markers for the effects of things like inflammation, and therefore they are likely to be a good reporter of the physiological age of a human being. And I think very important for developing drugs. I want to say that there's a kind of a an attempt in the field to get the FDA to approve clinical trials against aging itself. Currently, it's trials only against the disease like diabetes. So if you have a good clock like the Horvath clock where you can measure the effects of treatments against aging, you can have additional tools to assess whether drugs are working or not. A role of proper nutrition in aging. So of course, preven— nutrition is key for preventing obesity, overweight, which causes many problems including inflammation and insulin resistance, etc. Vitamins are very important, etc., a good balance. Maybe even setting the balance between growth and repair. That's something to think about. Fasting, and I believe will be a very important factor. Raju, any data that showed— oh, Shai Ben David, when you go over many models, don't you risk overfitting? So Shai, And what we did was develop 4 possible mechanisms and therefore 2 to the power of 4, 16 different models. And we used Bayesian statistics to make sure we're not overfitting. So we were very worried about that. And if you'd like to take a look at the paper, maybe you can Enlighten us if you think that's valid. It's Koreen et al., 2019. Michael Levy, do you get qualitatively different results without the white noise term? Yeah, without noise you can't make this work. You can't explain the data as far as we can tell with deterministic models. You need to have noise because of the huge increasing stochasticity. So noise is crucial. If you're asking whether white noise versus non-white noise— I'm not saying the noise is white, I'm saying that white noise is a minimal model that's sufficient to explain the data we have, but it could be the noise is not white, probably isn't in reality. Is there info on position of cells? Amir is asking. That's an emerging field. Right now there's a race to make mice that have good fluorescent markers of senescent cells that are readable at old age. And I would predict that what you'll see is stem cells with a column of senescent cells above them. So you'll see a punctuate. But there is one paper from the van deursen lab that showed some very low concentration of senescent cells with a huge effect on the heart. So anyway, that's fascinating. I don't know what to say. During healthy aging, what causes increase in senescent cell production with age? It's very simple. Stem cells divide, and when they divide, they mutate. Some of those mutations kill the stem cell or make it go away, but some of them are invisible to the stem cell and only expressed in the cells of the body, differentiated cells, making them damaged, and then they become senescent. And the amount of mutant senescent cells rises with the number of mutations going on in concentrates, so their number increases linearly with age. So all over the body you get more and more senescence. How did you test the goodness of fit for the trajectories? As I said, we used a Bayesian kind of simulator, and we used log likelihood for the trajectories. Aram, can a model for senescent cell population dynamics be bi- or multistable? Indeed. If senescent cells strongly make neighboring cells also senescent in a bystander effect, you can get a bistability, and then you can get a let's say a wound, let's say with senescent cells that don't go away, and surgeons know sometimes you need to remove senescent cells for the wound to heal. So that would be an example of bistability. So in your equation, Ismail is asking, the noise factor does not depend on time? Yeah, we tested models where noise depends on time, but we found we didn't need to do that. But it— then again, let's say a minimal model. So nice questions. Maria: Interesting model. I appreciate it describes the data, but does it provide any new predictions? So one new prediction was the lifetime of senescent cells that we actually thought was interesting enough to test experimentally. So that was a prediction, an unknown. Senescent cells, now we know, turn over 5 days in the young mice and 1 month in the old mouse. That was a prediction, panned out. And now another prediction is what I showed you at the end. We can now predict what a treatment would do to the incidence of diseases. So, could be that one day down the line we'll have data like that, and that can help us design new experiments. And that's a very important ability in order to design optimal treatments with minimal side effects. Maria, I hope I addressed your question. Neha, what about the production of these cells? Is exponential? The production is linear with time, but since removal saturates, it gives it extra boost that looks almost exponential. We don't think that production saturates.

Can I ask a question which appears, but I don't think you got to it? I think several people asked it. Why does the— why do you think the rate of production of sensitive cells goes up in time?

Why does that happen? So I answered it 3 times, but it probably wasn't clear. Okay, so the idea is that All of our— so we were thinking, what stays in the body for decades? It's not the cells like our skin cells that get removed every month, or blood cells get removed every 100 days. It has to be something that stays. What stays? The stem cells. They stay. Stem cells make all the other cells. Now the stem cells are dividing at a constant rate throughout adult life. Once in a while, they get a mutation. Some of those mutations make the cells they produced the column of cells above them damaged. Those cells think there's an injury, they're damaged, and they become senescent. Now the rate that happens, because the divided concentrate is linear in time, so you have a mutant stem cell here, then here, then here, then here, then here, and that's a linear process with time. Did I explain myself, Shafi?

Yeah, so the point is you're saying that senescent cells get created when they think that they need to repair something?

No, senescent cells are there, they make Stem cells are there. When they get mutated, they make damaged cells. And the damaged cells, what do damaged cells do? They become senescent. So the mutations, the problem is that you can't, every time cells divide, there's a chance for mutation. And therefore you get, it looks like an injury because you have damaged cells, but it's not really an injury. It's just a damaged stem cell making injured cells. And that, did I explain myself? Yeah, yeah, yeah, right. Senescent cells are doing what they think they're doing right. They think, oh, we're damaged, we have to call in the immune system. We have to stop cell regeneration. They're doing what they should, but we're not designed to live so long. We're not designed to live that long, and we're not designed to have so many senescent cells. It's just like having injury all over our body. Our poor macrophages and NK cells can't deal with it. They also, by the way, NK cells can't do what they're supposed to do. They can't kill virus-infected cells and cancer cells. And so when we have a lot of senescent cells, we can't deal with cancer and COVID-19, etc. Because those same cells are busy with the senescent cells. So it's multiple layers of decline that are rolled up in these senescent cells as far as we could. Yes, could senescent cells density ratio be used as a quantification of aging? Is there currently a marker? So I believe that if we could measure from a human being the amount of senescent cells, we would have a great marker for aging. Unfortunately, that's not possible right now in an easy blood test or something like that, even though there's a lot of attempts to do that. And I also want to say that the methods to quantify senescent cells are not the best. It's probably likely that senescent cells are many things. Each cell type has a different senescence program, and that because it's early days, we think of them as one entity, but in fact, there's many, many kinds of senescent cells with different effects the reality will turn out to be much more complex than we think. And so it's still the optimism of early days. But I do believe it's a really great quantifier of aging once we get it. Maybe the Horvath clock is related to that. There are few communities in the world—Okinawa, Sardinia—with highest proportion of centenarians. Genetics seems to be one aspect. Also some data on small communities at higher elevation. Yeah, there's communities with a lot of very old people. And I don't know what to say about that.

So— I think we're approaching our end.

Yeah, maybe we should have a grand finale. There's so many questions I didn't answer. I feel bad about that. Aloha Uri, Lee Altenberg. Hi Lee. He's on in Honolulu. Thanks for the great visit in January.

Isn't that nice? And yeah, thank you, Uli. This was like amazing. And in fact, I wonder if we can save the questions. So maybe there's a feature to do that. And if you want in your spare time during COVID you can send off answers.

We'll try to do that. I see that I only answered 50 and there's 60 more. Yes, so I really apologize if I didn't answer your question.

I think there's a way to save it, the question, so if there is, we'll do that. But thank you for a tremendous talk, and I at least, I'm on mute, you know, one of the terrible things about Zoom is that you cannot get the applause and the, you know, I'm sure the actor in you is craving for it.

I have kind of gotten used to it by teaching on Zoom. But the questions, and one person says similar questions, which means the audience made the audience think. Your questions made me think. Some of the questions are new to me. And, um, yeah, I hope we— I don't know, somehow I hope first of all we get to old age. It's better than the alternative. And also that we, I don't know, learn how to, you know, all those non-medicinal ways, all the relationships, the connections, the community, solidarity, having something meaningful in life, very important to keep us going. And then, Shafi, I really enjoyed it. What can I say? Tremendous.

Thank you so much. Bye-bye.