 Okay, first of all, this is a very important checkpoint called the start for the yeast. This checkpoint is to, for the cell to decide, should I go through the process of division or not. This is very important, why? Because I have to make the decision to make sure I have enough resources to replicate my genome to divide. You know, we all, all of ourselves are from one, a fertilized egg, a big stem cell, right? When they divide and divide and divide, they commit into different cell fate. So each cell type is actually a fate, and that's also a fate decision when you become a skin cell, when you become a neuron cell. And all the cells, they have one genome, but different fates. Actually, each cell fate is like a bunch of genes that win their muscle and their neuron heart. And stem cell is like nobody yet dominating. We can use mathematics to understand how and why a stem cell can become another cell fate, and another cell fate can become a stem cell. But I believe there will be new math, for example, new mathematics, simply by studying the life phenomena. Yeah. Yeah. So, like cellular decision making will make, will be new math that we uncover. For example, or how we think, how the brain, how the brain function, and how do we describe life? What's the mathematical language? What's the logic? Ni Hao, everybody. Welcome to Simulation. I'm your host, Alan Sakian. We are on site in the beautiful Beijing, China, at Beijing University. We are now going to be talking about quantitative biology. We have Dr. Chaotong joining us on the show. Hi, Chaotong. Nice meeting you. Thank you so much for coming on our program. Really appreciate it. Thank you for inviting me. You are super welcome and such a fascinating person. Check out this bio, everyone, for those that don't know. Dr. Chaotong is the co-discoverer of self-organized criticality and the SAMPIO model, which are revolutionary breakthroughs in understanding how complexity arises in nature. He's currently chair, professor of physics and systems biology at Beijing University, founding director of the Interdisciplinary Center for Quantitative Biology at Beijing University, founding co-editor and chief of the Journal of Quantitative Biology, fellow of the American Physical Society, as well as principal investigator of the Tong Lab, which has 20 to 30 undergraduate and graduate students studying quantitative biological systems. And you can find the links in the bio below to the Tong Lab as well as the Center for Quantitative Biology and the Wikipedia page. All right, Chaotong. Let's start things off with one of our favorite questions that we like asking our guests. What are your thoughts on the direction of our world? I am optimistic. I think science and technology will take us very far. I think that that's the main driving force for our world. Many things will change, including maybe the definition of what is life and our ethics, et cetera, but I'm excited. I think the world will evolve to a more and more wonderful place for us to explore and to enjoy. Yes. What would you say is a key principle that we can embody and pass along to the next generations that will help ensure that we have a prosperous world? First of all, we have to be peaceful. The most important. The world is really one world. We have just one village. I hope everybody realizes that so we can work together to have a global view, to have everyone educated, to live the poverty, and then go from there. Yeah. Peace. Peace. Unity. Yeah. No poverty. Education. Education. Yeah. And disease and all this. I think they will eventually be solved all these problems. Yeah. And I especially love interviewing leaders that have been pushing the edge of science and technology forward for such a long time because the advancements that are found at the edge of science and technology, at the edge of civilizations and knowledge about fields are typically what ends up eradicating poverty, increasing education, increasing health, eradicating disease. These are the big things that enable people to live healthier, more creative lives. And peace and unity is again just one globe that we have to share that everyone has lived on before us that built this beautiful world. Did you know that the number of people that lived and died before us is approximated to be about 100 billion people? No, I didn't know. Interesting. I love that statistic. It's one where I always try and get behind the eyes of the different people that helped build society over the big time. It depends on where you define when we start to be people. I think I believe the estimates are like 6 million years ago or so. And then it's always also interesting seeing if you know power laws very well and if you apply Pareto or the power law here, you can hypothesize that maybe just 20 billion of the people made 80% of civilizations advancements. And that's always interesting, well, who are those people and how did they think and what did they build? I love asking stuff like that. Let's talk about your journey. So born in the Jiangxi province. That's right. Okay. And who were you as a kid and how did you get interested in science? Okay, so my childhood was kind of interesting. I grew up during the Cultural Revolution. So essentially starting from the second or third grade of elementary school, the Cultural Revolution started and then there's no formal education for many years. So we just play and find whatever interesting books we can read. Well, there weren't much books you can find at that time. So there are a few books left on the shelves of my home science books. If I translate into English, it's like 10,000 wise. Sounds like a great book, yeah. And one of the books is about physics questions. I think that's one of the main reasons I got interested in physics. Plus, I had an excellent physics teacher in middle school. What? Okay, this book is great yet. It's 10,000 wise. I love stuff like that. There are many volumes for this book. But there are a few volumes left on the shelf. One is the physics volume. Yeah, the physics volume. They're 10,000 physics wise. And because questions is our way to probe reality and ask great questions, it gives you a better quality of life and not being shy and just asking the great questions are so important. Okay, what about... For those that don't know, I don't know. What was this like? What do you mean by like during the Cultural Revolution, what happened with school? Like, there was no teachers? During a long period of time, maybe a year or two, the school assembly was shut down. There were no classes. And then the class resumed, but the stuff we learned are very sort of very elementary and simple stuff. There's no serious class. And for example, there's only very simple mathematics and physics. You don't learn anything like history, biology, literature, geography, philosophy. So it's a very, very narrow education with a very thin piece of knowledge. But a couple years later, when you were in middle school, you already had a great physics teacher, so it had to come back. Yeah, well, yeah, I had a great physics teacher, but the stuff he was teaching in the classroom wasn't that complicated either. But that's enough. He inspired me here. So I got interested in physics. In some sense, maybe it doesn't matter how many books you read during your childhood. It's something you got excited and you feel passionate. Yes, and that's an important step in empathy for so many people. What would it be like if we just had like a two-year, just like absence of school systems in general, and it shows in many ways like just the fragility of what can happen in a child's life and in a culture's progress. And I think that's very important to try and get behind, like what would it be like, good thing you found the 10,000 physics-wise and this type of stuff. Okay, but then how about the pursuing of physics and you went to university in mechanics, and this was at the USTC, University of Science and Technology of China. Correct. How did you focus down this path? Yeah, after high school, at that time, we were still in the period of cultural revolution, so there was no normal channel to go to college. But then two years later, the Deng Xiaoping started his great reform and one of the things he started is to open the college to normal people, to a huge number of young people through examination. And was it the Gaokao back then? Gaokao, that's the first Gaokao after cultural revolution. What year was that first Gaokao? That's 77. 77. That's right. And prior to that, what was university for? Just how did people get into university before that? Well, before cultural revolution, there was Gaokao. It just stopped for 10 years during cultural revolution. Okay. And after Gaokao was reinstalled, I took Gaokao and went to the University of Science and Technology of China. I wanted to study physics, but at that time, our university, USTC, which was, I hope it still is, was the best university in China, the hardest university for young people. They don't take physics students in our province because they think our province, maybe the educational level is not so high. So they only have quota for students in the mechanics department. So I went to the department of modern mechanics. But nonetheless, I like physics, so I studied physics. I went to physics class and made physics friends. So I kept my interest in physics. Yeah. And mechanics and physics have so much in common overlap. So then, and this is a whole separate conversation, it would be so interesting to have someone on the show that knows Deng Xiaoping's cultural reform because there's so much nuance to it and I would love to eventually be able to interview someone on that exact subject. But this reopening of the universities was so important. And then you actually went on this cool program, Caspia. That's right. So during my third year in college, I think, and then we suddenly had this program. We heard about it. So at that time, there was no more channel for Chinese students to go to the US to study. There was no GRE, no TOEFL exams, and the US universities, they don't know how to admit Chinese students. That's no formal channel. But the Nobel laureate, Professor T.T. Li, he saw this and he wanted to start the program by himself. So he initiated this program. It's called China US Physics Examination and Application, 1980. So he contacted, by his influence, he's a great physicist, he contacted US physics departments of around 50 universities and get organized and then to to administrate exams in China and then to select Chinese students to go to US. So I was lucky. Again, the first year for this program to start, I called Wave, I took the exam and then ended up in the University of Chicago physics department to study physics, to start my PhD study. Wow, that program, you were telling me before we started that, it went on 10 years and then it stopped. I love stuff like this. If we can inspire more programs like that around the world that do these great cultural and knowledge exchanges across the planet, we have a much greater propensity for that piece and that harmony that we were talking about at the beginning. So this program brought you to the University of Chicago where you did your PhD in physics and tell us about the first immersion into the United States and what was that feeling like, the culture, the people, the science, the university, what was that like? Well, it's a big cultural shock from every aspect. The way professor taught classes, I had an American roommate. We get along fine, but then we had also some frictions. It's very different from China, how roommates get along with each other, but in the end we both understood each other. One thing shocked me is that the first day I went to the physics department, I got five, four, seven keys. So I had lots of keys. Every door I needed a key. So, hmm, that's new. Yeah, many of these small cultural shocks. But I enjoyed it really. I lived in the International House, University of Chicago's International House, built by Rockefeller, I believe, and University of Chicago was sponsored by Rockefeller initially. I think his initial belief is to build this International House is to have people together from different cultures. And that really served me a lot. So I spent first two quarters half a year in the International House. Although the rent was very high, basically all my salary I get by doing TA was paying rent and food, and that's it. Yeah, but I made many friends from different countries and different cultures, including many Taiwanese and Hong Kongese friends. So that was a great experience. Very similar with my first immersion into China. I felt a very beautiful spirit that took me in warmly. And the people and culture have just been the art and it's just been so fun to be immersed. And I almost feel like I get to live through your first experience. Other people can hopefully live through my first experience. Hopefully we can catalyze more of these first experiences around the world. But how did you end up picking what in physics you wanted to do? It's such a broad field. There are so many different ways to tackle it. How did you pick what you wanted to do? I somehow got into statistical physics late in my college time. I think one of the reasons is that at that time the other day there was a Nobel prize given to Prigodzin to award him in statistical physics in non-recurring systems. So maybe that had impacted me. But also maybe of my interest to sort of daily life problems, like why sky is blue, why we have clouds, why waves in the ocean are white. So all these problems were very closely related to nature. And then statistical physics is a tool to deal with all these matters, the faces of matter, how many different parts, particles, molecules put them together. How would they behave in general, collectively, together? Like we have many individuals, we have all our own personality but when we are together we can do something together very differently. So in physics it's very similar. If you have individual ingredients they look very normal. Nothing fancy. But if you put many of them together there's something phenomena emerge. So we call this emerging phenomena. Like superconductivity, phase transitions like water become gas, become ice, they suddenly make something change drastically. Laser, many, many examples. The analogy to humans is very important here because it's one that's super relatable for us that we as an individual have our own personality behaviors but then when you add a second person, is a person, your mom or your dad, someone very close to you, is a person, maybe someone you've never met before, how does that change the dynamic? What about when you're in a group of a hundred or a thousand people? Exactly, the best example is our brain. If you just pick a single neuron, it's not so fancy. It's just some spikes, some electricity, some spikes. You put them together and you get consciousness, you get everything. We still don't understand. But that's the best example of emergent phenomena. More is different. That quote goes, the whole is greater than the sum of its parts. Absolutely. And that's emergent phenomena. That's right. And this is interesting that the phenomena is evident at, like you were describing this molecular or neuronal cellular level and it's also evident at a civilizational level. Absolutely. Society level. The society is the collective behavior, it's emergent phenomenon. There are many individuals that together then we have this society with this hierarchies of societies, of countries. Economies and governments. Corporations. Exactly. And we have monetary system. We have stocks. Stock markets. Something completely imaginary. Yeah, the phenomenon of the civilization has incredibly interesting creations that emerge from it that if we were just the eight billion nodes separate, wouldn't necessarily emerge. So interesting. Okay, so I see how the worldview starts building out. So now Brookhaven National Lab. This is where the post-doc happened and this is also where the SAMPIO model and self-organized criticality happened. So these are massive discoveries. Walk us through your time at the National Lab and how these things emerged. Yeah, so this is actually related to my interest in collective behavior where many individuals that when they interact together in physics at that time. So in graduate school I, by some chance, I learned there's a phenomenon called one wife noise. It's a very rich phenomenon. It's a little technical but basically many things like the twinkle of the star, like the fluctuation of stock market, like the flow of river Nile, they're all fluctuating in time, right? Fast and slow and bright and dim stock market up and down. And if you ask, what's the time scale fluctuation? Seconds or hours or days? Stock markets are there in seconds? Or hours or days or months or even years? And then people found that the fluctuation is in all time scales. There are very fast fluctuations and there are slow fluctuations and there are slow fluctuations that are all superimposed onto each other. So this is a very fascinating example of scale invariance. So a phenomenon where you don't have a typical scale in time. And then also you have this phenomenon, you have scale invariance in space and in energy. For example, in earthquake, we hear about big earthquakes. We hear much less about huge earthquakes. Well, in fact, there are many, many more small earthquakes. You don't hear about it because it's not newsworthy. If you record all these earthquakes, small, medium and big, and then ask how powerful they are, they fit the power law perfectly. There are many, many small earthquakes. And some medium earthquakes, few big ones, very rare, huge ones. Again, there's no typical scale. There's no earthquake saying, okay, most earthquakes are three Richter scale or five Richter scale. No, from one Richter scale to ten Richter scale even larger. They all exist. It's just a frequency decays as power law. So that's another example of scale invariance. And we also have examples of landscape and sea line. If you use a stick to measure it, then you will find out that there are large wonderings and small menderings and even smaller little fluctuations in space. Again, there's no scale. So all this phenomena exists in nature. What's the mechanism? So in grad school, I was interested in one wave noise, the scale invariance in time. During my postdoc, I had a very, very free time. My supervisor, Perpak, is very, very liberal. So essentially, whatever I do, interesting, that's fine. So I can think about all these things, and then eventually we collaborate it because we think this is a wonderful, wonderful thing. And then with another postdoc called Wiesenfeld, we realized that all this phenomena possibly can be explained by a simple concept we call self-organized criticality. And by criticality, it means that things on the verge of break, it's critical. And by breaking continuously, you can generate all these fluctuations in space and time without a typical scale. As an example, it can be illustrated by a sandpile. That's why we use a sandpile model to illustrate it. So imagine in a beach, you build up a sandpile by adding sand. Okay, originally the pile was flat, and there's no little avalanches, only small ones. As the pile gets steeper and steeper, you add more and more sand, sometimes see larger, larger, larger avalanches. And then eventually, they reach a critical point where the sandpile will not get more steeper. It will just fluctuate around the critical angle of repose. But the fluctuation can be very interesting. You can have small avalanches, large avalanches, or some avalanches sizing between. So that's a scaling invariance. So this scaling invariance, the system is self-organized. You don't have to carefully change anything, like a temperature or something. You just drive it by adding sand slowly. You drive the system towards some instability. And the system will stay at this verge of instability. If you drive it too much, you have instability, there will be avalanches happening, they will return to a more stable state. So it's continuously fluctuating or stay at a critical point. So that's the name, self-organized criticality. What a fascinating discovery when we're in such a relatable example where so many of us are children and we put the sand and sand and sand and then we notice that one more clump of sand can sometimes do nothing, just add to it, or sometimes the little clump or even just a little grain can cause a big avalanche and then it goes back to stability. And the applications of this sort of understanding are so interesting. When we're talking about what is, like, what does this inner life of the cell that is smart enough to basically have some sort of an understanding of what's happening inside the cell, what's happening in the environment outside the cell, so, like, almost keeping, like, a ledger of what's happening inside the cell, what's happening outside of the cell, and then it gets to some sort of a point of, in a sense, maybe the amount of proteins continue increasing in the cell and then at some point the cell goes safe to divide and then cell divides. Yeah, that's a, I would say that's a different kind of complexity. It's also a complex phenomenon. Life is the most fascinating complex phenomenon. The self-organized criticality, it's a, compared with life, it's a simple complexity, let's say, although recently people found that within life there are also phenomena like self-organized criticality, like our brain activity. There are brain avalanches. Brain avalanches. Yeah, it's like neurons. They fire together. As soon as you say, when I go back to the United States and someone says, Alan, how was China? Associative web goes crazy. So many cruel experiences that I want to now share. Yeah, so it may have something to do with information processing. But life, on the other hand, offered us many, many different kinds of complexity. Cell is one example, right? Cell looks very, you know, innocent, but it's very complex, in fact. It has to face all kinds of difficult decisions to make. For example, a single cell organism, bacteria or yeast, it has to decide when to divide, when to preserve its energy. And it has ways to measure the environmental information and somehow integrate the information to help it to make decisions. So cellular decision making. So that's very fascinating. And also I studied cell cycle. Life is actually very robust. Imagine that all of us are product of a fertilized egg. It's a single cell. Divide, divide, divide, divide, divide. Tens of trillions of cells, yeah. And then not only this, they have all kinds of different, several hundred different cell types. How did they know to make your nose? Yeah, how did they know? And how did they not make a mistake when to divide? To copy DNA. Not put a billion, three billion base pairs to copy the DNA. How do they make sure that the DNA has no mistake? It's time to divide. And to grab the chromosome pairs, sister chromatids to divide equally into two cells. And all this we call robustness, biological robustness. So later on I was interested in complexity in biology. The first problem I worked on was protein folding. It was on proteins. Proteins are also very fascinating. Amino acid sequence fold into three dimensional structure very precisely. A unique sequence fold on a unique structure. Yes. No mistake. Occasional mistake. Occasional mistake. Yeah. And even the sequence has some mutations. In many cases it was also fold into the same structure. So this is called robustness. Okay. And the structure itself is very robust. It's the lowest free energy state. Meaning that any other fold way of folding it, the energy is higher. Some more dynamically. So I studied. I asked why proteins are so robust from a statistical physics point of view. Yes. Then I was fascinated. We found something also very interesting. We called designability. This is because the protein structures are highly designable. The way they wind around, they fold to each other. Not all the structures can be made protein structures. Only certain structures. They are so stable so that many sequences can fold them and make them the lowest energy state. Okay. So this is a designability concept. Then later on we move to study cell. The network. Cell cycle network, for example. You can ask a similar question. Why are they so robust? Why don't they make mistakes? In the cell it's very crowded. There are many proteins that bump into each other. They don't have eyes. They just diffuse. And then if we are friends, we just stay a little bit longer. And then we'll be hit by other proteins or water. The thermal motion will be departed away. So it's all this kind of very noisy environment, crowd environment in the cell. On the other hand, they can perform all these very precise functions. Like cell division. Yes. Right. And then you can ask why. And there we started from a mathematics point of view. Nonlinear dynamics. You can write down differential equations or Boolean logic network. Like how one protein is talking to other proteins. And then you simulate the network. You found that the system is very robust. For example, there's a huge attractor. No matter where you start, they will go do this very stable cellular state. And then the cell cycle path is also very robust. Even you make some mistakes. There are many noises like thermal noise or the fluctuations of protein numbers. But the dynamics, the relationship between these proteins will force you to converge into the superhighway, which will lead you to the cell division process. Yes, yes, yes. Interesting. You can map out protein dynamics and how they lead onto the superhighway towards. That's the idea. That's right. You can understand it from a mathematical point of view. From a mathematical point of view, the quantitative biology side of things. That's right. So this understanding of the source code of biology, mathematically mapped, most people say this is chaos theory. How can you possibly map it? What can it unlock for us as we do map it? What does it end up doing for us? So many incredible applications, which we'll talk about. You've started to unpack. I want us to visit this on the way. You mentioned this. It's just such an important understanding of our world. Scale invariance, one over F noise, power loss. So when I look at something like earthquake magnitudes, you gave us this example. And they're all over nature. All over nature. So in the rainforest, very similarly, 20% of the trees sequester 80% of the carbon and then they do cool things like sequester it and distribute it amongst its networks to other trees that don't get as much carbon. Where humans, we can maybe learn a couple of things from that style of distribution of excess. There's analogies there that I've been writing and speaking about a little bit. But okay, there's that one. There's so many of these examples. Okay. Then all of a sudden we see ourselves as well in the way that we also have acquired resources. 20% of the people have 80% of the wealth. Could maybe also be 20% of the people have 80% of the overall spiritual enlightenment, let's say. There's so many ways to view it. 20% of people own 80% of the land, all that type of stuff. So humans aren't actually separate from nature in the power law distribution. Okay, let's look at where humans are part of nature. We aren't, yes. We are not separate, we are immersed right in it. And also, by the way, I don't think all the power laws have a unique mechanism. There can be different mechanisms for different power laws. Different power laws. For power laws of different systems, for example. So maybe why 20% of the people occupying 80% of the resources is not self-organized criticality. It's something else. What would you hypothesize? I don't know, maybe it's some economic system, the way how the economy is running. And the rules and regulations that are maybe reshaped in favor of those that have accumulated more. It's interesting. In a way, I believe that society has been trying to optimize the efficiency. Economic efficiency, yes. Maybe there's some automated model, I don't know. One can ask if I make a model of economics, of productivity, if I optimize efficiency. Is that outcome? Is it most efficient? Economics is like this or not? I don't know, that's an interesting question. I had a message that I started communicating to leaders in my network. I started asking them, is there a center for economic simulation yet? There are. I'm curious how robustly they've tagged all of the variables that happen in an economy and then can do things like deploy a code to the economic simulation, literally a digital twin of the Chinese economy. Let's take the US economy, for example, and then you deploy an update, let's say the proposed basic income, $1,000 a month, the people deploy it and then see what happens in the simulation. Same thing with, well, what if we put a half a percent tax on all Wall Street transactions? Same thing, deploy it, what happens in the simulation? I'm very interested in that. I'm also interested, and you can explain this if I get this wrong, when you look at, let's take a 1 over F noise and scale invariance again, so when you take a wavelength and you zoom in on the wavelength, you see the same thing, same repeating form as when you zoom out, and you see the same form, although you may see different amplitude, but you do the very similar form. There's a powder distribution of different wavelengths. Coming back to the... And we see this in fractals too. We see this in fractals too. I love the fractal. It's just, you keep going. Same thing, right? I can't help, but coming back to the economics problem, let's say, you may argue that, or one may argue that optimized productivity may not be our purpose. Maybe we want optimized happiness of everyone. How to balance it? This is a whole open question. But your question is very interesting. What principles made this 80-20 distribution? Could it have literally been in the initial source code of when the Big Bang happened, that nature evolves this way? I don't believe it. Okay, just tell us why. I think Big Bang certainly created the universe. We are part of it. After we have planets, we have the solar system and then life evolved. And there are many accidents along the way of evolution. I don't think there's a fixed destiny starting from the Big Bang. It's just too many fluctuations and noise and uncertainties, even from classical mechanics, let alone quantum mechanics, intrinsic uncertainties. So we are actually a product of many accidents. But there are some... Which could also be programmed in? By programmed, I think, if you mean to say, there are some general principles that I believe there exist. Just like the amino acids accidentally misfolding when they fold into the protein? Yeah, why 20 amino acids? That's an accident. And after colliding with the planet, an accident enabling humans to evolve. Yeah, but why protein structures this way? If we have 26 amino acids or 10 amino acids, I think the protein structures are reasonably robust. If life evolved in mass and compared with us, I think that many details would be different. But many principles would be the same. For example, the distance from the star is a big one, of course. Yeah, for example, if you want to fly, you have to obey hydrodynamics. You have to have wings. If you want to swim, you have to have this shape. And we have two eyes because we can see the distance. Too many eyes are too complicated. And there are certain principles emerge, universal principles. And in fact, I'm really interested in, at this point, to look inside the complexity of biological complexity and try to find principles, universal principles. Let's start talking about those. And this actually came after, I mean, there's just so many other incredible things that you did along the way, spent time at the Institute for Theoretical Physics in Santa Barbara, who worked at Nippon Electronics Corporation, which actually Nippon is how, you were teaching me, that's how Japanese reference Japan as Nippon. Right, Nippon. Nippon is Japan. And then the US or the UK were just like, let's call it Japan. And the whole world just started calling you Japan. And just Japan calls Japan Nippon. But the rest of the world calls it Japan. This is crazy. It's like they used to call us Peking, right? We call ourselves Beijing. Very similar. Why are you called Peking? Which was the old? Yes, the old. By old meaning that some old British, Yoshi decided we called Peking. According to some rule, I don't know. Okay. And you were with NEC for 14 years. 13 and a half. 13.5. And also during that time, you pursued your interest in biology. And this totally makes sense with biology and physics being smashed together. Elected as a fellow at the American Physical Society. And then you were going back to China every year during the summer. And this was for conferences, giving seminars, teaching. And that's when you started the Center for Quantitative Biology. You were a professor of biophysics at UCSF for six years. And then you came back to Peking University to chair the professor of physics and systems biology and being the principal investigator at the Tong Lab. So now let's talk about how you got, you plus the 2030 undergraduate students are studying quantitative biological systems and you're finding these biological principles. How, first, out of everything in biology and physics, do you guys decide where to try and identify biological principles in quantum to take the lead? How do you pick with the study? That's a question every undergraduate student and to some extent every graduate student will ask how do you pick problems, right? I don't think there's an answer for that. There's no standard answer. It's a part of science, right? What problems are interesting? What do you see after a problem when you solve it? Is that meaningful? Would that have an impact scientifically or technologically? I think it's all personal choice in a way. So that's why science is fascinating. There's no common recipe to do science. But every scientist, for various reasons, historical training, interest, they have their own way to pick up problems. And eventually they learn this also. You may get lost. Some of my students got lost in the first few years how to look for problems. But eventually you learn and you get better and better. It's like Fermi and Ricco Fermi said for physicists we have the sixth sense. We have a smell of what is good science. In a way, the education for students is really the most important thing. They have to learn the taste of science to pick good problems. So that's a sort of general answer. In practice I let my students to pick their own problems. I just try to create an environment for them. An environment of bringing interesting problems, open discussion, and then they try to find their own problems. I think it worked. To be more specific, we don't pick problems because we are interested in a specific system or a particular protein or a particular disease or a particular pathway. So we don't pick problems and say we want to conquer cancer. Then we try to see what is the problem I can study to conquer cancer. That's not our way of picking problems in our lab. So we pick a problem we think is interesting scientifically because by investigating it, by solving it, we can gain a broader, general, hopefully universal knowledge. It's more like curiosity-driven. So to give you an example, why people study magnetism and electricity hundreds or even thousands of years ago? I don't think they realize we can have lights. We study electricity, then we can have lights. It's very purposeful. I don't think they realize that electricity and magnetism are two parts of the same coin, two sides of the same coin, and Maxwell unified it, and then we have electromagnetic waves, then we have wireless communication. We have all these huge applications. Nobody knows. They're just out of curiosity. Okay, what is electricity? What is magnetism? Oh, electricity can generate magnetism. Magnetism can induce electricity. What are the rules? What are the laws? What are the equations? Can you unify them? So that's how the most fundamental science advanced. This is electromagnetism, quantum mechanics, very similar. Not very purposeful to solve in a particular problem, but out of curiosity. I want to understand. And then the way we pick up biological problems, we want to understand. In a way of physicist point of view understanding, we want to use quantitative ways to understand. We want to have equations. We want to extract principles. So that's a general way of picking problems. So my lab is, if you look at my lab, they study all kinds of different things unrelated, but really there's a general theme behind it. We're going to dive deeper into this style of interdisciplinary and multidisciplinary thinking, which is very embedded in your life with the study of physics and then biology and how those merged. Let's go into, okay, as you are describing this, it comes up to me that a lab in a sense could say, hmm, let's apply a power loss style of understanding where we could potentially invest our resources. So can we find the 20% of quantitative biology that's going to give us 80% of the knowledge of how quantitative biology actually works? Okay, so if you take it from that angle, maybe you have like a think tank constantly happening at your lab where they're constantly thinking, what are the key scientific drivers of quantitative biology and then how do we conduct experiments to understand those keys that can then go off into the world and then inspire other scientists and labs to keep driving the understanding forward as it came up. But I also realized that the way that you talk about it inevitably leads to when you let the students pick and explore it, inevitably also leads to that, where you have a couple of the kids that are doing these core competencies of quantitative biology and then other ones that are maybe doing like more, like something to deal with like an orphan style problem, let's say where it's only impacting a smaller percentage of people but those people are obviously need science driven towards solving that style of a problem like an orphan disease or whatnot. Okay, now let's break this down. So you gave us a couple of these source codes that you're aiming to drive our understanding towards solving complex diseases, designing new therapeutic strategies but you got to know how the cell makes decisions and you call this cell cycle regulation. What is, how is the cell making decisions? Does it keep, like we were talking about, does it keep a ledger of what's happening within the cell? Excellent, excellent. What's happening outside of the cell? Do I have a ledger of both? In fact, one of the work which was just accepted was exactly what you said. There's a ledger. The cell has a memory about the past, environmental condition and you set the memory to decide to make decisions for the current. For example, for the single cell organism, budding yeast, the organism we use to make beer, bread, budding yeast, our friend, it's a mod organism. So we use that to study cellulatization, cell cycle, regulation. So we found that the cell integrates the past condition to help make the current decision. How do they do that? Okay, first of all, there's a very important checkpoint called a start for the yeast. This checkpoint is for the cell to decide should I go through the process of division or not. This is very important, why? Because I have to make the decision to make sure I have enough resources to replicate my genome, to divide. On the other hand, you can't wait too long because if you're a competitor, they make a smarter decision they divide faster than you, then you're out-computed. So this is an optimal decision. How do you use environmental information? How do you know that now is the right time to go through the division process? Wait but not too long. This is a cellulatization. Kind of like we wait but not too long. You can't wait for marriage. Exactly. You can't afford to wait forever. Like marriage, you can't find nobody. You can't wait until 50 or 60. Same for the cell. Through the billions of years of evolution they became very smart to make the decision. So this is a crucial checkpoint, a very critical checkpoint. And what makes them to pass this checkpoint to make the commitment of division is to take all this environmental information like what's the nutrient condition around me, how big I am, how much storage I have for the food, for resources and all this. And people know this and they study it. Although it's not completely clear, but people know that they use and integrate the current information. So what we recently discovered is that they even use the past information. So sell current ledger but also sell memory. Exactly. So they remember the past cycle from the last division to up to now, to current. How long I experienced? The longer the journey I experienced which means the condition is worse because you grow slower, everything's slower. It takes you longer to reach the current status. The shorter the journey means that the other conditions are good. So you grow fast, you copy DNA faster, you divide faster, you are at this current status. So this longer shoulder is the best sort of characteristic of past condition. So they remember this length of past journey by concentration of a protein. The longer the journey, the higher concentration of this protein. The shorter the journey, the lower concentration of this protein. And this protein happened to be the gatekeeper of this checkpoint. So the past journey is long which means the past condition has been tough. Just building a concentration will be high. So the bar to pass this checkpoint is high. So the cell has to grow longer or to get more signals. All the current conditions are really good to pass it. What an incredible way to understand sell memory. What an incredible way. The longer journey for the cell to be more careful which means our protein threshold is higher before we decide to take the next division. That's right. And if it was easy, protein threshold is lower. Correct. And then you don't have to be so careful you can just pass. But you have to integrate current information by the bar is lower. You can use microscopy Correct. We can quantify it. And your quantification methods right now use which tools? We label the protein with fluorescence protein. We fuse the protein with our with GFP, green fluorescence protein and then you can see it under the microscope. The brighter, which means more protein, higher concentration so we can quantify it. And how do you add the GFP? Oh, that's genetics. That's easy. Thanks for the revolution of molecular biology. You can fuse the gene of GFP to the gene of this protein in the genome. You can insert. And you're inserting the GFP gene into the first cell of yeast or which? Just one cell of yeast. All the generations will have this gene. Yes, into the first cell of yeast and then you then take as it proliferates whether it's slow proliferation or fast proliferation you take and sequence those and then you see that. We don't have to sequence that. We just enter the microscope. We can see the fluorescence. And then we can grow the yeast in good condition, bad condition, stressed condition. We add salt so that you can change all kinds of conditions for the past experience and see the accumulation of this protein. Okay. Is that our first understanding of cell memory? No, I wouldn't say the first understanding but this is the first understanding of how cells use memory to regulate this checkpoint. The cell cycle. This is... You call it the start checkpoint. S-T-A-R-T? Not me call it. Lee Hardwell called it. He discovered this checkpoint and called it the Nobel Prize. Beautiful. So beautiful. Okay, so now this is one of the cell memory plus cell ledger coming together into this cellular dynamics. Okay, so what are the other... These ones are obviously very important. This protein threshold and shall I, is am I ready to divide? Okay, this one is very obviously very important. What other ones have we uncovered? What other cellular dynamics like this? Or is that stem cell reprogramming? You know, we all of ourselves are from one fertilized egg, a big stem cell, right? When they divide and they commit into different cell fate. So each cell type is actually fate and that's also a fate decision when you become a skin cell, when you become a neuron cell and all the cells have one genome that's fate. Okay, and now it's very scientifically and technologically very important to reverse the process to take a muscle cell, a skin cell to transfer it back to a stem cell. So from one cell fate to another cell fate. Well, there are many applications. You use the stem cell can grow hard, maybe a pancreas, or can have many medical applications. But we're interested in the science of it. How can cell change fate? Yes. And how do we, this is so crazy. Yeah, and many years ago scientists including the Japanese scientists, Yamanaka they discovered maybe you remember the name Yamanaka. Yamanaka. They discovered that for a normal cell a differential cell when you add certain genes which are usually expressed in stem cell you can transfer this normal cell into a stem cell. So by gene transformation you express highly expressed genes which are normally expressed in the stem cell. For that he got the Yamanaka Nobel Prize. It's a very big discovery. But what's the principle behind it? Can we use mathematics to model it? If we can model it then maybe we can understand more. We can get deeper insights. Okay, and then we are very lucky. We got interested in this problem simply because we have a wonderful colleague Ong Kui Deng, a professor in Peking University. So he studied stem cell and they found something very interesting. They found that you don't have to use Yamanaka's recipe of the stem cell genes to induce stem cell. In fact you can replace some stem cell genes with some other differentiated cell genes still induce stem cell. So this is very counterintuitive. Say for example for a muscle cell to become a stem cell of course you want to intuitively you want to let the muscle cell express even artificially the stem cell genes. So then you have a hope. If I want to look like you, then I better to take a picture and make my eyes look like you, my nose looks like you and then maybe I can really look like you. This is not a good idea. Better to look like you! But they found that they can use some other genes. So it sounds like it really messed up the system and then the cell became stem cell. So we started collaboration with them. We started to write down equations, mathematical equations to describe the interactions of the genes. The genes interact. The stem cell genes they promote each other and then they have their favorite downstream genes to promote. But the downstream genes for different fates, they hate each other and then they also don't like stem cell genes. It's a relationship. So different genes, they have a complicated relationship in the fight. We can use quantitative biology and mathematical methods to take a stem cell and change it into whatever we want. Not whatever we want. We can use mathematics to understand how and why a stem cell can become another cell fate and another cell fate can become a stem cell. So we can map out the cell lineage of something as complex as a human being from single cell becoming just stating inside of mom baby that's being born and then that involving it into an adult. Not the whole complexity. Not yet. For certain special cases certain differentiated cells, not all differentiated cells. Some we don't quite understand. For those we understood the genetic interactions like what's the master gene for this differentiated cell. Why they muscle cell? Why is this a neuron cell? Because in the muscle cell the genes are muscle one. So they then they repress all the other neuron like genes. It won. You won. They find another one. They win. And actually each cell fate is like a bunch of genes they win. They are muscle and they are neuron and heart. And stem cell is like nobody yet dominating and only the stem cell genes are playing we are dominating. And then there are all kinds of signals there are all kinds of ways for the stem cell to be induced towards muscle or neuron. But all this can be described in certain cases when we know enough about the relationship of these genes by mathematical equations. At some point we go from the single cell to 2 to 4 to 8 to 16 etc. And at some point it becomes the brain cells the neurons start to win. The heart cells start to win the muscle cells, the bone cells. That's right. You have all kinds of signals during the development in space and time. So at a certain point and this bunch of cells differentiate into specific cells and at another point in another space these cells will develop into another fate. You guys call that cell fate or cell determination. Cell fate. Different cell types different fate. We call it cell fate because it's really a fate to become a neuron. It's a neuron forever. It's only artificially you can make the change, it's fate. How is that not similar to if Alan is born in Sioux Falls, South Dakota in 1992 to the specific parent to the specific environmental status and socioeconomic status that Alan will just over and over and over again have the same fate 80 years later besides maybe that slight mutation every millions of iterations. For all the cells in Alan the fate are more or less fixed. But Alan as a whole Alan as a whole could get hit by a bus. That's a different fate. That's many other environmental influences. Deterministic or accidental stochastic. So that's a different fate. For the cell fate it's actually more deterministic. More deterministic. And Alan is a little bit more open to free will let's say potentially. Versus when the sperm meets the egg this is a pretty deterministic that you're going to get a baby in nine months. That's pretty healthy. That's right and also this fertilized egg developed into a human being. No hardwired. That's pretty much deterministic. But also there are many things accidental. Especially later on the connection the strength of the connection of our neurons is by learning by experience. And then people can be very different. Given if you were born in the exact same location with the exact same parents with the exact same environmental stimuli at the exact same time that you would probably become who you are today. If everything is exact I would say of course we don't understand this at this point. The whole scientific community I don't think we completely understand this. I would become I don't know maybe 20% the same I would say 70-80. There's still about 20-30% and certainly variance. All those are purely due to cell variability. Even everything is the same. The two cells can be different. Simply because all this process of a gene making a protein it's stochastic. So to exact the same cell one cell can make this protein more and that will be less. So this is stochasticity and this can make a difference. Big difference. Down the line. Okay, so from the point of being one cell to being a baby nine months later less variability than once you come out of the womb into the world over 80 years more downstream variability potential. You gave it about 20-30%. Potentially, yeah. But even in the womb when it developed there's this kind of cell variability. Also about the same percentage you would say? Less. Like identical twins for example. Identical twins when they're just born that's an interesting question how similar they are. How similar their brains are to connectivity. I would imagine there's still some variability. But not by a lot. But later on they say large and large influence. This variability but not by a lot kind of reminds me of the hypothetical that there's another Dr. Chow Tong orbiting another star in another parallel universe where he's slightly different than this one and then there's another billion of those parallel universes running different Dr. Chow Tongs all at the same time and who knows what happens during dreams if maybe during those 8 hours that you go and visit those parallel Dr. Chow Tongs. Yes, yes. So this is all about, I love this cellular decision making and understanding that simplicity evolves to complexity. And the more that we build tools that understand the source codes of the simplicity the better we can understand over time and space how it evolves into complexity. What other tools do you and your lab want to build to understand the source code of the simplicity? Well, no specific tools that we really want to build. Mostly we use existing tools like microscope GFP differential equations and recently artificial intelligence. Yeah, I teach about this. So we use the existing tools and then we develop the tool usually you have to do the specific problems you are studying. And then of course we always keep our mind open to discover something brand new. So personally I believe although I may not be I'm sure I will not be the one to discover it. But I believe there will be new math for example. New mathematics. Simple. Simply by studying the life phenomenon. Yeah. Like cellular decision making will be new math that we uncover. For example how we think, how the brain function and how do we describe life? What's the mathematical language? What's the logic? I don't know. But I feel that our current math is not quite there to describe the essence of life phenomenon. So there will be new math coming and I believe there will be new physics. Okay, and all this physics we can explain quite well many small things, elementary things and also things are in equilibrium. But life is a as we said it's a very complicated, emergent phenomenon. It's an open system. We take energy we learn, we take information So what's the new physics? So I believe we have new math, new physics or even new computer science. Our computer is completely different framework, architecture than our brain. How we do computation is very different from computer. If we know more and more about our brain we can make better computers we can be smarter or machine can be smarter. So now the artificial intelligence the deeper neuron network, very hot, right be the gold champion, world champion it just learned a little bit of brain structure. It can be very powerful. So it's going to be very exciting to have this brain science and computer science coupled together. There will be many new tools and new science yeah. At this point we we use deeper neural network try to understand biological system try to extract the genetic interaction. As I just described all the genes, some are friends some enemies. The fight they help each other so it's very dynamic. When cell make decisions it's very dynamic. When they replicate it it's very dynamic. When they do metabolism it's very dynamic. When they do cell faith transitions, transformation is very dynamic. So if we know more and more about these genetic interactions we know more and more about the cell about the cellular interactions but how do we know how can we speed up this? Traditionally we do genetics biochemistry molecular biology trying to figure out so called low throughput by low throughput experiments so that's very powerful. It's very very efficient. But now there are more and more tools to observe the cellular behavior to see the gene expression. As I said we can feel the GFP to see the protein concentration go up and down with time when we can see many protein concentration go up and down in the cell. So we have all this information can we use this information to guess, to infer the genetic interaction other friends, other enemies so we use neural network, artificial intelligence to do this. It's been fun. So the lab has been identifying and you have been identifying now over decades the best tools for you to use with your own added ideas and added experimental processes to be able to understand quantitative biology better and better and then when you add something like artificial intelligence and computational capacities that are evolving so rapidly to something like the chaos theory of biology it enables us to faster and faster get the quantitative biological source codes that we want to know you gave this example of like what I feel like is a theory of everything that will eventually emerge you said probably not in your time maybe in mine who knows but that can merge quantum mechanics general relativity those two and then how that initial source code ends up creating complex biological phenomenon that evolve civilizations that then begin to poke at the source code that then potentially just make another cycle and of life. Okay so the theory of everything is not the theory of everything in the usual sense by the physicist and there they want to unify general relativity gravity and the every the basic forces of nature right I don't believe general relativity or special relativity is relevant to life in armina quantum mechanics maybe yeah so I I meant theory for life it's a new theory maybe new mathematics in fact at every level of nature at every scale there can be a new theory with elementary particles with quarks and then we have nucleus we have nuclei, we have we have a matter, we have solid liquids, gas and then we have life we have galaxy, we have universe, we have life and and all these levels they can all emerge very special theory for example in physics the thermodynamics and statistical physics it's emerged from many interactions of molecules and atoms it's a new law the phenomena you cannot observe in a single particle also it doesn't help if you understand a single particle you can understand the general behavior of water you understand oxygen, you understand hydrogen even if you understand single water molecule you still don't understand when you put many many water molecules together how they behave be it gas or ice or liquid so that's a new level of theory and the science is trying to link all these levels yeah but it doesn't mean that if you know the lowest level you can derive, you can understand all the levels it doesn't work that way sometimes you understand higher level first and then you try to link to the lower level sometimes you understand lower level and then push to the higher level so it's going in between back and forth mapping the source code in both directions that's right in biology and the higher level maybe it's psychology and then maybe society all these molecules interacting I have my personality, I have psychology how do they how does psychology emerge from biology to the universe of human civilization I I believe not what could be more complex than that then I think brain can be more complex than that but is the brain within all 8 billion of us together isn't that civilization include the 8 billion brains see you realize we have many historians and many great history books they can describe the human civilization reasonably well and they can even reason why they go from this step to that step the stone age, the bronze age industrial evolution there's no single theory yet to write the history book of our brain why we have consciousness how memory works why we can sit here and talk to each other yes, yes okay, I don't know it's just my feeling the level to what's as complex as human civilization is the human brain see I think the human civilization for us it seems to be easier to understand because we don't have to go from the brain level to understand it we just go from individual behavior like okay, human we have to eat we have to live and then they interact there's something for them to optimize they want the power, they fight so you can start from some higher level basic rules and assumptions and then to describe the emergence of human civilization right, so that's why I said sometimes you don't have to go down you don't have to understand the brain to assume some basic ingredients so what's the basic ingredient of human society right, the food the living resources air, water and shelter and then people fight for this, people collaborate for this and then organize to protect themselves this is kind of far wow so understanding source codes from civilization and human brain levels and the behavioral dynamics of the civilization like psychology and of course including neuroscience and the way that that emergent phenomenon works while simultaneously we go and understand the initial moments of the Big Bang and we understand quantum mechanics and general relativity and up we go to atoms and molecules and cells and then we meet and that together can unlock not only what gives us the better education, the better healthcare the better technology, the better society more happy people, more creative people better world harmony that as long as our ethics and our morals and our wisdom rises at the same time was very important but it also enables us to do things like take that source code and then run our own evolutions, our own simulations and run quadrillions of them and just observe what happens just like in this case you're running simulations of yeast and you're running and watching how the cell has a fate of becoming a heart or becoming a brain and the depth of understanding the source code of quantitative biology here is extremely applicable at all of the levels of understanding the source code so I think that's why I said in the beginning I think science and technology are really driving force for society and for the human civilization and really we have a very very very open field for us to explore and all kinds of opportunities and of course all this ethics they have to come up how about can you clone a human being when the AI is also developed how do you combine human and AI to what extent you can combine them there are all kinds of issues but I think we are optimistic I think as science advanced technology advanced we face all these issues but we are sort of advanced that's very exciting bright future likewise very optimistic I want to make sure that we talk about the importance of interdisciplinary and multi-disciplinary studies it's now become more and more clear that through our conversation obviously you took a very physics and biology and mathematical and computational now perspectives that are helping you with this but also just me as someone that's interviewing people on all different fields are giving me a very strong world view that is making very interesting connections across disciplines you guys even at your lab in your center for quantitative biology website design and marketing and selling and grant writing so you can get the newer equipment and you have to be good storytellers to inspire other people to care about quantitative biology and to be collaborators with you so if you can be a good artist and designer and storyteller while you are a good scientist and stuff you really have a creative edge and I want you to speak to why you care so much about interdisciplinary multi-disciplinary studies and how your students as well practice that this is really a mode of scientific research and the different modes you can stay within the discipline and you can go across disciplines I wouldn't say which is right or which is wrong or which is better which is worse I think we need all of them the trends in the last century also is that the discipline has become more and more specific and the barrier between disciplines has become higher and higher and in the last decade or two maybe even longer scientists realize that we have to cross we have to break these barriers and in fact if you see many new discoveries new breakthroughs they're really at the boundary of two or more disciplines and if you also if you ask some societal questions like air pollution like even cancer okay or how long can we live all these are really not a single discipline problems it's a problem of many disciplines so you need perspective from different disciplines you need people from different disciplines to work together from educational point of view I think at least some fraction of students they have to be educated broad minded so that the discipline is not a war in front of them so that they will not see a problem this is a math problem they can only solve the mathematical part of it or this is a physics problem or this is not a physics problem so I don't know how to solve it you just see this is a scientific problem right and then you just use whatever knowledge, whatever tools you can utilize and that has to be nurtured educated from the very beginning so that's why we have this interdisciplinary education program undergraduate and graduate and the science we do, quantitative biology it's biology the subject is biology quantitative meaning that we use other more quantitative disciplines to study it right, can be mathematics can be physics chemistry or computer science so that we can discover things if you only studied from a biology perspective we can discover new things yep really adore and just prioritize this style of thinking we need them all, like you listed we need people that are just going really hard with math as well of course but we kind of live we live in this world we both do you have Gaokao in China you have ACT, SAT all GPA, etc inside the United States and there's all different types of these and they do a pretty good job at measuring IQ and measuring math reading skills and stuff like that ok what about emotional intelligence what about the emotional quotient what about people skills empathy, growth mindset perspective taking emotional regulation, meditation, spirituality what about stuff like that and how can that even a little bit of it at a young age how can that drastically compound and make someone an even better scientist down the line and same thing with maybe a little bit of music or a little bit of art how can that make someone a drastically better scientist down the line all those are very important I I believe general education liberal arts education so I think students it's very important for students to expose to all kinds of things later on they can be specialized but at least in college level high school level or younger they should be generalists to learn many things, to integrate many things and find their only interest, their passion I absolutely agree we need all those you said including music and arts I have musician friends I have artist friends I really like to be with them inspiring I am a generalist I am a generalist I am a zong zai I am a polymath I am a generalist so I learned that one while I was here because I thought it was so important and the other one I am a journalist I am the journalist I am a journalist yes I am a journalist Yeah, I'm a journalist, I was a journalist. That was a journalist, but yeah. Yeah, wu shi zong zai. I love being... Wu shi zong zai. Wu shi zong zai. I'm a polymath, I'm a generalist. That's how I learned it. There's probably multiple ways. Chinese is so interesting and difficult. But again, this idea that if you expose children at young ages to all different types of tools and methodologies of learning, and then let them do things like, you're like, okay, here's the 17th United Nations Sustainable Development Goals. Pick and try and tackle these problems. Project-based learning, tackling these problems. I don't necessarily know SAT, ACT, Gaokao, that type of stuff. Probably excellent in many ways. At the same time, project-based learning around solving the sustainable development goals, helping children explore being generalists and stuff like that. I'd also welcome that a little bit more in our world. What do you think's the meaning of life? That's interesting. Everyone thought about it at a certain stage of his or her life, I believe. Now I don't think it's so much, actually. I feel fascinating that there's so much for us to understand. To contemplate, to explore. Nature, society, brain, and also all these possibilities of science and technology. All these consequences are unimaginable, imagineable and unimaginable. It's wide open. I'm just excited. What's the role of love in our world? I think it's important. Without love, we are too dry, I think, then we become a machine. We can be very good, but then we lose a sense of community, a sense of us. We need love, the big love, to share the globe, to share the earth, to be harmonious with each other, and to be happy with each other. So that's, I think, love is important, the gradients. I love that. And what do you think is the most beautiful thing in the world? Nature. Nature created everything. And we are trying to understand nature. Yeah. Wow, such a mind-blowing episode. Thank you, Charles. Thank you so, so much for coming on our show. It's a pleasure. It's been an honor. Thank you, thank you, thank you. Thank you everyone for tuning in. We greatly appreciate it. We'd love to hear your thoughts in the comments below on the episode. Let us know what you're thinking. Have more conversations with your friends, families, coworkers, people online about quantitative biology, about understanding the source code of our reality, about cellular dynamics, about everything that Charles teaching us on the show. Have more conversations about that. Push the edge of what's known in our world. Check out the links in the bio below to the Tong Lab. Also check out the Center for Quantitative Biology as well here at Peking University. Go and come and visit Peking University. Come and visit the Center for Quantitative Biology. It's a beautiful campus. It's a gorgeous campus. I can't, Peking University and Tsinghua University are right next to each other. The funnel for brilliant people in the world is so strong here. It reminds me of the MIT Harvard right next to each other in Cambridge. It's very similar. I love that. And also check out the links to simulation and are in the bio below. Support us, help support us. You can find us a PayPal Patreon cryptocurrency. You can design cool merch and get paid support. The artists, the entrepreneurs, the organizations around the world that you believe in. And go and build the future everyone. Manifest your dreams into the world. Thank you so much for tuning in. We love you very much. And we'll see you soon. Peace.