 All right, good morning, everyone. It is now my great honor and pleasure to introduce the next speaker, Dr. Robert Tysian, Professor of Molecular and Cell Biology here at Cal, since 1979. I am beyond lucky and grateful to have worked as an undergraduate in his lab since summer of 2021. And though my interactions with him may be small snapshots in his greater and much longer career, witnessing the manner in which he drives home essential points in lab meetings, conversations about recent papers and publications, and sage advice for both college and life in general, is a testament to the knowledge, experience, and brilliant mind he has. Tysian, or Tij as everyone calls him, utilized his training as a biochemist at Cal and Harvard to make his way into our textbooks as he pioneered our understanding of the molecular machinery that reads DNA and operates to drive gene expression and normal human cells in disease. While conducting biomedical discovery research and simultaneously teaching thousands of undergrads, he not only served in leadership roles at Cal, such as director of the Berkeley STEM Cell Center, faculty director of the Lee Kaeshing Center for Biomedical and Health Sciences, and head of the Siebel STEM Initiative, but also received many scientific awards, including election to the National Academy of Sciences, American Philosophical Society, Alfred P. Sloan Prize, the Louisa Gross Horwitz Prize, and California Scientist of the Year. Since 1987, he has been a Howard Hughes Medical Institute investigator, and from 2009 to 2016, he was president of ATHMI, the non-profit organization that supports basic biomedical research with an endowment of roughly $20 billion and an annual budget of $950 million. What further drew me to Tija's lab as a biochemistry slash business double major is the fact that Tija's also a serial entrepreneur, having co-founded Telerik, Incorporated, and ICON Therapeutics, alongside launching several other biotech companies as a science partner of the column group, a venture capital firm. Currently, he holds the Lee Kaeshing Chancellor's Chair in Biology and serves as scientific advisor to the Chan Zuckerberg Initiative and the Biohub. Furthermore, Tija and his wife, Claudia, a retired corporate attorney, have raised two daughters who both live in the Bay Area. Now, before I finish, we will have a camera crew filming the talk as a thank you video for homecoming, so please raise your hands if you do not want to be in the video. All right, perfect. In that case, without further ado, please would everyone give a warm welcome to Dr. Robert Tija. Well, good morning and you just saw why I'm still here at Cal. I have students like that in the lab. It just is amazing. Every morning I can't wait to get in the lab, talk to students like Gloria. They're humbling to say the least. It's a great pleasure for me to be here. I'm only gonna talk for maybe 25, 30 minutes, give you a little bit of flavor of what's happening here at Cal, particularly in the biological sciences. Part of my goal here is not only to give you the parents a sense of what, perhaps your students are going to or your children are going to experience, here at Cal, which I experienced as an undergrad. But things are very exciting in the Bay Area, as you know. It's the place where biotech was born and has matured in very interesting ways and Cal has been a major player. And one of the misconceptions I think a lot of people have, even those that are in the Bay Area and should know better, is that even though Cal doesn't have a integrated medical school, we do a lot of medical research. And in fact, people were wondering why I was the president of the Howard Hughes Medical Institute if I'm a basic research scientist. And the reason is because if you don't understand how basic biology works, then when you walk into that doctor's office, he's not going to know how to treat you properly. And that's really the premise of my talk today. And I want to give you a sense of not only what students like Gloria and others, including my graduate students and postdocs are doing in the lab, but for you to actually see the pathway from a very fundamental discovery all the way to what's hopefully going to be your bedside as we understand more and more complex diseases. And the one that I'm going to tell you about today in a very brief interlude is one of the most difficult, which is neurodegeneration. So let me, as a scientist, I can't do without slides. So this is where I've been living for the last 40x plus years as a faculty member, but before that, in the late 60s, I was a student here. And it was a life-changing experience. I trained with Dr. Koshland and Dr. Barker. You'll see two buildings with their names on it. And it was a privilege working with them. And then I went back east and I actually worked with Jim Watson, which some of you will know, was the guy who figured out together with Francis Crick the structure of DNA. And those studies back then sort of catalyzed my career. And what I'm going to tell you about is almost a direct consequence of what I learned through my studies here at Cal first, and then at Harvard, Oxford, and then at Cold Spring Harbor with Jim. So I also have to tell you that a little story. Before I came to Cal as a student, I had never been west of Philadelphia. And when I flew in to San Francisco Airport in 1967, and a faculty member from Cal who happened to be sitting next to me in the airplane, Professor Noonan from the law school gave me a ride in very nicely. And as we were driving across the Bay Bridge, which I had never seen in my life, I had this queer sensation that I was coming home. And I've had the same feeling for the last 50 years. I still can't explain it. Maybe it's the food, I don't know. But I think it's the university. So let me tell you a little bit about the way science works. First, let me say, even though the slide shows you a lot of microscopes, including some ancient ones, I want to tell you something that's curious about science and about life in general. I was trained by Koshland and others to be a chemist and a biochemist, which usually means the way we study how things work is we tear it apart, isolate the components, try to put it back together and make it work. That's really powerful and that drove my career for almost 30 years. But there was something nagging in the back of my mind and that is I knew that as I tore the machinery of the cell apart and isolated its components to study it, I was also missing out on something because it was no longer in a living cell. And it really frustrated me for decades that I couldn't actually study the molecular events that are going on inside a cell in a living system rather than in an artificial, what we call in vitro system, which is in a test tube isolated from its normal environment. Now that's not to say we didn't learn a lot, but I knew that we were only learning sort of one level of understanding. Part of the reason for that is that it's really hard to look into a cell and see what's going on. It's hard enough to even look at a single cell moving around, as you know, your naked eye is not good enough to do that. So since the 17th century with Hook and Van Loon Hook with the microscopes, people began to see things below the resolution of your naked eye, so microbes. But what I wanted to do was much, much more ambitious. I wanted to see individual molecules. That's about 10,000 times the resolution that you can get from these microscopes. The other thing that was very frustrating to me was that the evolution of the microscope had kind of stalled for a long time. There wasn't much advancement in the quality of the resolution of these microscopes since the 18th century. I won't go into today why that is the case, but one reason is that there was a physical reality which is called the light diffraction limit. And that is that you cannot get down to resolution lower than one half the wavelength of light that you're using to look at your sample. Now you say, oh, wait a minute, don't you guys have electron microscopes and X-ray crystallography? Can't you go down to the atomic level? Absolutely, on dead cells. But what I wanted to do was look at live cells. If you hit a live cell with an X-ray, what do you think happens? You fry it instantly. Same thing with an electron. So we required a revolution of sorts to overcome this light diffraction limit which has essentially stalled out live cell imaging since the 18th, 19th century. So how did we do this? And it's often the case, it's never one solution. It starts with a bunch of advances some of you may be well familiar with. Computers, lasers, transistors, and really, really sophisticated molecular biology. And it took several decades for all these things which were not designed to come together. They came together largely because of universities like Berkeley where scientists are encouraged to follow their nose, develop unbelievable things, sometimes not knowing how they're going to apply it. And then somebody else comes along, in this case, Eric Betsick, who is now a colleague of ours here in Berkeley, who put it all together and came up with what we call super resolution microscopes that can actually measure the movement of individual protein molecules in living cells. Unheard of, when I first heard that, I thought impossible, can I be done? He did it. He did this while he was a colleague of mine while I was president of the Howard Hughes. We had a laboratory in Ashburn, Virginia and he developed these microscopes and then I began to collaborate with Eric to use those microscopes to attack the problem that I'd been mulling over for literally two decades. And so what Eric was able to do, and maybe there are some computer people in the audience, this could never have happened without really fast computing. And somebody wants to ask me more details about how he did this, can come and talk to me later, or you can just go listen to his Nobel Prize speech, because he developed this in 2005 and won the Nobel Prize in 2014. That's one of the fastest timelines I've ever seen. And the best part of this was when we hired Eric to come and do this completely out of the box crazy idea. At HHMI, he was literally an unemployed physicist trying to build a microscope in his living room. So suffice it to say super resolution microscopy is revolutionizing biology today. And so what I thought I'd do is first just show you what just for fun is I'm gonna take you into the microscope room in a sense and show you what the microscope actually is recording. So you see that little dot swinging around on the left side here. That's a light detector detecting a fluorescent protein zipping around in a living cell. Actually, this is a human stem cell. And the trace in the middle is what the computer's doing is keeping track of where this thing's going. It's keeping track of how fast it's going, how long it's going in a straight line before it turns, et cetera, et cetera. It's called single particle tracking. So in essence, it's kind of like what GPS is doing to you when you're driving around. It can figure out exactly where you're going, how fast you're going when you're turning around and when you stop, which is remarkable. And this was done in collaboration with a young scientist, brilliant young scientist that Berkeley managed to extract out of Paris, which is not an easy thing to do, Xavier Darzac from the École en la Male Supérieure in Paris whom I met on a sabbatical and convinced them ultimately to come to Berkeley and join me to do this. So what are we actually learning? Those of you a little bit more scientifically inclined, this will be obvious. Those who are not, bear with me. So what you're looking at is in a live cell and actually even in a whole organism or in a tissue, you're peering into the life of a molecule as it's zipping around doing its job. You can pick whatever molecule you want by making that molecule fluorescent using genetics. So you can figure out right away, what is the concentration of the molecule? How many molecules per cell there is? That's very difficult sometimes to do without this technique. Diffusion coefficient, that's just a fancy word for saying I can follow its movement and how fast. It's like the speedometer. Diffusion behavior is a little bit more subtle. It turns out even, maybe there are some chemists in the audience, even though we learned way back when, even in high school, that molecules an aqueous solution, which is a cell, diffuses by something called Brownian motion. It turns out in living cells, that's usually not what's happening, which is why we didn't understand what the heck was going on. The other thing we can tell you is when a molecule stops, that's called a fraction bound. So for a given population of molecules, certain population is bound because it's stuck to something and it's not moving and then the other parts are moving. So you have the bound and free. And then finally, resonance time just means it's a fancy way of saying how long did it stop for? You have to stop because you wanted to take a break or because you're carrying out some important function in the cell. So that's resonance time. All right, so those are all things that until this technology came along and Xavier and I and Eric got together to do this, nobody had ever measured such a thing. Here's just an example of a embryonic stem cell with two key proteins of which one, I have fluorescent label and I'm tracking the movement and I happen to know from other biochemical work that this protein is supposed to bind to DNA, your chromosomes. And when it does so, it stops and slows down a lot. And that's shown in this plot up here. That's short lived and long lived. And so this is the long lived guys that are bound. And I can make a mutation, just a single mutation that gets rid of the long lived which means it no longer binds DNA. This is an incredibly powerful two. If I could have a drug that did that, I could solve a lot of cancer, for example. Here's another one, this is Huntington's disease. I have on one side normal patient cells and on the other side a disease patient cells. Pretty obvious what's going on. You have aggregation of the Huntington protein. But the question was, are the aggregates actually doing something bad? Well, I can go a step further and ask when those aggregates around are in the cell, in those brains, what's happening to other molecules that are in that same cell? And when we ask that question, so the green big blob is the aggregate of a Huntington's patient. The purple fireflies zipping around is a really important molecule that reads DNA and keeps that cell healthy and see what's happening to that purple molecule. It's getting stuck on the surface of the green blob. That's one of the reasons Huntington's patients have problems. So this is already a dynamic view of something going on in a living cell that we never understood. I'm now gonna take you a little deeper into a problem which I hope none of you have experienced directly in family or friends, and that is a devastating disease of the young people called Rett syndrome. Rett syndrome is a mutation, usually a very single mutation in a protein called MECP2. Don't worry too much about names. It usually afflicts girls. Why? Because it's X-linked. It's on the X chromosome. Girls have two X chromosomes. Boys have an X and a Y. Now, the sad news is, the reason why you don't find too many in boys is because that mutation, which can still allow the girls to survive for a certain amount of time, is deadly for boys, because you only have one copy, and if that copy is messed up, you're out of luck. My collaborators at Baylor College figured out that there are some rare young boys who are alive and have Rett, and they really wanted to study that mutation. So they found a boy, actually his picture is shown here. I'm very sad to report that he unfortunately passed away this last year, but we sequenced his DNA, figured out what the mutation was, transferred it into an animal model so we could study it in detail, because the goal here is eventually to figure out what good is it if we know what's wrong, if we can't fix it? And the goal will be eventually, you'll see why the information I'm gonna give you will help us perhaps figure out a therapeutic strategy that will work for these patients. So here's the disease, those of you who are not familiar with Rett, it's the X-linked postnatal neurological disorder. The children, they seem normal for six to 18 months, and then things go really bad. You see all of the morbidities over here, it affects one in 10,000 births. It's the most common monogenic, meaning single gene mutation that affects intellectual disability among girls. The good news is this is not a terminal neurodegenerative disease. In other words, you're not losing the neurons, they're just not functioning properly. So if we could actually go in there and tweak the system so that it worked better, you would solve the problem. That's the good news. So how is this crazy technology I just showed you about tracking molecules gonna help us in any way? So a little bit of background here. This is just to remind you this is a collaboration with Baylor College of Medicine. Huda is an old friend of mine and we've known for decades and we worked together on this problem. MECP-2 is a gene that she discovered to be the causative agent of Rett. And she knows exactly what mutations are causing what effects. And this work is done by a postdoc in her lab together with a postdoc in my lab. And what happens even in the mice, so we took the mutation from this young boy and transferred it into the mouse. I know it sounds like fiction, but actually we can do this quite easily. And lo and behold, that mouse shows all the symptoms that you would see in the human. Now again, I'm not gonna spend a lot of time showing you how we can actually measure the intelligence of a mouse, but it actually works very well. But look at the other things that it affects. It affects body weight, brain weight, reduced lifespan, impaired motor coordination, hind lamp clasping, all the things that are correlated with the disease in humans. So this is a very, very good model. And using this model, a postdoc in my lab, Claudia Kotoglia from Milano originally, she just became an American citizen this week. She began to use this incredibly sophisticated ability to track individual molecules moving in these disease cells of the model and to directly measure what's going on. Why is protein not doing its job? It's only got one little point mutation. It's not like the protein isn't there. And this is just probably a little more detail than you want most of you, but it's actually pretty easy. This is why we do this experiment in a mouse. We can't do this in the human because we actually have to cross a female with a male so that we get a pure strain of males that have that mutation and nothing else. And then we sacrifice the animal, pull out the cells from the cortical neurons. This is from the brain, and then do our crazy experiments. So we'll go right back to doing what I said we could do, find its distribution, the diffusion spectrum, the fraction bound, the residence time, all those things that our assay is designed to do, but now in the context of a disease organism and a physiological state. First thing we know is that the protein is there. It's produced in somewhat lower amounts, but it's in the right place and it's in the right place at the right time in the right cells. So it's not like the protein's gone. That's important. But right away we notice a problem. If you look at, so remember who can track these proteins, right? So the block up here is the normal and here's the mutant. It's pretty obvious what's going on with the mutant. It's moving a lot more than the wild type or the normal. That tells me right away why this protein is having a problem in these patients because it's not able to do its job. It's like it's got a very short attention span. It can't stay on the job long enough to do the job. So that's one problem. And it gets more sophisticated. It turns out we can analyze why this thing is moving faster. No, I don't wanna get into the weeds here, but think about it this way. If something is spending more time running around, you can ask one of two things. You can say, okay, it's running around more because it's residence time is shorter. So it's in the unbound state more often. That's called the dissociation rate. But it could also be because it just has a hard time finding the place it's supposed to bind to. So it's just not as good at finding its location that it wants to be in. That's called the on rate, how fast something comes on. It's really important from a drug discovery standpoint which of these is going on or if both are going on because it'll completely change the way you're gonna deal with the problem. Does that make sense? So nobody up to this point has ever been able to measure either the on rate or the off rate of this protein in the normal disease state. This was huge and I think will lead to some kind of therapeutic strategy. And it turns out that it in fact is affecting both the on rate, how quickly and how fast it finds its target to do its job. And it's also remarkably and unexpectedly sticking around longer which in and itself could be a problem. So in biology very often too much of something or too little of something is equally bad. Everything has to be incredibly well balanced and probably all of you know that from your own life. Okay, so that was just a little vignette of really fundamental experiments that we did. Most people would say well that's very nice but how the heck is that gonna help anybody? And I'm now gonna transition to me something that Cal has been incredibly effective at doing and nobody knows it. And that is when we take a very fundamental idea that looks very esoteric abstract and we build a company. That company becomes very successful. The drug or the process or the materials or the computer chip gets used by millions of people. So I'm gonna give you the final set of my talk here is to take you through an experience that I've had since I first came to Berkeley because I arrived here as a faculty member in 1978, 79. Genentech was just starting. That was the first actually two. CEDIS and Genentech both in the Bay Area, first biotechs ever. And I was on their scientific advisory board as a young faculty member and really saw the revolution that was taking on in the pharmaceutical business. I'm now gonna take you decades forward into technology that uses this fast microscope imaging system that I just showed you and how that can be converted into a cutting edge biotech, ultimately a pharmaceutical technology. So let me remind you, I told you that my previous life I was taking cells, tearing apart and isolating some components to study them and that was good. But as I said, I was throwing away 90% of the stuff that's in the cell that I didn't understand. So this slide is just to remind you that the cell and particularly the nucleus of the cell is an incredibly crowded place with molecules bumping into each other constantly. And the whole point here is when you track the movement of individual molecules in the normal physiologically living context, their behavior is completely different than when they're in the test tube. And this company that I'm gonna tell you about really took advantage of this to try to industrialize the ability to do dynamic imaging in individual cells to study drug discovery processes. This was a collaboration, so there are four of us who are founders of this company, including Eric Betzig from Cal who won the Nobel Prize I told you about. Xavier, my young colleague from Paris and a colleague of mine from the Howard Hughes who actually designed all the chemical dyes that we need for this experiment. So again, it's a collaboration of many different technologies that all have to come together from different organizations, although three of us are here at Cal. In fact, we collaborate with many people all over the world to do this. The company is called Icon Therapeutics, those of you who are curious can go look it up. It's in a funny place, it's in Hayward. Most people haven't even heard of Hayward that were east of the Sierras. And it uses single particle tracking as the primary platform for discovering drugs. It uses single particle tracking as a way to test literally hundreds of thousands or millions of potential drugs in any number of diseases. We are completely agnostic as to the disease, although primarily Icon is currently focused on oncology, which is cancer, but we're also gonna probably do something in neurodegeneration. We'll probably do something in immunity. We can do something in infectious disease. It doesn't matter. That's the power of the basic technology that Cal introduces to the world is that it is widely applicable. So it's to create drugs targeting the dynamic movement of protein. So to put it in a very simple minded way. Most drugs today are trying to, it's kind of like trying to hit the target with a sledgehammer. You're just trying to completely knock the function out or you're completely destroying the function. We decided that it's a lot better to tickle the system, tickle it up a little or tickle it down a little. That means either slowing the molecule up or speeding it up, okay? That's very difficult to do unless you can measure the movement and actually follow your GPS system and know whether you're slowing it up or speeding it up. This harnesses the power of another really important thing. This is a very quantitative assay. In other words, we're not taking guesses of, is it on or off? It's like, no, it's on and, but it's 5% better than the other. That's called quantitative biology. It's a new area. Cal is absolutely on the forefront of it because it requires computer science. It requires optical physics. It requires sophisticated molecular biology and brilliant students like Gloria. So this is just to summarize. Just to give you a sense of how much we're helping the California economy. In the meantime, as we're discovering amazing things, having a lot of fun doing it and hopefully ultimately benefiting patients. So this company was founded in September of 2019. Our timing wasn't very good. We shut down three months later. But we only shut down for a month because we had so much space and so few people, it didn't matter. We only started with like 15 people back in the middle of 2020 in 25,000 square feet in Hayward, right off of the bridge today, which is not that many years later. We're over 210 scientists. What's really great about this company that's different from any of the other ones that I've ever started is that half the people are engineers. They're software engineers, robotics engineers, optical physicists. It's amazing. And they have to work every day hand in hand with the molecular biologists, the geneticists and the biochemists, and of course the chemists. We manage to raise a lot of money and the screens are going incredibly well. And just to finish it, I'll show you how, why it is that sometimes you get a great idea in a university and you realize you cannot take the next step. So, when Xavier and I and Eric figured out how to do this, and I showed you the experiments, to do that imaging that I showed you, those little videos with the light jumping around, takes my graduate student or postdoc about a week to collect the data and about three weeks to analyze it for one cell, too slow. The company wants to speed this up by orders of magnitude. I really didn't know how fast this could happen. So we went from that scale that I just told you to what we're now, what Icon is capable of doing now with technology I don't have time to go into. They have completely different microscope platforms and everything is roboticized. The human is not touching that assay. They are doing 100,000 a day. That is so far beyond my own imagination that I had to go there to see it for myself. And the first thing I said to the CEO was, can I have one of your microscopes? So we were also very lucky and this again, I think is a testament to the traditions and the quality of people that Cal produces. We managed to recruit Roger Perlmutter who was president and head of research of Merck to be the CEO. He told me just last week after we had our board meeting that he knew the technology was good and that's why he came, but he didn't know how good it was and that the progress he's seeing is even beyond his expectations which was good for me to hear because I really didn't know how far this would go. We're not there yet. We're a long way from actually getting a novel drug, but I think the path forward is very interesting. And so with that, I think I'm done about on time. So I try to give you a sense of how a very fundamental scientist like myself, I'm motivated by answering deep fundamental questions of biology, but I'm not shy about then using that information to try to help the broader community in ways that go beyond my publications. And I think the several companies that I've been involved in and my activities as a venture capitalist actually all feed into that and some of you may have some comments about that later, but that's where we're at and I'm very happy to take many, many questions from you since we have plenty of time. Thank you. I have a question. But are there certain diseases that are not amenable to the SBT tracking? That's not a naive question at all. That was a very profound question. Almost certainly there are gonna be diseases where the causative agent will be more amenable to this type of analysis. So for example, you can imagine that could be a mutation that's causing a disease that's not even changing the mobility of the protein at all. That said, we've now looked at about a dozen and yet to find one that doesn't. Part of the reason is because our ability to measure small differences have allowed us to detect things we never could detect before. It's kind of like the web telescope. It's like, okay, I had the Hubble, that was great. And now, wow, the web is telling me stuff I never could measure, same problem here, or same solution. But that's a really, really good question. Yeah, there were lots of other questions. This fluorescent technology, is it only with proteins? And my interest is thinking about Alzheimer's dementia, the tau protein, and tracking, can you track something early and get it before it becomes the disease? That's a great, great point. It's not limited to proteins, although tau is already on our list. Tau's gonna be very, very interesting. You obviously already saw our interest in Huntington's, which is even more complicated than tau. I think we're so early, we're at the bleeding edge of this technology. The good news is we're the only company that has a microscope, and that's also the bad news, because what I really want is to have this microscope so everybody who's studying a disease can use it, but we're not there yet. But that's a really great question. Yes? When you're adding the, when you're adding the fluorescent protein, aren't you changing its weight, its 3D structure, and that ultimately changes its behavior? Absolutely, absolutely. And that's, this is a really, really sophisticated question and something that Roger Promo, the CEO, and I worry a lot about. The good news is we can always test it. So there are proteins when we append that fluorescent tag onto it that behave completely inappropriately, but we can tell that right away. And so we either can't use that protein, or we have to put the tag in different places. So if you know anything about protein structure, the protein is folded up in a certain way. If you put that tag in the wrong place, it's kind of like putting a wedge into a door and you can't close the door anymore, and that's not gonna work. But the surface of the molecule is vast. So you can put it in many different places. We almost always can find a place to put it where we won't have that effect. That said, we always worry about that. So very often, once we have a signal that something's happening and we've detected it, we usually go back, chop off the fluorescent part and re-study it in its isolation, just to be sure. Weight doesn't matter. Turns out that the amount, many of the proteins we care about, compared to the dye, is so big, it's like having a little mouse grabbing onto a tail of the elephant. It really doesn't matter. Yes? Just curious if numerical models to basically go hand-in-hand with the research? Yeah, so the question was, is anybody using computing numerical modeling absolutely? And I could spend half an hour talking about all the modeling that goes on. I should say that because of the capacity of this microscope to do 100,000 assays a day, the amount of data we're creating, get ready, it's a petabyte a week. Petabyte, okay? You can't even use the cloud to store this. So we need a whole new strategy to go back and reanalyze the data and do the kind of analysis you're talking about, which is why half of our people are software engineers. Yeah, it's a huge problem, yes? Fabulous. I was just wondering, we've heard over the last couple of years a lot about RNA technology and how that targets and goes in and can manipulate proteins. So just if you could help me understand, so how does the work that you're doing in this area inform other platforms or how does it all work together? Because I think you made an opening comment about, once we understand, we need to figure out how to solve. And if you could help bridge that, that would be really helpful. Yeah, absolutely, absolutely. And to answer your question, so we can tag RNA molecules. We can tag DNA molecules. We can tag the membrane. We can tag any molecule that's genetically manipulatable. But your more profound question is, how does this technology help anti-sense modalities? I think what this technology will do is that it will tell the anti-sense people what target that should be going after and why. Because what this technology is really powerful for is understanding the underlying molecular defect. That's probably one place. But there are other things that are happening which I could talk more about. But the answer is yes, it will absolutely have both broad application and very specific application. Yeah, another question here. What is the physical limit of how small you can get with lattice light sheet microscopy? Oh, well, you know, you can have this hard discussion with my friend Eric. You know, we're down to the kind of microscope we're using actually is different from a lattice light sheet, it's higher resolution. Lattice light sheet is actually not that high resolution. We're down to maybe five nanometer resolution right now. The problem is not so much resolution because you can increase resolution just by hitting it with more light. You remember what happens when you hit things with light? Those cells are not gonna be happy. So one of the huge advantages which we didn't fully appreciate when we first started this work is that I can collect megabytes of information from tens of hundreds of thousands of molecules in about two seconds with this technology because our frame rates are so fast. That means that before the cell gets really unhappy, I've already figured out what's going on. That's key. I'm wondering if the issue of looking through water versus air was a factor for the microscope. Oh, big problem, big problem in our first iteration. You know how most high resolution microscopes light microscopes use oil to get over this light liquid air interface they use oil? Well, oil is a mess. So we had to design water immersion microscopes. That's what we're using now. But of course, Eric, the Nobel Laureate figured out an even better way where we don't have to do that anymore and his new microscope will increase our resolution by a factor of three and our speed by a factor of 10. And it's kind of a hybrid between our last light sheet and a high low microscope. It's really amazing. It's called a oblique line scanning microscope. I know it sounds like gibberish, but it's amazing stuff. Any other question? These great questions, by the way. Yes. What do you see as the biggest challenge in scaling up the number of microscopes that are able to do this work? Actually, I don't see much problem in scaling up the microscopes once we decide that that's what we want to do. I think right now, you can remember the company's less than three years old. And so we're literally been flying the plane and building it at the same time, which means that we keep changing the modules. And so we haven't settled on what our first real big prototype is going to be yet. Once we have that, producing it is not, these are not super expensive microscopes. Don't get me wrong, they're only cost, only cost about 300,000 each. Whereas an electron microscope might cost you five million. So we can produce a lot. And part of it is the engineering. So one of the things that we have to do with this, imagine if we're measuring something that's moving around in the micron resolution, this microscope cannot vibrate. There can't be Bart driving by. It has to be on a platform that cannot move more than angstroms. And that's what's costing money. It's figuring out how to isolate. It's called isolation, vibration-free isolation. And we've figured that out now. But to do that and to have a fully roboticized is quite a feat. But I don't see any technological barriers. What I see is biological barriers. Yes. Is the business model to sell the microscope or is it to discover the drugs? Yes. So the question is, are we gonna sell the microscope? This came up when I first started talking to investors of this company. Half the people really wanted us to sell the microscope. And the other half said, don't bother, just do drug discovery. I think we're gonna end up doing both. But it's a very difficult thing to do because it's two completely different businesses. So this is a business decision which I will have very little input into. My CEO will make that decision, which he should. I don't think we have an answer to that. But I also feel that if this microscope is as empowering as I think it is, we will not be doing the world any favor if we don't make it available. So one way or another, we have to make it available. But I think you can become a service provider by developing the cloud platform and having the computational functionality there and then just having people subscribe to your service. We're looking at all of those models. The trouble with being a CRO is it's distracting when you're trying to make drugs and drug making is ultimately what we really wanna do because we're probably three to five years ahead of anybody else. So we sort of feel like we owe it to the pharmaceutical industry to actually do it ourselves. So my sense is that the company will primarily be a drug discovery company and drug development company. But I'm not eliminating the possibility that there could be a spin out of some sort that will do what you're talking about. And I would like to invite you to American Canyon to set up your office. Yes, please. So it seems as though the focus of this technology is on defect situations. So what applications do you see for non-defect situations? Well, here's a really interesting thing. I don't know if something is in defect unless I know what is not defect. So most of the times we're studying what we call wild type situation. Now there's a normal situation. Which is actually not easy to figure out. One of the perplexing realities of biology is that we are organisms made up of billions of cells that have evolved over four billion years. And boy, do they have tricks. They had a long time to figure out tricks. And we're trying to peer into this black box to try to figure out how things work. So the answer to the question is we have to know what the normal situation is before we can even know how to fix something if we don't even know what fix means. So I spend most of my time just trying to understand what the normal process is, which is very complex. Yes. How often do you run into a situation where the problem is with the receptor rather than the protein? And how do you figure out which of the two is? Oh, yeah. So your question is fundamentally aimed at how do we know what target to go after. Well, it turns out most receptors are proteins anyway. It's just that they're proteins stuck in the membrane. That makes life really easy for us, actually. So we can actually not only look at the movement of the receptor but the things that bind to the receptor, the ligands. So we can make them both fluorescent in two different colors. And we can actually watch the ligand binding to the receptor, something no one's ever done before. I didn't even talk about that part. So key for atherosclerosis have been, right? I haven't worked on lipoproteins yet, but we will. This is work that was done by a very, very good old colleague of mine, Brown and Goldstein from Dallas, Texas. And I think this technology will be used broadly in every molecule you can imagine. Because there is not a single molecule in your cell that's not moving. I kind of think of it this way, life is movement. And until now, most of our understanding of biology came from fixed dead samples. It's not very satisfying. Question back. Is the company conducting independent research in areas that you believe are novel or interesting? Or are you sort of collaborating either with industry or other institutions who are in a particular therapeutic area already asking for sort of the advanced technology and the advanced data that you can bring to their research? This is exactly the stage where Roger and I and others are thinking about is how do we democratize this microscope? That's what you're really asking. And we believe, even though we have investors who want to make sure we're going to be a financially solvent company, we believe that when we get to the point where we can manufacture significant numbers of these microscopes, we should make it available by one mechanism or another, whether it's selling it, renting it, whatever. Because the amount of information that's out there that no single company can possibly ever collect, that's really what you're getting at. And of course, as a Cal person, as a public university, that's really what I want to see happen. So I think it's going to go in that direction, but it has to be a, it can't be mutually exclusive to the company's goals. But I think they can be actually complimentary. Yes. So the question is, are there applications outside of biology? Oh, absolutely. But I think we're so new at this that we haven't even tried yet, although my biophysical friends and engineer friends are very interested in it. You can attach that fluorescent tag onto some complex hydrocarbon or something. Absolutely, absolutely. I mean, one of the limiting things right now, I'm sad to say, we can only do this in two colors. Because the technology isn't there, I would really like to do this in 20 colors. So I can watch 20 different molecules doing their dance together. We're not there yet. But I suspect we'll get there. You guys are better than my students. Can ask some questions, great. Are you able to apply this approach to the protein folding process by tagging the protein in multiple places and see it how it actually come to its structure? Yes, we can. But that only happened literally this last month when we figured out how to look at two molecules or two different regions of the same molecule talking to each other in real time. Turns out to be very difficult. It turns out that the folding problem is, if you know anything about alpha fold and deep mind, the folded parts of the molecules that run our bodies are pretty well figured out. But the sort of the black hole of biology or the dark matter of biology is that many proteins are intrinsically unfolded, which means that they're flopping around in the aqueous solution in ways that people used to think, oh, they're just flopping around so they can't be functionally important. So I'm just gonna chop it off and study the parts that are folded. So all of the data from alpha fold is based on folded proteins. Well, unfortunately it turns out that especially in humans, the vast majority of all of the most interesting disease causing proteins have major unfolded regions. Nobody solved that problem yet. That will be a computational problem. We have time for one last question. Last question. How are you gonna analyze all this data that's come out of each microscope? I'm not. My students and the team at ICON will, it's mind blowing, it's mind blowing. We're gonna need really fast computers. We're gonna have to figure out better ways that right now our only way to store the information is biologically. In other words, if we wanna do the experiment again, we have to go do the experiment again. We can't just look at the data again because the data is just overwhelming. It's both exhilarating the think that I can generate that much data. If you know anything about AI and machine learning, it's all about the amount of quality data. We're generating a lot of quality data. We haven't figured out how to fully mine it. We're mining it at, if it was a 10 mile deep storage of information, we're mining the top one inch right now. Thank you very much. Thank you.