 So, what questions really do you think you can solve? What's next? Because this is like Tommy said, it's like driving hypothesis, so you have the ability to do it. So what are the next questions? So I think the two current things, the two big games we have at the moment are twofold. One is to try to understand translocation events. But I think on the more simple folding level, it's to try to understand misfolding processes that occur. So in the same way that you have folding processes beginning on the ribosome, you also have the potential branch point for misfolding events to go on. So there are decisions to be made for a nascent polypeptide. And as has been studied for a range of isolated polypeptides, the idea would be to understand how the cellular machinery, in particular how the ribosome surface that I purported today to suggest was behaving as a chaperone for a nascent polypeptide, how it can potentially chaperone the polypeptide to avoid misfolding processes, in particular in tandem repeat domains and many other repeat domains that occur, but also in misfolding prone sequences. There are a range of cases where, of course, you have single point mutations that alter not necessarily the native structure of the protein, but the dynamics that are sampled by the protein. So the idea would be to understand these processes as they occur during biosynthesis. And there's also already significant evidence to suggest that protein misfolding is occurring on the ribosome. Up to 15% or more of nascent polypeptides are targeted for ubiquitination and degradation as a result of misfolding events. There's a significant amount of stalling that occurs on the ribosome. And there is machinery, the listering machinery that comes in and separates the two subunits and targets the nascent polypeptide for degradation. So understanding these events, I would imagine, would be the next frontier for us. So you're talking about falling back on the ribosome and that maybe helps. And you made mutations working the protein and on the ribosome. Did you see, did you check your mutations on the ribosome with other proteins other than the one that you showed us? And does it affect in different ways? So we haven't done that. There's actually a paper in the Bajo archive that's just come out showing that you can actually, by taking out some of the similar L24 and L23 proteins, you can alter the position at which an emerging nascent chain begins to fold on the ribosome in a similar way. Now, the purpose of these proteins is unknown. There's a high conservation between prokaryotic and eukaryotic ribosomal proteins at the exit tunnel. But their influence on folding appears to be unknown as of yet. So all the quality control branch points for misfolding that you talked about were actually things that were worked out in eukaryotes, like the degradation. And you've been working most entirely with E. coli ribosome so far, right? Not entirely. The work I didn't actually show on misfolding events were on eukaryotic ribosomes, in particular, kind of yeast ribosomes, and rabbit reticular sites, where we actually see more of these misfolding events that are occurring. Yeah, that was actually my question. How much? I didn't realize you were able to do it with eukaryotes. The eukaryotic ribosomes are clearly more complicated than the prokaryotic ones. But that's mainly, actually, in the initiation machinery. Translation initiation, obviously, more complicated in eukaryotes. At the exit tunnel of the ribosome, there's actually a significant amount of homology that exists between all of the ribosomes. And actually, in some of the CRISPR targeting that we made, we actually targeted the very small extents of differences that exist between the eukaryotic and the prokaryotic to try to examine some of these events and some of the shifts in the initiation of folding kind of seem to be reflective of the differences between eukaryotic and prokaryotic ribosomes. But certainly, I mean, as you may imagine, the structural biology, in particular NMR spectroscopy associated with being able to do things on the E. coli ribosome is significantly easier than being able to produce these nascent chain complexes on the human ribosome and so forth. We need specific labeling events to happen. But electron microscopy is changing much of the face of that, where we can take a range of relatively crude complexes and be able to make classifications from a range of states that are being observed in that case. Certainly, working with eukaryotic ribosomes is certainly the way forward. Sorry. What about the secretory proteins? You don't have this interaction with the surface of the ribosome. Do you think there's something replacing it? Well, I mean, I'm assuming, for example, in the serpents that we work with in the microsome states that we use to examine these, we see some extent of similar interaction with the surface of the microsome. We don't know whether it's specific to the ribosome. I mean, these are things, as I was saying earlier, are pitifully under-investigated. And even I pretty much showed you the state of the art today in looking at E. coli ribosomes with simple immunoglobulin systems at a high resolution. So these are really things that need to be investigated in the future. Aaron, you're talking about proteins which are inserted into membranes. But this is co-translationally. If it happens co-translationally, the SRP receptor immediately binds to textbook things. So the tech of the SRP is recognized and then it immediately takes it. So it doesn't go through this. And then once it's injected into the lumen of the ER, there will be all the chaperons of the ER to help it formed. I mean, but there are chaperons even on the solix side, for example, that many of the systems we work with. The trigger factor, for instance, is omnipresent and actually at higher concentration in the ribosome, but doesn't influence the folding equilibrium at all in the states that we've examined. Not necessarily the case that the chaperons will come and mop everything up. I have a question for you, Joan, and for you, Tommy. So from listening to the first talk and the last talk of today, so I think an important aspect is going to bridge from the structure to the interactions, especially for those proteins that assemble at the surface of membranes, which is what you talked a lot about. So what do you think it will take to be able to look at those interactions in terms of structure and dynamics? So I think the level of snapshots, so the level of snapshots, they're already quite interesting efforts as well of doing cryoEM directly in-site in cells. So there's cryotomography that is happening, which is quite impressive. So I think the snapshot part can be done even with today's methodology already when you're looking at things in cells. On the dynamic side, at least the part that I can bring is we can visualize where the molecules are. Sometimes you can put sensors that can give you some information, maybe even folding. You can do maybe some fret or something. I think that's possible now to do. So I think it's possible. It's happening. I think that in terms of the technique that can really give you a strong understanding of dynamics is NMR and really tell you about very low populations of intermediate states on folding and misfolding pathways. I think traditionally, NMR spectroscopy has been a technique that has been one of the purists who has tended to prefer to develop very complicated pulse sequences on small ubiquitin-like molecules. And I think what I quite like about the things that we're doing is that we're beginning to use NMR on increasingly larger systems. And I think that is what's going to allow the bridge with some of the work we've seen, because I think that it's actually in terms of exploration of large molecular machinery, NMR typically is in its infancy. And higher magnetic field strengths will begin to turn the corner in this regard. So we can begin to provide highly complementary data to that. And I think working more closely with people, I came and I was very, very impressed with these talkers. I think it was everyone. And I think that the capacity to bridge the resolution scale would be absolutely mouthwatering. Just have a question, right? I mean, I brought up that in your talk. I mean, the experiments that you're doing are still ensemble experiments, right? And so I am still confused when I'm looking, let's say, at the various states that you had, whether that was all of them were homogeneous or you were looking at different subsets from different molecules. That wasn't really clear to me, right? And that's why I was trying to get at the time connection on the snapshots that you were having. So maybe you can comment on that. So in terms of the electron microscopy that we're doing, we're clearly having various classes of the nascent polypeptide that we're seeing in different states. So they, for example, form the basis of a, just alone, they form the basis of a simulation where the system is started off and restrained according to these sets. Now, if we, the problem with time scale then goes even further with including the equilibrium NMR data into a process. And NMR has traditionally been a case where you have a set of restraints. You say this distance is so-and-so and you have a myriad of those types of distances and angular restraints. And you feed these into a structured determination with a view towards achieving a minimum in the normal way that you know about. The way that this is achieved is through, normally through a molecular dynamics simulation that has a time element associated with a simulation. But these things can be easily recognized, reconciled in sort of biased, restrained molecular dynamics methods. And this is a massive area of NMR spectroscopy that is absolutely routine in this case at the moment. So I'll answer your question somewhat. Thank you so much. Any more questions for John, if not, because he needs to catch a train. I'm very sorry. I really, really enjoyed this meeting so much, but I should get back for my own, no. Give it to her. Help. Thank you very much. Thank you very much. Bye-bye. Thank you for your question. He looks so tired. We're going to ask a general question. What did you use the microphone for? No. Yes, for them. So that they can record. Ah, no. Then of course not. I don't want to be recorded at all. Actually, the question is connected with the fact that this nice, very deep and interesting biological meeting takes place in the Mathematical Institute. So I have a general question to all of you maybe except the last guy. But still somehow it can be applicable to him, too. The question is, can you formulate a not mathematical but theoretical question inside your work, inside your subject on which you can't answer by biological methods only. And for which you need application of some mathematical methods. Not like data analysis, which is statistics, and we all know how it is useful. But exactly mathematical approach which will answer some question which you have and which needs this approach. So it is a question for each of you to formulate such theoretical question or to say no, we don't have, which is also OK, too. Please. Start from you and go this way. OK. I couldn't have lunch today because I talked to Misha 1, Misha 2, and Andrei. And also I think you're in the discussion of lunch, right? All the Russians, you were not included because you went away. So the discussion we had had to do with image pattern recognition, right? So we have all this data. It's very nice to look at it, right? And how the hell do we now get information out of that? No, but you didn't say, OK, you don't say in general whatever, but you don't. OK, it's very nice to have a brain of pattern recognition. But again, what biological, it is nice to formulate the same biological question. Yes, this is exactly my question. It's a biological question, not like a tool to pattern recognition. Biological question, on which your pattern recognition will answer? Why not? No, no, of course yes, of course, why not? Yes, but this is not my question. Imagine that I will look, imagine that I'm going to look at every cell in the brain, OK? And I want to see all the organelles of every cell in the brain. So imagine that I map every single organelle from every cell in a tissue. And now you can look at different responses that the tissue has to pathophysiology, to the standard cell biology. And you can do that without putting markers on that. So you just look at general way of imaging and you can recognize all the organizations inside the cell, right? So I wouldn't have to generate these specialized cells. I wouldn't have to do transgenic things. I mean, just general. And then I can go and do self-physiology, for example, at high level of detail. You don't like that. I don't have to like it. Yes, I accept your answer. OK, I'll give you another one. I'll give you another one, OK? I'll give you another one, OK? So in the brain, when you're developing the neurons, right? There are cells. What? Oh. Now you stop. OK, talk to me. OK. So, OK, you're developing your neurons, not your. But she was developing your neurons. And there is cell-faith decisions that are taken, OK? OK. So now you have an outcome which is the cell. You're getting a neuron of a certain type or a cell of a particular type. But there were signals that happened that said, OK, go to that direction or that direction. Now, up to now, when we have been mapping the signals, they tend to be typically by genetic means, right? You interfere with a pathway and you look what the response was, right? You have the notch pathway, which is a signaling form. You eliminate that and you see, OK, the animal goes gaga, right? And you decide, OK, the neurons doesn't work. That's very primitive, right? I mean, that's OK up to now. But imagine that I can now follow the actual functioning of the pathway. I can follow exactly the where the molecules are. I can tell how many molecules got activated, what regulation went on in gene transcription. And then that is happening at the second level. The self-made decision and the outcome happens hours after, right? How can I integrate that in the same setting from beginning to end? That's non-trivial. But that's biology in action. I expect that mathematical formalization or mathematical model can give you an explicit answer on this question. I have no idea. Maybe I just don't know. I mean, the only thing I know is that when I was showing you this data, I was showing you this. Wow. Can I finish my? So I was showing you, for example, this lipid sensors business, right? So this is actually the result of a lot of molecular interactions, binding constants, rates, et cetera, right? So it's very nice that I can say this by words, right? But is this really true? So we had to simulate. You had to make models. Now, maybe for you, that's very simple. These were differential equations, et cetera. For me, it's impossibly hard, right? So I need your help. It's not that simple at all, but I still was trying. No, thank you very much for your answer. It's very important and interesting. The only thing that I still did not catch the explicit question. You said that it will be nice, but okay. But never mind. It's me. You answered very well. Thank you. So we'll talk. Let me translate from Russian. No, no, no. I think you... You will never go to other people. I also want to ask a question. I think she's asking for falsifiable hypothesis that kind of falls out of the data. What data? What? A falsifiable hypothesis. What do you mean by falsifiable? Well, something that... Is that true? That's something that falls out of the data. And you could... Oh, I'll tell you. Yes. False. No, it's true. It's not a false hypothesis. It's a falsifiable hypothesis. Yeah, of course. How can I do that unless you... Help us. Yes, yes. We need to help us. This is the case. This is not the case, yes, but... Still, still, yes. I understand. Well, frankly, I'm not quite sure I want to answer, but let me try. So let me give you some very concrete examples, and I'm probably gonna get in trouble with a lot of people for saying these things. But for example, in trying to understand in the context of small molecule drugs, where they go in the body, drug distribution, what the bioavailability of those compounds are, they're very large data sets. And we have very simple rule of thumb kinds of rules for what kinds of processes are. But I think models that are based on large data sets, parameterizing those structures, those properties of the compounds, would be very desirable. I mean, I don't see that as a mathematical problem so much as a way of formulating and structuring the data. Now, I'm getting even more trouble, but I think that there are large fields of biology where mathematical formalism could help a lot. Evolution being one, where I think that having more formal definitions would be helpful, development, those kinds of problems. I have no clue how to start working on those things, but I think that these are areas in the future. Then you also explain how generally it could be useful, but my question was, do you yourself have some very interesting, very precise question on which you can't answer by biological means only and which desire exact involvement of some mathematical methods to answer explicit one, your question. And I'm well aware that mathematics in general was very needed and well, I asked exactly about your question in your field, in your work which you presented today and not like general concept. So the very specific answer, using databases of hundreds to thousands of compounds for which we have known property, say bioavailability. How can I use that data to predict a new compound which has equal bioavailability, but some other property that I wish to have? So I think that these are existing data which are not fully analyzed, which if they were, would help greatly in our work. So in another words, you would like, for example, to have a tool in which as an input, you have some described and formalized property and having the databases and applying this tool as an output you get which of these compounds meets the expectation for this. A prediction of new, prediction of new non-existing compounds which have either better or shared properties. So I'm not gonna give you any answers you find satisfying. I know that. Don't expect that you have. I just exactly asking, do you have such question? No, no. Yeah, I mean. Exactly my question. Why you should have? This problem that we're working on with the Wolbachia and how they affect the reproductive behavior of the progeny and females and males, the differences in infection. Those things that were at a very primitive stage in terms of understanding just the molecules involved. But at some point we'd like to know, people have model. There are a lot of evolutionary biologists working on Wolbachia. What percentage of males need to be infected before it becomes an advantage for females to have basically a defense against that by being infected themselves and having an antidote. I'd like to be able to link that with the molecules that we have if we know what's the concentration of those molecules, when do they get in? How can we relate those molecules to the predictions based on very broad evolutionary arguments about what's required to establish an infection? I mean, I've always get puzzled by that type of question here. So one is, is it that I need theory, mathematical theory, or is it that I'm looking for help with computational methods or to formalize, okay? And I must confess I, so at the beginning I thought that one in the mathematical theory type thing, I've shifted and I just feel that right now what I think we need is help with the formalism and the computational part, right? That's, which is, it's a practical thing. I mean, the biologists don't get trained to do that. The people that went through that path don't get that way of thinking. And I think this is part, something we need, right? Whether it's applied to what you're trying to do, that's, so. I'm more interested in the questions formulated in a way that they really seek some theory, as you're saying, and less computational, so there must be, there are problems that are like, right now for me and for the biologists, I mean, I feel, the field we, I feel need dramatically the help with the process. Yeah, I think the evolutionary, someone mentioned that, that's the traditional area where there's real theory, right, and then a lot of the stuff we're doing is dealing with statistical models to try and say, is this unusual or something interesting? So in the genotype to phenotype thing, you have all these potential interactions, but you never know if the network that you map is just a random thing, or if it's something that really is enriched for those interactions that suggest certain pathways are connected and associated with a phenotype. And it's hard for us to solve that problem, but I don't see any theory, math theory, or axioms coming out of it. I think this is a question kind of for everyone, but mostly three versus one. I'm just curious, no, not really, not really. I'm just kind of curious how, Wait for the question. I'm curious how, like, I think that a lot of microscopy people see things as seeing as believing, or you can really understand something rather than a genetic interaction model, because all of you guys are doing interaction not through necessarily my microscopy. So I'm kind of curious how you feel about the microscopy going on in Tomas's lab, and then the vice versa. If you think the microscopy is more powerful, like how are your different ways of looking at interactions? What are the pros and cons? I'm going to grab the microscope and correct you. It is seeing is perceiving, not believing. We have to be very careful, meaning that seeing is very powerful impact on our brains the way we think. And I think that can be useful in that it can persuade us to think again about something that we thought we already understood. It can revise our views on something where we have very strong preconceived notions. And I think that's very useful in science because often we sort of get stuck in a rut and we need a large jog in order to get away from that. So I think that that's where imaging has such a powerful role, but it is also one tool that has, well, the point of parking lots for molecules was brought up. How do we know whether what we're looking at is an active state or a biologically relevant state and so on. So we have to be careful in sort of the biases that we bring to the experiment because it can have a powerful impact on how we interpret the data. So my answer will be less philosophical. And just to say, first of all, that it's not the three of us against Tommy. I mean, we actually all use microscopy in some way or another. We just don't use such sophisticated microscopy. So even we are interested in where the molecules are as part of understanding how they might be working in the cell. It's part of knowing their concentrations, knowing when they appear and how they interact with each other. So it's just a part of the understanding. For us, it's not the only way of looking at things. But what Tommy's point of view is now is there's so many things that we never even thought of thinking about. I mean, perceived before, just seeing something so new is a way to think about new areas of biology. So I think microscopy has lots of nice functions in terms of promoting new ideas and new ways of thinking about things. I'm supposed to say something. Are you both sure those are colonies? Yeah, yeah, yeah, look at the size of it. We don't think that is. I mean, the yeast model system is built on largely on cell biology and genetics. And we try to do biochemistry as best we can. And so I think you need all those things to really figure something out. And now, obviously, computational biology is the other component that is really driving everything. And when you put it all together, then you might be able to figure something out, right? Why did you ask that question? Okay. I think because I feel like this, I'm wondering if you feel like this technique is powerful enough at some point. I mean, okay, so with biology, we were doing all kinds of stuff because we actually can't see what's going on a lot of the time. So we could skirt around the problem by doing knockouts or whatever. And so I think I was curious whether you felt that the history of biology lies in these super high resolution imaging techniques. So before the anatomists existed and they were doing their sections, there was a perception of what life was, right? An animal or a human, right? And then the guys cut and looked and they started to have thoughts. But of course that was a dead thing, right? But it was influential, right? So they were looking. It didn't define things. It wasn't the last word, but it gave you a mindset where you can then keep moving again, right? I think this is the same. I mean, I think there's not one or the other. It's a, as it was mentioned right now, it's you use all these methods, you glue them together. Each one has properties that allow you to do things better than the other, right? When you're doing the imaging, you cannot see the whole global system. It's impossible, right? There are too many variables. You're doing the, right? So you just, there's different ways in which you are handling this large scale of information and you keep dissecting to try to integrate at the end, right? As it happens, there's a burst right now on this imaging. And that burst, that burst is, this burst happens to be in optical microscopy. And I don't think it's the last word. I think it's just a burst, right? Same as electron microscopy of molluscs cryoem. There's a spike right now, right? And et cetera, right? It's a process that keeps evolving. And there was the previous spike was in CRISPR. Yes, it doesn't keep each field, just. Yeah. So it's not like explosion. So this is, I have a follow-up kind of, but I know, I don't know if all of you will have an opinion on this, but in terms of, do you think that my, right now I think one of the few things that microscopy can do really well is chromatin stuff, looking at it with the dynamics of chromatin. And I'm curious whether you feel like, I don't know, I feel like Mark, you're maybe, because you have this nucleus, maybe you'll have to move into doing some of that. I don't know, chromatin immunoprecipitation sort of things. And then I guess with the microscopy, how can you, do you think that it will ever move forward enough to hit that, a few years ago, that deep? So I just can tell you that a few years ago I was fascinated by splicing. So I had a friend of mine in Lisbon, Karma Fonseca, I said, hey, Karma, can we just look at splicing? So she developed a system, and then we managed to follow splicing in real time in a single locus and the coupling to transcription, right? And that was done by imaging. There was a huge amount of background before that, based on biochemistry and genetics, but there was also lots of discussion on whether these things were correlated or not, and the experiments were done as an ensemble experiment, right? So suddenly when you were able to look at this and really see this, oh my God, they happened to be coupled, okay? Some people had postulated that and some people were against that, okay? No more discussion, right? I think it depends on the problem, it depends on the time. I think what you're saying, it's fascinating that people trying to do that, just trying to map where transcription factors are coming and the kinetics and dynamics and how they're walking through, and yeah, I think it's, yeah, lots of new cool things. The following up, unless there are other questions, I think that there are sort of two kinds of processes that we can look at. We can look at bulk processes, which are fairly easy, and usually amenable to ensemble methods, but then if we want to look at, if you will, specific processes at a specific genetic or cellular locus, then we need imaging or other tools that differentiate them from everything else that's going on. So I think that's where the power lies, and well, I certainly have a bias toward things that have to do with genomics because that's my background. I can think that other people who have interesting endocytosis or other things see unique places and want to understand what happens exactly here, not just everywhere, and that spatial temporal information is really powerful in understanding biology. I'll pass. I'm not sure if this is working. Oh, it is working, oh great. So I was wondering about two related things, and it's a question for some of you more than others, I guess. So there is this giant mountain of amazing data that's coming out, but I would argue that it is mostly available to people who produce the data, then maybe a little bit to colleagues after it's published, but it's very hard for mathematicians, computer scientists to get access to this data and play with it. So I'm wondering first, do you, would you say it's important to invest into making this data very available for non-specialists, necessarily, and the second part of this question is, when you're looking at this, this is awesome complexity, this data, and we get to more and more and more layers of this awesome complexity. Would you expect that ultimately when we have maybe mathematical apparatus, maybe new language, new ways of looking at it, it would simplify? Is it your expectation that it looks so amazingly complex because of the units that we choose, cells and genes and units that we choose, molecules? Is there, you see what I'm saying? Is there expectation that once we understand there's the laws which would bring it to beautiful simplicity? No, I think for sure. I mean, it's in the ultimately, like with the endocytosis model we saw today, it's simple chemistry that's driving it, but there's a lot of moving parts and you have to figure out what they are and then come up with a model and then ultimately you should be able to formalize that. And so I think it ultimately will be very simple, but there's a lot of moving parts. There's a practicality. The movies I show you today are the small movies that we have. There's a 30, 40, 50, 60 gigabyte, each one of the movies. Those are the small ones. We have data sets that are a third of a terabyte. One movie, we have a data set which is 14 terabytes. Tell me how do I, I mean, forget about putting in a database, right? Inside the lab, I don't even know how to look at these damn things, right? So we're having a major, major problem and this happens to be in our side. I think the same is with other big data things. So one day it will happen. I think maybe the answer is to have games. So if there will be games for kids and they will use large terabytes of data, then things get solved, right? I don't think that we can drive it based on science. Has nothing to do with compression. This is not a compression problem. Look, I have a movie like I show you, right? And I would like, I just would like to see, I would like to be inside of this fish and I want to watch the cell crawling towards me, okay? I don't know if you managed already to download this YouTube thing, because I think the internet is slow here. You will see that, right? But we try to do that in the lab, has been very, very hard just to do the math, to do the VR, et cetera, is non-trivial. I mean, you were asking me about mathematics. I mean, how do I make, the data was collected all in the same intensity, but I need now to make it translucent with gradients, right? This is a mathematical problem and I don't know how to solve it. When I talk to people, they all look to me and they glaze, right? So yes, we need that. Final question here. Okay. I want to move away a little bit from the mathematicians, but to the team of this conference, it's from molecules to cells to human health. And you were defining today, where is the block in this, well, this steps from a molecule to a cure. And one of the things is that we cannot predict yet how a drug will act in a body and what type of effect it will have on different systems. And now, Tommy is showing these beautiful movies in which you can see multiple cell types. You can follow individual molecules. So could it be that this next generation of movies and imaging could help us to overcome this step by looking at not just the effect of one cell, but looking on the effect of a system or a couple of cell types, maybe in more complex structures like organards or different models that now are used for personalized medicine. So what are your ideas on that? Well, certainly I would say that assays in more complex experimental systems are desirable. High throughput assays when possible. High throughput in this sense means thousands, not millions because animal experiments, in order to be reproducible, take quite some resources and those resources are, well, rarely available to academics and in drug companies, they're becoming less and less widely distributed. I don't really have a solution. The sort of high and imaging approaches are appealing, but again, today rather low throughput. I mean, when it takes a day or two to acquire a data set, it's not something that we can sort of routinely dedicate to high throughput kinds of studies. So I'm afraid I don't have a simple solution. I guess one solution that I would have is, let's try to make sure that the data we do have is widely available and in accessible formats such that information that is perhaps not very organized is perhaps in the literature, but not in a format that is available for most people, becomes more so so that it can be used. So I'm sorry, I'm not able to answer in a more optimistic way, but I think that the challenges are great. Maybe the institutions that are in charge of funding need to think again about whether there are ways of making such data more widely available and collecting it. So we're actually doing an experiment test to follow what you just said, right? So we happen to be looking at infection, viral infection. And so that has a biological problem because when you infect, you present cells with viruses and the number of particles that a cell sees is very high. The MOI is very high. So only one or two viruses will infect but maybe hundreds of particles bound and were taken into the cell. So now you want to see a drug that is interfering with infection. You know that that happened because you did the high throughput screen and it's fine. And you even know what the target is, you know everything, right? Now you would like to see what's going on, right? I think that the only way is to do the imaging and we're doing exactly that, right? So we track in many cells, hundreds of viruses. You put the drug and you say, okay, where did I get the block and what stage, et cetera, right? It's a huge computational, I mean, it's very complex computational. It's non-trivial, right? But it's possible and it's happening and I can see that we already, there's a virus called rotavirus, right? They give you diarrhea to the kids. So we unravel the pathway of entry of the virus that way, right? It was confused because people were thinking they were taking him by endocytosis. And it turns out that that's not true. Particles are taken by endocytosis. The majority enter by endocytosis. A few particles are trapped in the plasma membrane and that's how they penetrate. No way you could have done it unless you see, right? So that's my pitch, that, you see I'm a salesman. Well, I could tell you a story. Yes. So I did my, just cause we're mathematicians here, right? I did my undergrad, 50% in math and 50% in chemistry. And then a bunch of biology on the side. And I studied math because I knew I was really bad at it and I didn't, I knew I wanted to learn something at university. And then at the end of my degree, the chair of the math department called me into his office and said, and you could tell, you know, they could tell that I wasn't a mathematician because I wasn't the sensitive ponytail guy. I wasn't, you know, reading a novel during my fuzzy logic class, like the brilliant daughter of mathematicians, friend of mine. Anyway, he said, you're not going on in math, are you, Charlie? And I said, well, no, like we know there's just no way. And he goes, okay, well, we don't want to slow you down your other career. So we'll just bump this mark up a little bit. And if you promise never to take another math course, you can graduate today. Sadly, sadly, this I'm afraid is not a unique or unusual experience. Biologists do tend to have a fear of numbers and a fear of, well, the sort of challenges of mathematical formulas.