 Okay, so apparently we made it. I think I'm going to continue on the track that Angelo said last week and earlier this week. And the idea is to give you first, as I understand you have a broad background. First, an overview of the field of research of applications of polymer physics to chromosome folding. And after giving you a sense of what is distilled about and what we do, I'll try to enter, say in this slightly more technical details to give you a sense of how theoretical physics, statistical mechanics is used in this context. Of course, please stop me whenever you think I'm going berserk or I'm not expressing myself. This is aimed to be general and so do not hesitate to ask any question you think I may have been skipped. So I want to start from a very, very stupid and general title, which is why our genome, our chromosomes cannot be considered just like random spaghetti in a bowl. And to try to start from scratch, let me go back to the sequencing of the human genome. You know that now since almost 20 years, our genome is sequenced in the sense that we know the string of the bases, the letters, the four letters which compose our DNA. Yet, this is considered only the end of the beginning in the sense that we know the letters, we know the sequence, but we still do not know how it works. I mean, why a given gene is active in one tissue and must be turned it off in another tissue? Why a gene which has been silent for years suddenly turns on maybe a non-co-gene and starts cancer. So we can't answer this today and what we are trying to do is to move in that direction. And trying to answer those questions, how is our genome regulated? Of course, it's the key to access not only the fundamental tissue of life, how life works, but can also, from a more practical point of view, open the way to understanding and hopefully treating diseases such as cancers. I mentioned that, but also congenital diseases. Congenital diseases are diseases which are linked to mutations in the patient genome, congenital, so the patient is born with the fact. And in all our parotid families, there is someone with a congenital disorder, one kid in 20 is born with a congenital disorder. Often, luckily enough, they are just minor things, a little strange thing on your ear or being taller than the average. In other cases, they can be terrible diseases. Think of body malformation. And in other cases, they can be lethal. And so answering the question I'm going to discuss today, hopefully will give progress also to deal and to make diagnosis and to treat such diseases. And what I want to discuss with you today is some important reason for this progress which has been made in that direction. And in particular, I want to discuss with you a new dimension of DNA, its third dimension, how it folds. But let me go to the very scratch of the story. So, again, please stop me if I'm repeating things that you know so well and roughly speaking, our DNA is 3 billion bases long. So it's a sequence, it's a string of 3 billion letters. It's six billions because we have two copies roughly of our DNA. And when the human genome project was accomplished, we know the sequence and then we know the genes. And now we know that humans have roughly 20,000 genes. Genes are a key portion of our genome. They are segments along the string which code for proteins. Proteins are the building blocks of cells. So the system is self-contained because you have written somewhere how to build what you need to work. And so the genes are called the coding part of our genome because you know there that is the code to produce those proteins. However, as I told you, we know today that humans have roughly 20,000 coding genes. In that number, the beginning was surprising because it was expected one order to imagine more because 20,000 to give you the sense of scale is not much bigger than the genes, the number of genes that much simpler organism have such as drosophila. Drosophila is the fruit fly. You have seen that. This is the little fly flowing around and around fruits in summertime in Italy. We have seen that for sure. And in drosophila, 16,000 genes. So it's practically as many as we have. What is also surprising is that in the human genome, genes are a very tiny fraction in length. It's less than 2%. The coding fraction of our genome is 1.5%. So the vast majority of our genome is known coding. And up to 15 years ago, maybe even less, that non-coding fraction was named Jean-Guenet, as you may know. Biologists thought that it was the relic of evolution. Once we had a tail and there was a gene for the tail, which is no longer used. And so it's misused somewhere, dispersed in the 98% non-coding. Well, that view has radically changed in the last 10 years or so. And we know today that in the 98% known coding of our genome is the secret of the regulation of the 2% of the genes. And so this is what I want to discuss now. By the way, from an evolutionary point of view, what correlates with the complexity of an organism is not the number of genes. It is the known coding part of the genome. So if you take very simple organism, bacteria, or even early eukaryotes, early eukaryotes means early organism with the nucleus, where DNA is in the nucleus. In the bacteria, genes are roughly 99% of the genome. So the more and more complex is the organ. The bigger is the fraction of the known coding DNA. So I try to tell you what has been discovered, one of the things which have been discovered about how the known coding portion of our DNA may control the activity of the genes. And more or less it's as simple as you see in my picture. It has been discovered that along the sequence of the DNA that are regions, those in green in my slide, which are known coding, so you would call them junk in the old terminology, which are essential for the regulation of genes. And they were discovered beginning by chance. As often happens in real good experiments. They were trying to find genes or something and cut pieces out of the DNA. And they cut pieces in the known coding portion. The expectation was, well, this must be a control. Nothing happens. And instead they had a brutal effect on the organism. And that was the first indication, one of the indication which led to the discovery of such distal regulatory elements. So along the sequence, you see there are comparatively short segments which have the role to regulate genes. It has been discovered originally that if you cut that out, you have an effect of a given gene. And now we know some of the mechanisms whereby the control occurs. And one of the key mechanisms which has been discovered is shown here. What happens is that the regulators can act on the gene by folding, looping onto it, informing a physical contact. And in this way, the genes turn it on or turn it off or fine-tuned. And this is occurring, not for one gene. This is occurring at the same time for the 20,000 genes in our genome. And you, I think, have understood that the way the system folds is dictating what the cell is going to do. And so, and this formidable origami whereby genes and regulators, 20,000 genes and their vast number of regulators, it is estimated that there are, on average, four regulators per gene in humans. So in this huge amount of sequences and DNA pieces, the way they fold, they come together. The origami they form is the way in which the cells control, specifically control the different activities of the different genes. I find this personally surprising because there are a number of questions which I will try to discuss, but any you come with is a good question, I think. But I try to list what I have in my mind. How can a regulator find its target gene? How it comes a day, see each other in the darkness of the nucleus and come together? This is not trivial because this is functional. So you may not risk not to meet. You die, you should not meet. Second question, how is this coordinated for 20,000 genes and their corresponding regulators? You see, this is far from trivial. If you are in Piazza San Pietro in Rome, the Pope is giving his mask. And there is a crowd there, 20,000 people. How do you find your friend if you have not your mobile phone? It's far from trivial. So what I want to build on is on this question. But first I would like to give you the sense of what are the discoveries in the quantitative experiments which are hopefully opening a way to answer this. From scratch, you know that in our cells, in humans at least, in your currents, the DNA is not a single filament, but it's made of in human 23 pairs of filaments which are named chromosomes, as you know. And those chromosomes are linear filaments of polymers and they are all included in the nucleus of the cell. So this is a human cell that is an organelle inside which contains the DNA, the 23 chromosomes. And I want to try to give you a sense of scale. The nucleus is order of magnitude 10 microns in size. The DNA content in each nucleus of each cell is roughly two meters. If you take the 23 chromosome pairs and you attach one to the other, it's two meters. And those two meters are packed in a 10 micron linear size object organelle. So there is an astonishing problem of packing. And it's not just packing because the way you pack is going to set what you are going to do. If you leave, if you survive, if you become a muscle or if you become a bone or a number of tissues that we have in our body. And an important discovery on how chromosomes pack into the nucleus was made, say at the beginning, say around processing of this century. And the scientists who contributed really to that are two brothers, Dangelo Knowswell, the Kramer brothers, physicists and the medical doctor, Turner biologist. And what they discovered with tools I'm not going to discuss now because it's exciting, it is a nice story. We can chat about that at lunchtime, how they discovered it. But what they found is essentially depicted here. This is a much later experimental observation. This is based on microscopy. And you have understood that what I'm showing you here is a nucleus of a human cell in the different colors you see that are different chromosomes. This type of picture is obtained with a technique which is essentially based on fish. Fish is a technique whereby you can stain DNA. And in particular, in the case shown, you can stain differently, different chromosomes. And so you see by eye how the different chromosomes locate into the nucleus. And this is a real image, a real image, a microscopy image. And I hope I conveyed you the excitement I have about that because if you look at this, you immediately realize that the system is far from randomly organized. Suppose you have a spaghetti, colored spaghetti, and you cook them. And then you put them in a dish. You know what you expect to have, something like that. And instead, what we see at this discourse is that it is as if each chromosome knows that it has an identity, compactifies in, we called it in Italian, a nido, spaghetti, a nest. You see, the right chromosome has folded on itself and the others as well. And they know how to locate one with respect to the other. So it's a highly organized structure. Far from being a random mixture of spaghetti. And so again, the question is, what is the invisible hand which produced this? And how is that controlled? With microscopy, this is a picture, I think, of 10 years ago, with microscopy, major advancement had been made in recent years, but including a Nobel Prize and so on. But yet there is a problem, fundamental problem of resolution. Because it's still very difficult to investigate what happens inside the chromosome. And there is where the excitement is. Because I told you that distal regulators typically are on the same chromosome. And so if you want to understand how life works, we must be able to resolve inside a chromosome. By the way, in fact there are functional contacts also across chromosomes. So the activation of one gene depends on what is the contact with another chromosome. But in general it is insist, this is the way biologists name it. So regulation depends on what is on the same chromosome. But anyway, the key question is to resolve what is happening there. And microscopy has a resolution problem there. And so maybe as Angelo told you already, in recent years, important progress has been made to devise the thing of technologies to allow to circumvent microscopy and to try to understand who's contacting whom at very fine scale. And I want to introduce the topic. I want to briefly mention the technology that we produced with Ana Pombo in Berlin. And I want to do that because it's very easy to explain and it's essentially a statistical mechanics idea. And the idea is the following. Suppose you want to measure who's proximal to whom. So which an answer, which regulator is in contact with which gene? Well, and you cannot use microscopy because you want to go finer. The stupid idea we had is summarized in this slide. Suppose you can cut slices, random slices through the nucleus of your cell. And suppose that you have two sides of interest, the, I don't know, the red and the green. Well, if you cut a tiny slice through the nucleus, it's very unlikely that the red and the green are both in the same slice. Usually you cut a slice and you don't find any. If one of the two is present in the slice, say the red, it's very unlikely that also the green is present in the same slice. If the red and the green are randomly positioned. Instead, if the red and the green are close by, are in physical proximity, then it's very unlikely that in a slice you find the red. But if you find the red, you also find the green. And so by just cutting sections to different nuclei and collecting the statistics, you can resolve who's contacting whom at the level of the single cell and at the resolution, which is in principle only dictated by your ability to sequence DNA. So in practice, what we do is the following. We cut slices through a number of nuclei, random slices, how we cut. That's the easiest part. And it's something which is well, well, stamped in molecular biology. They have ways to cut, to cut thin sections and it's really mechanical. So with the sides, that's 10 micron, I told you. In this slice that we cut is in the paper that I guess you see, please sit there. The sides of the slices we cut is 200 nanometers. But this is not the real technological difficulty. The real technological difficulty, if you want to go that way, is to extract DNA from those slices without burning it. This is where we really had hard time with. So the story goes that this is a statistical mechanics idea, simple statistics, then you have to implement it and that took time. So the difficult part is to, once you have extracted the slices, what we want to do is to know what is inside the slice and that's based on sequencing. And you know that sequencing technologies have made huge progress in recent time. And so you can really sequence it. You can really know which find details, who's there if you do not burn it. Because the way we do it is you can't slice then with laser ablation, you extract the section of the nucleus and you have not to burn the DNA when you extract it. And once you have extracted it, you have it in a tube and then you just sequence it. And so after sequencing for each section, you know which bit of DNA was present or absent. So for instance, in that slice, the three loci of interest are all present. In that one, this is missing. In that one, this is the only present and so on. And you have this for hundreds or so slices and with some computing, it's not really difficult. There's only, there is some math, but nothing extraordinary for statistical mechanisms like us. With some computing, you can reconstruct the contact probability. So what you see here is real data. This is an example of five mega on chromosome six. And this is a so-called contact map. I think Angela mentioned that. The contact map tells you for each pair of sites what their contact probability. So how many times, say, what's the fraction of cells where they are found together in physical proximity? And again, you see that there are patterns. So I don't know if you have processed that, but suppose DNA is at least within a chromosome that is randomly mixed. You expect to have uniform matrices. Maybe there is only an effect dependent on the genetic distance. So how far they are along the linear sequence of the genome. And so you have to expect something like that. No patterns, because the system is translational invariant along the genome if it is random. And instead, you see there are complex patterns emerging. Also at the level of folding of chromosomes within a single chromosome. And those patterns are telling us the story of the contacts between genes and regulators. That's why those data are very exciting. Because this is qualitative data. This is not saying, well, I've seen a couple of times chromosome X contacting chromosome Y. No, this is telling you the frequency. This is a quantitative measure context. And so for physicists, this is very exciting because now we can make models from data. We can test our models against data. We can make predictions and test the theories. Okay, so first glimpse on what those patterns are. I think by high, you immediately see that there is sort of a hierarchical organization. Because if you stare at the picture, you see that there are sort of blocks of interactions along the diagonal. And those blocks, though, are included in bigger blocks, which are themselves part of even bigger blocks. So this means that contacts are organized in a hierarchical way. And in the picture, which is emerging, of how chromosomes are folded, is a hierarchical organization. So if you could zoom into a chromosomal territory I showed you beginning, then you would see something like that. At least this is what this type of data. This is, by the way, the first technology introduced to map contact, which is the high C technology. In what happens at the nuclear scale, the current picture is this one. So this is a schematic picture to give you the idea of what happens. What high C data, what gamma data, this new technologies I told you about, tell us is that each chromosome produce a strong network of contacts. However, there are interactions across chromosomes. And with the technologies I told you, it looks like that they are much weaker. So on average, interactions across chromosomes are two orders of magnitude smaller than interactions within a chromosome, but they are significant and biologically functional. And so the picture that overall is emerging about chromosomes are organized in the nucleus is, I would say, a net of nets. In the sense that each chromosome has a strong network of contacts, functional contacts, but the different chromosomes interact with each other, forming a global net of nets. So I try to give you a summary of what I discussed. So the impression is that to regulate gene, you have to fold DNA because in answers, regulators in general have to come in contact, physical contact with the genes. And so at the scale of genes, you have sort of organization of the genome. But that organization is not limited at the scale of single genes. It extend hierarchically over entire chromosomes. And there is a structure also at the level of the entire nucleus of a cell. So this is a crash course in chromosome folding, technically speaking, and how chromatin folds. Chromatin is a technical term to say, not vague chromosome, but the real chromosome, how they are inside a cell. And so with all the machinery of molecules attached to them. If I have time, I will tell you more about that later on. So now where physics enters? And pictorially, to tell you what this community of physicists is doing in this field, let me try to make an analogy with the physics of atoms and nuclei. The big development of quantum mechanics is linked to experimental and technological progress. At some point, spectral lines could be identified and it could be understood that there are patterns unexpected from classical physics. And you know, so there is a long story which take us from democracies to modern quantum mechanics. And in a sense, the aim of this type of physics applied to biology is the same. We want today to understand not what's the structure of the nuclei of atoms, but what's the structure of nuclei of cells. And so what are the principles which control that? And then what are the principles which control life? And the implications I told you about. So this is what we try to focus on. And this is what I want to discuss in my lectures later on. But first I would like to give you an overview, an idea what are the ideas that are being discussed in the literature at the moment. So I want to get back to the original starting question I had for you. How can a regulator and a gene find each other? And I want to try to give you a sense of scales. A gene in human is order of magnitude a few thousands of bases. And we know today that regulators can be as far as one million bases away from their targeting. So one thousand times more distant than the length of the gene itself. And often regulators can be a few hundreds or a thousand bases long. And so again, the big question is how do they find each other in the darkness of the nucleus? What are the mechanisms whereby they come into contact? Or in the terminology I use here, it's the glue which brings them, holds them together. And a theoretical physicist approach to this is I would say even trivial. If you have a force which takes two objects together, there must be a particle which mediates the interaction. Nothing more than that. We know this from basic physics. Of course here, the particles which mediate interactions are not fundamental particles, but they're stupid molecules of biochemistry. And so the idea is really trivial. You have molecules which can bridge the gene and the regulator. And you can show, I will show that to you later on. They produce an effective field, an attractive force. In fact, there is a thermodynamic transition whereby the two, the stable state of the two objects is to come in close proximity and to be hold in close proximity. And so the idea is that, well, we have just to find the standard model which describes such interactions. And then life is just a matter of solving an Hamiltonian problem. I don't know if I'm clear enough. And so this is the line of thinking. And in this way you can think that you can reconstruct the structure. If you understand what's the Hamiltonian of your system, you can think, you can reconstruct the structure of chromosomes and predict the way they fold. And you see here an example of how the genome around the SOX9 gene loops, complex structure, nested structures. You remember the hierarchical organization I showed you emerging from the data at the beginning. And by the way, the reason why I'm showing SOX9 is because this is a gene associated to human diseases to congenital disorders. And so understanding how is the regulation of SOX9 occurring? So when and how are its regulators interacting within has also very practical implications. Originally these were movies on my computer but this is a PDF, I cannot show you the movies in their full structure. But this slide was to give you a sense of how you can control differentially a given gene in different tissues. In this example, this is a regional stretch of DNA, it's roughly two mega around the alpha-globin genes. And I highlight in yellow and red the gene and its regulator. And you see here the static picture isn't fully expressing the richness of the dynamics of the problem. But what I'm showing you here is on the left how the genes and regulators are located in the DNA of mouse embryonic stem cells where the genes are off. And how they refold, I guess it's difficult to see in this time snapshot. But anyway, this is the way it refolds in erythroid cells when those genes must be activated. And I am fortunate to explain you more details with Abel because you don't see the movie. What you should notice is that genes and regulators are far apart in this case and they become much closer to entering contact in this other case. And so this is how specific regulation of activity is occurring at least for the alpha-globin genes at least in those two tissues. And as I said, yes, yes, the idea is precisely that now that you have the tools, you go and try to make sense of what is happening in embryonic stem cells. I try to summarize for those of you who are less familiar. And embryonic stem cells are cells which are called pluripotent because they are the cells which can give rise to all the tissues of our body. And they are known to be differentially controlled with respect to other tissues. And what you ask is, can we make sense of how is this differential regulation occurring at the genomic scale? And this is exactly the direction where we are going. And I will come to that later on. That's why I'm answering very, very shortly about that. Because instead to try to come up and wrap up my introductory part of the lecture, I want to mention the application of this type of concept. And I want to discuss, for instance, the case of genetic diseases, which I mentioned at the beginning. An example of genetic disease is a disease induced by a mutation in genome, a deletion in the example shown. So there is a kid which is born without the cyan pieces for some reason. And one of the key problems in genetics today is to predict the effect of that, to make a diagnosis. Is the kid surviving or not? What's the impact of that? Is this a minor impact? Or is it lethal? And today the only way that genesis is used, or virtually the only way genesis is used to try to make a prediction is to see if the deletion or the mutation in general is affecting a specific gene. If you miss a gene, well, we have a chance to say, yes, this is risky. But I told you that genes are less than 2% of what you know. So the vast majority of inherited mutations nowadays cannot be diagnosed. And I think you understand what's the impact of that. We all have family stories about it. So you can imagine what's the leap forward of starting from first principle to try to make a prediction and say, no, look, that mutation is not involving a gene, but it is lethal because it is changing the way in which that gene is interacting with its regulators. Because for instance, a trivial effect is that you take out a piece, and then a regulator we should act on another gene starts interacting with the wrong gene and activates the wrong gene and induces a phenotype. This is the name in biology and genetics. And I will show you examples of that and how we can predict this. And let me just have a quick tour to give you the sense of what we can do today. This is a real human patient. And this is another genomic region which is linked to human diseases, in particular, lima formation. This is the region around the FA4 gene. And this is the contact map of that region. This is roughly 2, 3 megabase long region. But anyway, without details, I will tell you that more about that later on, that this is the contact map in the so-called wild type case, so the healthy normal case. And you see here the interaction of FA4, PAX3 is another gene in the region. And when it takes, this little thing here is an enhancer of FA4, is a regulator of FA4. And you see here what's there, the structure, the interactions in the locus. Locus is a named biologist, used to say a region. So when I will say locus, I mean the region around the given gene. So this is the healthy case. And this is what happens when you have a deletion here. This is a deletion only on one of the two elites. And so what you see here is the combined map. And that deletion produce, record actually, so a mouth formation of length of hand. And what this is showing is that when you have that deletion, what happens is that the enhancer of FA4, you see starts interacting with PAX3. And what was shown by our collaborators is that PAX3 is unregulated, sets on, starts producing these proteins. And that's leading to the mouth formation. And what I think is even more exciting is that I deceived you because the top data is modal predictions, theoretical physics-based prediction. The real experiments which are only controlled are beneath. And you see, it is a good agreement between polymer physics and experiments with the patient cells. And so I guess without now entering too many details, you see what's the future that we envision today by using this type of methods, theoretical physics methods in biology, in genetics. Is that we can really change the world of diagnosis and treatments of diseases. I see in the short-term future the possibility of revolutionizing diagnosis. And if not for my generation, I'm confident that for your generation, having we understood the principles of control of genes, also treatment will be available for a number of diseases, such as those I mentioned, which nowadays are not credible. So let me wrap up and then we stop for, I don't know, half an hour, what's the standard? My wrap up is the following. Hopefully what I just started introducing you to is the fact that the genome has an imported physical dimension. The way it falls in space is crucial for the regulation of gene. And by combining progress, technological progress in biology, molecular biology, in the principles of theoretical physics, we start, we start, only start understanding what are the principles whereby life itself works. That is to say how our genome is controlled. And this may open, I think it can open, a real revolution in genetics and medicine in the way I briefly summarized for you. And I'm stressing that on one hand for the excitement that we have as basic scientists in this, but also to give you the sense of opportunity that in this emerging field is for people like you, I should say like us, but I am pretty old. I mean, this is a field where there are wonderful opportunities for basic scientists because of the world of discoveries that is ahead of us, where there are huge business and then job opportunities in very high-profile companies. There's an entire section of alphabet which is working in this just to give you the sense of what are the opportunities in the job market for this type of topics. And so unless you have questions, which I'm very happy to answer, I will stop here and meet you in half an hour and we start with a slightly more technical lecture. So thank you and open to question only if you have them. Okay.