Everyone, welcome to theCUBE's presentation of the AWS Industry Technology Partner Showcase. This is season one episode two to kick off the series covering the exciting parts of the Amazon Web Services ecosystem. Talk about enabling global collaboration and game development. I'm your host, John Furrier with theCUBE. And today we're excited to have, we're joined by Chris Lee, general manager of immersive technology at AWS and Zach Blitz, head of core technology at Epic Games. Here for the opening session of the showcase. General thanks for coming on, great to have you. Thanks for having us. A lot of action going on in game, in the news, the audience are growing, the game culture, gamification, gamers 10 years ago and now 10 years older and they're getting younger and better. It's just fun to see the game environment the way it's growing and continuing to innovate with the technology and just overall experience been great, great conversation. Let's get into the first topic. Game production in the cloud, big part of the evolution, faster time development, better experiences, it's getting bigger scales. So the first question is, what's the current state of game development in the cloud? Let's get into it. Yeah, so I think game development in the cloud right now is pretty strong. I think throughout COVID, I think the cloud really enabled game studios to go remote, enabled to be kind of spread globally. And I think about game, sorry, game development in the cloud and kind of two facets, everything that's in the game server and then everything that's outside of the game server. And I think Amazon specifically has done a lot to help with things that are kind of outside of the game server, things like a game lift have been, I think, really, really welcome additions to the industry, especially in areas where startups would have to build kind of game server management platforms on their own. And I think it's really, really awesome. Places I'd love to see us go, maybe CICD, Source Control, those things are still for small studios, I think really, really painful to get started and set up and usually it takes like two or three people working at really odd hours to get builds out and to get things tested. But yeah, so far pretty strong. Chris, obviously Amazon got the scale, Epic Games, leadership position, you guys working together, but talk about some of those trends. It's still early days in game development, certainly as the collaboration increased democratization, participation, the customer could be part of the production process themselves. All kinds of new dimensions are changing. How do you see this early days of game production? Yeah, I think Zach teed it off really well. Like the pandemic was this really transformational time for game developers, where we had to radically shift from a lot of culture around building games in the same building together to not a more hybrid workforce or fully remote workforce and then getting more distributed around the world where we've focused on how to support games that run in the cloud for a long time and live service games and orchestrating game servers and products like Game Lift. And now we're seeing game developers really want to think about how they work coming out of the post pandemic. Now that we're three years later, what does hybrid mean for us? How do we want to set up better ways to work and how could we leverage the cloud to do that? We see some definite trends there with more customers today asking about and looking for solutions from AWS, from our partners to be able to achieve better outcomes in this remote environment. Zach, you mentioned the game server and then everything kind of around it. What are some of the workflow changes you're seeing in this new environment? Yeah, I think obviously throughput is a throughput in capacity and performance in boxes are huge when you have remote build farms, when you have remote device farms, everything in the CI-CB stack and the source control stack. And so, the compute asks are only getting more intense I think for the cloud providers. And also the need to be elastic, which is not something that metal has typically provided. So, yeah. Guys, global collaboration is a big part of it. The workforce is our developers, right? They got a code, they're working together. What's changed in the makeup of the developer? And then we'll get into some of the dynamics around the game development workforce. What's the current state of the mindset or makeup of today's game developers? The same as the traditional game developers devolved, has it changed at all? What are some of the observations you've seen, Zach, and Chris around that persona? Obviously, there's more developers coming into the workforce, but what's the persona look like today? Yeah, I think games as a service has really been the big change. I think when you were shipping a box product and you kind of had like two years to ship and kind of like a 10 month sprint towards the end of that release date with a real fixed release date, crunch was really intense and you kind of got into these cycles with devs where they were just burning out pretty rapidly. I think the mindset now is a little bit more optimistic in that games as a service provide the opportunity both on the back end and the front end for us to kind of take a one step at a time approach launched with an MVP or something we feel really good about getting into players' hands and then iterating from there. Similarly, I think games today go out rather unoptimized and then kind of gradually become more optimized just as any business I think would over time. And I think the cloud definitely provides the ability for devs to do that. Chris, I want the global workforce. What are the challenges? You got all kinds. He mentioned he last Disney before. Devs are in the cloud, right? I think one trend we see is developers have always tried to push the boundaries of what's possible with the highest fidelity, find that perfect experience for gamers and achieve their creative vision. And we are seeing you have more access to a global talent pool now with this remote and hybrid work environment. And then you also see this greater demand for compute and fidelity where we have bigger data that lets you put better pixels on the screen and we have much more compute required to get that ready for game developers built and cooked into your game. So I think we do see some trends with more distributed teams than we had pre-pandemic and definitely see people leveraging these diverse pools of talent where they're located. And then just really pushing the needle of how do we help those that distributed team collaborate together while bringing the bigger data, more content, more demand for kind of generating these assets at the same time. Zach, I want to get your thoughts on the high level trends. Obviously agility has been a big part of the cloud since day one, speed, agility. Now with data and generative AI you've got a lot more generative activity whether it's at the edge, latency is now important and these games are getting more competitive. They just demand for faster turnaround times. How do you see that evolving in this global workforce? Cut distributed workforce, okay, great, check, more talent. How do they become more productive? What are some of the challenges? What's the implications? Yeah, I think if anybody thought that network engineering was dead when we moved out of the data center and didn't need to do like ACL changes anymore, I think, man, there's two, I was just thinking about what Chris said and I think there's two really important use cases there. The first is like internal WANs and network engineers who can do that stuff really well, getting builds to and from kind of build servers. I mean, that's a huge job and a really, really important job at large game studios. I think the edge in terms of gaming, yeah, I don't think you can have eSports be as prolific as it is without servers nearly everywhere. And I think Amazon's doing a great job at that. I mean, just look at Korea compared to America. I think the reason why League of Legends is so popular in places like Korea and China is just how far servers are towards the edge. I mean, playing games at two or three milliseconds is insane and it's a huge competitive advantage. I'm looking forward to kind of the last mile and a lot of the edge stuff kind of, I think growing and changing the way that we look at the types of games we can play over the next 10 years. I think if VR and especially competitive VR is going to find a place, it will be when images can be rendered directly from servers that are two milliseconds from a user's home. Obviously we're not there yet, but I'm excited about the potential for all that that brings. The high-frequency trading or high-frequency gaming, if you will, really is going to come down to also more equality in terms of the latency and competitiveness. I think that will open up a lot more environments and developers have to manage that. Chris, you guys are always working on reinvent this past year. The big discussion is about networking, interconnects, what's around the chips. This is systems architecture and it's global and that's going to have implications. Yeah, we are very excited to get the edge closer to players. We actually had work we did over the past year where we worked to help create a local zone in Dallas to really reduce latency for players in the United States to help with some of these competitive gaming scenarios. And so we're always looking at the way that our customers want to engage their player base and where they need the edge in relation to those players to make sure they have a great game experience that meets the unique needs of their games. Not every game needs that super fast latency, but when you do, you really need it. And then you have to have a great way to be able to get the cloud like a local zone for AWS where we can put compute, storage, right next to these tech centers or population hubs that people want to get into to be able to engage their players. Guys, what are some of the entrances you're seeing in the industry on the horizon?  What are some of the challenges that are going to be solved quickly as we certainly well past the post-pandemic world now with a better, faster infrastructure coming on with cloud. Zach, what are some of the big trends you're seeing in game and game collaboration and development? Well, I think right now, the industry's had a rough go with the economic environment. And I think that has a lot to do with the lack of elasticity around fixed costs, whether that's people or compute. That's, I think that's a big deal. And you see it right now. I mean, there are a ton of startups thinking about the better ways to use spot instances to save cost. We're always thinking about better ways to kind of optimize the bottom line. And elasticity is a big deal. I don't know that that's actually super innovative, but a lot of the ways that games have scaled in the past is super inelastic. And a lot of the applications industry wide are dinosaurs in that way. And so those are things that, although maybe not the sexiest answer, like are definitely things I think we're looking at, 23 into 24 for sure.  I need to guess how many players are gonna come in the front door and win. And then if I needed to scale up, when I had this crucial moment in my game's launch, when I have the most attention on my game right up front, if you would get those guesses wrong, you can really pay at price where maybe you don't have the ability to get all those gamers into the door to try it when they're most excited to try your game experience. Or maybe you over provision a lot and you end up spending way more infrastructure up front while you're still fine tuning your game, have the right engagement with your player base. So I think this has been something that really helps game developers make sure they're getting the right amount of infrastructure at the right time and to be able to scale it quickly when they need it. I think that's one of the big advantages of working with AWS. I mean, I'm blown away by Epic scale, League of Legends, Fortnite, Zach. I mean, this is a huge scale in just in terms of the game operations and then the development. Give us an insight into the footprint, how you guys look at that, how does that work on the operating these games, whether it's exposing it to many people, but also as you manage it, you got to run this. You got to build, you got to design, test, run. You got to develop and it's the scales massive. Yeah, I think observability, single panes of glass are huge for us, both in terms of where players are, where they aren't, you know, how to be elastic in the right environments. I think like, you know, service health, all of that stuff is a huge, a huge deal.  I think quite well to make it so that we can run these things together seamlessly. We can do monitoring and alerting and logging. But yeah, there's a ton of information and a ton of brain power that goes into trying to figure out how to do that. And I would be doing a disservice if I gave a more specific answer than that. We have a lot of data too, the data observability. I mean, the sea of data, every single data point matters. Yeah, petabytes, petabytes.  I think people have a tendency to want to, you know, then use that information to make conclusions and ferry it from one place to another, which gets really expensive. And I think having excellent data people manage at both of those companies, I think is really, really, really critical to both business health and operational wealth. Chris, this is really going to be a big topic. We've been talking about platform engineering. It's evolving mainstream in the enterprise. Data engineering is coming as a huge discipline. You got all this data, generative AI is on the doorstep here at the beginning stages of that trend, only going to make it better on the build run, grow side to make the experience better for users and developers at the same time. This is a huge portion of the conversation. Yeah, we're very, very excited about generative AI at AWS. We really want to help every game developer figure out how they can incorporate that into their game production. We want, I mean, one of the things we talk about is helping every company be an AI company if that's their goal to be. So we focus on a number of things there for choice in kind of choice and model and we've built great things like Amazon Bedrock where developers can choose different models that best suit their needs. We don't believe one model will rule them all. We also believe in helping game developers customize and support their models that they choose to use. We've got a way to securely make sure that you're training your models and doing those IP protections that are important to you as a game developer. And then we provide a robust set of tools for developers to use and dedicated infrastructure much like we do in the other parts of our AWS portfolio where we have chips like the Inferentia and the Tranium chips to actually lower the cost of how you deliver those things to game developers. And so I think when you think about building your game model that fits into our build model where maybe that we have dedicated solution areas for AI and ML for games. We also have things like game production in the cloud like we're talking about here today and game security, game servers. Then we get into more of the run phase where we're talking about live operations and maybe data and analytics in that growth phase to really help game developers understand what's going on in their games and what's going on with their population to make sure that they can understand the best ways to engage their players and make the best experience for them possible. Games are apps or platforms. Platforms are now global. Guys, great conversation. I have to ask you, since you're both in probably one of the most envious jobs if you love gaming, I mean, I'm a little bit too old to get back into the coding game inside of things, but for the folks that love gaming you're both in the middle of it. Zach, you're the head of core technology at Epic. What's the hottest thing going on right now? What are you most excited about? Give us a peek behind the curtain around. What's like there? Someone who's watching us may be thinking about getting into gaming or maybe entrepreneurial activities high as well on gaming. You mentioned some of the smallest news of doing well, managed services are hot. I mean, it's a lot of opportunity and people are just curious. What's the cool stuff going on and what's coming around the corner? I think it's kind of an interesting question, right? Because I don't know that there's any one thing I'm excited about. I do think that the pandemic obviously opened more people up to gaming in a way that they've never been opened up before. And that's inclusive of like, I got boomer parents who love playing the New York Times, spelling bee every day. And whether or not it's web-based or in sessions playing Battle Royale, I mean, these are all games. And I think just everybody's kind of growing acceptance of it as culture I think is really exciting. I would say if you're looking to get into the industry, this is the advice I give pretty much all the time. Most jobs that you're doing in a corporate setting carry over to games. Like we still have people who are like taking notes and meetings. We still have people who are lawyers.  And although it's kind of a boring answer, I think if you just kind of start with where you are and think about, hey, does this job actually exist in games and talk to somebody in it? Generally the answer is yes. You know, so it's, and we need all those people. It doesn't take just Zach's and Chris's to launch games. It takes a lot of blood, sweat and tears from legal, from PR, from marketing, you know, a number of people in a number of different disciplines. Awesome. Chris, your thoughts on, you know, entrepreneurs out there, people who want to see peak behind the curtain, immersive technology, where are we in the progress of the ideal virtual reality, real time world? I think it's an exciting time. We see, in AWS, we always want to help empower the creatives and the developers that are bringing their vision to life. So it's exciting to see new headsets come out. We have some new headsets from Meadow with the Oculus Quest 3 and the MetaQuest 3. Sorry.  So it's exciting to see how there are so many great partners and players that are bringing to life new headsets, new experiences and new visions for what virtual reality and mixed reality can be. So I think that's an exciting time. It's very early days. We're seeing new experiences and new ways to leverage that both in gaming and across industries where we see people finding ways to collaborate or communicate differently, do training. And so there's an exciting evolution where game technologies are the technologies that people are using to bring a lot of those experiences to life. Chris, Zach, thanks so much. And Zach, congratulations on continued success at Epic. And Chris, you got the cloud growth coming, more entrepreneurs, more developers, more developer goodness, a lot more observability, a lot more data, a lot more user experiences coming with gaming across the world. Thanks so much for kicking off the Global Collaboration and Game Development series. Appreciate you. Thanks for coming on. Thanks for having me. Thanks, John. Okay, thanks, Zach. This is theCUBE's presentation of AWS Industry Tech Partners Showcase, kicking off season one, episode two. We'll be back after the short break.


If you have never run an educational programme, I first recommend you to do some workshops in a public library or in a local institution and then think big, think education. It's a big thing. Be patient because teachers take their time to get convinced that Wikipedia is a good thing. What makes sense is start small and then scale up. You should really start small and experiment. Our success came from, first of all, starting small, not taking too much upon yourself and understanding your limitations and focus on something and persevere in it. Right now we are about three or four people who are actively working on educational projects, mentoring students, trying to find contacts with other professors, with other lecturers. Class size is something that we learned many, many times can really impact negatively a classroom assignment. So we recommend you keep the class around 30. That's really ideal, at least in our programme. That's what we've seen. In the beginning it was quite difficult because we thought that the way to grow our programme is to write emails to all the professors in the Czech Republic. And the turnout rate of that was very low. We hardly got any answers. And then I started contacting my former professors and people I knew from the academia and that was the key point. The key to a successful education programme is a dedicated teacher, for sure. You need to find an ally. You need to find a champion in the university. If you're working in universities and you get instructors who decide they're going to really take this on, they're going to make this work in their classroom, it starts to spread. It's better to have teachers who already are, even if they're not Wikipedians for ages, at least to have experience with editing. We used our personal contacts and people that we knew to bring in the idea of Wikipedia in educational context. It's really important to find good collaborators, to find the right people to collaborate with people who understand your point of view and what it is that your mission is. We try to use some kind of peer productions or peer projects while when a teacher believes Wikipedia or trusts Wikipedia we try to use him as a virus to spread among the university and also among students. So the idea is to bring not only quantity of people but also quality of information into Wikipedia. So part of it is outreach not only in education but also in GLAM. And obviously these two overlap and the best way to go about it is to go from institutions that are overlapping like a library which is a GLAM institution that is inside an academic institution like a university or archives or museums that are on campuses which we have a lot so that would be one way to integrate in the two. The second is working with for instance librarians which are in many times their educators. So they teach others how to use resources etc. so librarians are usually good partners to us.


What else can I predict? So, people have done these experiments to see that if I take a polymer, let us say I take this polymer, I take some random polymer and I look at two sites along this backbone let us say one site here and another site there ok. If I walked along the backbone, these two sites are separated by whatever is the distance between them let us say some S base pairs ok. But I that is the genomic separation between these two base pairs, but I could also ask what is the spatial separation between these two base pairs, what is the distance between these two base pairs right. And I could do people have done experiments to find out these quantities, what they do is that this technique is called fluorescence in C 2 hybridization, fluorescence in C 2 hybridization of fish. So, what they do is that if you want to know where a particular sequence of this DNA is, you create a complementary. So, you create a sort of probe which is exactly the complementary sequence to this region right. So, you create a complementary probe. So, if this is A T T A this would be T A A T right and you attach a fluorescent marker to it. So, this probe will go and attach to this specific region of the DNA that you are interested in right. So, you can know that inside this nucleus where exactly is this this segment of the DNA. You can do this for every segment and therefore, get. So, if you if you tag this segment for example, this using a different target of course, depending on what the sequence is over here, you would get where this sequence is on an average inside the nucleus. And then you could say therefore, what is the average separation between these two segments. If you did it for a standard polymer, so just purely from from a let us say a random walk polymer, then again this separation between these two base pairs which was separated by s distance along the contour would typically go as s to the power 2 nu where nu is equal to half for a random walk polymer ok r square would go as s. So, if nu is half this is s to the power of 1. So, that is what I would expect for a typical polymer. So, it would keep growing like this. So, if I plotted r square s versus s it would grow something. You can do this. So, if you do this experiment for actual genomes. So, here is a plot of that this is actual fish data this is the r square as a function of the average genomic separation between base pairs ok. So, this is averaged over what? This is average this is not for a particular pair of base pairs. So, if these two base pairs are separated by s I take the average of these two over many many cells, but I take such average over all base pairs which are separated by s. So, this is one pair one and two then this could be another pairs if I start somewhere over here the other pair would be somewhere over here right. These two would also be separated by s. So, average over all base pairs which are separated by this s genomic distance and I find out what is the average separation between such base pairs and it turns out that yes it initially does increase, but then slowly after a point after around 1 megabase or so, it quickly approaches the saturation ok. So, it does not keep growing indefinitely as I would expect or as I would expect naively it reaches some sort of saturation pretty quickly and what could the reasons for this saturation be? A trivial reason of course, is that it is these polymer systems are confined you are putting them inside a confined volume. So, they cannot really go beyond the confines of this nuclear radius right. So, there is some sort of a confinement effect. There could be other effects like tethering it is known that there are proteins on the surface of the nucleus which I will discuss which actually like to bind which actually like to bind to these chromosomes and that again will restrict what sort of confinations are allowed and then there are also protein protein DNA interactions. So, if you have proteins which bind regions of DNA something like this if a protein came and bound two regions of DNA like this then these two base pairs over here and here even though they are very widely apart on the backbone would be extremely close in spatial separations right. So, this sort of a leveling out of this R square could in principle be due to confinement, it could be due to tethering, it could be due to protein interactions or more likely due to all of these together ok. So, we have, but whatever model you sort of build should reproduce this sort of a feature that it should sort of flat out after around 1 mega bases or 2 mega bases yes. No. So, this is I think this paper was for a mouse genome. So, this is averaged over all sort of what do you mean a single DNA. So, let us say this and this. So, I have a fluorescent protein marker over here which tells me the location x, y, z of this segment. So, it is not a single it is not a single base pair level. So, you build the target strand has I forgotten maybe around I think it is around 30 to 50 base pairs. So, you build a complementary strand sorry ok. So, that was the conclusion. So, it is not a single base pair it has it is a target sequence basically ok. So, then that target sequence will come and seek its complementary target sequence and bind. But in the level of this genome which is like 10 to the power of 9 base pairs, the 30 to 50 base pair probe is like almost a single point effectively right. It is a very small point on this whole DNA back point yes I see ok. So, you mean that so, that is true. So, you have this DNA double helix which has let us say a t whatever t something like this and I am designing something which has anyway this complementary sequence this t a. So, in order for it to come and actually bind here. So, one thing is that. So, I am not sure of the exact answer of this, but one thing is that you therefore, have two sort of possible binding partners right. It could bind to this complementary strand or it could bind to this target strand right with equal sort of energies if I have not done anything else right. So, even with even if I were to do nothing else these would occasionally bind to this target strand and cause a fluorescence. Maybe in only half the samples, but I am not sure that is the answer actually I think they might do something. So, that the energy of attraction to this complementary strand is slightly lower. So, that it preferentially wants to bind to this. I am not sure if they do it by opening up the DNA somewhat in this region or through some other method, but I can look that up. But even if I were to do nothing I would still expect the sort of fluorescent signal because I have introduced this target probe. Yes, no right you have two copies of every chromosome. When I say humans have 23 pairs of chromosomes, you have two copies of 1, two copies of 2 and then 1 x and 1 y. So, 22 pairs and then 1 x and 1 y and then when the cells divide you know this chromosome sort of ok. So, let me just the confinement is sort of obvious that because you have a nucleus nuclear volume you have some confinement effects. Let us look a little bit about this tethering proteins. So, here are particular class of proteins called lamin proteins and these lamin proteins are preferentially found along the periphery of the nucleus periphery of the nucleus sorry forget this curve just look at these things. So, there are different categories different species of lamin proteins closely related for example, this lamin B1, this is lamin B2 whatever. But the key thing is that these lamin proteins are found sort of distributed along the periphery of the nucleus and people have done experiments to show that there are regions of the chromosome which bind very strongly to these lamin proteins of the periphery. So, you have these sort of tethering interactions which says that this polymer is not simply a free polymer inside this volume, but there are constraints which says that certain regions of the polymer will actually be bound to this nuclear to this boundary which is the nuclear nuclear wall or the nuclear lamina as it is called. So, that is one class of interactions that can also provide some sort of compaction. So, if you had a polymer which was interacting with the wall in this way the statistics of that if you were to calculate average r square s would be different from the statistics of a free polymer floating in solute floating inside this bounded volume. So, that is another thing you have these tethering interactions and thirdly you have these protein mediated interactions which sort of comes under this very interesting thing called DNA looping. So, often people have found that there are these proteins which loop distant segments of DNA. So, this is a this is some protein which takes together two segments of DNA which are very far apart and it sort of brings them in close proximity to one another. So, what I have got now over here is what I would call a DNA loop and people these loops play a lot of functional role. So, for example, you could have a gene activator region. So, for example, let us say I have a gene I have a gene sequence like this and in order for this gene to be active then there is a promoter region which sits very far away let us say somewhere over here far away along the backbone. If there was now a looping protein which sort of bound this region to that. So, that my confirmation now looks like this then this could be then a protein could come and bind to this activator region and start transcription of the DNA or stop transcription of the DNA need not be an enhancer it could be a depressor, but by bringing together distant segments of the DNA you could play a role in regulating the genome itself ok. So, the structure of so, the structure is important in order to sort of understand how the genomic into information that is there in the DNA itself is interpreted and that is interpreted through this higher order sort of regulation. So, if you bring together distant parts some genes may start expressing some genes may become silent and so on. So, they say something about actually this looping protein. So, it is actually quite interesting or rather it is something that I am interested in so, I might as well tell you. So, what is known is that you form these extremely large loops or rather loops set up on all length scales it is not just simple short loops that you would expect randomly, but you form extremely large loops of the order of kilo bases. So, hundreds of kilo bases even and in occasionally even mega base long loops and these are very well known in the literature a lot of the functional roles of these loops are also known. What was not known is that what sort of proteins form these loops. So, what people hypothesized a few years back 3, 4 years back is that you have some sort of a protein which is called which I call an extrusion complex or which people call an extrusion complex which has something like the structure it looks like two rings ok. What it does is that it comes and binds to this DNA topologically. So, that the DNA is if I have two rings you imagine a thread passing through both of these rings and then as this proteins as this sort of DNA I could pull on this loop or this thread and I could get a longer and longer loop ok. And if this loop formation would stop at certain specific sequence markers which are called CTCF motifs. So, these are certain sequences on the DNA which tells these proteins to sort of stop and that is where the loop stabilizes ok. So, it was not really known what these proteins were this was more for hypothetical model, but recently a couple of years back people have found out identified certain candidates for what are called this is loop extrusion complexes. So, these extrusion complexes are called the loop extrusion complexes. So, for example, this is one such protein it is a protein called cohesin which has a structure like this. So, this is a dimer actually. So, it could be that this DNA sort of one arm threads through here and the other arm threads through there and you form this sort of a loop. So, here is sort of a cartoon you form a loop and you sort of thread through it and you keep growing the loop until you hit the markers at the ends which tells the proteins when to stop. So, how do these proteins actually extrude loops and people have done these experiments as well these are a nice set of experiments. So, this is a DNA so, what is called as a DNA curtain. What you take is that you take DNA fibers you take DNA strands you take a DNA strand for example, like this take many, many such DNA strands and you put these proteins on them and you put these proteins this cohesin proteins on them and you tag these protein cohesin proteins with some fluorescent marker. So, as these cohesins move about on the DNA you can follow the trajectory of these markers and see what sort of a trajectory you can. So, these are these multiple DNA curtain. So, this is called the DNA curtain. So, these are these multiple DNA strands ok. So, you could think of this like an ensemble ok. This is an experiment which is many, many copies of this simultaneously happening and then you can average over these ensembles to get average statistics ok. So, that is what we will forget about the lower figure. So, if you look at any such strand of if you look at cohesin on any such strand and you look at the trajectory of cohesin. So, now you have to imagine that this axis is time ok. This is axis is where my DNA strand is ok. So, as I move along here. So, these are continuous snapshots of that fluorescent marker. So, as I see like this what I am seeing is the trajectory of that cohesin molecule on the backbone of the DNA and you can calculate. So, let us say I start from somewhere and I can calculate how far I have gone in a certain amount of time right. So, I can calculate this by averaging over all these DNA strands and all times I can calculate how much this cohesin moves on this DNA backbone as a function of time and it turns out it moves perfectly diffusively. So, for diffusion this coefficient should be one and experimentally I think that determine this coefficient to be 0.98 ok. So, which is pretty impressive right. So, it is actually moves diffusively not only that you can measure therefore, the what is the diffusion coefficient and they have done that experiment and they have measured the diffusion coefficient at different salt concentrations. Physiological salt concentrations is roughly somewhere over here between 100 to 250. So, inside the cell you have certain salt concentration. So, over there typically so, at physiological salt the diffusion coefficient of this cohesin molecule which is forming this loop would be somewhere 0.1 to 1 sort of micron square per second again disregard this lower figures. What does it mean? So, this further what you see is that this cohesins are actually topologically bound to DNA they are not chemically bound. What does how do I see that? So, for example, I can look at locations where this binding and unbinding of cohesin happens. So, you will see that binding. So, here is my DNA strand tell me if I am not being clear. So, let us say here is my wall to which I have attached my DNA strand and here is my cohesin protein which is doing a diffusive random walk on this DNA backward ok. Now, you could figure out where these cohesin proteins bind ok at what region they bind and you will see that if you look at the histograms if you look at the histograms they bind sort of so, this is my position along the the y axis is the position along the DNA and this these histograms on your left are your binding events. So, they bind sort of everywhere along the DNA. There is some sequence specificity, but on an average they bind everywhere. On the other hand if I look at where they come off they come off only at this only at this end of the DNA which is this free end of the DNA which is floating around ok. So, it does not once it is bound it does not unbind from random positions, but it sort of travels to the end and then slips out from here which is consistent with this sort of a ring idea that one has. It is not a chemical bond, but it is a sort of topological bond. So, it forms a ring like this and then that ring does a diffusive walk and then only when it reaches the end it sort of falls off. You could show that for example, if you capped this end as well if you if you capped this end of the DNA as well then this would actually not fall off. So, this flow is basically you these are in done in nanofluidic channels. So, you have some fluid sort of flowing to push the cohesin along this direction. So, when the flow is when the flow is on all the cohesins are at this end they have all reached the end, but they cannot come out because I have stopped I put a cap over here and then when the I take the flow off it goes back to doing a diffusive random walk. Again I start the flow again everything gets pushed here and then again when I turn it off it goes back to doing a random walk. So, it has extremely long lifetimes. So, it is not a chemical bond that binds and unbinds it is actually a topological bond or at least some of them form a topological bond which once its bound stays on for a very long period of time ok. So, it is what is the y axis? This is the DNA strand. So, let us say it is 0 base here and then whatever 50 kilo bases at this end. So, you have to imagine the y axis as the DNA strand and x axis is time ok or it is the position of the cohesin molecule on the DNA strand there ok. So, you could ask that well ok this is how cohesin moves on DNA it diffuses on DNA that is nice, but we just saw that DNA inside cells is not just bare DNA it comes in the form of these nucleosomes right it comes in the form of these nucleosomes. So, these are these histone complexes and the DNA is wrapped around that which is a sort of a large object right the histone protein is a protein octamer. So, it is a large object. So, what happens when this diffusing protein this cohesin faces a large object on the on its way. So, again they have done these the same set of experiments actually have a reference. So, this reference over here in 2017.