Good afternoon folks, live here again at the Think Tech Studios downtown Honolulu, Ted Ralston with our show, Where the Drone Leads, have an incredible cast on today. We've got Dr. Peter Quigley. Peter, thanks for coming on board. First time around on this show, and that means you get to come back. That's the rules, okay? And on the telephone from far, far away in Kailua, right over the Koalaos, we have Jim Crisifuli of D-Bed. Hi, Jim. Aloha. Oh, well, how about that? Okay, we got a tropical response out of Jim. That's good, Jim. Good seeing you there. Well, you wanted Italian or what? Italian would be fine, sure. Right. When's autumn though? Okay, so now we're all squared away. So we bring to the public on this show topics of current and topical interest on UAS or drones, as they're sometimes called. And this is an incredible week coming up because we do have the Aerospace Summit that D-Bed is putting on, Jim Crisifuli is running it, along with Jeff Wong, down at the capital. He'll be running basically displays up starting on Saturday, running through the week and then the heavy part is a Tuesday and then Wednesday when we have the various panel discussions and outbreak sessions to develop aerospace plan for Hawaii for the next five years. That's right. And Peter, you're running that. You can be the emcee anyway. Well, actually, I do have the privilege to introduce the governor and then I moderate the first panel, which is basically the overview of the summit. And so we've got some heavy hitters there in that first panel. Mike Bruno is the Vice Chancellor of Research at UH Manila. Of course, we have a lot of activity at Manila around the Hawaii Space Flight Lab. He'll speak to that and give us a little bit of an overview of that. And Mike Jones is the senior advisor from Paycom. He's going to be on my panel, Hank Rogers, of course, of Blue Planet fame and other enterprises. He's also the chair of the Pisces Project, and he'll be there as well as Captain Vance Johnson, who's commanding officer at PMFR. So it's quite a lineup to start the venue. And again, it's sort of the umbrella conversation that then, you know, allows the other panels. Yours is coming up to two after that, topic two, but it's going to kind of set the stage for what people are thinking about, what the various users have from a needs perspective that Hawaii can help fulfill, and that UH can fulfill, I presume. UH, you've got to be a big part of this. Well, certainly UH would like to be and has been with Luke Flynn's work at the Hawaii Space Flight Lab, as well as the drone work that's been going on in the College of Engineering already with some of the research. And of course, we have drone activities at UH Hilo. We've got some Arthur Cunningham is out there, and he's been running some programs and some non-credit training. And so there's been plenty going on at UH. But the whole point to this, like so much in Hawaii, is making the connections across sectors and across agencies because nobody can do it alone. That's pretty cool. And Jim, great credit to you for putting this on. Jim, tell us what your expectations are when Thursday is over. And the day has ended. What do you want to have as a take-home, take-away, and new information for people to have? Sure. Well, what we're looking at is trying to depict what's the aerospace industry in Hawaii. Why it is such a strategic growth sector for the state and basically identify some of the low-hanging fruit for development moving forward, say, over the next five years. So we've identified a number of areas, including commercial space launch, developing of a UAS test range system in Hawaii, focusing on a whole new range of tech development capabilities under the rubric of planetary sustainability. How can we test, validate, and implement technologies in Hawaii that can improve the state of our planet and those same technologies of renewable energy, broadband telecommunications, additive manufacturing, those all can be applied to help advance our reach through the solar system, exploring the moon, trips to Mars and beyond in a very exciting way. And Hawaii can play a big role here because of our moon, Mars-like terrain where you can test and validate these new technologies. And in fact, we're going to be signing a new Space Act agreement with NASA that will basically form an alliance between the agency and Hawaii. And in particular, NASA aims research center in California and the Big Island because there are many of these high-tech firms that work in Silicon Valley and based at the NASA Research Park in Palo Alto that are very interested in developing new technologies for future robotic and human missions to space. And what better place to test them than in Hawaii on our moon, Mars-like terrain, especially with our international connectivity because our motto is really, or I say our approach, is to focus on international collaboration, build public rather than compete, collaborate with our various international partners so we can share complementary resources to help develop new cost-effective ways to sustainably get beyond low-Earth orbit and then build public-private partnerships between Hawaii and various organizations around the United States and internationally that are interested in corporate development of space.  And you mentioned a key word along your dialogue, and that was additive manufacturing. Hawaii is not seen as a state where we manufacture a lot of things. We have the shipping issue, we have the natural resources issues, and we have the lack of a sort of what you might call an airplane assembly-trained workforce here, and so, or space system-trained workforce. So in terms of the Hawaii contribution to this, I can see the additive manufacturing concept is really interesting, Jim, because that doesn't require necessarily the large industrial footprint and base that we think of for airplane assembly and truck assembly. So sub-assemblies and miniaturized components could well be made here, as well as could be the engineering and the research and the testing that is going along the way to make those happen. So Hawaii has an interesting and kind of a non-traditional potential future here in the entire world of the evolving aeronics. Yeah, can I just support what Jim was saying? He used a word that's key to my heart and that's collaborate. He used it in the international framework, but if you look at the folks that are coming to the summit from industry, from academics, from the military, this is once again a great opportunity for Hawaii to pull its energies and its policies together around an economic area that has great potential for the state. As you're pointing out, there's lots of abilities to go in engineering directions, advanced manufacturing directions. We've already shown in the flight program with Luke Flynn out of UH Manoa that we've connected almost all the campuses that have been involved in that space launch effort that he's been involved in and launching a small satellite into space in partnership with PMFR. But that collaborate issue is crucial because we're not gonna get, we're not gonna continue to get a lot of chances. I mean, I continue to think about that word with San Diego, they saw 30 years ago, they had tourism real estate and a naval base and they knew they had to have more. And they got together intentionally and strategically and moved from a $3 billion regional economy to a $178 billion regional economy in less than 30 years, but they did it intentionally. And so we can't, one of the urgencies around this summer, we just had a statewide conference this morning at the Colau Resort, educators, entrepreneurs, private sector folks, government folks around just STEM in general. So this is one piece of that STEM muscle that we've got to grow and tourism's great, we can add to tourism, we can add tech to tourism, come and see, be part of the anti-aging research and recreational area, come and be part of the science and technology advancements in Hawaii with new energy and new agriculture. But if we don't do that, then our economic future is questionable. So this is crucial not only for the opportunity about aerospace and aviation. I include aviation in this frequently because it's also low hanging fruit for us in a number of ways. But you're right to sort of put manufacturing and quotation marks, because we do the same thing with aviation, some other areas. We did it with drones when it first showed up. In 2010, FAA said, well, by 2020, there might be 15,000 drones. In 2016, they modified that projection and say, oh really, by 2020, there's gonna be 542,000 drones. And of course, the economic impact changed from $1.9 billion of an economic impact nationwide to $10 billion, of course. So we have to step up to some things that don't look quite possible. They look, well, we don't do that. We have to write them down first. If we don't have the vision and write them down and extrapolate our minds, we'll never get there. That's right. There's no opportunity to do that. And what goes through my mind, I've been working with Jim for a couple of months, putting ideas together to get to the point where we're gonna have the conference next week or the summit. And this gathering of people and the mental capital being spent putting it together and the discussions that'll occur, that really becomes a body of well-informed knowledge that the legislature can turn to, the business community can turn to, the venture capitalist people can turn to. If we could somehow keep that energy going and keep that group of people together in some virtual way, because people who write laws, people who make bills, people who generate development plans and investment plans need that body of people to turn to. So I wonder, maybe we can turn to Jim. I guess that's a d-bed function. Jim will slow it over the wall to you. How do we keep that body of people together and keep their connectivity going and make them responsive to our legislature and to the business community? Well, the bottom line is the magic wand, but realistically, if we can find a common denominator, what is gonna be maximally beneficial to the greatest number of people? And I'll give you one example with aerospace. Our big economic drivers in this state are the visitor industry and the military. Well, aerospace and the new opportunities that are being discussed here can certainly help expand and diversify our two big economic drivers. In the visitor industry, we're looking at developing a commercial spaceport at Kona International to allow space planes to launch and land at this airport and so bring basically space tourism to Hawaii, which I like to say it adds the orthogonal dimension to our visitor industry. And then in the military, we're working on developing a new test range for unmanned aerial systems. We're looking at possibilities for commercial launch into both polar and equatorial orbits all from the big island. And this is another way to expand military operations, but again, collaborating, bringing the private sector, the public sector, the military, the university into partnerships so that there's a bottom line of benefits. Okay, and that partnership that we're speaking of, Peter mentioned collaboration, you mentioned the partnerships and such getting it all together. That's what I see is a great value here. We're gonna assemble once. This grand bunch of people with a lot of information and visions and each one has their own networks that pull things together. And they're all more or less marching in the same direction. Somehow we have to create a, give them all a card, they belong to a party now. What if that party, I'm not quite sure, maybe it's the aerospace summit party. Let's talk about that, how we're gonna go forward, ask after we get back from a first break. Sure. Aloha everyone, I'm Maria Mera and I'm here to invite you to my bilingual show Viva Hawaii on Thintech Hawaii every other Monday at 3 p.m. We are here to talk about news, issues and events local and around the world. Join me, Aloha. I'm Jay Fidel and I'm the host of Research in Minoa, Mondays from 12 to 1 on thinktechhawaii.com. Take a look at us and learn about geophysics, learn about planetology, learn about the ocean and earth sciences at UH Minoa, you'll really enjoy it. So come around, we'll see you then. You're watching Think Tech Hawaii, meeting people we may not have otherwise met and helping us understand and appreciate the good things about Hawaii. Great content for Hawaii from Think Tech. Welcome back everyone, Ted Rawlson here and our guest, Dr. Peter Wrigley, UH Assistant Vice President of Community Colleges. Very close, very close. Okay, close enough. Oh, it'll do. And way over the other side of the co-allows, we have Jim Crisifulli from D-Bed standing by in our Kailua studio. Anyway, what we were talking about is this aerospace summit coming up next week, which happens too infrequently, quite frankly, Jim, message. We ought to do it once a year or maybe even more often than that. Anyway, it's a gathering of a lot of people in the government, in the state, in the legislature and in academia, and private citizens as well, private business, to come together and understand where the strong points are of going forward with an aerospace future in Hawaii. And we were talking just before the break about how to keep that activity going, how to keep that connectivity going, how to keep those ideas forming and flowing and nurturing them somewhere. So, Jim, we talked to you and we tried to have D-Bed pick that up and I think you may have skated past that a little bit, talked about the grand ideas of collaboration, but didn't say how you are gonna make this collaboration happen. Yeah, well, what we're looking at again is a common denominator where we find a residence with common visions and common objectives and goals and that common denominator, which is built through, again, public-private partnerships and looking at the multinational alliances. I think a third dimension to all of this is to get the public engaged from the get-go as major stakeholders. If the public understands what the benefits of space exploration are and the opportunities for them and their children, the next generation of scientists, engineers, and entrepreneurs to engage, that will certainly get the momentum going. But we're looking for common denominators in terms of interest moving forward. And again, on these two planes in planetary sustainability to improve life on our home planet, but also extending our reach to the solar system and opening new worlds and opportunities, again, for our kids, grandkids, the future of humanity, and in a way that's collaborative. That's not like a mission that you have at UH or somebody has at UH to figure out what the interests are of the future and how those interests can be nurtured and fed and generate training programs, educational programs that fit that picture. Is that a university function? Well, sure. And as a matter of fact, we're about to launch a new website that's workforce focused in January. Social media. Well, this is gonna be a live website that has all the jobs in the state of Hawaii, but also all the projections of where the economy's going. And it also will show us where the opportunities are, but also where the danger is.  And that is the fact of the matter is we're going, we've organized a set of meetings around the state, sector by sector banking health, you name it, the hospitality sector at the top, because, you know, frankly, industry's tired of having meetings. They don't wanna have another meeting. They want to be part of a coherent conversation that moves forward. Now this aerospace summit will be part of our strategy to build our own strategic plan for the aerospace aviation industry. We're gonna take information and of course, people's participation and build it into this strategic plan. Why, to put another plan on the shelf? No, it's, we've gotta make an argument like we do across the economic sectors about the return on the investment. If we grow this sector, if we do some of the things Jim's talking about in terms of space flight and drones, again, I'll make the point that drones, as you pointed out, this article, suddenly we're seeing drones move into HECO's mode of operation. Yeah, what you're referring to is yesterday's announcement in the Star Advertiser that HECO has created an unmanned air systems division. And today or yesterday had a demonstration of this in front of the press and everybody else. So HECO, a main line, main infrastructure in Hawaii is moving forward in that division. 10 years ago, I remember saying to my wife, watching a helicopter fly sideways up the side of the coal aisle, inspecting those lines, I go, it's a drone job, right? So it's moving and Bezos and others are talking about a commercial application, et cetera. But the point here is that we've got to do our work. You said, what is UH doing? I'm, UH, as I said before, cannot do everything itself. We've reached out to the private sector. We're reaching out to the government sector, DBED and others to have this conversation around the state with economic sectors. But to do the same thing we wanna do here with the Aerospace Summit and the Aerospace Strategic Plan, what is the return on the investment? If we grow this sector, how many new jobs? Living wage jobs. And the fact of the matter is, is that many of the jobs we produce today in the sectors that are prominent are 40 plus percent. They don't require a college degree, right? And so there's real question about, how much living wage jobs we're producing and how sustainable that is. So this is part of not only the excitement Aerospace, but the quality of future life for our children and for the life in Hawaii. Jim Lolley, who's a venture capitalist, lives on Kona, he was Kleiner Perkins and HP and he's been part of a lot of UH and community efforts. He said something really important. I thought when we were talking about moving into these areas, he said, Hawaii is one of the most beautiful places in the world and it should be one of the places where some of the smartest things in the world are going on. This is a smart area for us to get involved in. We're voyagers, we've got a history of voyaging, and so now the drones are becoming sort of mainstream as you point out, but somebody looks at the second panel and it's something about space transportation and they go, well, that's too wild. Well, a few years ago, drones were too wild and now it's becoming mainstream. So we can take the space transportation piece, for example, and decompose it down to the pieces that Hawaii can take on. That's the sort of reality we need to form out of this. We'll take the common denominator and the partnerships Jim's talking about and put teeth in it with what you're talking about, which is how does Hawaii really grab ahold of this the day after the conference is over and start moving forward? Right, but you need more than plans, right? We need these partners, we need to collaborate, but we also need a policy environment. Bingo. I need the government that helps incentivize this forum as well. We need to take this group of this intelligence coming together next week and help it drive bills and legislative decisions in the legislature and maybe we need that as an output of this as well. And you've taken, you've thrown drones on the table three or four times and you're waiting for me to take the bait. I know that, Peter, because I've got the session on drones, it's 11 o'clock in the morning on Tuesday, folks, down at the Capitol. And I've got a panel, too. It's gonna be something, and we're trying something new here. Aerospace, I'm from aerospace. Aerospace is frankly all sucked up in PowerPoint. And we do long PowerPoint presentations that don't necessarily get anywhere. That's what we do in aerospace. And we're trying to take the reverse of that in this drone panel. We've got 11 people, either 10 or 11 on the panel and I think two of them are actually phoning in. So we're giving them very short time to quickly get to their point. What's the message? Because there are so many opinions, so many points of view, they're all need to be listened to. Some have more strength than others, but we've got investment people, we've got the FAA, we've got education people, commercial practitioners. Exactly, and we've got the PPUTRC head. There's a number of, so many dimensions to this UAS thing. But the other thing is it's interesting, when you speak of the economic future and the return on investment, people like ESPN are interested. Way non-traditional aerospace names are coming into the game, Amazon, Google. And this thing moves at a different pace than those traditional aerospace. So we have a whole new dimension, or center dimension, we have to wrap our arms around. How do we capitalize on that in tourism? I mean, tourism, we need to think of that aspect of it as well. It's a, you know, you talked about the pace. It's a, clearly a disruptive technology. And as you point out, fascinating, that HECO is moving in to the area and starting to normalize this activity. You can't help but think about refrigeration beginning of the 20th century. You know, we had refrigeration, it was in a few homes, but ice harvesting lasted for two more decades, right? So we approach change in an interesting way. We're afraid of it, or we don't notice it. But the point I think that's so compelling that you're making is that, hello, it's here. Exactly, hello, it's here, and get ready. And don't expect that the traditional paths of the past are gonna carry us in the future. There are new paths we have to discover and may happen. And we have to learn how to say yes. I mean, frankly, one of the first reactions that we saw in Hawaii was the attempt to legislate against drones, because of the association. I think that's based on lack of information. Once again, this team can really help put together the information that's in a positive sense. Now, I will say, in the case of drones, counter drone is just as big as drone. Counter drone means identify, track, and locate.  And same for a construction site, same for anything. So, one of the big factors that we're slowly coming to realize, maybe fastly, you're not coming to realize, is that people have to begin controlling their airspace by knowing what's going on. And so, counter drone will become a big part of the business. So this, again, that's a dimension nobody would have thought of a couple years ago. No, and then you mentioned research and UH. I mean, one of the big areas not only in the control of airspace, which we've got some history with, with aircraft transportation, et cetera, we're pretty good at it. But this is a new dimension, will require new software, new technologies, new jobs. I mean, I can't underwrite that enough. New software, new analysis methods. And the new virtual technology that's coming on. You got people wearing the virtual goggles, flying drones, for God's sakes. And actually, I like that. I'm thankful for that lead as well. Twice you've been me that leads, I'll finally take it. We use the word drone racing. It's gonna be a big event out in Kuala Lumpur later this month. Drone racing could be looked at as the drag racers, or some other low end of life, right? It's not really that. It is all about multiple streams of information and cognition, seeing in one or two D and computing it in 3D, acting in 3D, dealing with real and imagined object disturbances in your flight path or objects that come up that you have to deal with. And getting around that course as fast as you can. Racing is how you measure it. But that is really where the next step is going in fact. We were out at one of the power plants around here earlier this week trying to fly inside the smokestack. And I tell you, the drone racing guys would do a much better job than I can flying inside that smokestack. So we're gonna give that a try here. But there's a lot to be added by that component. It also adds competition to the mix. And let's not forget also. I mean, 10 years ago, I remember talking to faculty about distance education and online education. They go, well, you know, it's clunky, it doesn't work, it doesn't do it. 10 years later, it's a totally different industry, right? And so what we see today is not what is. So don't imagine that the barriers of today are gonna carry through tomorrow, break them down and figure out. So Jim, we're helping you out here. And we're... This is so much. And I hope people will consider coming down to the Capitol again. It's gonna be on Tuesday and Wednesday, October 4 and 5, starting at 8.30 on Tuesday morning. And Peter is gonna give an overview with the panel on the low-hanging fruit, aerospace and Hawaii, why it's such a strategic growth sector for our state that will focus on these various areas, aerospace, space transportation, UAS technologies, aerospace education and training, developing the next generation of entrepreneurs that can take this to the future. And there is a free lunch involved, isn't there Jim? Sorry? Is a free lunch involved, is it or not? Yes, it's free lunch. Free lunch. Oh, there you go. How can you beat that? There you go, I'm there. Okay, you have to be there. You're opening the show. I might stay for lunch. Yeah, okay. It's food for thought, what can I say? Okay, and once again, I think the takeaway is from this conversation here, which I hadn't thought of before, but this issue of keeping this pool of information flowing, that seems to me the most important thing here. And it will have this event, but there'll be a lot of ideas that are, you know, the sunshine principle. You shine sun on something and it expands into some new ideas. So how do we collect, Jim? Let's throw this challenge at Jim. Jim, how do we take the ideas to start that people have a week later and have them come into the picture, have them harvested, have them reacted to? Sure. Because I'm sure people are gonna have four times as many ideas the week after than they have at the conference itself. Sure, as Peter mentioned, we're gonna be incorporating all of the recommendations from the conference into a strategic development plan, sharing that with the governor and the legislature, coming up with a vision for the next five years and thinking about, okay, five years from now, where could we be? Where would we like to be? And then working backwards, strategically and tactically to figure out how to begin from here to there. Who needs to be involved? What kind of partnerships do we need to build? What type of funding resources? Public sector and private sector can be complementarily linked together to move us forward. But the bottom line in all this, this has to be massively participatory, not only getting the private sector and the public sector together, but Joe Q. Public. If the common man understands the value or what? And the future potential of space exploration. I think generic, I think. That's our future. Okay, and so maybe we can use the community colleges as part of that connectivity into the public. Isn't that about the closest thing that high value things go out into the public through the community colleges? Sure, and certainly the nice thing about drones like aviation, I'm a real proponent of aviation because if you wanna get a kid interested in math, put them in a cockpit. Suddenly they wanna learn math because they wanna do that. Same goes with drones. We know about kids that didn't like math science. They're taking these things apart, putting them back together, and it's fascinating. So it's a great portal to STEM careers and STEM interest in math and science and engineering. So I know we have to add economics to that because that's what the ultimate goal is of understanding where the payback is and such. But maybe the community colleges, once again, play a significant role here. They are outplaced, they're everywhere. And I think I know somebody who's sort of in charge. Well, count us in. And that would be you, Peter. Dr. Peter Cully, thanks for coming on the show today and talking about the Aerospace Summit and Jim Chris Cully. Looks like the Skype goddess from Kailua to Honolulu, no problem. That sounds terrific, thank you. We'll see you guys all Tuesday. See you down there tomorrow, setting up, Jim. Thanks a lot, folks. We'll see you next week. Alrighty.


Live from Las Vegas, it's theCUBE, covering HPE Discover 2017, brought to you by Hewlett Packard Enterprise. Hey, welcome back everyone. We're here live in Las Vegas for theCUBE's coverage of HPE Discover 2017. I'm John Furrier with SiliconANGLE. My co-host Dave Vellant, Dave with SiliconANGLE, Wookie Bond, our next guest is Xavier Poisson, VP in Indirect Digital Services at HPE and Craig McClellan, founder of ThinkOn. Guys, welcome to theCUBE, welcome back. I know Dave interviewed you in London. I wasn't there, but welcome to theCUBE. Thank you. So Xavier, I got to congratulate you on the prestigious Cloud Leadership Award in 2017. So congratulations on the Data Cloud Europe prestigious award. Yeah, yeah, it was announced yesterday in Monte Carlo. And I believe it is a good recognition from the industry about what we have been doing, but not only me, you know, as a collective work with our partners, with the HPE people, and really to bring the best of the value of Cloud to our customers. So like, Monte Carlo, Vegas, okay, tough choices. I'd like to go to Monte Carlo, it's not a bad place to visit, hang out. Cloud 28 is really expanding, really kind of lightning in a bottle with what you've been doing. So this speaks to the general industry trend, the way that you're riding with Cloud and Enterprise. Talk about why Cloud 28 is doing so well and what's the dynamic, what's the driver? Well, you know, I checked back out of the prize, we believe that the customers deserve to know more, that they need to have the choice and also that our partners are playing a significant role to make it happen because we cannot believe that one single company will do everything. So the digital transformation of our customers is involving that more and more capabilities are put in place in order that we answer the right needs at the right moment in the right geography. And this was, you know, the foundation of Cloud 28 Plus to make it happen like that. We call it, you know, how you can make a global ecosystem in the sense of the sharing economy, putting the resources together, in order really that one single partner can find with another one the way to achieve his goal instead of thinking, I will do it myself and I will lose my customer at the end of the day. And believe me or not, but the customers recognize that. So this is why I believe it's growing, it's growing fast. And the open source community is really expanding as well. And if you look at the technology providers from the global system integrators down to the front lines of channel partners, Cloud is changing the game, customers expect coexistence. Craig, you're in the middle of all this. What is some of the frontline dynamics with customers because they're going to be getting a lot of services from a variety of different vendors and suppliers. No one size fits all anymore. And that's so true, more than ever. I think it falls into three categories. One is all the customers expect partners and their service providers to focus on the integration with others, treat each other as peers. Whether you call it collaboration or co-optition, it's still an issue that the customer more than ever is expecting their providers to facilitate. Secondly, they're very impatient. Everything is about now or five minutes ago and there's very low tolerance for the traditional engagement model. And the third item is technology is changing so fast that customers in many cases have stopped trying to stay on top of it and they're now looking for the service providers to be effectively their proxy with the underlying developers. The patient thing is a good point. I want to drill into that because what we're seeing is a move to Cloud highlights the anti-waterfall concept which was really great for project management back in the days of ERPs and those 18 months to 24 months POCs. Now, people are under a lot of pressure to drive top-line revenue and cost consolidation so Cloud can give you that. So how has that changed the nature of the customer? I'm not sure they're impatient but how has that changed structurally how they engage with partners? So what I experience in our day-to-day is the customers are eager to fail fast. Failure is an acceptable outcome as long as it doesn't take them 12 months, 18 months. They're also expecting service providers to embrace a similar DevOps mentality where they're looking for service providers to be innovating all the time. So there is some forgiveness I think that occurs from the customer base if we're all in this together but they really, back to what I said earlier they just do not tolerate, we'll meet next Thursday and talk about it. They really want to move to the action. They want the action. So Craig, talk a little bit more about ThinkOn sort of why you founded the company. What's your journey been like? I'm really interested in the transformation that has been affected as a result of Cloud28. Well, so we believe very strongly in ecosystems, participating ecosystems. We're a wholesale provider so we enable the traditional vars to go to market faster and we looked at the Cloud28 marketplace as just another example of an ecosystem where traction inside the ecosystem is growing faster than if we were to do everything ourselves. So not only do we embrace the notion of partnerships but we also leverage the channel to help them develop faster go-to-market strategies in their chosen niches. So how did it work? I mean, how did you guys engage? Is every do you find partners like this? Did they come to you? They're already part of you? Oh, I believe it's both sides. Sometimes, yes, we discuss, I believe HPE has a responsibility to discuss with our partners to explain that the world is changing and that there is an opportunity. So we do our job and creating the relationship with Craig has been done by the HPE team in the country and diversity matters. We need to respect also what is happening into the country. The ecosystem on the way business is done into the country. So in this case, it was HPE. Some other cases, and I have a very good example, it was in New York, the account manager of VAR was called by the VAR to say to, I want to join. How I can get in touch with Cloud 20 plus because I see the opportunity to partner with some other vendors, meaning ISVs or SIs, and I want to be there. So it is both sides. We have a lot of calls from ISVs because the software vendors, developing applications, and as you said, Craig, is going very, very fast with cloud native development. So you have more and more startups coming and developing new products and they want to reach the market very, very quickly. And with the exposure that we have because we are worldwide, and we started in Europe, in Eastern Africa, but we are developing Cloud 20 plus now from December onwards in the United States of America, in Canada, Latin America, in Asia Pacific, you would be amazed what is happening in India, for instance, where cloud is just popping up and where all the good ideas are coming. So it is both sides, either from HPE engaging with their partner and say, okay, there is an opportunity. Do you want to join? Or sometimes, as I said, it is the partners reaching on us, they say, we want to be there. We want to accelerate with you. Now give us some metrics on the program. So as of today, so remember, we opened the platform. It was in December 15th and we're together in London, if you remember. Absolutely. As of today, it's 18 months after, 500 members. It's amazing, 500 members. We cover more than 300 data centers of our partners, like the ones of Craig. 300. And we have published nearly 18,000 cloud services on the platform out of 2,000 unique. And we have nearly now 40,000 hits per month on the website. It's really amazing. I can tell you, it's a snowball effect. And it's not only the end user customers, but we have a lot, a lot of traffic inside the platform. Between members, we are building new offering. So for instance, we have been speaking here at this cover of the Ormuko offering that has been announced. We're running on this cover. This is coming out of Cloud 28 bus. Typically, and we see that there, there is another offering that HP Pointnext is proposing now as a service, which is a legal identity by Lekwa, which is a software company in the Nordics, coming out of Cloud 28 bus. So expanding dramatically. So this really highlights the pay-as-you-go cloud business model. And it gives ISVs and VARs and VABs the portfolio approach. So they're kind of organically putting this together versus the old channel model of predefined programs and products being shipped out to partners. You can pop services in here and then your customers can roll their own solutions. That's right. Is that, am I getting that right? Absolutely. I also think that one of the things that is a real value add is a lot of organizations are concerned about vendor lock-in. And when you build a consortium, like what the HPE has done, it forces the service providers to participate in a way that avoids lock-in. Every service provider wants to build a lock-in strategy, but there's subtle ways that you can do it that aren't offensive, and then there are offensive ways. And I think the Cloud 28 consortium is really doing a good job giving customers the comfort that they can adopt services, but they're not locked in. Let's call it sticky. What's the best way for somebody in the channel to create stickiness and loyalty with their customers? In my experience, they have an existing ecosystem that they've been working with for a long time, whether it's HPE or Veeam or another software vendor. And that's an ecosystem that their sales organization understands. It's an ecosystem that their own support organization understands. I think you should always start a nice, simple step within an ecosystem you already know and then take the next step, turn it into a recurring revenue stream without trying to start from scratch. Blank Slate is always exciting to the people that are paid to do it, but unfortunately, the outcome is usually not on time and on budget, but there's lots of little steps you can take with existing ecosystem partners. Kind of familiarity, ease of doing business, track record, all those kinds of things. Customer trust. I mean, we use the term lock-in, but that's really what we're trying to achieve is trust and loyalty. The new lock-in is scale, openness, and trust. Question on some of the tactical things. Channels have always been a beautiful thing in direct to sales. There's a great cost per order of dollars. The numbers are great, but you got to get it going, right? So you got some, the flywheel rolling with Cloud 28. How do you nurture this? I mean, obviously it's organic. There's some community involved. Training and getting out there.  I mean, I'm just trying to understand this is really good formula. Is there a magical formula? Is there certain training? Is it done in the community, peer to peer? So it is amazing because it is driven by listening to the people, and I would say educating everybody in the value chain and the sales people at HPE, the pre-sales at HPE, and the people within our partners and the end-user customer that they need to think business outcome. And once you shift from transactional selling to thinking business outcome, all the things are getting together because you think what your customer, on your customer's customer wants to do, on how you will help your customer to achieve these business goals. And you spoke about agility, time to market. These are things you can create with assembling all the, all what is in to Cloud 28 plus. I have a big example of, we use our Cloud 28 plus to answer very large multimillion dollar RFPs. Why? Because multi-cloud is a reality. So large governments, enterprises, wants to deploy clouds in many areas, not always putting everything in the same data center. They want also that you have a good mix of technologies, a good mix of usage, and then you end with RFPs which are giant. And especially when everything is coming to IoT, to the sprawling of data, you need to have data analytics, high performance, it is becoming a nightmare. So we have a very good example with a big RFP in Europe. It was all about collecting all the open data that are produced by the satellites in the sky and to put all this data available for all the SMBs in Europe. I can tell you it was very complicated to do. You will not believe me. In less than three weeks, we were able to discuss with the right partners inside Cloud 28 plus to be the consortium and to be three weeks. It was available. Well, the thing about cloud too, as you get into these scalable, horizontally scalable data opportunities, you also need specialism, you need to have expertise. And that to me really is an application specific, not peddling product, you actually, to your outcome perspective, your solution providing, right? It's based back to listening. So, okay, final thoughts, guys. HP Discover 2017, what's the takeaway? Greg, Xavier, this year, what's the big story out? So we heard Meg Whitman compute is kind of being redefined and scaling. What's the big story here from your perspective? Well, for me, I was excited to hear about the customer having a more open mind about where to put workload. I would say two years ago, there was this mad rush to the cloud without really understanding the cloud. Now there's a more seasoned reality is that workload has a multitude of locations where it can be. And I've been saying this for a long time, but as a small organization in Canada, not everyone's listening. Well, you're nimble, you're in the front line. That's right. So it's nice to hear that it's being seen around the world in the enterprise space. That's my big takeaway. Xavier, thoughts? I believe that Hewlett Packard Enterprise is interest and confidence about the journey we have designed with Meg Whitman. We are to cross different phases of transformation and it is not finished.  You know, when you have been perhaps listening about this new stack that was pre-announced there, I was thrilled with the process. This product has been built just because it was by essence connected. When they were designing the product, two clouton tape plus, that would be a resource provider for the new stack. This is the way we invent product now. So we put the customer and the channel partners and the ecosystems in the center of the design of the products that we are doing. So it's no more a product on sale. It is a product that is ready to be sold because it is fitting customer or channel partner outcomes. This is a big transformation of Hewlett Packard. I would just say one of my observations is, again, education on the cloud is key. And then, you know, this ability of the tailoring solutions, not a one-size-fits-all, you know, here's hyper-converged or here's composability, having the customer mix and match, whatever they need. Guys, great conversation here inside the Cube HP. Discover, HPE Discover 2017, this is theCUBE, I'm John Furrier with Dave Vellada. We'll be back with more live coverage. Stay with us after this short break.


Hi, everybody. I'm Lincoln Stein. I'm the director of informatics and biocomputing at the Ontario Institute of Cancer Research up the ways in the same place that Michelle is from. And I'm going to be talking about network and pathway analysis, building on the material that Quaid and Wyeth have given you earlier. I'm going to also, after this lecture, Robin Hall, who is right here, is going to lead you through a tutorial on using some of the tools I'll talk about. And I will be here for a little while to answer questions, and then I have to sneak out because of a combination of academic and child care responsibilities. So obligatory, creative commons, open access, open source, et cetera. So do I have a laser pointer? I do. This place is really well equipped. So you've previously talked about various types of functional pathway analysis. You've had lectures on overrepresentation analysis. This is what the David tool runs on. It uses Fisher exact test in which you take a set of differentially expressed genes, partition them into the up-regulated and down-regulated groups, and then compare to a set of reference genes to see if they're drawn from pathways to see if there's an overrepresentation of your down-regulated genes in those pathways. Another way of doing something similar is a functionally called functional class scoring. You'll have heard of this as GSEA, where you don't need to pre-select the cut-off point, but instead you rank your genes and it identifies the background for you. The third class, which we're going to touch on, is pathway topology tests, where so each of these systems is based on the idea of taking the whole gene space and creating arbitrary bins. So a gene is in DNA, one gene is in DNA repair, another gene is in apoptosis, a third gene is in mitotic regulation. And viewing your data in that context, the problem with that is that it's a very naive way of thinking about the genome. In fact, every gene has multiple functions that can sit in multiple of these bins at the same time. And there are relationships between those bins. One up regulates the other, another is an inhibitor, another is a catalyst for an essential step. And so if you have a map of the interactions among genes and the nature of the interactions, you can get additional information. So David, GSEA, and then we'll be talking about the Reactome Functional Interaction Network and a tool called Paradigm. So this is basically what I've already said. So the advantages of pathway analysis is that you can draw on these large curated databases knowledge bases of biological pathways. Pathway databases are usually curated. They provide a biochemical view of biological processes similar to intro biochemistry with detailed diagrams of each step along the way from one substrate to another. It captures the cause and effect, the detailed biological information, the mechanisms, and it provides, can they provide you with human interpretable visualizations of your data set in a pathway context? Disadvantage of pathway databases are they provide a sparse coverage of the genome. So in general curated pathway databases don't cover more than about a third of the gene space. And because these databases are created by people, people never agree with each other. And so the different databases disagree on where one pathway starts and another pathway ends. But you're very familiar with some of these databases, I'm sure. How many people here have not seen or used KEG? Excellent. So as you all know, KEG is a heavily curated and very rich database of biological pathways in human and many, many other species, actually started out as a prokaryotic database. It focuses on the enzymatic aspects of biology. So in this diagram, for example, which is part of sugar metabolism, it's showing all the enzymes as EC numbers. But it also does cover higher level pathways such as cell division or tyrosine kinase cascades and signaling. Reactome is the database that Robin and I work on. It's about 10 years old. It has slightly different focus from KEG. It starts out with human high level pathways, such as signaling pathways. And focus, it stays at the human level. We don't cover other organisms. Reactome uses a common formalism for drawing its pathways called SBGN, Systems Biology Genotation, in which every action is represented as a node in the diagram. And then there are a series of inputs into that reaction and then one or more outputs. And it's used to represent things as diverse as a unphosphorylated sugar molecule and ATP coming into a reaction being catalyzed by an enzyme and the phospho form of the sugar coming out. Or it can be used to describe a movement of one of an extracellular molecule from the outside of the cell to the inside of the molecule or of post-translational modifications. It's a pretty general way of describing biological processes. As you can see from this, you can just sort of follow. You probably can't read it at this resolution, but you can just follow along the steps in a series of biochemical reactions in a way that's very typical from reading textbooks. So, reactome is hand curated, it focuses on human. It's very rigorous, which is why it's also a very slow and painstaking process. Every reaction in reactome is traceable to the primary literature. So you can go from our website and step right into the PubMed entry, which describes the experiment or experiments which prove this reaction occurs in the human cells and what the characteristics of that reaction are. It does not, we do not try to annotate pathways in non-human species, but we do this automatic projection via orthology onto other species, so you could actually download pathways in non-human species, although they're not, they don't have the same accuracy as the human pathways do. And we provide a series of user interface tools. You find that we give you a Google map style reaction diagram diagrams that you can zoom in and out of. You can overlay your own data on top of these diagrams. You can also overlay other people's data, such as interactions with small molecules from drug databases or interactions with protein-protein interactions from proteomics databases. You can upload a list and find pathways that contain your gene list. You can do a simple gene overrepresentation among reactome pathways, although there are many better tools for doing that outside reactome. And you can give it a pathway in human. You can find corresponding pathways in other species. Most important thing about it is its open access, meaning that we encourage people to download the entire data set incorporated into their own tools, build on top of it, and republish it. And many people have used reactome as sources for more sophisticated tools. One example of this that I want to draw your attention to is the Pathway Commons project at Memorial Sloan Kettering in Chris Sanders Group. This is a resource that was developed in response to the problem of proliferating pathway databases which don't agree with each other and use different representations and data models. What Pathway Commons does is it enforces a common data model on each of the pathway databases using a data language called Biopax. Thank you. I was saying biopsych. What is it? Biopax. And so on a regular basis, each contributing database, there are nine of them at the current time, dump all their data into Biopax format. It gets taken up by Pathway Commons and then you can search through it. And it will give you a list of all. So you can put a gene name in here and it'll give you all pathways from all pathway databases that contain your gene and you can compare them directly and decide which pathway is most suitable for the type of research you're doing. So what kind of analysis can you do on top of a pathway, on top of pathways? Now, the ultimate type of analysis is systems biology. So if you have the binding constants for each of the proteins and small molecules that are interacting and if you have the kinetic constants, you can take a pathway diagram and you can build a kinetic model consisting of a set of partial differential equations or using a methodology called Boolean modeling and predict exactly what will happen when you increase the expression level of a gene or knock out a gene with a mutation or inhibit part of the pathway with a small molecule inhibitor or a drug. And that's the ultimate kind of modeling one can do. The problem is that in the vast, vast majority of cases, there's not enough information to do systems biology with pathways because we don't know the binding kinetics. We don't know the rate constants. We don't even know simple things like the concentration of salt in the cellular compartment in which the reaction is taking place. And systems biology is getting there, but it doesn't do a good job yet at modeling the movement of molecules from one part of the cell to another, which is a major feature of most interesting reactions. So we're left with a set of much more much simpler and naive tools. And the most simplest and naive is pathway colorization. And you can guess what it is. You upload a gene list. The database finds all the pathways that contain your gene list.  You can see where they are in relationship to each other and maybe make a guess of what common mechanism links the members of your gene set. And there's simple colorization where it just marks your genes in red if they're contained in the list. Or you can, or most databases, offer a heat map scale so that you can give gene and its expression value. And it will give you a graded picture. And some will give you a little, if you have a time course, some will give you a little movie of how the pathway changes. So here's an example from a reactome of pathway colorization. We have here a list of genes which are mutated in glioblastoma multiforme. It's from a TCGA publication a number of years ago. It's in a standard format. We upload it. And then reactome gives dozen overrepresentation analysis and gives you a list of all the pathways that are overrepresented in that gene set along with a p-value. And then you can step into this, click on it. So we can see the signaling by PDGF has the smallest p-value. So we click on that. And that shows you a pathway diagram in which it's colorized each of the genes that was on the list according to some color scale that you give it. In this case, it was the severity of the mutation and shows you how they're related to each other.  If you mouse over them, it shows you the color scores for the members of that protein complex. So this is fun and can give you useful information. It can give you figures that you can use in talks and papers. But it's very limited. And the main reason that one of the big reasons it's limited is that most of the genome isn't in the pathway database. So if you have a list of 500 genes that you're interested in, you'll be lucky if 150 of them are actually in any of the pathway databases. So the rest of them are terra incognita. And that's because pathway databases only capture the well-understood portion of biology that's been published in peer-reviewed journals and has been confirmed. So in order to apply network, apply pathway analysis to the rest of the genome, you need to start reaching into less well-understood relationships. And you get into the domain of high throughput data, genetic screens, phenotypic screens, co-immunoprecipitation, physical interactions like yeast 2 hybrid, as well as conceptual links like sharing of go terms or adjacency in pathways mentioned in the literature. So we're going to talk about networks. So networks are very different from pathways. In a pathway, we have the precise topology of the relationships. We know the nature of each interaction and what it does. In networks, we have much looser relationships between two genes or proteins. So we're going to spend a little while talking about networks. So for example, a typical network that you might work with is a network of protein-protein interactions. And tomorrow, you'll get a great talk from Quaid on gene mania, which is the uber database of gene and protein interactions. You can use a protein or gene network typically consists of a series of vertexes, also known as nodes, and a series of edges, which connect the nodes by some relationship. Very different from the reaction diagram that I showed you before, where the node is actually a reaction and proteins and other molecules feed into and come out of the reaction. So these are always bimolecular. There's only one edge between every two nodes. Things that you run into in networks is you can run into, you can have cycles. So that's a closed path through two or more nodes. And you can have different kinds of edges. You can have undirected edges, such as this one. So gene A interacts with gene B, but they're peers of each other. Or you can have weighted edges. So for example, if I'm talking about proteases, I can say that gene A is a protease that cleaves gene B. Or gene A is a activator of B. There's directionality there. And I can also have weighted edges. We can talk about confidence scores. I'm very confident that this is a real interaction because it has a score of 10. I'm not so confident of that one because it has a score of 7. Of course, the meaning of any of these weights or directions depends on the context. So to map biology to a network, you need to choose what the nodes mean. You need to choose what the edges mean. And you need to know what the directionality if any means and what the weighting if any means. And you can use this as a very general way to represent protein-protein interactions, regulatory relationships, genetic interactions. So gene A is a suppressor of gene B, protein sequence similarity, pretty much anything. But it's critical. But you'll see lots of networks thrown around. And it's always critical to understand how this network maps the biology. So I'll show you a few examples here. Here is a proteomics experiment published about a decade ago in which a group did pull-downs of protein complexes in saccharomyces cerevisiae. And they did mass spec to dissect the components of each complex. What you're seeing here is a network of 500 or so genes. And the arcs, so each node is a protein in this case. And each edge connecting them indicates that those two proteins were co-immunoprecipitated. So they're in the same complex. Here is something that is similar, but it represents sequence similarity. So this is all of uniprote, in which they did a blast P against between each protein pair. And they connect them together if they have a high protein similarity score. And you start to see protein families coming out. They're color coded in different ways. And it's a representation, which is very similar to the protein protein interaction network, or the protein protein complex network, but it means something quite different. So here we're looking over evolutionary space. And here we're looking over something that's existing in a current cell.  So going back to our abstract picture here, the degree of a node is the number of edges that connect it. So this node is degree 4 because it has four edges coming out of it. This one's degree 2 because it has two. The shortest path is very simply defined as if you choose any two nodes in the network, what is the shortest path that will get you from one to the next? So the shortest path for this pair happens to be two hops. In a social network, it's between five and six hops. You've heard of six degrees of separation. In the internet age, it's about five. I can send a message, I can send a letter to anybody else in the world through five intermediate people.  Then there's this interesting concept of between-edness, which is harder to understand. And what between-edness is, it's a measure of how important that node is for the connectedness of the entire network. And so the way to explain it is every two nodes has the shortest path that connects them. So between-edness of a node is a number of shortest paths that go through that node. So you take every node in the network, find the shortest path between them, and every time that shortest path crosses a node, you bump up that node's count by one. So this fellow here, who has degree four, has between-edness six because six shortest paths pass through it. Here's another one that has degree four, but he's not as popular. He only has a between-edness of five. And the interesting thing about high between-edness nodes in a biological context is these tend to be the bottlenecks in information flow. These are the regulatory molecules. They interact with a lot of other genes or proteins. And a lot of the information processing passes through them. So they tend to be highly enriched for lethal genes. And they also tend to be interesting genes that pop up in cancer screens and a lot of other disease processes. There are also targets for drug interventions. So the final concept here is the idea of the scale-free nature of biological networks. How many people have heard about this? Yeah, OK. So there are many different ways that you can link up a network. You could start with 10 nodes and just add edges randomly between pairs. And that would give you a network like this. Or you can use a rule in which the chances of adding new edge to a node is proportional in some way to its degree so that more connected nodes get even more connected. And that gives you something which is much more bunchy like this. It's called a scale-free network. Or you can have a rule that you choose one node, which is kind of the super node, the controller. It has highest degree of freedom. And then there's another tier outside that where there are only a tenth as many connections. And this continues.  Now, as these have different properties, the one that's easiest to explain is the relationship between the degree and the probability of that degree, nodes of that degree, occurring. In a random network, you get a bell-shaped curve around the mean degree. So this is k here is the degree. And pk is the probability of a node of that type occurring, basically the number of such. And so you get a nice peak. In a scale-free network, you get something quite different. You have an exponential relationship between the degree of a node and the probability of that node occurring, so that you get lots and lots and lots of nodes that have a low degree and very, very, very few nodes that have a high degree. So here we've actually plotted the degree in a log scale. You can see a linear relationship. A hierarchical network will have the same properties, although there will be this bunchiness in the relationship because of this kind of rule and how we hook up the different tiers of nodes. So the way you can distinguish between a random network and either the other two is by a graph of degree versus probability, how do you distinguish between a scale-free network and a hierarchical network? Well, it turns out that if you can cluster the nodes by using community clustering algorithms that have been worked out for the World Wide Web and other places where you basically find little communities of nodes which are interacting with each other. And if you graph the degree of a node versus the clustering around that node in both a random network and a scale-free network, you actually get a linear relationship. Your neighborhood remains pretty much the same no matter what your degree is. In a hierarchical network, however, you get an inverse relationship as the degree increases, its clustering gets tighter and tighter. And you could actually see that visually here. The reason that this discussion is important is that biological networks turn out, by these criteria, to be scale-free networks. There are a very small number of genes that have a large number of connections. And these are choke points in the biological network. You remove them, and the network falls apart. A large number of genes have a very small number of connections. They tend to be leaves. You remove them, nothing much happens to the network. The genes cluster quite a lot because of this property. And when you look at what's happening biologically, these tend to be functional pathways. They tend to correspond to pathways that are known and pathways that are not known. And the clusters themselves are scale-free, meaning that there are lots and lots of small clusters of just a few genes that are working together with each other. And then there are a few large mega-clusters, like the one around P53 that encompasses hundreds or thousands of genes. Before I move on, any questions about the network jargon? More properties, or any of that? OK, great. So we had pathway databases. Now we're going to talk about network databases. Somebody has to put these together. So there are more network databases than there are pathway databases. And part of that is due to the fact that you can build them automatically using computation. There are also curated network databases. Their popular sources of curated networks includes BioGrid with about 600,000 genes in it, intact 60,000 genes, and Mint, a smaller number of genes, but a lot of interactions relative to the size of the database. You can actually see this very really reflects curation priorities, whether you're going to go broad like BioGrid with lots and lots of genes, but relatively few interactions of the number of genes, versus intact, which has fewer genes, but it has more interactions than BioGrid does. The nice thing about bi-molecular databases is it's very easy to combine them. You can take all of these and put them together, and you'll have one big network that has information from them all. But you have to be careful, as we'll see later. Then there are ways of building network databases without curation. So you can take text mining approaches. One very popular thing to do in the early 2000s was to take all abstracts from PubMed, or all the corpus of all the bodies of articles from PubMed, and look for, identify the gene names and find an association score between them. If they occur in the same paragraph or the same sentence, then they're probably related in some way. And you can build up a big network of related genes. It's much faster than hand curation, but it's not perfect. For example, if you see Hedgehog mentioned in a paper, you're talking about the gene Hedgehog, or the protein family Hedgehog, or you're talking about the Hedgehogs that live in hedgerows and eat ants. And to resolve this problem, you have to do natural language processing, but this is difficult. Google can probably do it, but most academics can't. Popular resources built on top of these include iHop, which is a fantastic resource that lets you hop from one literature reference to another, following the gene links, and PubGene, which is a part of NCBI. Other ways you can make a network database of interactions are high throughput experimental screens. East to hybrid protein interactions are a very popular source. Protein complex pulldowns, mass specs, as you saw in the yeast example is another one. Genetic screens, such as synthetic lethals and enhancer suppressor screens. Also another popular thing to do. Again, none of these sources of information are perfect. In particular, yeast to hybrid interactions have taken the proteins out of the human cell, if that's what you're interested in, and put them in a different context in yeast. And so you may get interactions which occur in yeast that don't occur in the human cell, or vice versa. And even if you do see a physical interaction, it doesn't necessarily mean that it's a biologically important functional interaction. And so for example, actin comes up as a major interactor in all these screens because it happens to be a very sticky protein. And probably not all those interactions are real. The same thing with protein complex pulldowns, actin comes up all the time. And genetic screens are hardly sensitive to changes in the genetic backgrounds. If a suppressor in one Drosophila strain or yeast strain, it may not be a suppressor in the other because of other interacting genes that you don't know about in the genetic background. So what does one do to reduce the error rate in big network databases so that we can actually get useful information out of them? You'll hear tomorrow about Quaid and Gary's approach with Gene Mania. And I'm gonna give you some simple examples of doing this and then talk about my own group's research on the problem. So basically the way to reduce the noise in these big networks is to use multiple sources of evidence to up way interactions that have several different independent sources of evidence pointing to them and down way ones which only came up in one screen or another screen. Or to add external information to the problem. So a simple example that worked very well for filtering these two hybrid interactions is this concept of a party hub that Mark Vidal's lab came up with that Dana Farber in Boston. They noted that their initial yeast to hybrid screens had probably about a 40% false positive rate. So 40% of the interactions they called were not real interactions so when they tried to validate them, they did not validate. They came up with this idea of party hubs in which you look for partners that are expressed in the same time and in the same place in the cell. So if they're both expressed in muscle cells in the Golgi apparatus, then they're more likely to interact than if one gene is expressed in muscle, the other gene is expressed in liver, they're never co-expressed, or if one gene is a, lives in the Golgi and the other lives in the nucleus. Those are likely to be false positives. And that very simple filtering will reduce the number of false positives from 40% down to about 10%. More complex ways to do is you take many different sources of curated and uncurated evidence and you combine them in some intelligent way. And so that's what the Reactome-FI network is. So to illustrate this, when we started building this network, it was really in response to my group's difficulty in using Reactome to analyze cancer data sets because there wasn't enough coverage of the genome. And when we started building this, we were at version 35 and there were only 5,000 proteins in about 1,000 pathways in Reactome, it's a lot, but it's only covering 25% of the genome. So three-quarters of our cancer screens were coming up with nothing. So we wanted to expand Reactome's coverage. And so what we did, as though first we took all the curated pathway databases or a subset of the better ones and combined them together. So we took Reactome, we took the National Cancer Institute's Protein Interaction Database, Panther System, Keg of course, several others, and from these we extracted all the interactions. So we took these complex pathway diagrams and turned them into a big network of bimolecular interactions, but each edge is curated and it has a direction. Usually we know what the role of the protein is. One is activating another or inhibiting the other. Okay, and so that got us up to about 30% of the genome. And then we took a large source number of uncurated high-throughput screening sets. We took human protein-protein interactions from several of the protein interaction databases that I talked about before. We also took interactions from protein-protein interactions from flyworm and yeast and translated them into via orthology and to human protein-protein interactions. We took text mining protein interactions from a resource called GeneWays. We used gene co-expression in order to capture this temporal co-expression of the genes. We took go annotations and we took PFAM interactions. And for each protein pair in the genome, we gave it a weight indicating how many of these independent sources of evidence were supporting that interaction. And in order to do this in an intelligent way, we used machine learning, we used a naive Bayes classifier, which is the very simplest kind of machine learning system, in which we learned how to weigh the each piece of evidence by training the classifier based on the curated interactions, a set of curated interactions taken out of this. And we trained and tested and trained and tested until we entuned the thing to have a false positive rate of about 1%. So we wanted a very accurate functional interaction network. And this gave us a network of about 11,000 proteins corresponding to 9,500 genes, 210,000 functional interactions. And we increased the coverage of the genome to 50%. I wish we could get higher than that, but there just isn't enough information anywhere to allow us to annotate the other half. Here I'm showing 5% of the network. And you can see the scale-free properties showing up pretty well here. Here are those clusters. And you can see a lot of little clusters and a few really, really big clusters like that one. Because of the way we tuned our classifier, our false negative rate is about 80%. A lot of that is due to just missing, not having genes in the network. Okay, so how would you go about using this? Well, the methodology we've developed and implemented for using the functional interaction network to make sense of gene sets involves active network extraction, clustering, and annotation. The steps are simple. We take this, the functional interaction network. And this network is a melange of many different cell types at many different developmental stages. It doesn't say anything about any particular cell type or developmental stage or disease state. In order to do that, you take a gene set that you've derived experimentally, say all the up-regulated genes or all the genes that contain copy number changes, and extract from the melange network a subnetwork consisting of the altered genes. And then you apply clustering algorithms to create a map of interacting clusters. And then you can use the over-representation analysis that you've heard about over the last two days to annotate those clusters with over-represented pathways. And so what we've actually done is discovered our gene sets from the network and then annotated it using the over-representation analysis you've heard of. And typically this will take, you can start with hundreds of genes that are altered, and it will reduce, this methodology will reduce this to a manageable set, usually about a dozen or two dozen disease modules, which you can then step into and look at the relationships among the genes in order to develop hypotheses.  So the TCGA project published a set of 900 genes, which have recurrent apparent driver mutations of them in ductal adenocarcinoma of the breast. They published this back in September. When you take that list of 900 genes, extract it from the interaction network and cluster it, cluster and annotate it, you get this map. You get a large network corresponding to focal adhesion and interactions between the extracellular matrix in the cell. You get tyrosine kinase signaling. You get adherence signaling. You get notch and wint signaling. You get DNA repair. These are all things which are previously, these are all pathways which are known to be altered in breast cancer. In addition, you get some other things which are not previously seen. Axon guidance, which was recently published as a altered pathway in pancreatic cancer, shows up as a very strong signal, as well as ubiquitin mediated proteolysis and interactive ligand receptor interaction. As well as things which are probably false positives such as the mucin cluster which show up in lots of cancer screens. This gives you a nice snapshot of what's going on. It's easier in many ways than going through the long lists of often contradictory gene-set enrichment analysis results. Here, oh, I've lost it somehow. Okay, I have gone on to the... It's crashed, has it? Okay, let's go into the good part, too. Yes, of course, that's a good time to do it. That's correct. So, the procedure, and this is part of the tutorial that Robin will lead you through, the procedure is you discover the modules in your data set. And then you annotate those modules from the databases of your choice. So you can annotate it actually with keg pathways or with gene ontology terms or with any of the other tools that you've learned from this morning. But the advantage is you're labeling 12 different modules. You're not labeling... not any of the big list of 100 significantly enriched gene sets. There can be contradictions between the annotations. If you label, for example, with keg pathways versus reactant pathways, keg pathways may... keg annotation may say something like pathways in cancer, which is not very helpful. It often does that whenever you're looking at a signaling pathway. And reactant might say row GTPases, which would be more helpful. Or it might be reversed and reactant may give you something unhelpful and keg might give you something good. So there's still some amount of inspection you need to do. Does that make sense? Yeah. Yeah, they may appear in multiple places. And they often actually do. And it can be real. So to give a recent example, we're looking at pancreatic cancer. We know the pancreatic cancer that carries mutations which are enriched in axon guidance. It's actually a signaling pathway. Okay. Yeah, I can go. All right. But in the module map, actually, there are two different modules that are labeled with axon guidance. This is an older picture from when we had in Suicones this morning. But here's module nine is axon guidance. In later data sets, we actually have a module 10, which is right here, which is also axon guidance. It turns out that there are different sub pathways in axon guidance. Okay, so here's pancreatic cancer mutations. This is from OICR's sequencing work. And the interesting thing is we see some of the same modules. We see Winton could hear and hear again as they were in breast cancer. We see extracellular matrix, which was there in breast cancer, axon guidance, as they said. And then you see things that these are sort of common to different cancers. And then you see things which are unique to pancreatic cancer, such as MAP kinase and hedgehog signaling pathways. You see a big signal from the B-cell receptor and EGFR signaling. KRAS here is huge, and that's a major driver of pancreatic cancer. And so you could actually use... Oh, actually, this one does have it. Here, we have module seven is axon guidance and module nine is axon guidance as well. Good. You could actually use this as a signature of different cancer types. And then here's another way in which it's useful. We're obviously very interested in using the molecular information in pancreatic cancer to find subtypes of the disease, which might correlate with different clinical outcomes. So here we've taken 45 different patient samples and put them on a map in which we've listed each of the genes, which is mutated at least once. And we've marked the genes if they're mutated in that sample, tried to do hierarchical clustering on it, and you get a whole bunch of nothing. We have no clustering, whatever, of the patient samples, except for possibly this cluster of two patients who are KRAS negative. Yes. On the previous slide. Sure. You said that a figure from further analysis of axon guidance were basically just like subtypes. That's correct. Is there anything to be made from the fact that it doesn't look like there's a lot of connection between the two? There actually are a lot of connections here. It's not projecting very well because we reduced the intensity to make the picture look nicer. But I have not explored this enough to be able to answer your question intelligently. Actually, I would just be hand waving. Yeah. I suppose you don't have to answer that. I guess you would have to know enough about the biology of axon guidance to know why the two subnetworks would be differentiable from each other in this particular analysis. Why aren't they just sort of along together? One of them is Roboslit signaling, which is a signaling pathway in its own right, which is part of axon guidance. The other is Ephron signaling, which is also used, also part of angiogenesis. My feeling is that these really are two separate pathways, which for historical reasons both contribute to axon target finding, and where I've been put together as axon guidance in the literature, but are not very strongly connected. That's how I interpret that. What its relevance to cancer is, which I think is what you're asking, that I can't even really speculate on. Yeah. That's right. Then why not name them? Well, that's why we call them Nodule 7 and Nodule 9, which then makes the wet lab people annoyed. What's Nodule 7? I forget what that is. That's axon guidance. I thought you said Nodule 9 was axon guidance. Well, that was the same thing. That was one, too. Well, we try to organize the data to reflect our best understanding of biology, but we cannot, without really annoying people, do wholesale namings of pathways, renaming of pathways just because the data says different. You can see lots of examples here of K-RAS is involved in B-cell receptor pathways or B-FGF and EGFR signaling. It's a key point. The entire module is named after all four of those pathways, and we can't say that it's one or the other. It's a little bit of all of them. This is the disease-specific pathway. Any more questions before I move on? Getting back to this, this is disappointing. There's no clustering of the patients. However, if you cluster the modules, cluster patients on the basis of the modules, you get a very distinct pattern. Here we've scored each module according to the number of mutations that occurred in that module, also weighted by the frequency of that mutation. Again, here we have the patient samples. Here we have the modules. Now we actually get very good clustering. We've got three patients who are K-RAS negative, axon guidance positive. We get about a dozen patients who are K-RAS P53, and I forget which one this is, and axon guidance negative. You can count one, two, three, roughly four different patient populations. If you look at their survival, this set has a very poor survival. The others have better long-term survival. We're now following up on that to see if it's reproducible in independent data sets. From what looked like a very disappointing result, we can actually distinguish apparent subclasses of pancreatic adenocarcinoma. We're not the only people who do this. I want to draw your attention to other pieces of software that do similar work. There's Hotnet from Ben Raphael's group at Brown University, which works on either expression data or single nucleotide variation analysis, and offers you a cytoscape visualization. It basically does something very similar, but uses a different model in which it models diffusion of heat across a lattice. That's why it's called Hotnet. And WGCNA, which is AR package, which is specialized for expression analysis, will also give you disease-specific modules from expression data and an underlying network. I'm going to end with another example of using network analysis to discover biomarkers. One of the problems in cancer is that you have a large population of patients, all of whom get treated with the same chemotherapy. Some of them respond to the chemotherapy. Others don't respond to the chemotherapy, and you don't know why. Ideally, you'd like to have some sort of marker, which would allow you to distinguish between the patients who are likely to respond from the patients who are likely not to respond, so that you can treat the people who are going to respond with the drug and avoid the unnecessary toxicity for a treatment that isn't going to work for those who don't respond. And you could also use this to stratify risk. If you know that you have a population of patients, some of whom have an aggressive disease that's going to kill them in a year and others have an indolent disease that you could follow if you knew who they were and only treat them if it starts to behave badly, you could stratify them into a high-risk group that you treat and a low-risk group that you don't treat, thereby avoiding the complications of treating everybody with an aggressive therapy when they don't need it. So the problem, so this is in the cancer domain, there is a lot of work to discover biomarkers based on single nucleotide mutations or expression changes or copy number changes, which will differentiate one group of patients from another. The problem is that when you're looking at 22,000 genes, it's very easy to over-train your selection. You can take any random set of genes and probably distinguish between the responders and non-responders just on the basis of chance. And there's actually a paper published recently on breast cancer which showed that selecting genes randomly can find biomarkers very easily in that dataset. And that's because there are just so many different genes, you can find them by chance. As soon as you take a biomarker found in one clinical cohort and try to replicate it in an independent cohort, a lot of them fail. The field is littered with these unreplicated biomarkers. Other problems are the diseases are heterogeneous. If you have lots of subtypes of disease and you don't know how they correspond, how they're distributed among the patients, then you need really large cohorts of patients. And another problem is that the tumor itself can be heterogeneous. You can have a single primary tumor which has two subclones, one of which has mutation which makes it sensitive to the drug and the other of which doesn't. And you treat the whole tumor, you get 50% response and then the resistance subclone goes out. So here's an example of using the disease module maps from the functional interaction network to discover a biomarker. And the general principle is you start with a disease module map from the previous step. You then take an expression analysis set or a CNV set or a synchromucleotide variation set from multiple patients and you can perform a principal component analysis on the modules and then select clusters of modules which correlate with clinical parameters. Or in the easiest case, you just test each module against the patients to see if the presence of a module correlates with differential survival or response. So here's an example from breast cancer again. This was work done by Guanming Wu in my laboratory. He built a network. He extracted the active breast cancer network from a New England Journal of Medicine article published in 2002. That was 295 patients, 12,000 genes analyzed. And then he validated with an independent data set from 2006 roughly the same size. And he found a module named Module 2 whose expression level correlates with more aggressive survival. And so in particular in estrogen receptor positive breast cancer which is usually considered a more treatable and a better prognosis subclass.  This is a Kaplan-Meier curve. How many people are unfamiliar with this? We haven't seen it. What we're seeing is these are years here and at year zero 100% of the patients are alive and then as you go further on the number of patients who are still alive decreases because they've died of their disease. And then died of their disease. And you remove patients who have died of other causes or are lost to follow-up. And so it's got a good p-value of 10 to the minus fifth here. You have to ask does it replicate? Yes in the independent data set. It replicates very nicely. And then we went on and replicated it in multiple other data sets and it seems to be a good marker. Now what is module 2? Module 2 is because it's derived from the functional interaction network we know how we can annotate it. We know what it does. It corresponds to kinetochore maintenance pathways and aurorabicinase signaling both of which are involved in mitosis. And so we're showing that an upper module that's involved in mitosis if it's up-regulated it leads to decreased survival and this makes sense because of the higher mitotic rate of a more aggressive tumor. So that's a nice story there. Now I'm going to stop my examples here and move on to the tutorial. I'm just going to end by saying that there's much more work that needs to be done in this type of network analysis. The main thing that the Reactome functional interaction network doesn't do is it doesn't allow you to look at multiple types of molecular lesion simultaneously. You can look at expression data or you can look at mutations or you can look at copy number changes or you can look at methylation or whatever but you can't integrate them together in an intelligent way. And furthermore, the analysis that I've showed you doesn't take advantage of the directionality of the edges, the directed edges. Although we know that a particular mutation is activating of KRAS, we're not then following the arrows downstream of KRAS to see what the overall effect on the activity of the pathway is. And so that's what a new generation of algorithms exemplified by Paradigm from Josh Stewart's lab at UCSC attempts to do is that given a pathway, a network diagram representing a pathway, Paradigm will take several different types of molecular data and integrate them in a way that follows the arrows so that increase of activity of MDM2 will decrease the activity of TP53 and then it propagates this effect so that at the end of the day of gene expression and mutation changes, it tells you whether the pathway has changed, has gone up, its activity has gone up, its activity has gone down, or it hasn't changed. And it will give you a heat map like this where we're looking at samples versus pathway activities. What this is trying to show is that in all samples, this pathway, whatever it is, it's increased. This pathway is decreased in some patients and increased in others. And so here's a nice example from a paper they published about a year ago on how this works showing that in glioblastoma multiforme, they're able to capture four known subclasses of glioblastoma multiforme very nicely and they've annotated the pathways and they've changed here on the right. They also have a nice display which allows you to see all the raw data on top of the pathway activity data. Here's the inferred activity of AKT1 pathway and you're looking at copy number changes here, expression changes here, mutations, and so on. So the bad news about Paradigm is that you can only get it in source code for me. I have to compile it. When I tried to compile it, I couldn't do it. I had to ask Guanming to do it for me. It's not documented. They don't give you repositories of formatted pathway data. They don't give you any examples of how to convert experimental data into the input files. And basically, right now, it's a package that only its authors can run. The good news is that at Reactome, we're working on a web service, an implementation of Paradigm that will run on top of the Reactome functional interaction network and you'll be able to run Paradigm from within Cytoscape probably about a year from now. Okay, so take-home messages. First pathway network analysis gives context to alter gene sets. It goes beyond what you can do with gene set enrichment analysis. The type of analysis you can do differs greatly in the complexity, power, and usability, from simple things like path theory, diagram colorization to very complex things which I can't give you a ready-made tool to use yet. The type of analysis will work in progress, but I think that all signs point to it being a very powerful tool in the future. And I've given you some URLs for you to look at afterward. So, time for questions.


Hi everyone, my name is Will Mahaney, and I'm going to be taking you through a day in my life here at Syracuse University. A little bit about me, I'm a senior studying math and data analytics. I'll get into more of kind of the clubs I'm in on campus as we go throughout the day. But it's a Monday and I need to get to campus, so come along. At Syracuse, you have to live on campus for your first two years, but then you have the option to live off campus for your junior and senior year. Now as a senior, I live off campus. I live in a house where we just have the first floor apartment. So it's just two of us living in an apartment together, which is really nice, me and my roommate. But yeah, I'm just about a 10 minute walk from campus, maybe less, depending on how late I am for class. So I'm walking right now and I'll see you when we get to campus. I made it to campus. I'm here in Heinz Hall, which is home of the iSchool. And I'm here just in time for an orange preview day. So as an admission master for the iSchool, I am giving the presentation to about 10 or so families that are interested in the iSchool today. So I'm going to head over to the conference room and get that set up so I can present to all of them. We just got done with the information session upstairs. I'm now hanging out with Ella and Carrie and we're just hanging out until we have class. I got lunch today with my friend Ella in the Life Sciences Cafe and then headed to my first class, which was big data management. I only have two classes on Mondays, my other one being social network influencers, which is really nice. My Monday, Wednesdays are pretty light Tuesday, Thursdays. I also only have two classes, two math classes to finish up that degree. As a second semester senior, I only have four more classes to take to graduate. So I've been able to do a couple of other things outside the classroom that are more fun than I enjoy doing. One of which being I'm on an intramural volleyball team. We have a game tonight, so you'll get to see a little bit of that later. I also play in the pep band. I play trombone for all the basketball games. If we make it to the NCAA tournament, I'll get to travel with the team, which I think is so, so neat.  I'm down here at our Goldstein Student Center on South Campus. This is where a lot of students come to use the food court, do laundry, do homework, things like that. And I'm using two of those tonight. I'm doing a quick little laundry and going to work on some stats homework while it's in the wash. Alright, so I just got back from our volleyball games. We played two every Monday night and we won both of them tonight, which was really exciting. That's about all I have for my Monday. I'm going to finish up a few more homework assignments and then head to bed. I appreciate you following along and I hope you enjoyed the day of my life.


So I was 22 when I started the industry and I would not be sitting here today if I just went and got my license and tried to go sell, you know, without Rory and the mentorship there and the structure and, you know, having, you know, Grant to lean on and all that. So I think I guess the best way to say it is you've got to be able to find, you know, partners and mentors and people that you can really trust in order to do that. I was thinking he was going there. I'm sitting here thinking of Lord Minter and like finding good people and learning from people. And that's where you went with your dad. Exactly. Exactly. Yep. Someone to lean on, someone to learn from.  You know, did you learn from him? I'm like, absolutely. And that's still the first person I go.  I mean, that's the first person I still go to, you know, if I have questions about this.  Most people are ashamed to admit that other people are helping them, too. I don't know why that's a thing, but it's a thing. People are like, it's almost like it. De-credibilizes them. You know, that's not even a word, but you see where I'm going. Like that someone helped me get where I am. It's like, dude, every successful person ever had someone help him get somewhere. So it's all and successful people are more likely to admit it. It's really unique. You know, some people, a lot of people just they're like, oh, that's that's I did it.  And, you know, but I love hearing people do that. Well, especially 92 percent of people aren't in it. You know, if you can find one of those 8 percent and learn from them and we've been able to accomplish and, you know, half the time what Roy was able to accomplish.  So that's what if any bring up a super good point that if anyone's out there like struggling, you have to plug in and find someone that can help you, right?  You just can't do the second person to today that said we typically don't work well with people that are contract chasing. You know what I mean? And just chasing the higher contract. The remiss of the same exact thing. They both said the same exact thing. It's weird how successful people kind of think alike, you know.