What is up all you beautiful people? It's Ashley here and today's little video is based on an article called AP Psychology also known as Advanced Placement Psychology Apparently you shouldn't be intimidated by the fact that it's an AP class But at the same time you should not take it lightly. If you want to understand in the United States classes start in August So I'm pretty sure that there are a lot of you guys out there who are now Currently taking an AP psychology class now the article itself is meant to give you guys some useful tips and resources In order to help you understand the content of the course and I guess I'm just gonna let you guys know What all of those links are now when the lovely author of this article have to prepare for a regular unit test They got a Barron's AP psychology book Fifth edition It's especially meant to help you out if you take a class as topic specific. Not a fan of reading, huh? Then head on over to Crash Course Psychology's YouTube channel Got a vocabulary quiz coming up? No problem because the author has got you covered Go on over to Quizlet and quiz yourself over the terms that you've been studying Either by playing games or using the flash card option The author has thrown in two more Tumblr links if you want to get that A on that test or five in the exam psychology master post and studiousness.tumblr.com Now I want to wish the best of luck to all of you beautiful people who just started out taking AP psychology classes and Of course Christmas is on its way So happy holidays to all of you beautiful people and I hope you guys open lots of presents


Bingo! Here it is. It's a 12 o'clock rock on a given Monday with Marco, Mina, who is not here, and me on a Monday talking about energy on a Monday. Welcome back to the show, Marco. You rock, my friend. I can think of a better place than time to spend with you and the two of us. And, Mina, if she's able to join us then every other Monday and talk about the rock and rolling world of Hawaii energy. You bet. That's what we do. And we can rumble and ramble around and find material that lasts and lasts and lasts. There's always something new. Let me just say that the Clean Energy Day program on, what, August 16th last week was very good. And we had a lot of speakers and they were, for the most part, they were candid. And they, you know, they, I think a lot of them told it like it is. And that was good. David Ige spoke, Randall Iwasse spoke. A lot of people in the energy community spoke. And I think, and of course the utility was there and spoke. And Sharon Moriwaki and Mike Hamnett gave awards, six awards out for transformational achievement. And it was one of those coming together kinds of conferences. So I think that adds value and it's probably at least my observation. It's the best energy conference at least that goes on in Honolulu. And we're going to keep doing it on a regular basis and you should come, Marco, next time. You should come. I'll get you involved, all right? Well, thank you. And I have a deep and profound philosophical existential question for you, which is, what is the meaning of is as far as you discussed what is going on energy-wise? I mean, what is the definition of is in that sense? Well, there were, I'll answer that by telling you there were, you know, panels on how things are moving.  That now we're faced with putting more technology on the grid. And Juan Electric said they had a bunch of new boxes they were testing.  That's going to take some cash money. There was a lot of talk about storage. We had a panel on storage. And that was very interesting because now everybody agreed that batteries were the solution. Batteries have their limitations. Of course, Stan Osman was talking about hydrogen as a transportable storage device. And of course, there was a lot of discussion about exactly what the technology is in batteries. And PS, there were a number of articles here in the last couple of days on the MIT technology energy review about the new batteries and the taste for batteries and so forth, which I would like to use as a starting point for discussion of electric vehicles. And finally, we had a panel on transportation where the State Department of Transportation told us about all the things they were doing off the highways to be efficient and clean energy. But there's not too much happening on the highways over fuel or cars. And that's really a problem. And finally, we had a wrap-up panel where Rick Rochelow was very interesting. He's my favorite energy scientist for sure. Him and we had Mark Lick there and Kyle Data of Uluponu, all very candid. And I think what Kyle said, which was a closing remark on the thing, so I would leave you with Marco, is he said, you know, we haven't talked about the limitations on the technology. In this discussion, we have assumed that the technology will take us wherever we want to go. So now it's a question of getting our act together and using the technology and getting on board with it. And I think he's right. And that distinguishes this panel, this program from other programs in the past where we wondered about whether the technology would take us there. I think we all agree it's available. It's out there. It's just a matter of getting our act together. And I think one interesting thing, let me add this one point came up in the colloquy, that is we have, you know, a few simple things and they present as a kind of, at least a pro forma solution. Number one, we have solar. We have an interest in solar. We have political acceptance of solar and that's good. Two is people are not jumping off the grid. They're staying on the grid. One speaker, you know, talked about how he got off the grid and then he regretted that. I was very interesting. A guy who writes for Civil Beat, a fellow named Eveslin, E-V-S-L-I-N. And, you know, bottom line there is that we have interest in storage. We have a certain technology in storage. And I think we have really all the elements and we know what a smart grid is like. So if we put all that together and make it better and better, we'll have what we want. We'll get there. Because you could generate an awful lot of electricity with the solar panels. You could store a lot of electricity with the batteries. You can handle the peak hours with the batteries. And if you put enough money into it, I'm talking about billions, you'll be able to replace the whole system with solar, both residential and, you know, scaled up, utility scale. Smart grid for, you know, demand response. And, you know, these batteries, we could do it. We have it all there. It's all there. It's just a matter of making our minds up. So that's my takeaway from the program. Well, thank you. And I would appreciate it, hopefully, in this conversation if you would share a little bit more about why this individual who went off grid decided that it wasn't such a good move. But let me, I'd like to respond to a couple of things that you said, which is I've taken what I consider to be quite a deep dive into residential energy storage, residential battery storage over the past several months. And I can tell you, Jay, that there is a substantial, a substantial where the capital asks, if not the whole thing, capitalized. There's this substantial disconnect between what one hears at conferences, what one reads in MIT journals and other parts of the popular press, and what is available here and now at what price here in Hawaii for residential energy storage. And the reality is, and I tried to make this point in my piece on the Star Advertiser last Wednesday, the reality is that the actual choices to homeowners today, I'm not talking about when the gigawatt factory in Nevada Desert is completed in turning out product, or other future dates, but today and in the weeks to come, the choices to the homeowner for residential storage are limited and they're very expensive. As a way of comparing, let's say, a battery less system, no batteries to a battery-based system for no export connection to the grid where no surplus power essentially, or little to no surplus power is permitted to be fed back into the grid. If you take a five kilowatt system, this will make the math really easy and you're installing it at four bucks a watt, four dollars a watt with no batteries. That comes up to $20,000. If you're putting in adequate storage for such a size system, given the usage pattern that we have here on this day, which is less power consumed during daylight hours from eight to five more power consumed between five to ten p.m., you more or less double the cost from $20,000 to $40,000. And that is going to be a huge hit on those consumers, those homeowners who have yet to go solar who may still want to go solar. So again, the disconnect being that the options and the technologies and the hoopla and the spin and the buzz and all the wonderful hype words and phrases that people like to use in the energy business that just is disconnected from the practicality and the affordability and the availability of the options available right here and now. And that's of great concern to me. I agree with you. And it should be of concern to the large number of people who cannot afford $40,000. But it isn't a surprise. It isn't a surprise that the batteries are going to cost about as much as the panels. And that doubles the price for those people who can afford that. And by the way, many more of them could afford that if the state renewed the solar tax credit, which I think the state should do to encourage the progress in this area with that and the utility doing it on the other side with solar farms and large scale facilities and community solar also. And I'm not hesitating here. Put billions of dollars into batteries. It doesn't have to be all in one day, but over time, and then retire the power plants, we'll have a system that works that will be all renewable. And yes, it will cost money. It will cost money on both sides of that equation. But there's no surprise. Retiring the power plants costs money. Putting in high tech, putting in new infrastructure, making the whole thing independent of fossil fuels was never going to be cheap. And I don't know where people got the idea that this was going to be cheap to adopt new technology around the state, to transform the system. I think we should all be very realistic about this and understand it now even if we didn't understand it before that it's going to cost a lot of money. So I don't think anybody's fooling themselves about that. I think it will cost money. And the people who think, oh, it's going to get cheaper and cheaper, it's not, not in a short term, not in the intermediate term. It's going to cost billions to do this. And we've got to do it. No question. We've got Madeleine coming down on us. It's been raised to a class three storm. It could put the lights out. And the guys with solar, the guys who are, if not off grid, there aren't many of those, but independent of the utility for electricity, at least during the day and maybe even during the night, those guys are going to be in much better shape than the rest of us. We've got to be resilient. Climate change is going to give us Madeleine after Madeleine. And we've got to have this ability. It doesn't matter what it costs. Oh, it doesn't matter what it costs.  I mean, I think it matters tremendously what it costs and it matters tremendously who's going to pay for the cost and under what terms and over what length of time. And I mean, we are going in the right direction. I'll just put another plug in for my friends at KIUC. I mean, they're moving forward with their third utility scale PV project that will have dispatchable power, battery power. Yeah, 15 megawatts. I was very, I'm always impressed with Dave Bissell. I'm always impressed with him. He talked about that project. And one thing he said, you know, he took his cell phone out in the middle of his remarks and he said, hey, I got lithium ion in my cell phone here. But I know that a year from now, my lithium ion is going to be about half as good as it is today. I know that if I buy even the best batteries made today, they're going to lose their, you know, they're going to lose their ability to hold the charge in a year or two and they won't be as good. So I have to know that going in. I cannot make any false assumptions about what batteries can do for us or about how long they will last. He's right. And he and KIUC go into that whole program, which I agree with you is very successful and they are really smart guys and they have great relationships really unbalanced with their community. But they know going in that there are issues they're going to have to, and that sense of reality of being realistic about things, very important to dealing with their customer base. And I mean, for what it's worth, I mean the company that they are dealing with for their third project is Solar City. Solar City, which loses more money quarter after quarter year after year and has never made a penny a profit ever since they became Solar City years and years ago. So I mean, there's this odd, odd disconnect in the sense that we have some of the more innovative companies, solar companies out there, that are pushing the frontier, the boundaries of the frontier in terms of storage and in terms of innovative means of finance for solar systems, both residential and commercial, and yet they've been notoriously unprofitable forever, the forever being ever since they have incorporated and started doing business. So I mean, how much longer, how good a business model, how reliable, how solvent a business model is it when we've got these monster solar companies from San Edison, which declared bankruptcy back in April, two companies such as Solar City and other major financial development companies, solar development companies, which have never ever been profitable. So I mean, I don't want to diss Solar City too much here because I take my head off to them in terms of what they are doing, what they've done on the island of Kauai. But this is all coming at a tremendous cost in terms of losing their quarter Q2 losses whereas over 250 million with an M, $250 million just in one month, one quarter. Well, limiting it to Hawaii for a moment, one of the things that came up at the conference I thought was really interesting is that if you have this RFP process and the utility is out there looking to essentially buy renewables from developers, the developers make bids, they all make bids. And one of the factors they put in their bids is a factor that covers the risk. What risk are we talking about? Aside from the ordinary business risks, the risk of doing energy development here in Hawaii is the risk of permitting. And also I guess the risk that the utility will cancel your deal for some reason. And so you have bureaucracy and uncertainty all around and any self-respecting energy developer is going to build the risk of those uncertainties into his price. The result, of course, is his price is higher. And if his price is higher, our price is higher. If there weren't so many risks, if this was all sort of settled down and the developer would know pretty much what was going to happen and could be confident about the permitting and the solidarity of his contract, he would bid less. And if he bids less, our price is less. So I don't know if it works that way in the mainland but I think here one of the reasons we're paying so much for renewables is because of these bureaucratic uncertainties. We have them in all areas. That's why housing is so expensive too. You don't know when you're going to get your permit. If you ever get your permit, you may have to, you know, one Castle and Cook project took 40 years to build the project out in Mililani because they had to go through all these gauntlets. And the result, of course, was the housing was far more expensive than it might have been. And I think the same rule applies to energy and that's too bad. If we addressed that on a governmental level, we could fix it. Or at least in part we could fix it.  Well, I think you're absolutely right and I'm just kind of harked back to, I don't have the latest reports from Moody's Fitch and S&P standing in pourers right in front of me, but my recollection is whenever these rating agencies most recently do seem to come up with analysis as far as the energy situation here in the regulatory environment that one or more of them has been noting the uncertainty in the regulatory environment, which has, like you were talking about, notes the risk involved in terms of doing business out here. And I mean, next year I certainly learned the hard way after being invested here for, gosh, you know, more than two years and we know how that turned out. So there's a tremendous amount of risk and I get it. I mean, you want to factor that in in terms of coming up with pricing just to kind of round out, just to go back briefly to residential storage. My rough estimate, Jay, is that the cost of residential storage needs to come down somewhere in the 70 to 80% range, 70 to 80% compared to what we're seeing now, people like me are seeing now as far as what providers are offering for storage right now. It needs to come down by, you know, three quarters or so in order to really hit more of the mainstream. I believe, I really believe that will happen over time, but it's not going to be on a month-to-month or month-over-month basis. It's going to be more incremental and I can only hope that there will still be enough, enough of a PV industry here in the State of Hawaii to be able to stick around long enough so that when those prices do come down as they ultimately will by that kind of margin, which I pretty much hopefully do, that we'll still be around to... Yeah, that's so true. I saw that in your article. I certainly agree that whatever is happening, it's deteriorating the PV install industry and that will have an effect going forward because they won't be around anymore. But speaking of which, we're not going to be around for one minute. We'll take a minute off, Marco. Marco and me can take a minute off. We'll be right back. You'll see. Aloha. I'm Richard Emory, host of Condo Insider, a weekly Thursday show at 3 o'clock that goes all summer long talking about issues living in a condo association. Each week, we bring experts to talk about the rights and obligations of owners and boards of directors to successfully run their condominium. It's a great educational show. It answers a lot of questions. We hope you'll visit us sometime. Aloha. Aloha. I'm Chantal Seville, the host of The Savvy Chick Show, which you can watch every Wednesday at 11 a.m. on thinktechhawaii.com. On The Savvy Chick Show, we are all about inspiring and empowering women and girls to be the best they can be by having amazing guests from all around the world. So we hope you'll join us every Wednesday at 11 a.m. Aloha. Bingo, we're back. Marco and me on Monday talking about energy. That's energy with an IE, right, Marco? So we're going to talk now about the end of that case. And I guess the question is whether it's really the end of that case. So Meena Morita, you know, brought an action to, I guess, for a declaratory judgment or set aside the appointment of Tom Gorak and the circuit court judge who heard that didn't take very long. I thought he might wait, like until December or January, and that would have made it moot because the Senate by then had an opportunity to confirm. But no, the judge decided within a couple of days that the governor could make an interim appointment and it did not require anything special because there was some reference to that in the Constitution. And it leaves me wondering about what the word qualified really means in the statute. You know, it's supposed to be, you know, you can't seat anybody until it's rather, that Mike Champley would not leave until his successor was both nominated or appointed and qualified, quote. Qualified is an unusual word. It's not the same word as consented to by the Senate. And I think the government took the position that the word qualified meant that he had the qualifications. But I can't buy that because that means the same fellow who appoints the member of the commission also determines his qualifications. You still need a third party to determine qualifications. And presumably that would be the Senate. Nevertheless, the decision was to confirm Tom Gorek as an interim appointee with a full authority of a commissioner. What do you think, Marko? Well, yeah, it was pretty darn quick. It was, Jay. I mean, Thursday, Mina had her proverbial day in court and Friday, the judge issued his ruling. Certainly didn't waste any time. The two-part ruling that, as I understood it, was one is that I think he ruled against her standing to sue that she did not have standing to sue. And second, substantively, he ruled that the governor was, in fact, within his prerogative as chief executive of the state of Hawaii to be able to do what he did. So Mina's choice at this point is to decide she's played it as far as she wants to, or to appeal. And I believe that the appeal would be not to the intermediate court, but directly to the Hawaii Supreme Court. And then, of course, if she were to do that, it would be up to the Supreme Court whether to even hear it. And if they did, then they would rule on it. So I'm too bad we couldn't get Mina to join us today because she'd be in a better position to address this, but that sounds to me that's the decision before her at this time. Yeah, and the standing thing really sort of gets in the way because, well, I have two reactions to that. One is the Supreme Court, if it ever got this case, could deny an understanding and without even getting to the merits of deciding any law. The other reaction I have is that, well, if Mina Morita doesn't have standing, if she doesn't raise any special standing different than you or me, it doesn't matter that she was former chair of the PUC, that doesn't mean anything, not in the context of standing. But then who would raise this? Suppose there was a good and wonderful argument in favor of striking the appointment. Who would raise this? You and me couldn't do it under this ruling. Who could do it? I gotta believe that if Mike Champley, who lost his job, not because he wanted to lose his job but because his term expired, I think Mike would have, would have had standing, would have standing both past and present to challenge it if he so chose. But as far as I know, he's made no move to do so. But I mean, it seems to me that he would or at least should have standing if he chose to do so. What about Consumer Protector? What about the government agency? Not that this would happen, but the energy office in D-bed. See, the problem is that they're all part of government and government is the one who did this. Government is the one who made the appointment. So it's unlikely that government agency would go against its own appointment. So if an individual citizen doesn't have standing, and Tom, rather Mike Champley, doesn't want to come around and take a position on it, take that position on it, there's really nobody practically speaking who could. Well, conceivably, Che, think that out for a moment. Conceivably, perhaps the Senate could have, if they wanted to, but interestingly they chose not to and instead filed an amicus brief, a friend of the court brief in support of Mina's position. I actually read that over the weekend. The Senate majority, number one and number two being led by Senate President Ron Cochee from Kauai. So the Senate majority attorneys filed an amicus brief in support of Mina's position. So I guess I'm thinking, because it's kind of loud here, not being a lawyer, that if the Senate instead of Mina had chosen to sue, that perhaps they would have had standing to sue because they could have argued that the governor was stepping on their toes, their powers in terms of qualifying a nominee for the commission. I don't know that much about balance of powers. Certainly there's a balance of powers question here. It's constitutional in the sense that one part of government may be violating the powers of another part of government. But I'll tell you one thing, I've never heard of a legislature or one chamber in the legislature suing a government body. That would be the first time, at least in Hawaii, that I've heard about that. Well, I think on the national level, I believe that there have been lawsuits by either the majority in the U.S. House of Representatives or the majority in the U.S. Senate. I mean, I'm not a legislative historian, but I believe it's not completely out of the realm of one branch of government, either the executive or the legislative branch suing the other. Okay, sorry, I'm not an expert in that. But let me add that they'll have their voice. They'll have their voice because soon enough maybe, what, 90 days, 120 days from now, they will be able to obtain consent of the Senate. The very same consent. And if they don't like Tom Gorak, then they won't approve him at that time. And it's hard to stop that if that's what they want to do. Although on a substantive basis, I'm not sure there's a lot of good reasons, aside from the appointment process itself, to deny consent for him to sit as commissioner. And I think I read something, this has gone just back to yesterday, seemingly a long time ago, read something yesterday regarding these proceedings that Gorak would conceivably be the sitting commissioner, the interim commissioner through the whole session, which doesn't end until the first week or so of May. So I'm not sure how that works. I saw that too. I think that's the law in the Constitution. They get to consent. The person appointed, and I don't know if it supplies to the PUC, but in general, gets to sit until the end of the session. If he's not affirmatively confirmed by the end of the session, then his nomination is not valid. Anyway, we're about done, but I do want to make one comment before we close about the article you sent me over electric vehicles standing for the proposition that things are going to happen faster than you think. That the technology is moving so quickly, and people in general would like to see electric vehicles, you know, avoid range anxiety. And of course, the wild card is the automatic vehicle, which by the way started operating in Singapore this week. How do you like that? They're ahead of us. But all this technology moving so quickly that I think that we may be on a roll here. We may find electric vehicles are coming around the corner at a high speed, and we may find that the next time people buy them, there'll be more charging stations, more of them. There'll be 200 mile range, which is pretty good, like a tank of gas, and we will be going somewhere. It's happening. I think it's actually in a funny way, it's reached critical mass. It's no longer an infinitesimal percentage. It's an infinitesimal percentage with large prospect for growing. What do you think? I think the magic numbers are 300 in terms of a standard range, which Tesla can hit if you're willing to spend over 100,000. That's 30,000 or less in terms of sticker price. And third, which is something you haven't mentioned, it'd really be great if we had a company that was selling these vehicles that was profitable, that we could have some confidence was going to be around for the duration, duration being at least more than a year, two or three. I think it's exciting and once we start seeing vehicles that can have that kind of range and do have that kind of price tag, I think that is going to be a very big deal, but we're not there yet. And I keep on going back to being the owner of a small business that can't afford to be losing money year after year that is going to be very important to me that vehicles or manufacturers are going to be able to be sustainable over time and make a reasonable profit. I agree, they'll have to make a reasonable profit and it may not be an American company, it may not be Tesla, maybe somebody else who can do it cheap. Tesla says they can make a car with 200 miles plus for $35,000, that's what they're on to now. But I suggest to you Marco that maybe we'll have that plus an automatic car which will really develop interest among the driving community and I think that will catch on. I actually think if you marry the two elements together, electric and automatic together, it would be irresistible assuming the regulators allow it to happen like it's happening in Singapore. And furthermore, let me suggest that if it doesn't happen by a company in the US, a US manufacturer there'll be somebody in China who does this they're probably working on it just as hard or harder than we are and they can put it together and presto they'll be all around the world before we get out of the starting gate. So we really have to push on it. Not only the people who care about renewable energy not only the public, but also the energy, rather the automobile manufacturers in the US including Tesla and the investing market. How about that? Well one of the major players in China is BYD in terms of vehicles in EV and I know that they're big in China they don't have anything close to the Tesla range and of course the Chinese have shown over decades that they can be very how we say imitative imitative in a not a pejorative sense, but I mean they do a very good job of copying and doing things that they copy cheaper and of course I get some in all kinds of trouble sometimes in terms of trade wars and so forth and allegations of dumping and so forth and so on but I think you're absolutely right that if Tesla's not able to make a profit and I really do hope they make a profit sooner rather than later and I think it's out there who will be able to learn from what Tesla's doing right and what Tesla's perhaps not doing so right and come up with products to be able to make it much more mainstream make electric vehicles much more mainstream and I think we make the combination here in Hawaii of more EVs that are powered by renewable energy sources and we're going ways to hitting that magic 2045 goal of 100% renewable and power generation hopefully off-sitting a lot of imported fuel for transportation so amen to that brother yeah I got one more point you know the recently Uber entered in a partnership I think a 40% interest in its competitor in China a competitor that does Uber, taxi cab kinds of things a smart move instead of fighting mother nature they went along with it and now are in a capital concentration together with the competitor what will rule the day in China on that kind of business well this could happen too, Tesla creative guy he could get into a joint venture or some kind of partnership with some other company in the world bail them out of any financial issues find new technology, new innovation and maybe the answer ultimately for this marriage of the two technologies will be a capital concentration that is on global scale you can do it and that would create a global market right away anyway Marco we're out of time so thank you so much for joining me today as always I look forward to talking with you in the interim and two weeks from today I look forward to having another conversation about all the things that are happening in clean energy here and everywhere else you are my rock to my role okay


always ask the turtle first is a thought I picked up from a story I read about a lady who told the story when she was a tourist in Thailand she saw a turtle climbing up the side of the road and she stopped the car picked it up and took it back to the river and released it. What did the locals saw do that and said what are you doing? She says I saw this turtle so far from the water I thought I'd rescue it and put it back in the water the locals said you should never do that the turtle probably took six months to get to where you found it it was going there to lay eggs somewhere secretly I think that's a good go-to idea to have about people stop rescuing people who didn't ask you to rescue them stop being over responsible for people diving and picking them up taking them to where you think they should be making them who you want them to be when they're never asked to be rescued helped in the first place hope that helps cheers


I've wanted to become an apprentice with Wales and West Utilities for a long time. When I finally got in, I was over the moon, it's a great training setup they've got. We've got a training environment that we go on to and learn everything before we go on site and it gives us chance to get our mistakes and to learn everything properly before we go and do it for real. What I would say to other individuals that want to become an apprentice or thinking of doing an apprenticeship is that it's never too late. I think that at any age, 18 to 50 years old, it's a fantastic career to have.


This will take two seconds and would drain the swamp for good. This is a personal message to President Donald J. Trump. You're having a really rough go lately. But at least you and I agree on one thing. We must drain the swamp. Our government isn't listening to the people anymore. It's totally corrupt. It's entrenched. It's broken. And American families are paying the price. You're the anti-establishment president and people love that about you. You've also proven that you're willing to use executive orders to do things that Congress won't. Some of those things aren't so popular, but we've created something that is. It's powerful and it's called the American Anti-Corruption Act. It pulls at 87% with both liberals and conservatives and would drain the swamp for good. Our plan to pass it works, but it will take years. This will take two seconds. This is an executive order we've crafted just for you. It includes as much of the anti-corruption act that can be passed with a stroke of your pen. It's not everything that needs to be done, but it'll hold up in court and it's immediate. This is how legacies are made by striking boldly at the establishment in ways that the left and the right can get behind. Mr. President, let's drain the swamp. Also, we left a little something in there to grease the wheels. That's irony.


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Okay, now we're going to talk about network and pathway analysis. So you've had an introduction already to gene set enrichment analysis where you are looking for enrichment in groups of genes in a gene set of interest, so we're going to go a little bit beyond that now. So first we'll do a little review. So why does one want to do pathway analysis on biological data sets at all? The main reason for this is statistical considerations. When you have thousands of genes, you have thousands of hypotheses, and that causes multiple testing correction problems. When you reduce thousands of genes into a smaller number of pathways, often you can achieve a reduction down to dozens of pathways, you're increasing the statistical power. So now rather than looking for overrepresentation of a disease process in individual genes, you're clustering and combining those genes together and looking for overrepresentation in a smaller number of pathways. It allows you to do things like find the meaning in the long tails that we often see in disease processes. So for example, in cancer genomes, when we're looking at somatic mutations, we usually see five or six genes that have a large number of mutations and then a very long, long, long tail of genes that are currently mutated at a low level, and we don't know how to deal with them, but often by clustering them into pathways, you start to see statistically unlikely clustering and you can find the meaning in that long tail, and that enables you in turn to tell biological stories and get your papers published. So I'm going to be talking broadly about pathway slash network analysis. This is a very heterogeneous field of many different approaches, but the way I define it, it's any analytic technique that makes use of biological pathway or molecular interaction information to gain insights into a biological system. Because we're in a cancer institute here, a lot of my examples will be drawn from cancer, but by no means do I mean to limit this just to cancer. Pathway network analysis is a hot and very rapidly evolving field, and there are many different approaches that people use for this. So let's look at what the difference between a pathway and a network is. So again, this is my definition. It's not the broad definition by any stretch of the imagination, but the way I like to look at it is a pathway is the detailed biochemical view of a biological process. The thing that we like to draw on the blackboard when we're explaining the causation of a particular biological phenomenon. So for example, here is a traditional pathway view of the epidermal growth factor, receptor pathway, the initial part of the signaling pathway, where EGFR receptor and the EGF ligand bind to each other, forming a complex. This is inhibited by a negative inhibitor, then there's a dimerization step, ATP is hydrolyzed to ADP, causing creating a phospho product of the dimer, which then has further pathway, further steps below, and there's a positive regulator SRC1 here. This is generally the way we create our hypotheses and interpret them. Unfortunately, this type of representation is very difficult to scale, and it's very difficult to do large quantitative computation over. So often, if not usually, this very detailed mechanical model gets turned into a logic model represented as a network. And in this network, we're throwing away all the detailed information. That is, there's a dimerization, there's ATP hydrolysis as a phosphorylation step, and instead we're just capturing the logical association. EGF activates EGFR, LRG1 inhibits this interaction. The nice thing about the network representation is you can start adding other information where other types of interactions, where you don't know the exact mechanism, but there's say genetic information, or there's proteomic information that shows that these additional genes, KRT17, are interacting somehow, we don't know exactly how. But we can do some network analysis even with that limited amount of information. And so you can convert, by the way, from the pathway representation to the network representation, but you can't go back the other way because there's information loss. So I'm going to give you now three different, talk about three different types of pathway network analysis. They start simple and they become increasingly complex, but they all start out with the same set of ingredients, there are basically two. One is a list of altered genes, proteins, or RNAs that you will be analyzing, so this comes out of your own experiment.  That's your list. The second is you need a source of pathways or networks to analyze your data set with. So let's talk about where those ingredients come from. So pathway databases, and there are quite a few of them, are databases which collect the mechanical, mechanistic, biochemical representation of biological processes. They are usually curated. They provide an intuitive human readable view of biological process. They capture causation, unlike networks, and they have a human interpretable visualization because we have been educated to understand these diagrams. Disadvantages are because they're curated, they don't usually cover the whole genome. It's human labor and it's also limited by what gets into the literature. And in addition, it's very subjective. Different databases will disagree on the boundaries of pathways. So going back to my EGF example, the EGF signaling pathway actually leads into the RAS signaling pathway, which is then fed into by a bunch of other signaling pathways, including TGF and others, which actually interact all together. So when you carve out something called the EGF pathway, where are you drawing those boundaries? And different researchers make different reasonable choices. So one pathway database is EGF pathway may not be the same as another pathways. That's a problem in communication and reproducibility. Here's an example of a classic pathway. This is pentose, the pentose glupuronate pathways represented in keg, the Kyoto encyclopedia of genes and genomes. I think most of you have seen these diagrams. Keg is the oldest and most storied of the pathway databases. It curates biochemical reactions in multiple organisms and maps them onto multiple species from the carriots up to human. And annotates everything using EC numbers. So it's ability, it's a good sort. PC numbers are the enzyme commission identifiers for enzymatic activity. Its advantage is that it's quite comprehensive. It covers multiple species. The disadvantage is that because it's based on enzyme activity numbers and there is not a one-to-one mapping between enzyme activity and genes and proteins, can be a lot of source of confusion in it. In addition, keg has changed its business model over the last few years and it's no longer completely open and you have to license it. The reactome database, which I'll talk about a lot because it's a product of my lab in collaboration with NYU, EBI, and OHSU in Oregon, is another pathway database. It's probably the largest open pathway database for human and it's created by a team of curators who read the literature and create a data model from which we can create these pathway diagrams. And it focuses largely on disease-related pathways, signaling pathways, developmental pathways, response to infectious disease, and other pathways that people are interested in. We also take a somewhat opportunistic approach if we're approached by a researcher who says, well, you really don't cover the field that I'm working in such as retinal development. We say, oh, well, that's great. We'll work with you to curate retinal development. Can you spend some time reviewing articles for us? And so it is created, reactome is created by a collaboration between curators, expert authors, and it's peer reviewed. So each of the reactions that we represent has been reviewed by at least one peer. So we have rigorous curation standards. Every reaction is traceable into a reference in the primary literature. We do cover non-human species, but a lot of that is automated. We do computation through orthology to create non-human pathways. And at the current time, we cover a little bit more than half of the human genome, 10,651 in the last release, across 2,132 pathways. It provides a Google-style zoomable map that you can overlay your and other people's information on. And you can do some pathway and network analysis using reactome tools. We'll go into that in more detail. And it's open access. Anyone can use it. There's no fee. You can republish it. No restrictions whatsoever. So those are a couple of pathway databases. There are many more. I'll give you a resource where you can get the comprehensive list in a bit. So networks capture more of the genome, including the less well-understood portion. And networks can span a large variety of interactions. Any two molecular entities, whether they're lipids or proteins or DNA or DNA and proteins, you can make a network out of. So you can create networks that span genetic interactions. For example, you can capture suppressor enhancers. Physical interactions like proteomic pulldowns or these two hybrid experiments, co-expression data, sharing of go terms or literature citations or authors who publish on them or adjacency in pathways. When you're working with networks, it's really important to understand this key fact that there are many, many different kinds of networks. So you have to understand what the most appropriate network is for the problem you're trying to solve. Pathways are much more homogeneous in that respect. So network databases, again, they can be built via curation. They have a group of people sitting in a cinder block room curing literature and building these up. Or you can build them automatically from high throughput experiments. And most network databases are built in both ways. They have extensive coverage, much more extensive coverage of biological systems. Some of them have all the genes in them in one way or another. However, they have a higher error rate. They generally, the underlying evidence is weaker and the relationships are less well understood. And so the interactions are less reliable on the whole, not that they're not useful. Popular sources of curated networks include biogrid, intact, and mint. Each of them have tens to hundreds of thousands of genes, some in one species, some in multiple species. The comprehensive list of pathway and network databases can be found at this resource called Pathway Commons. Which is hosted by Memorial Sloan Kettering and multiple in New York and multiple collaborators. And here you can get a comprehensive list of all the network and pathway databases with a brief description of each one. And you can download information from a large subset of them. They've created MSKCC and Pathway Commons has created a common interchange language called Biopax. That allows various participating pathway and network databases to submit their data in this common language. And then they get integrated and merged together with a web-based interface. So you can actually get pathway and network data here as well. Okay, so those are the two ingredients where you get pathway and network data. What do you do when you've got them? There are basically three different classes of pathway and network analysis that I'll take you through here. The first one you've already seen, this is Gene Set Enrichment. And in Gene Set Enrichment the basic strategy is to take a pathway or network and break it into clusters of related genes, proteins or other molecular entities. So a classic way to break it is by using the gene ontology. And you divide the biological network into 500 or so categories based on what biological process or what subcellular compartment that gene participates in. Other ways of doing it are you accept, you take a pathway database like Reactum or Keg and you use the pathway name. Whatever the curators decided was the correct boundary for that pathway. And then using your gene list, you look for overrepresentation of the genes or proteins in your gene list in one or more of the bins that you created from that pathway or network database. Okay, that should be familiar to everybody. The, at this point, the advantage of this is that the statistics are very well understood. It's straightforward. You have plentiful tools, both online tools and command line locally run tools to do this. And people basically understand what the results mean. The disadvantage is that there are many, many different ways of slicing and dicing the genome. There is a tendency, it's subjective, and there's a tendency for people to keep picking different gene sets until they find the answers that they want. And there's that bit of subjectivity that is not good. Second is the problem that in most gene set, that most gene set enrichment algorithms don't do well with genes that belong in multiple sets. Some of them will correct for it, most do not. And you can end up emphasizing one bin at the expense of another based on an arbitrary decision of which bin to put a gene into. However, that being said, GSEA is the gene set enrichment analysis is probably the first thing one should do, and it gives you a lot of information. The second class of pathway network analysis is subnetwork construction and clustering. And in this technique, you do not start with a preconceived notion of what sets the genes belong to. Instead, you discover them from the network by looking at your, through your data. So you start out with a network of all possible interactions, whether they be physical interactions or genetic interactions or sharing of go terms or whatever. And then you superimpose your gene list on this network in order to identify clustering, statistically unlikely clustering of your gene list inside that network. So you find, for example, that instead of being scattered throughout the entire network, all the genes in your list are clustered in one little section of the network. And then by extracting that cluster and looking at what genes your list are interacting with, you can start to tell stories and understand what is going on there. So it's an unsupervised style, discovery of the potential role of the genes that are on your gene list. And there are quite a few, quite a few published and heavily used programs that let you do that. The final method, the most complex one, is pathway modeling. And here, instead of losing the information that happens when you go from a pathway into a network, the algorithms attempt to build a computational model of the pathway itself so that the sequence of events, the cause and effect relationships remain, remain there. And this shades into systems biology, but it allows you to put your gene list on and see what the integrated effects of alterations in that gene. So a typical example is you've sequenced a cancer genome and you found two different genes in the TGF pathway that have been altered. You don't know for sure that that's a significant finding, but you put it onto a model of the TGF pathway and you find that, yes indeed, those two genes will interact with each other in the pathway in order to constitutively activate the pathway. Or perhaps they will do nothing, or perhaps they will decrease the pathway activity, but it gives you a prediction that you can then test. And then there are a number of pieces of software for this, all of which are harder to use and less commonly used in literature, but are gaining in importance. And so you use these three methods to answer three different types of questions. For gene-set enrichment, it helps you ask, answer the question of what biological processes are altered in this disease. I'm using cancer as my example. For the Novo subnetwork construction, you can discover new pathways or identify differences among tumors or among patients that are related to which subnetwork are affected by the alterations in the genome in that patient. And for the final way, it allows you to ask, how are the pathway activities altered in the particular path of a patient? And to ask and answer thought experiments like, can I identify members of the pathway which are targetable by drugs or by genetic manipulations? So let's go through these in more detail. Enrichment affects gene sets. I think I said everything on this already. But the main advantages are it's easy to perform and there are lots of tools to do it. And the disadvantages are the arbitrariness of the gene sets.  For the Novo subnetwork construction and clustering, the process is very straightforward. Using the appropriate algorithms, you take your list of altered macromolecules, genes, proteins and RNAs, and you apply them to the biological network of your choice, and the algorithm will identify topologically unlikely configurations. You can then extract those clusters of unlikely configurations, and then usually there will be a small number of them, you know, half a dozen to a dozen. And then you can annotate the clusters to understand what it is that you've discovered. And in fact, kind of in an iterative way, you can annotate the clusters that you discovered from the network analysis using gene set enrichment techniques.  You can now annotate the whole thing and say, oh, these are all part of the ribosome, for example. So here's an example of doing this with the Reactome, Reactome. This is from a publication, Genome Biology, about seven years ago now. What we did here, and this was done by my colleague, Juan Ming Wu, who is now at LHSU, is to take the curated pathway databases that were present in Reactome, turn those into a network, and then added uncurated high-throughput interaction data. And at that point it's mostly yeast to hybrid data, since then we've added modern code transcription data and other types of networks to create a big hairball. And then we used machine learning techniques to weed out false connections from the yeast to hybrid data in order to reduce the number of false positive interactions. And that gave us 11,000 proteins and 270,000 interactions. The network is still grown, has grown since then. And then the next thing one does is to take a gene list, and our usual use case was somatic mutations and a cancer, apply that to the network, and then to use the same algorithms that are used to identify social groups, social clicks in Facebook, or on the web, or other web-based social networks to find groups of our genes which are talking to each other, interacting with each other more frequently than you would expect by chance. And this pulls out a series of clusters, which we can then annotate, and in cancer these end up usually, these end up being signaling clusters, or clusters having to do with cell cycle, or cell motility, or usually things that you would expect. If you do it on autism spectrum disorder, you get lots of clusters having to do with neural development, which is reassuring. And then what do you do after that? Okay, so I'll give you an example in a second why this is useful. So, okay, as of the current date, the ReactMFI network has grown to 12,280 genes, 230,000 functional interactions, has 60% coverage of the genome, so we still can't say anything about the 8,000 genes which are not well expressed or not well annotated with literature or don't come down in yeast to hybrid screens. We believe the false positive rate to be less than 1%. However, the false negative rate is quite high because there's a lot about the genome we don't know, so that's a big caveat. And it's a very big complex network. I'm just showing you 5% here. And you can kind of see a clustering that has to do with complexes and pathways. Okay, so here's an example of using this. The labels didn't come out very well, but you'll get the general idea. This is exome sequencing of, or whole genome sequencing of 52 pancreatic cancers. There are more than 200 recurrently mutated genes in this. And here's a typical long tail with a few genes, KRAS, P53, SMAD4, being very frequently recurrently mutated. We can immediately say these are driver genes, but then there's a long tail that goes way, way, way out of things which are still way out here. There are five out of the 52 patients that have recurrent mutations. They have roughly a 10% mutation frequency. We don't know whether these are real or not. They don't meet statistical cutoffs for calling a driver gene by recurrence alone. So what do we do with this? Well, if we do that clustering and extraction, we end up with a total of 11 clusters of highly interacting genes. And by and large, they are what we expect. We have a large module which has KRAS right in the middle, which is highly frequently mutated in pancreatic cancer, and involves ERB signaling, FGF signaling, EGF signaling, and axon guidance, which is one of the processes known to be altered in pancreatic cancer. We have another one for P53 signaling, another one for Hedgehog, TGF beta. We have all the extracellular matrix interactions that have been associated with metastasis. And then we have things that are coming from other cell types in the genome, such as lymphocytes. So we have MHC class 2 in here, and we have the spliceosome. You can then do fun stuff with this. So for example, one of the things we were able to do with this pancreatic cancer set is to identify four major genomic subtypes of pancreatic cancer based on which modules are mutated in any individual patient. So one type is characterized by absence of the first three modules, and mutations in module 7, I forget which one that is. There's another one characterized by mutations in modules 2, 1, and 10, one that's characterized by 2 and 10, and so forth and so on. If you attempted to do this just on the genes themselves, you don't get any, you get very little clustering at all. So this gave us some subtype information. In this case, however, the four different subtypes didn't have any difference in prognosis, so it was of academic interest. However, when we did this in breast cancer, we actually very readily found a strong biomarker of survival. So here is an example in which one of the modules that we found in early stage estrogen receptor positive breast cancer involved genes from M-phase of the cell cycle and Aurora B signaling. It clearly has to do with mitotic activity. And when you apply this to breast cancer survival, high expression of any of the genes in this module are associated with a much worse disease-free survival. Patients do much worse than if they have low expression shown in the red line. In fact, the effect is so strong that patients with high activity in these modules have as bad a survival as patients with triple negative breast cancer, which is generally considered highly actionable. Patients with estrogen receptor positive breast cancer would usually get a kind of non-aggressive therapy, surgery, a little neoadjuvant therapy, and then watch and wait. Patients with triple negative would get much more aggressive treatment. And this argues that there is a subset of patients in the estrogen receptor positive group that should be treated more aggressively. So there are multiple algorithms that will do this type of clustering. Gene Mania is a fantastic web service that was developed by Quaid Morris and Gary Bader at University of Toronto right here. And it uses a birds of a feather principle to identify genes that are related to an experimentally defined set. You upload your list of genes to the Gene Mania website and it extracts and creates modules for you. And it has several hundred different networks that you can apply to this. So you can choose the network that is best suited for your experimental questions. One of the disadvantages of Gene Mania is that there's ascertainment bias. And genes which are very highly connected have been very well studied, like P53 tend to create clusters no matter what you do, just because they have lots of connections and they end up coming down. The hot net program that was written by Ben Raphael at Brown University in Rhode Island avoids this ascertainment bias by modeling the network as a metallic lattice. When you put an up-regulate, you then put a series of up-regulated or down-regulated genes in it and it creates hot or cold spots and then it uses thermal diffusion to identify hot regions of the network. When you have a highly connected gene like P53, it has lots of connections so the heat disperses more quickly. I've never understood why this way of modeling networks actually makes any sense, to be honest. But it does produce very useful results and has been used to identify drug targets and to identify tumor subtypes. So it works even though if you can't explain exactly why it works. There are then a couple of site escape applications and you've all used site escape at this point, right? Which I recommend. One is called hypermodules and this is a very good general-purpose tool for finding and extracting network clusters. It allows you to co-cluster with clinical characteristics. So if you have a gene list of up-regulated expressed genes and you're looking for a clinical characteristic such as high rate of migraine headache, you can put those two together and we'll find clusters which are correlated with patients who have the migraine phenotype. And then the Reactome Functional Interaction Network has its own bespoke site escape app. You'll actually be doing an exercise using it after I finish this lecture. And this is more limited than the others because it uses the Reactome FI network and you can't change it. But within it, it offers multiple clustering and correlation algorithms including hotnet, including paradigm that I'll talk about and allows you to do survival correlation right within the application designed specifically for cancer genome analysis. Okay, so then the last type of network pathway analysis I'll talk about is pathway-based modeling. So in pathway-based modeling, there is a... You or the algorithm creators have created a computational model of one or more pathways in which the relationships between upstream and downstream events, the positive and negative regulatory relationships and their weights have been preserved. And this then allows you to apply a list of altered genes in the direction in which they're altered to the model, compute over it and see what the effect is on downstream... downstream effectors of the pathway. So you can do very specific things like looking for increases or decreases in a particular phosphorylated...active phosphorylated product or look for increases or decreases of ubiquinolation of a product or the presence or the activation of a transcription factor. So it attempts to preserve and integrate the multiple molecular alterations and it is kind of a babysat step into systems biology. So there are...this is where there's an incredible diversity in the community. I'm just giving you a very superficial list of the most popular tools. There are many different...many different formalisms that people use. The oldest is partial differential equations based on reaction kinetics. Software like CellNet Analyzer is used. These are mostly suitable for metabolomics and biochemical systems such as yeast and fermentation, undergoing fermentation where you're predicting from the reaction kinetics what happens when you add maltose to the mix. They're good for up to a couple dozen genes after which time the computation becomes increasingly intractable. Unless anyone who is using metabolomics, you probably won't use these. Then there are information flow models that are designed for signaling cascades, usually mostly for kinase cascades. The two that I'm most familiar with are NetForest and Networkin from Uppsala University in Sweden. And they model specific signaling pathways. They have pre-built models of most of the ones that you would be interested in. And you can perform experiments on these models to see what the effect on signaling would be from mutations or other alterations in that pathway. For transcriptional regulatory networks, the best software that I know of and the oldest is from a call to RACNI from Andrea Califano's group at Columbia University. What this allows you to do is give an RNA-seq or microarray data across a series of perturbations, typically used in knockdown experiments or transfection experiments. It will build a cell-specific regulatory network for you and allow you to identify the key master regulators of the process. So the transcriptional switches. And it does this using an information theory paradigm. And then there's a large family of methods based on probabilistic graph models. These build a logic model of the pathway with arrows of influence between each pair of genes. There can be positive influences and negative influences. And from a data set, you can learn the weights of these influences and then integrate across them so that if there is an increase in the activity of a gene up here at the top, it'll propagate that changes down through the various positive and negative arcs. And tell you what happens at the bottom. The most popular of the algorithms that are used is called Paradigm from Joshua Stewart's lab at UCSC. And it's been used very extensively for cancer analysis. So I'm going to give you an example from how Paradigm works. This is a very simple example. We have a very reduced view of P53 where the MDM2 gene inhibits P53 activity and P53 activity promotes apoptosis.  If you increase MDM2 activity, a P53 activity will go down and apoptosis will go down. Very simple model. Now even this model turns out to be much more complicated at the genomic level because you can have alterations at the gene level or at the RNA level or at the protein level. So you can increase MDM, let's say decrease MDM activity by knocking out the gene or you can hypermethylate the gene and reduce the amount of RNA or you can degrade the protein. And I have a question here. You can absolutely put the non-coding genes as well as long as you know what they're doing. So if you have a link RNA and you know for sure that it is activating a gene in apoptosis or some other pathway, then you can absolutely put that in. Same thing for P53. Oh yeah, I'm using the wrong mouse. And so alterations at any of these levels can have a different effect on the activity of the protein or gene itself and then the proteins interact with each other to cause the effect. So here's some examples of the various things that Paradigm was developed to handle. So you do a genomics experiment on a cancer, you can find mutations, you can find changes in RNA expression, sorry about that. You can have copy number changes, you can assay by mass spec and Paradigm is set up so that you can put one or more or all of these types of omics analysis into the model and get a sensible result out of it. And it actually works quite well if you apply it to a complex cancer type such as a glioblastoma multiforme. You readily find subtypes of the cancer which have to do with the, which each of which has a different set of network activities, pathway activities that are either up-regulated or down-regulated by the particular combination of mutations in that cancer. So what we're looking at here are, whoops, I'm clicking, our patients going across the columns, so each one is a different tumor, and then the rows are members are the integrated and inferred pathway activities of a series of pathways they built models for. So for example, the interleukin gata pathway here is very strongly down-regulated in subtype 3 and not significantly down-regulated in the other three subtypes. For EGF receptor, types 3 and types 2 both tend to up-regulate these. So this has been used to discover multiple cancer subtypes. In some cases they've had clinical significance. Now the good and bad news about paradigm itself is that it's actually rather hard to run because it's distributed in a source code form, somewhat hard to get installed in your machine. And making it even more difficult is there are no pre-formatted pathway models. You have to go in, carry the literature yourself and create the models, which can be quite difficult. It also takes a while to run. The good news about this is that it's one of the algorithms that we put into Reactome F5 is in the Side Escape app. It runs paradigm and we have pre-populated it with pathway models based on Reactome. It also improved performance so that it runs in a reasonable time and can actually run in minutes to hours rather than days. So you will get a taste of Reactome viz when Robin Hall gives his workshop in a few minutes. And that is the end of what I wanted to cover. It's just an overview of the field. I'm ending with some useful URLs for databases and for algorithms that you can use. They're all in your materials. And I want to thank everybody for your attention and then happy to answer questions for a few minutes after which I need to run off to a meeting, which explains why I'm dressed this way in case you're wondering. So any questions? Comments? Yeah. Well, in this case we're clustering individual tumors by mutations in the genes. So protein coding changes. Okay. And there are six frequently mutated genes and another 200 which are not so frequently mutated. We tried to find patterns in just the mutations of the genes. Didn't really get anything. When we turned it into network clusters, we got very strong patterns. It wasn't, no, that's exactly the point. So our biggest module was KRAS and KRAS is mutated in 95% of pancreatic cancers. But it turns out there are many other genes which are more rarely mutated and sometimes you would get no KRAS mutation but a lot of mutations in genes that are in the pathway. So they end up getting clustered together. So the question is whether the tools can be used to analyze data from model organisms or non-model? The answer is that in many cases, yes, you can, but there has to be a source of the pathway or network information. And so it needs to be a well-annotated genome in order for any of this to work. So you need to have the best scenarios in which the animal you're looking at is related to some model organism that has been annotated. So it's a nematode. Say it's a nematode, then you can use the CL against annotations for it and then use orthology information to transfer over go annotation terms or networks and pathways that have been built. It will require some upfront work in order for you to do it. And if it's a species that has not been well studied, then you might be out of luck.