Hello and welcome. I'm Lynne Freese, producer of Global Political Economy, or GPE News Dogs. Today's guest is Pat Mooney. At the end of this year, 2021, a meeting is being held to rubber stamp a corporate strategic maneuver to take over global governance of the entire world food system. Effectively food production, research, and finance. Pat Mooney will be talking about all this in the context of the long food movement and his report, Transforming Food Systems by 2045. The report shows the stakes are high because food systems are being rapidly transformed as food and agriculture go digital. This is the last chance to change course. Pat Mooney is lead author of that report, produced by IPAS Food in collaboration with ETC Group. Pat Mooney is leading IPAS Food's Long Food Movement project. He's co-founder and executive director of ETC Group that's monitored corporate power and commercial food farming and health for over four decades. He's an expert on agricultural diversity, biotechnology, corporate concentration and global governance. Pat Mooney was awarded the Pearson Peace Prize in Canada and received the alternative Nobel Prize, the right livelihood award. Welcome Pat, thank you for joining us. Thank you for having me. Pat from farmers and fishers groups to cooperatives and unions, the Long Food Movement calls on civil society and social movements to unite and collaborate. This is a forceful counter position to an agribusiness led transformation of the food systems. Your report Transforming Food Systems by 2045 maps out what this kind of ground up collaboration could achieve. And as the title suggests, you're looking decades ahead. So what was the impetus behind that? Well, we back in 2016, in fact, we began to talk about the need for a strategy that was not so short term as it has always been. It can't just be two or three years of thinking we need to be thinking further down the road. And we were expressing our general frustration, many of us in civil society that we're always trapped into these cycles of funding, which are so short that we really can't do the horizon scanning that's important. So we talked about, well, let's build something different. Let's try to see if we can imagine not just what we would like to have down the road, but how we would get to it. We all have the same kind of dreams of the way we'd like to see the world be, but can we really get there? Can we politically, practically do it? So the exercise of the Long Food Movement was to not just dream of what we want, but really do the politics of it, what's really viable in terms of moving institutions, moving money around to get where we want to be. The Long Food Movement is for decentralizing control and democratizing food systems as the key to feeding the world, as well as regenerating ecological and other systems vital to people and the planet. You say achieving that will require policy frameworks at every level of governance from local law to international law that support and empower smallholder and peasant farmers all over the world. So talk about policy frameworks that have moved in the opposite direction by supporting and empowering agribusiness and the role of agribusiness in getting governments to make those policy choices. For example, what did agribusiness want and get from government, say back in the days when biotechnology was the then new technology? Back in the even the late 70s and in the 80s, agribusiness was saying we have a technology here, biotechnology, gently modified crops, which will feed the 500 million. They said at that time there are 500 million malnourished people in the world that would solve that problem. They would take care of that and that they had the only tools that would actually be able to do it. They said that they needed some help to do it though. They needed three things basically. They needed government regulators to get out of the way, given the freedom to act as they wanted to. Secondly, they needed to be able to be given a regulation, a certain kind of regulation, intellectual property rights over life, over plants and livestock so that they would own it. And so no bad regulations but the regulations they wanted which gave them more corporate power. And then thirdly, they needed to turn the public sector researchers in their culture into basically servants for the private sector. So do the basic work for us and we'll do the rest. Just to clarify that third point about what agribusiness wanted was to turn public sector agricultural researchers into servants for the private sector. So this was to get the sort of research they wanted. In other words, research that advanced the interest of high input chemical intensive agriculture that eventually will feed into profits for the main agribusiness players. So pro GMO research. The green revolution sort of research we've been hearing about forever and all the developments coming out of universities and government research stations around the world for agriculture as well. The research money in the public sector goes into again support services for the private sector, basic research for the private sector. What were some real world consequences of this policy framework that agribusiness wanted and got? Take one example. I'm thinking here of corporate concentration in food systems. What happened there? Well we went from roughly 7,000 private sector seed companies in the world in when I first got into this work in the 70s to where we now have really what five or six of the most in many ways it's really only three or four companies that really control all of commercial production of seeds and pesticides together. So it's vastly concentrated compared to what it was. So there's been a lot of corporate takeover and buyout activity. Yeah on a massive scale. I mean it's been a huge convergence really started in the 70s and it's kept on going. It hasn't stopped. It is transforming itself. Who's doing the converging has been changing over time. When I was first dealing with this the biggest seed company in the world was Royal Dutch Shell. They had bought more than 100 seed companies and they thought they were going to be big in the market. They decided they couldn't do it after a while when they got out of it and more conventional crop chemical companies took over and bought the seed companies. Now of course we're seeing a new development where it's the big data companies that are moving in and taking over large sectors of the food system. And you think there's more to come that this trend shows no signs of slowing down. It's coming because again the industrial food chain is changing. It's no longer the chain with all the links in it that we used to have. Seeds used to be sold and owned separately from pesticides and from fertilizers and far machinery companies were stuck in the business of producing tractors. The traders and the cargoes of the world and the processors and the retailers were all different folks with big data management and the ability to manipulate not just digital information but also to manipulate digital DNA to actually adjust technologically computer wise adjust living materials. Makes it possible for the biggest companies with the biggest computers to step in and really try to govern the large chunks of the food chain. So seeds and pesticides have become one basically with the farm machinery companies and the fertilizer companies they could actually just become one big input sector. The grain trading companies are kind of lost in this whole exercise. They're not quite sure that they've got anything that anyone else wants anymore. The processors and the retailers are coming together more and the big data managers behind all of that the Amazons and the Alababas of the world the Googles and 10 cents of the world from whether it's China or Germany or the United States are saying well we can actually manage that better than anybody else can. So you get Alababa advising peasant producers in China on how to grow pigs and gardens as well as how to market their products as well as setting them up for retail sales in stores. I've been emphasizing that the big data managers is the ones who are really at the front of this now and calling the shots and deciding what to do with with the food system but behind them again are asset management companies the Black Rock and State Street and Vanguard and so on who are huge companies Black Rock has now more than nine trillion dollars in asset management power an enormous amount of money and Black Rock has shares in firstly every part of the food chain every every significant company in the food system is Black Rock is there. So Black Rock has that knowledge of what's happening it's sort of like being at a poker game when Black Rock is the only one that can walk around and see what all the cards that all the players have they know it all and they can then make decisions they think are important to make for for their profit and they have again the the massive control of data press more than anybody else does to to know how to use it most effectively. So in going digital food and agriculture is generating massive data and with it massive profits for food and the non-food corporations like data platforms asset management firms and others that moved in on food to get control of those data and profit flows and with consolidation and concentration mega corporations can amass vast profits. It's a very profitable enterprise really it's food is something that you do three times a day if you're lucky and it's something where you're shopping for all of the time so it's your repeat buying is built into the into the idea but and that means a lot for the companies it means an awful lot of data can be gathered you can attach things to that as you see with grocery stores that have bigger and bigger other you know areas and merchandise beyond food and so on there's lots of ways you can build from a food base to to control more the retail markets but also of course with big data management you control the production side much more easily it's possible to go to farmers and say if you take this package of of inputs that we own and control and have proprietary rights over and we can monitor that for you we can help you understand the weather understand the markets deciding exactly how much of fertilizer how much of pesticides what seed varieties you should be using they can control all that package of information and then attach that even to to cropping sure and sell that to the farmers so you end up with the company like John Deere for example as the world's largest farm machinery company having the the sensors on its tractors on its combines that really let it control the entire production process John Deere is in the field at the beginning of the growing season planting and dumping in the fertilizers the pesticides it's there at the end of the growing season picking up the harvest so it's knowledge of what's being produced and what will make it to the marketplace is is vast and much stronger than any other company. From seed sector consolidation in the 1980s into the present you've charted a trend of rising corporate concentration and control and food and agriculture since agribusiness got the policy framework it wanted you pointed out policymakers embraced what agribusiness wanted on the back of promises made by agribusiness at that time promises that biotechnology so genetically modified crops would solve problems like world hunger you debunked agribusiness promises then and you debunked them now and as an expert on corporate strategies you demonstrate agribusiness has a narrow focus that puts profit before people in the planet and systems vital to their well-being the ecological system the knowledge system the social system your early work exposing the terminators seed being an iconic example of how that works tell us something about that and read through into the present well terminator was um something we worried about even back in in the 1980s that as we saw biotech developing we and it wasn't doing well slow and getting into the marketplace and when it got into the marketplace in the mid 1990s it ran into a lot of opposition and you know it's a matter of just following the money something you know about it's paying attention to to where the greatest profit is and the biggest profit was going to be if farmers couldn't save their seed if they were prevented from saving their seed now they're legal preventions of course that you can apply through patents but that doesn't work as well in such a decentralized world of producers and so what the company's dreamt up was was an idea of what they call the technology protection system tps and it was a system which they described literally as being a way to help the farmers of south asia have access to the best possible seeds and technologies in a way which assured the companies who produce those technologies they wouldn't be robbed that the farmers wouldn't steal the technologies so that meant the seed had to be developed in such a way that it would die at harvest time they'd be able to take the those seeds to give you the end product but the seeds would die at harvest time so they couldn't be planted again so farmers would have to go buy back to the companies and buy seed every year they call it technology protection system we call it terminator seeds and there was such a reaction against that that the united nations came down with it with a moratorium against terminator technologies uh the companies themselves were forced to publicly say that they wouldn't use the technologies the biggest companies and that's held out they have not used the technology it hasn't been deployed into the marketplace but they didn't end round run around that again and we realized they would by by the end of the 90s we were saying that that well what they really want to have is not genetically modified crops they want to have crops that that will react to chemical use or can be changed internally you don't need to have genes move from one species to another you actually just simply change the the DNA itself within the existing species and that's now what we call gene drive technologies where you can go in and you can edit the DNA of plant variety or livestock species and alter it as you wish and there's no gmo involved as we traditionally know it so that's that's the next step of that control and and they are trying to get that into the marketplace they're saying that we can't have food security without it they're applying that to both health and to the food systems it works in both cases to malaria for example getting rid of insects that's that's that's part of of the approach they're now taking and again we don't know whether it will work we don't know if it does work for whether it will work too well be too dangerous what its longer term implications will be and we certainly know that we will not be the ones that control it Pat in your report you lay out two very different approaches to technology that as they run their course over the next few decades would map into two very different futures for food in a nutshell can you explain that there's two ways of looking at how they approach technologies they split or how we're looking at technologies that are making this probably quite unfairly simplistic but on one side we have high tech and the companies are saying they're the high tech gurus they know how to handle this they can go into their labs and they can do at the nano scale literally change life change dna manage systems in such a way that they can apply those those lab based technologies to the world on a macro scale and against that high tech approach we have what I would describe as a wide tech approach which is where you have production of food and systems at the level of of watersheds at the level of of ecosystems where peasants produce food and make changes create innovation but innovation which is set in that very specific narrow context that the the nano context of their community or their their farm area so one is wide in the sense that deals with everything in that ecosystem but but focused on the farm and the other one is is high in the sense that it has small application small innovations that can have little black global applications and I think the world is much much safer if we have a system where where the innovation comes from the 350 million labs that are farms around the world and the hundreds of millions more of scientists who are the producers around the world who can really be innovative with 7000 different crops not with the 12 crops of the companies work with with with 38 different livestock species not the five that the companies work with to get us through the problems of climate change and the threats of new pests and diseases and so on and biodiversity loss to to to have a decentralized system of of short short supply chains to me that's what makes sense for the future so for the common good for you what makes sense is policy frameworks that support and empower small farmers and peasants all over the world so from local to global that would reset the trajectory of food production research and financing systems to put people on the planet before profits so explained earlier under agribusiness as usual the opposite policy framework has prevailed for decades knowing that I was quite surprised to see you report smallholder production has performed so well it was even a bit of a surprise for us as well we knew that there was a lot we didn't realize how much it was and it's but conservatively speaking again peasant production smallholder production and that's fisheries as well as livestock keepers as well as as farmers together urban and and rural production was a lot is being produced in urban areas as well we put that all together then about 70 percent at least of the world's people depend upon that that smallholder production to to stay alive to to feed themselves and that also comes out to roughly the same percentage in terms of the amount of food not just the number of people but also the amount of calories and so on that are being produced which really begs the question of what are the other guys doing what's agribusiness doing the instance of not doing very much they're they're feeding perhaps 30 percent of the world's people they're doing that with more than 75 percent of the world's land and resources water etc and the food would use they're causing tremendous environmental damages and they're creating amount of over consumption of food because so much of the food in in the industrialized world at least goes to over consumption which causes health and environmental damages sum up briefly why you say history shows agribusiness has promised a lot and given a little and on the back of that what it's promising today well industry hasn't been successful agribusiness hasn't done what it said it was going to do its promises from the 1970s with biotechnology its claims around its capacity to manage the inputs to deal with long supply chains none of that has worked and it's manifestly true even to governments that that they recognize that that that so the promises haven't haven't proved to be valuable or to work for society so industry is is struggling at this stage trying to figure out what it does about that how do they make the case now that they they can they they've learned their lessons and can can come up with something which which really works for for governments and for people who want to eat food and they do that of course by claiming that they're really kind of doing what agriculture is doing that they have regenerative agriculture and the language they they grab onto is is that we get the message we're going to move towards climate smart agriculture we're going to work towards systems which have a full life cycle attached to them from cradle to cradle production we've understood those messages and we're doing it ourselves but we're attaching to that and improving it by having big data management by having our highly sophisticated supply chains by using block chains to track commodities from the field to the to the table they're capturing the language of of what was called the organic movement now the agriculture movement they're renaming it calling it regenerative agriculture and they're just saying they're going to tweak it with their proprietary technologies to make it better as we're now of course moving towards what's going to be a food summit so at the end of 2021 they're saying they will accept that all of these systems of different of agriculture can live together as possible for agriculture and peasant producers to be side by side with larger farms and the industrial processes of agriculture on the other side of course civil society levia camposina the world's largest umbrella of peasant organizations and and others are saying that we can't do this thing side by side if you're using pesticides you're using synthetic fertilizers if you're managing the marketplace for your purposes that destroys our livelihoods and our ability to to feed the 70 percent of the people that we are feeding in this context the report warns food security is under threat from agribusiness if governments rubber stamp what agribusiness want you've explained even governments now recognize the technological solutions for world problems promised by agribusiness have not worked out or been of value to society you talked about how in moving towards the summit at the end of the year that's the un food system summit to make a convincing case its new technologies are of value to society agribusiness has reframed its promises i'll just quickly quote a comment on that point and on what agribusiness wants in return for those promises at the summit this is from an interview you did at the launch of your report agribusiness has a very simple message the cascading environmental crisis can only be resolved by powerful new genomic and information technologies that they argue can only be developed if governments unleash the entrepreneurial genius deep pockets and risk-taking spirit of the most powerful corporations to do this the world needs a new governance model a multi-stakeholder round table where governments companies and civil society reason together if the summit embraces this governance model companies say they'll be able to apply artificial intelligence big data management digital genomics robotics and blockchain driven supply systems to sustainably feed two billion more mouths a quarter of a century from now so pat in return for those promises agribusiness wants the un food system summit to embrace a new governance model the multi-stakeholder model so that means a un stamp of approval to shift un institutions and food and agriculture so the existing governance structure of the world food system to the multi-stakeholder model of governance i should note for viewers that multi-stakeholder institutions promote the multi-stakeholder model as a vehicle to reset the world system of global governance a leading multi-stakeholder institution as reported in other segments is the world economic forum and these multi-stakeholder institutions are funded by the world's most powerful corporations and philanthropic capitalists like the gates foundation pat to unpack all this for us start with some of your thoughts on the multi-stakeholder model that's the model that we've seen i mean the world is walking up a little bit because of the cobit experience that we have covex which is the construction of the building the gates foundation together with welcome trust and where they've they've said let's have a multi-stakeholder group that brings together the pharmaceutical industry that are going to produce the the vaccines they should be there with those who are going to give the money to make this thing work so that's going to be the the foundations the big the the flat or capitalist foundations and we have to have the governments are going to give money as well we'll sit at the table we'll invite wh o to be there as an advisor so you have this facade of the world's governments participating in this process but they're not really decision makers in it and and those with the money and those with the technologies will make the decisions about how to distribute to vaccines around the world which has not worked very well for the vast majority of humanity it may not work well for humanity for for years to come so the world has seen that as a multi-stakeholder model but we're seeing that multi-stakeholder model also being proposed in the context of agricultural research in the context of how to restructure again the UN's normative functions for food and agriculture and investment and food aid and so on they're saying that that's that kind of models what they want to put in place there yet we're seeing the corporations making their through the world economic forum in particular saying here's how we want to restructure the food system in terms of these institutions at the United Nations level here's how we want to change the control of big data and manage big data for food and agriculture in our way and here's how we want to take agricultural research the through the international research body the the consultative group on international agricultural research and here's how we're going to change that to work for agribusiness and for the world they say and it comes down to this this language of multi-stakeholderism which i think is the most insidious and dangerous concept that we've seen and since world war two in terms of how the world will govern itself and companies are fundamentally saying let's all reason together let's all just sit around together and we'll just talk these things out and sort out how best to do things in the future we we have to recognize that we're all stakeholders here at the table together so let's talk and their definition of that is that we need governments at the table of course because they finally have normative functions we need to have the industry at the table because they got the money and the innovative capacity we need to have civil society at the table to sort of keep everybody honest but of course the civil society that they wanted at the table are kind of store bought civil society the ones that the companies have built themselves and funded themselves to to be there who have been co-opted into the system and they're really there for almost camouflage purposes the real negotiation is not multi-stakeholder it says a negotiation between governments and corporations and how will we how will the governments facilitate what the corporations say they need the resources they need to have and the regulatory systems they want to put in place to let them do their what they say is their job as corporations and so it's a complete falsity to call it multi-stakeholderism and that's just such a false description of the reality of the world that we think it has to be rejected in other words under that false description of the reality of the world that you were talking about powerful corporations are positioning themselves to directly call the shots on how the world will be governed and as the vehicle for this is multi-stakeholderism it's not so hard to understand why you reject that model your long food movement report maps out what the next 25 years have in store if agribusiness as usual gets the multi-stakeholder model of global governance that it wants and what that mapping shows is the keys of the food system are handed over to data platforms private equity firms e-commerce giants putting the food security of billions at the mercy of high risk AI controlled farming systems and accelerating environmental breakdown our conversation today is not so much to talk about the nitty gritty of the dystopian future for food people and the planet mapped out and the report is about how to prevent it in other words prevent the corporate takeover of global governance of the world food system by means of the multi-stakeholder model so in the time we have talked to us about immediate threats of this you point to three big agribusiness plays on structures of global governance of food and agriculture being pursued right now in 2021 those being the 2021 UN food system summit a play to get the summit and so the UN to mandate and embrace the multi stakeholder model for governance of food and agriculture and the other two fronts want to play on global governance on big data and food and agriculture and thirdly agricultural research so let's take them one by one in reverse order so first the play on agricultural research you talked earlier about how from the 1980s agribusiness got major governments to turn their public sector research institutions into a servant for the private sector so agribusiness the play in 2021 is to turn the world's international public agricultural research institution into a servant for agribusiness that research body is the consultative group on international agricultural research or CTIAR so who's spearheading that it really is I mean it's the Gates Foundation together with people who used to work at least for the Sinjenta Foundation which is now a Sino-Canvas property with the UK government and the US government a couple of others who are saying that we need to create a public international public sector research body which really is working hand in hand with the biggest private sector companies to deliver food security in the future and it's only the biggest companies that actually have the technologies again and the money for those technologies that can get us out of this mess so in the past whereas the CTIAR organization which I'm a critic of by the way historically has not been a great organization but still it was trying to do something in theory for the south and collaborating with governments in the south now that that body will really be a body which says here's what we want to do to you guys if you want our money and our technologies then you've got to go along with what we recommend to you it's really covex all over again but for agriculture it is the the the biggest companies with the biggest money pockets saying you can have our vaccines or you can have our our food technologies but only under our conditions and and that kind of control is really I think quite scary and that's what they are pursuing on your point that this play is covex all over again but for agriculture as we can't go into that here I'll just point out for viewers a good resource for finding out more about covex and so read-throughs to other sectors can be found online it's a report by harris leckman called covex a global multistakeholder group that poses political and health risks to developing countries and multilateralism so pat to get back to your comments about the world's international public sector agricultural research body cgiar the plan table as I understand it was for so-called unification of the cgiar system corporate framing uji code is meaning turning the system into a single corporate entity with stronger than ever connections to agribusiness so what's the state of play there and has this cgiar unification been achieved yes and again there's there's counter-transition as well a big that's what they're doing they've achieved it they've actually amalgamated the 15 institutes that are part of the cgiar system into one they've the gain the controls they want they're streamlining their technologies and research they're pushing out any smaller enterprises and marginalizing scientists in the south to be involved in their own food systems they're doing that but they're still stuck with a legal structure which is grounded in about 14 different countries around the world that any one of those countries can treat what's happening as a kind of merger as they would any other merger and acquisition and could stop it and there are many good reasons why for example Peru or the Philippines or Mexico who have these institutes in their own territory could step in and say no no no this this we're not allowing this merger to happen and under the headquarters agreements which exist now and have existed for some time those countries could literally take over those institutes and make them national property bring them entirely into the public sector of that country so all you need to break stop this this takeover of agriculture research is to to have two or three countries just say no do it and that i think is what we need to be pursuing in the discussions leading up to this food summit we need to say governments recognize you have power here exercise that power or you'll never have it again you'll lose control so mexico then is one of the key countries in this and mexico of course has been under intense pressure from agribusiness since by presidential decree they banned glyphosate and gm corn absolutely i mean mexico is where the international center for the production of maize and wheat takes place in the world it's right there in mexico just outside mexico city if mexico says that our headquarters agreement has been violated by this takeover by by the gates foundation and friends then that they can step in and and and take over all of those resources including the gene bank with the enormous diversity of maize and wheat seed in the gene bank and say now it's now it's the property of mexico and we'll cooperate with the rest of the world but we're not going to we're not going to surrender to this private sector initiative let's move now to another of the immediate threats to global governance the world food system from agribusiness led plays for a shift to multi-stakeholderism earlier you talked about the massive data and profit flows being generated as food and agriculture go digital so this play has to do with control over global governance of data in food and agriculture specifically the creation of an international digital council for food and agriculture so tell us about this first of all is it already a done deal no it's not it's still up for discussion i think it's encouraging that while we know what the companies want to achieve here they want their multi-stakeholder group to make the decisions for that the german government has stepped in and said we got to look at this more closely and they've gone to the united nations to the u.n. food and agriculture organization and they've said we need you to set up the government structure for this and this has to be a discussion with the world's governments so while there is a tendency to push in the same direction that they have with the world health organization marginalizing it and giving it sort of a a cameo appearance in the process i think there's still a hope that the digital council could be one which is is a intergovernmental body and which is a negotiated process with peasant producers around the world as well as with the governments to to decide what should be done it's not too late i am worried that the secretary general in new york has created this wider digital body that's looking at the use of digital information in every sector of the economy outside of agriculture as well and they've surrendered that process to a multi-stakeholder group led by the biggest companies but there still is a sub-sector around food and agriculture that's being negotiated and there's still some hope that we can protect the interests of of the food insecure and the food producers so then global governance of data in every sector of the economy outside of agriculture has already been surrendered to a multi-stakeholder group and that group's a body proposed by the u.n secretary general i should note for viewers that body's been dubbed big tech governing big tech in a civil society campaign to get it revoked by the u.n secretary general and that in several other contexts civil society continues to call on the u.n secretary general to rescind and desist from actions that surrender the u.n multilateral system to multi-stakeholderism the classic case of the role of the u.n secretary general in normalizing multi-stakeholderism inside the united nations was his signing of the united nations world economic forum strategic partnership agreement in 2019 other experts that i've interviewed report held that agreement was signed without internal discussion among u.n member states or public debate as reported by a transnational institute at the time an open letter sent to the u.n secretary general by over 400 organizations denounced the agreement for formalizing the corporate capture of the united nations in this conversation we've been talking about three big agribusiness plays bringing the threat of multi-stakeholderism to food and agriculture and you say when taken together put the entire structure of the u.n multilateral food system on the table in 2021 so far you discussed to the remaining play is the u.n food system summit and as context reviewers i'll very quickly note that this summit was convened by the u.n secretary general and as envoy to the summit the u.n secretary general appointed a recognized proponent of multi-stakeholderism and that of agribusiness that envoy agnes kalabata is a member of the world economic forum global agenda council now known as the world economic forum global future council she's the president of the alliance for a green revolution in africa agra a letter addressed to the u.n secretary general signed by 176 organizations working in africa and their allies called on the u.n secretary general to revoke that appointment to no avail in your words the summit is the brainchild of the world economic forum so comment now on the 2021 u.n food system summit it's really a shocking move we were we felt that there was a need for a food summit and one that really looked at the architecture of how food and air culture services are dealt with around the world but not one this is not the one that's being talked about by the world economic forum again which is this sort of multi-stakeholder strategy for governance which is behind the scenes sort of and the response has been from the world economic forum and those who are pursuing the summit from the secretary general's office is to say well it's really going to be a people's summit we're going to open up to absolutely everybody i likened it to being it's not a they call it the world food system summit to me it's much more like a disney world food system summit you can go to the you can go to the park you can get on any of the rides you want to the rides are bright and colorful there's never neverland there's frontier land there there's what else they have there all of those fantasy places you can go to but when you go on the ride and have enjoy the ride you end up exactly where you started when you get off it nothing has changed so we're all going to be on this ride moving towards the food system summit but at the end of the day it will only be those who are managing the process who will be able to interpret what came out of the process so much will happen there'll be so much discussion in so many different areas and there is no effort to actually create a final decision-making process where governments say here are the conclusions it'll be up to the organizers to say at the end of the day here's what we interpret to be the conclusions here's what we've picked out of this wonderful sort of potpourri of activities that went on here and and that's the scary part because they know what they want already and they will they will claim that they got it and they're they're welcoming all the noise and fanfare and activity along the way but but they did they decide there's been significant press coverage reporting controversy and protests surrounding the summit including out-and-out calls for boycott i've had it especially interesting to see the summit criticized in letters and statements not just from civil society and social movements but also un insiders and un independent experts for example i've his foods Olivier de chuteau the un special rapporteur on extreme poverty and human rights the former un special rapporteur on the right to food yes and Olivier and and others have tried to give it a chance of we all in in hypers food we felt that we should you know the very beginning of the process we were invited to be involved we agreed to be involved to at least see if it could go somewhere we're giving it the best effort i think there's an overwhelming recognition now from those in civil society and academia that have have tried to to to participate that that is not working and i think there will be an exodus away from the summit and i say that with knowing that having been around the un for more than half a century the un can paper over almost any disaster and governments are very good at making disasters look okay this is such a disaster in its organizational processes that i'm not sure it can survive it i don't think anyone's going to be able to paper over this this mess for me one statement in particular really seemed to capture a lot of what we've been discussing today and that was a statement made by the world-renowned economist and a former assistant undersecretary general at the united nations jomo ks he wrote big ag claims that the food ecological and climate crises has to be addressed with its superior new technologies harnessing the finance entrepreneurship and innovation only they can offer but in fact they have failed instead triggering more problems in their pursuit of profit as the new food system and corporate trends consolidate it will become increasingly difficult to change course very timely a long food movement is an urgent call to action for the long haul you still seem to think the long food movement can get to where it wants to be by 2045 that in other words it's not too late to turn things around i'm an optimist i think it's probably uh in my genes to be optimistic and i'm sure someone will offer to gene edit me to get it out of me but i i do think that that we're not too late in this again the surprise for us was that civil society is doing better and moving better than we thought uh is more coherent than we thought they would be they've got to do more but it is possible and the other surprise is that again 70 percent of the world's food system is produced by smallholder producers not by the big corporations as much as they'd like you to think otherwise the reality still is that 70 percent of the world's people are living from the the the bounty and and the work of peasant producers fishers and others around the world who are who are getting the food on the table so that we've got the majority of it still there is still an enormous amount of diversity beyond that which is held by science which peasants have in their fields they've been saving their own seeds they've been nurturing their own diverse livestock they're working again with 7000 crops with much more diversity than the industry is working with i mean just compare this one figure which i think explains it a lot about 45 percent of all agricultural research in the private sector focuses on one crop corn or maize one crop farmers are working again with 7000 crops so if you're trying to survive climate change who do you want to trust to get you through it you know you're just going to eat popcorn the rest of your life or you're going to be able to to in climate change or you're going to be able to to have that diversity of foods that couldn't get us through different growing conditions and different pests and diseases so there's a lot still on the side of the peasant producers pat mooney thank you no thanks for having me and from janeva switzerland thank you for joining us in this segment of gpe news docs


In the ABCs of health consequences of obesity, if A is for arthritis, and B is for back pain and blood pressure, C is for cancer, and D is for diabetes, then E is for encephalopathy. Encephalopathy means brain disease. There's consistent data linking obesity and middle age to higher risk of dementia later in life. Overweight individuals have about a third high risk, and those who are obese in midlife seem to have about 90% greater risk of becoming demented. The risk isn't just limited to future dysfunction, though people with excess body weight don't appear to think as clearly at any age. Obese individuals show broad impairments in what are called executive functions of the brain, such as working memory and decision-making, planning, cognitive flexibility, and verbal fluency. These play a critical role in everyday life. People may think about their obesity and the resulting stigma they experience as much as five times an hour, but the cognitive deficits do not appear to rise just from distraction. There are structural brain differences between normal weight and overweight individuals. A review entitled Does the Brain Shrink as the Waste Expands noted gray matter atrophy across all ages among those carrying excess body fat. This reduced brain volume has then correlated with the lower executive function. A compromised integrity of the rest of the brain, the white matter, suggests accelerated brain aging, even in young adults and children with obesity. Cognitive deficits in young populations suggest it's something about the obesity itself that's affecting brain function rather than a later clinical consequence, such as high blood pressure. Purported mechanisms for this executive dysfunction include obesity-related inflammation and oxidative stress. So does weight loss improve cognitive function? Based on a meta-analysis of 20 studies, mental performance across a variety of domains can be significantly improved with even modest weight loss. Though no studies have yet been done to determine if this then translates into a normalization of Alzheimer's disease risk. F is for fertility, or rather failed fertility. Overweight couples struggling to have children should be educated on the detrimental effects of fatness. One meta-analysis concluded as weight loss is associated with an improvement in pregnancy rates among infertile women. Men also may suffer impaired fertility. The heavier a man is, the greater the risk of having a low sperm count or being completely sterile. This may in part be due to the effects of excess body fat on testosterone levels. Fat isn't just the primary site of estrogen production in post-metapausal women, but in men as well. There's an enzyme in body fat that actually converts testosterone into estrogen. Men even going from obese to just overweight could potentially raise testosterone levels in their blood 13%. A more dramatic cause of infertility in obese men is called hidden penis, also referred to in the medical literature as buried penis, concealed penis, or inconspicuous penis.  It's also called trapped penis, because the moist and folding skin surfaces can result in a chronic inflammatory dermatitis leading to scarring, requiring a surgical intervention. So F may also stand for free willy.


So welcome back everyone. We will do a quick microarray example. So I told you about microarrays. And microarrays are one of these major inventions that actually forced bioinformatics to a new level. Don't forget, no, I already started recording. I said that when I was muted, so, no, sorry. Okay, so microarrays are a tool and they are one of the main tools nowadays for analysis of gene expression. We also have like RNA sequencing nowadays, but microarrays are still, still very useful and they kind of highlight, oh, you were watching a commercial due to a page refresh. Yeah, yeah, I don't like the commercials, but like it's like, yeah. I can't really do anything about it. Twitch needs to make money as well, although they're owned by Amazon, so they have more than enough money, you would think, but they kind of don't. So, and the nice thing is, is you're watching commercials, so that means that you are not the product. So on Zoom, which is free more or less, right?  So anything that you say on platforms like Zoom, they record that, even though they say they don't, but I don't trust Zoom at all. It's from Chinese origin, so. Anyway, so that's why I do it on Twitch. So, gene expression and microarrays. Let's get back into the flow of the lecture. Microarrays can measure the expression level of more than 20,000 genes in a single experiment. And that is a major advantage. Like in the 1990s, beginning of the 1990s, we could only measure like five or 10 genes at the same time. And when I started doing my PhD microarrays were miniaturized already a lot, but they were shaky. And nowadays, microarrays are really, really good. So there's two types of microarrays. We have one color microarrays versus two color microarrays. So one color microarrays allow you to measure a single sample. And two color microarrays are generally used in kind of a case control experiment. So you put normal tissue on there, and for example, cancer tissue. And then the microarray with the color shows you which gene is highly expressed in the one and lowly expressed in the other one. So how does this work? So we use a fluorescent dye, Psi 3 and or Psi 5, depending if you have a one or two color microarray. And then we have a signal intensity. So the intensity of the dye is read out by a scanner. So a little bit of an overview on how this worked. So we have the annotated genomic structure. So we have, for example, the human genome, with its 20,000 genes. So the first thing that we do is decide which parts of the genome we want to measure the activity on. So to do that, we make something which is called PCR products. So we amplify probes. So we define the regions that we want to target. These regions are done using PCR. We take out these regions from the genome and put them on these little glass plates. Then we have two samples, for example, normal lung tissue and lung cancer tissue. And then we extract the RNA from this. We color the RNA using Psi 3 or Psi 5. And then we take both of the samples, we mix them together, and we put them on the microarray. There is then a laser, which are actually two lasers. So one measures the red channel. The other one measures the green channel. And in the end, we get raw images of each of the channels, which are then using computer science or more or less tools put together to create the overview of a microarray picture. So the microarray here, if a dot is green, it was highly expressed in sample one. If a dot is red, then the gene was highly expressed in sample two. If it is yellow, then it's not interesting because there was no difference between the two samples. And if it's black, that means that the gene or the probe was not expressed in any of the samples, in any of the two. So that's kind of how a microarray works. So we define which areas of the genome we wanna measure.  We then put our samples on. So I always say that microarrays are kind of a fishing experiment because here you're kind of fishing with little DNA probes to see which sample has which genes that are expressed. So the workflow for a microarray is more or less the following and all of the bold parts are parts where bioinformatics is involved, right? So the first part is to create these oligo arrays. So to create these arrays with these little DNA probes on there, a DNA probe is also called an oligo. And of course there you need a bioinformatician to take the whole genome sequence, figure out where the genes are, and then cut them up and make a microarray. So even nowadays, custom microarrays still need to be made. We recently did an experiment and in that experiment we did ice on microarrays and we were really interested in a regional chromosome three. So the commercially available microarrays did not have as many probes there as we wanted. So the thing that we did is we kind of saturated our region of interest with hundreds and hundreds of additional probes. And of course when you put more probes in one region you have to remove them from the other one. So it's always a cost benefit analysis, right? Because we're interested only in this region so we saturate the region with probes. Of course the acquiring of samples is generally done either in a mouse house or in a greenhouse. So that's where people, biologists work, they do their experiment and they extract their samples. They extract and label the DNA from the samples that they collected. This is then hybridized to one of these microarrays that were designed. And then of course scanning occurs. And already at the scanning point bioinformatics starts becoming more important because you need to know how the dyes are reacting to the different lasers. So there's kind of not all dyes have the same dynamic range and so also there bioinformatics is already involved in how to build better scanners and miniaturize it even more. Of course after you scan these microarrays there's a lot of data that comes from it, right? If you're measuring that because the machine just takes kind of a picture and this picture is stored in kind of a diff format but these pictures are huge because these dots are tiny. So you have images which generally are like 5 million pixels by 5 million pixels which generates images in the order of like 100 to 200 MBs. So this data needs to be stored somewhere because of course you have to do something with the data later on. And you wanna keep it for later as well. The next step is of course data normalization. So data normalization is something that we will talk about more but also there of course statistics is used to kind of get rid of unwanted variation. For example, there's a little air bubble on one of the microarrays or there's a hair that got into the machine. The next step generally after data normalization is to do gene expression clustering because we're measuring 20,000 genes, right? We're not interested in a single gene but we want to see if for example a whole pathway of genes comes up, right? If you think about plants we might be interested in the glucosinolate pathway. If you think about mice we might be interested in for example the glucose pathway in the liver or if it's some pathway in the brain that is highly expressed in people with Alzheimer's versus lowly expressed in people that don't have that. And then of course the next step is data interpretation and there bioinformatics is responsible for searching through all of the available literature using automated literature scanning to see if we can kind of interpret our data and see how our data fits into all available knowledge that people have collected in the last dozens of years. So just a step by step going through so creating the arrays, a bioinformatician determines which sequence are measured. So humans have around 25,000 genes, a little bit more than was determined in the human genome project because we know now that there's also things like genes that do not code for protein but code for things like microRNAs or other types of RNAs which are biologically active. Each gene in the human genome is like 10,000 to 15,000 base pairs long. So if you multiply these numbers together you get an idea of the amount of base pairs that we could measure but we only have probes which are like 20 to 40 base pairs long. So we definitely have to make a decision on what we want to measure and what we can't measure because we can't measure everything. Even though microarrays are really good they still have a limit. So there's around 5 million probes that you can put on but 5 million times 40 is not the same as 25,000 times 15,000, right? So we do have to make a decision on what goes on there. So using bioinformatics we design these probes and we estimate the performance of these probes beforehand because not every probe will work because of temperature, sensitivity or like too many A's and too little C's so the binding is not good enough. And so this estimation of the performance is something that a bioinformatician does together with the design of the probes. So after the microarrays are scanned which is kind of what we get here. So this is a real picture of a two color microarray and this picture is really, really huge. Like it's tens of thousands of pixels by tens of thousands of pixels if it not a couple of million. And but if you zoom in on a two color microarray you see that there are very, very tiny dots and each of these dots have a certain color, right? I told you that green is high in one sample red is high in the other and here we see kind of a one color microarray. So that just has green and then the green intensity the more intense the more a gene was expressed and you don't get any information on it's not a sample one versus sample two. But hand microarrays are scanned and then of course we get these pictures and these pictures that need to be converted into intensity values because like we can't just use the picture. We have to have a matrix with numbers. So how does that, how do we do that? Well, we go from this picture using several software tools into something which is called kind of the gene expression matrix. So generally on the rows you have the different genes and then on the different columns you have the different samples and in the middle you have the gene expression level. So generally this fits in Excel depending on how many samples you have. Like 10 years ago Excel only allowed to have like 16,000 rows and there was always an issue so you couldn't use Excel so you had to find a different format and generally these are big comma separated files which just for every gene and for every sample have the gene expression level in a certain range, right? So the next step then is to normalize and why do we need to normalize this data because if microarrays are so good why are they not perfect? Well there's always some kind of non-biological variation per samples, right? One sample we just pipe it or one microarray we just pipe it a little bit of more RNA on the whole microarray compared to the other one, right? You can pipe it exactly 10 nanograms of RNA. Sometimes you pipe it 10 comma one, sometimes you pipe it 9 comma eight, right? So this little variance in the total amount which is put on needs to be normalized away. There's also the problem that there's a different dynamic range of different scanners so we can't just use two different machines and then expect the results from the first machine to exactly match the results of the second machine. And of course if you're doing gene expression on like 300 or 500 individuals then not all individuals are scanned on the exact same machine and on the exact same day, right? Because also these machines are sensitive to things like environmental temperature. If the temperature goes up by two degrees Celsius it changes the ability of DNA to bind with other DNA so at a temperature but also the humidity has a massive influence on the intensity of the arrays and all of these effects are something that we are not interested in. We are interested in which gene is different in cancer tissue versus normal tissue. We're not interested in like how the temperature of the room affects our results. And so there's many different algorithms that exist to normalize microarray data and we'll be getting to that in later lectures. So in later lectures we will be talking more in detail about microarrays and which tools and which algorithms there are to analyze this type of data and to normalize away non-biological variants. And of course this also has a risk, right? There's also the risk that we throw away real biological variation by normalizing. So we will have an example of that and you guys can then in R do your own kind of analysis on a little bit of microarray data. The next step is gene expression clustering. Like I told you, the microarrays has five million probes and they measure like more than 10,000 genes and we want to focus on the main result because we can't just say, well, there's this gene which kind of follows this. No, there's a holistic approach, right? So we need to take into account all of the genes of the microarray and we need a way to visualize this data. We can't make 10,000 box plots and look at the box plot of gene one and then see, oh, it's different in our sample and our control and then look at the second box plot, right? So the most common strategy here is to visualize the most differentially expressed genes. But this is difficult when you have a lot of different groups that you're dealing with. So we might have not just a case control study but we might have like 10 varieties of grain and all of these varieties of grain. They have a very similar genome but of course all of these 10 varieties have their own unique gene expression patterns and of course we want to visualize this and we want to show the biologist, yes, we did this experiment and the high yielding grain has this gene which is generally expressed higher or has this cluster of genes which expressed together seems to increase the yield of the plant. And of course we use again algorithms to create these groups and cluster data together. So the common strategy is not to focus on a single thing like you do with QPCR but really to look at all of the genes in the genome and then figure out, well, this group of genes is probably the group of genes that I have to have a closer look at. So how does this look? Well, I gave you a couple of or two examples. So one is a clustering of genes where we look at immune cells, the sort of paper where it comes from is located here. So we see we have CD20 minus cells. So those are cells which are not expressing CD20 and then we have CD20 high cells. So those are cells which are expressing a certain surface marker on there outside of the cell wall. We have different samples, right? So here we have five samples measured with the CD20 and we have like five samples that do not have and that have and then we can see here in the rows, the names are not here but each of these rows and there are literally thousand of rows have we can see that some genes are expressed lowly in CD20 cells, other genes are expressed highly in CD20 cells and the opposite here. And we always focus on differences, right? Because if a gene is expressed highly in both cell types, it's probably not interesting. But if it's high in one, low in the other one, then we can think like, oh, there might be something going on there. We can also do something different, right? And that means we can calculate correlation. So I assume that most people know how correlation works but here, for example, we look at different tissues. So in this research here, this is the preprint and what they show is indeed that if you look, for example, at brain tissue, I hope it's readable. It's a little bit small but I can just read it.  If you take a vector of measurements from, for example, hypothalamus, then, of course, the vector of genes in the amygdala are very highly correlated. But you can see very clearly that brain tissue has a very different gene expression pattern compared to, for example, things like skin or the different arteries. And so correlation also helps us to more or less group these things into data and it allows, correlation in this case, allows you to look across different tissues and figure out what is unique to these tissues and if there are any tissues which are very similar. And so, for example, here we can see that actually in the pituitary gland that these samples are, again, more like brain tissue and not so much like, for example, testis or cells that were taken from lymphocytes. So different ways of visualization and this is what a bioinfantation does, right? We get big matrices filled with intensity values and then we visualize it in a way that people can understand, oh, all of the genes in CD20 cells that don't express all the genes in CD20 negative cells, all these genes have, for example, something in common. And that is the next step because the next step would then be to see if there's any pathways that are overrepresented in this data. So gene expression, there's many, many different ways on how to cluster gene expression so you can use different partitioning methods like hierarchical clustering or fuzzy clustering or density-based, but all of these measurements depend on having a certain distance measurement because we have to define which samples look similar and which samples are different. And this is not an easy thing to define because what is different and what is similar is, of course, very dependent on how you think about differences, but all of these things can be caught mathematically in something which is called a distance measurement. And during next lectures, we will talk about different distance measurements that people came up with, like Euclidean distance, Manhattan distance, Minowski distance. So there's different distances and all of these distances give you a score for how similar two things are or how different two things are. And then you can use these differences and similarities to kind of cluster them together in trees using a kind of tree visualization. Of course, we also use a lot of statistics because we need to determine which effects are significant, right? And significance is something that we have rules about so we generally take like, hey, we want to have at least a false discovery rate of 5%, meaning that if we claim that these 100 genes are different between the two samples, have we kind of agreed that, well, not all 100 are probably different, but 95 out of 100 definitely are, right? So significance in a statistical sense means that the likelihood that the differences are observed are real is high enough for us to trust the results. And of course, this comes back with the fact that we're testing tens of thousands of genes. Yes, so there we have to account for things like multiple testing. And we'll go into much more detail in the microarray lecture to kind of show you guys how you can more or less still have these significant effects, even though you might have a very low amount of samples and you've might have measured like hundreds and hundreds of different genes. So the data interpretation generally occurs via something called genontology. So genontology is kind of the tower of Babel in biology. It is an up-to-date comprehensive computational model of biological systems, which defines molecular levels larger than pathways like cellular and organism levels. So we will be talking a lot about genontology. I'm not the biggest fan on genontology, but the thing that it does is that it provides you with a structure vocabulary so that you can talk to each other, right? In the past, we used to have like five names for the same gene, but not only that, but someone talking about the insulin pathway. Of course, what is the insulin pathway? Which genes belong to this pathway? This is something that genontology defines for us. So it is a structured way so that using a computer, you can see if a certain pathway is overrepresented or underrepresented in the genes that you found differentially expressed. Yes, so that's the handiness of genontology, is that it's a structured vocabulary. So instead of just having words which are defined by the context, it's words that are defined by an ontology, which is something that a computer can understand and reason about. So how does genontology look? Well, genontology is a tree, right? So we have, for example, the genontology, which is the biological process ontology. The biological process ontology is split into two different categories, right? So all of the genes are in here. So all of the genes are involved in some kind of a biological process. But some genes are involved in, for example, cellular processes, while other genes are involved in responses to stimuli. Of course, if you think about responses to stimuli, these can be responses to endogenous stimuli. So endogenous means from within the cell, but it can also be responses to stress, right? The same thing for cellular processes. Cellular processes can be cell communication. So one cell talking to another cell, but it can also be cellular physiological processes. So processes happen within a cell which have nothing to do with cell to cell communication. And all of these genes, so if you belong to a cellular process, you also belong to a biological, you're also a biological process, but it's a tree, right? So for each gene, we have assigned this gene to one of these categories. All right, that's one of the annoying things. So let's do ban. Where's the ban hammer? Ban, all right. Yeah, first one, wanna become famous. No, I don't wanna become famous. I just wanna do my lecture. Oh, my moderator was quicker. All right, so gene ontology structured vocabulary allows us to talk about things which might have been defined differently in the past. I, it sees that one message was deleted by moderator just when I clicked the button to delete the message. But I did the ban, so that's good. Had so, but gene ontology really, really useful. Interesting, by a moderator, but not you. I only have one moderator as far as I know. Or did Daniel become a moderator again? And is he sneakily watching the lecture as well? I don't know. Anyway, we will be talking more about gene ontology, but every member gene ontology is a three and it allows a computer to reason about concepts like cell death, what is transcription, what is dependent transcription or regulation of transcription. All right, so very small example. So bioinformatics is involved in the design of the oligonucleotides that go on to the array, so the array probes. Bioinformatics is involved in normalization of expression data, clustering of data, creating visualizations, interpreting the results and also storage and management, right? And that's what people often forget, is that a big part of a bioinformatician is to kind of keep all the gigabytes or terabytes which are generated within a group of people working together on a single project or on a single biological entity had to kind of keep that data. Hey, it happens a lot that the professor comes in and well, you notice experiment that we did like 15 years ago, how does the new data that we have relate to that data, right? And then you have to be able to go to your computer, fish out the data from 15 years ago and of course you have to do this in a reasonable amount of time. You can't say come back in a week and I will have sorted the data for you. So had the management and the storage is a big part in bioinformatics as well. Of course, programming is essential. And like I told you guys, the course will not teach you to program. And unless everyone wants that, right? We can have a poll on Moodle and everyone says, well, I wanna learn how to program but just go to my YouTube channel and watch the lecture that I did on programming in R. So I did a whole programming courses and we will use some R's for some basic analysis. And of course, when we will have assignments where it is required that you program, of course these parts will be introduced. But the focus of the course is to get you to know which databases are there, which hold the data, things like GenBank and Ensemble. And so where can I find my data but also to kind of give you an idea of which tools are out there. Because as a bioinformatician, you can't program everything that you want from scratch, right? So you have to use tools developed by other people. So the homework for today is for you guys is to kind of prepare your computer that you will be doing the course on. And things like, hey, you need to install at least a good text editor, install the R programming language because all of the assignments will be related to R. But also to install the Git version control software and create a GitHub account and link your local computer to your own GitHub account so that you can collaborate with other people. Because we will be doing some assignments where it will be kind of group assignments. So for that, people can work together. And I'm actually a big fan of that. Like when I do computer programming courses, I don't think it makes sense for one person sitting behind a computer and trying to come up with an answer. It's much better to have like two or three people looking at the same problem and coming up with different strategies and discussing these and then together implementing it. I'm a big fan of kind of peer programming where people sit together behind a computer and figure out the best way of doing something. Because one person is just an island and you can't be an island. You're always collaborating with other people and the same thing holds for people who program. So Git is more or less the most common version control system. Was developed by the guy who also wrote Linux kernel. So if you've ever installed the Linux operating system, you use this software, but Git is made by him. GitHub is a collaborative platform. So it's one of the biggest and I use it and there's other providers of these things as well. The nice thing about GitHub is, is that since you guys are part of the university and you're students, you can get a free pro account, which is always nice that you can get something from free. So the homework for today, we will be doing after this session and we will just take an hour and I will guide you to where can you download the text editor, the R software, Git and these kinds of things. And we will be going through the steps needed to link your GitHub account to your local computer so that you can send data or code from your computer to an online source and then online work together with other people to generate a solution. All right, so the coming lectures. Introduction to bioinformatics, that's the lecture what we did today, right? So we did the course overview. I give you a little bit of history, some definitions. Why do we need bioinformatics? I hope I convinced you by now that bioinformatics is essential in modern day biology. Some examples, well I gave you one example on microarray data. So the next lecture will be all about phenotypes or traits as people call them. So we will be talking about Mendelian traits, about qualitative and quantitative traits, about statistical analysis, multiple testing, sample size and project planning. Had to get you guys familiar with everything surrounding phenotypes. And then we will be just going up the level that I showed you. Right, I showed you the arrow with the different biomolecular levels. So the first level that we will be discussing is DNA. So there will again be a little bit of history about DNA and who did what, who invented what. We will be talking about different sequencing techniques for DNA. We will be talking about genes, like how is a gene structured, what are transposals, also called jumping genes. We will be, let me take a sip. We will be talking also about regulatory elements. So how is DNA expression regulated? And we will be, and I will be showing you some examples of other types of DNA, like mitochondrial DNA and chloroplasts. So if you're a plant biologist, then you need to know about chloroplasts because they have their own DNA, which is separated from the genomic DNA. And of course we will be talking a little bit about biomarkers. The next level is RNA. So there will be a whole lesson about RNA, about the history, about the different types of RNA, about RNA expression, expression analysis. Again, RNA sequencing, the structure of RNA, which is very important for the function. The next level is proteins. So the history of proteins, structure prediction, function prediction, how do we define which proteins belong together? What are phylogenetic trees? These things will come up in lecture number four, right? So this is one, two, three, four, five. So lecture number five. Then we will be talking about the metabolome, so the metabolite. So everything that you either put into your body or that your body creates, which are not proteins, RNA or DNA. And we will be talking about things like, what are metabolites? What is mass spectrometry? How do we identify different metabolites? Like, how does the police know that you've used some type of drugs? And we will be talking about different databases which contain reactions, right? So how does cocaine break down in the body? Well, there's several databases that can allow you to do that. Well, the database doesn't allow you to break down cocaine, but it allows you to see how this chemical substance is transformed in, for example, the liver or the kidneys into something that is not harmful for the body. We will be talking about keg and the reactome, which are more or less the two greatest data or the two biggest databases. And we will also be talking about cytoscape in this lecture. And then I think there is one lecture which is canceled because of Christmas, which is good because everyone likes Christmas. But there will be a holiday break. I think it will be after the metabolomes and pathway lecture. And then we will be talking again about phenotypes, right? Because then we go full circle. We start at the highest level. So the phenotypes, the things that are interesting to us, like because that makes the money, right? The milk yield of a cow is the thing that we're really interested in. And then we dive through all of these levels to get back to the same level. And then I want to talk to you guys about QTL mapping because that's what I did my PhD on. So my PhD is about high throughput methodologies for QTL and genome-wide association studies. So I'm kind of an expert on that. I wouldn't call myself an expert. I know a little bit about QTL mapping and genome-wide associations. So I just wanted to have one lecture to talk about that. We will then switch to something primary. We will then switch to primer design. Primer design is actually a pretty interesting lecture because it is very applicable because if in your master project you are bound to have to measure a gene or something like that, right? Like in biology, we measure genes and proteins. So knowing how you design primers and knowing the basis of polymerizer chain reaction is good. Although it might be actually that all of the students that are following the course have already some experience with primer design. So if that is the case, then do let me know because I think in the bachelor there's also a course which explains PCR and primer design. Although I always love talking about Kerri Mules. Kerri Mules is one of my favorite guys in biology because he's just like a very interesting character. And when we have the lecture, I can talk more freely about Kerri Mules and his drug history and the fact that he thinks that polymerizer chain reaction actually, he claims that the method actually was given to him by aliens and aliens look like multicolored ferrets. It's such an interesting figure. He got a Nobel Prize for his invention and he's one of the smartest guys, but he's very, very strange and fun, right? So I think that that makes biology interesting, that you have all kinds of these figures of all kinds of people who have their own kind of handicaps in a way. So I love talking about Kerri Mules. So if everyone knows primer design, we can skip the primer design lecture. If not, then I get to talk about Kerri Mules. The next lecture will be databases. So how are databases organized? What features are there? What are important databases? And then we will also be talking about Biomart. So Biomart is a software tool for the R programming language which allows you to connect your R session directly to Ensembl and directly pull data from the Ensembl database, which is really, really useful because then you don't have to go online to ensemble.org and click download and because the computer can then automatically talk to the database and Biomart is a very smart tool. We will then be talking about sequence analysis. We will be doing alignments, alignments and alignments and we will be talking about things like homology, what are sequence motifs, how do you find sequence motifs and what do they mean? And then of course we will have gene expression analysis. So we will be talking a little bit about the experimental design, the data organization and preparation, statistical analysis and visualization of differential expression results. So that's more or less the lecture about microarrays because we will be using microarrays as an example. Of course gene expression can also be measured using QPCR or RNA sequencing nowadays. So the lecture is focusing on microarrays because it's small enough for me to give you a data set because otherwise I would have to give you an RNA sec data set which is like 500 MB or something and no one wants to download that. Then the last couple of lectures will be the standards for bioinformatics and statistical analysis. So we will be talking about different types of files, different file structures about naming conventions that are there like how do I call my file names, headers and different variables. We will also be talking about documentation and testing. So how do I write proper documentation for software that I developed? What types of documentation are there? And testing of course is very important because if you develop new algorithms then you have to have a test suite, right? Because you have something which is kind of a gold standard. So we will be talking about that. And then the last lecture that we have is about scientific literature, mining and management. So that is where you get taught on how you use PubMed Advanced Search but also how you can more or less automatically churn through like 40 years of biological data. And I also have a part of the lecture which is about reference management software which I think is very useful for people that write a master thesis or write a bachelor thesis because you shouldn't do scientific citations by hand. You use a computer program, right? Why would you spend time formatting your citations if there's just software out there which can do that for you? And then of course we will have a couple of your own choice lectures. So if you have a data set or if you have something that you think is missing from this list and for example, oh I want to learn everything about machine learning in the context of, for example, the new Google algorithm for protein prediction, protein structure prediction. I think it's called deep protein or something. Then we can do that. So if you have something that really interests you and you don't see it on the list, send me an email or just throw it in chat and I will make a list and we can vote on it on which course we want to do. But I always like having a little bit of free space for you guys to decide what you want. All right, so the next lectures. So we decided we will get the lectures from one to four instead of from two to five. Assignments are necessary to pass the exam. So I will make sure that the lectures are around two hours, three hours when we do the assignments together. But there will be questions on the exam about the assignments. And the assignments are the useful part, right? You can listen to me talk about databases and stuff. But if you don't go to the database and click on the links and see what's there, then you don't learn what's in there, right? Then you just get a very global overview. So the exam will contain questions about the lectures and the assignments. So I'm just a tip for me. I've, in the past, I've noticed that people who don't do the assignments generally don't pass the exam. There are exceptions to any rule, of course, but there is a direct correlation between the people that do the assignments and ask questions and the people that pass the exam. All right, any questions so far about microarrays, other things, any people that already have a suggestion for a course that we wanna do, then throw it in chat. We will have, oh, I'm a little bit on time. So we will have like a 10, 15 minute break. So I will try and be back at like 4, 10, 4, 15. And then we will start getting your computer ready. So then we will start downloading some stuff.  Thank you for cheering. It's good that you're here. We can set up your computer properly as well. So that will be after the break. So we will have a short break. I think this break will be birds. Yeah, Daniel's here. Welcome, Daniel. Oh yeah, I didn't tell you guys, but like up here, you can see something which is called the mood box. I actually made a button for that as well. So let me show you, these are the commands that you can type in chat and then you will get your own little avatar there. And then you can change it. So I made this thing for Twitch. So I can do stuff like monocle and then I'm in the list and I have a little monocle. I just came here to ask you something about R because I have problems with the date format. What do you mean the date format? You already asked that yesterday. Like I was confused yesterday if you meant date or data. But dates are very simple, right? It's like the day minus the month minus the year. I will ask you in the evening. All right, but stay for the rest of the lecture because it's good that you get Git installed and make a GitHub account as well. You probably have Notepad++ and R installed so like the first 10 minutes won't be that interesting. All right, so I will stop the recording.