This is Mises Weekends with your host, Jeff Deist. Ladies and gentlemen, welcome back. Once again to Mises Weekends, we've got a really interesting topic this week that appeals to me enormously. We've also got a great guest, his name's Michael Watson. He is a former summer fellow here at the Mises Institute. He's presented a couple of different times at our research conferences. He is a PhD candidate at George Mason University and professor of economics at Belmont Abbey College in North Carolina. He's finishing up his PhD. So Michael, thank you for joining us. Great to see you. Thank you. The topic, ladies and gentlemen, is what lies outside of economics. This relates to a paper Michael just delivered about a week ago at our research conference last weekend where he was talking about some things like Thomistic personalism, which is a form of Catholic thought and applying this to economics and to prexiology and to necessity and thought. So I thought that we would broaden this a little bit and talk about what sort of actions we ought to think of as economic and what we ought to think of otherwise. So that said, Michael, let's start. I know this is a huge topic, but let's start with Mises presents this idea of prexiology that humans act. They have a means and mindset, but he also defined economics as a subset of prexiology. So in that sense, Mises appears to be thinking that there's human action that takes place outside of economics per se. So we need a definition of economics and economics we usually talk about at least in Austrian school, catalactics that is exchange, market exchange and barter. So buying and selling. But then there's also another part of prexiology. We can talk about the gifts or we can talk about violence. Mises talks about autistic action that is unilateral action. There's no bilateral. There's no buying and selling. So if I take my fists and I punch you, that's violence, that's an autistic, quote unquote option, a unilateral action. He would say that's within prexiology. My end is to harm you, whatever the, maybe it's some, I want to rob you or I want to get the enjoyment out of beating someone up. So I take my fists, I hit you and the means is the fist, throwing my fist and that time that I used to inflict that violence. But there's also the opposite, giving someone something. So I, one can imagine a situation where someone's unconscious. They don't have the ability to say no. And you help them, someone in a coma. We are gifting our resources to maintain that person, that person is an end. And we could talk about persons as means or persons as ends. We treat a person as a means we can treat them as a slave or some type of property. We could, or we could treat a person as an end. And we treat a person as an end when we, when they're good is our end. We, their wellbeing is our end. And so we help them, whether they're in a coma, they're unconscious, something of that sort or perhaps a beggar who is, or someone who's starving, you give them food and they accept that food. The other thing to consider as well is when we buy and sell, we're not treating each other as means. We're actually helping each other to achieve our separate ends, right? It's so, some people who are very concerned about ethics and morals, they see the market and they see people using each other as means. And it's, but it's, there's a, the voluntary relationship there is that you have different ends. You have your family. You want to take care of I have my family. I don't have a family yet, but some someday I will. I have my family out. I'm going to want to take care of and you, I have $5 and you have the apple. I buy the, I say to give you $5, I get the apple or apples go to support me and my family and the $5 goes to you and to achieve whatever energy you need to achieve. So the market is the means of which we are able to pursue our ends in a voluntary manner. And also it allows us to maximize the ends of which we're willing to achieve. Well, but I just want to clarify with you a couple of things. First, Mises sees means and ends as a rational decision, not as having a moral or ethical component, at least as regards prexiology and economics itself. And second, you know, how are we think about altruism then about gifts about supposedly selfless action. We think of them as occurring generally within economic action or generally without outside of economic action. So regarding altruism in economic action and outside Mises has a quote in this in human action where he talks about a business owner hires his buddy and pays them more than his productivity level. That margin above the productivity level is a gift. That's what he says it is, it's a gift. It's not, the business owner's taking a loss. He's engaging it in consumption, which is the technical term we use in economics which a lot of folks care about ethics freak out about but it's just a technical term. It's a formal term, you know, it's some, so he does talk about that. Another thing about means and ends, yes. So means and ends also seems rationality that we can are able to understand cause and effect that we can reason about what means will achieve which ends. Of course, Mises also talks about the ultimate end as well, but he doesn't define that. He defines the ultimate. He calls it happiness, satisfaction, contentment, whatever formal term you want to use, the praxeology doesn't talk about what is the ultimate end of human beings, right? He, it's all about the means and ends framework. Praxis, action, it's not about how should our ends be? What hierarchy of ends is moral and proper? What is the ultimate end? That's ethics, metaphysics and things like that. Well, I think what gets confusing for people here is sometimes is that Mises insistence that economics per se is a value free science. It's concerned with looking at catalactics and means ends comparisons, but that of course, the average person knows that all human action is imbued with moral and ethical questions. We can never completely separate them, but conceptually it's important to separate them in terms of studying in an academic discipline, in terms of science. Yes, so the way I handle this often is I say we start with our priority truth or the axiom, whatever you want to call it, of action. Action implies choice, choice implies scarcity. Is that value neutral? Is that true? And then scarcity implies law of returns, that there's an optimal output if we hold all variables constant and just change one of the input variables. And so I bring that up with folks and folks who are usually opposed to Austrian economics will say, okay, I agree with that. Okay, so that's the first step to make. So at some point, praxeology needs to be integrated with ethics, but we can do logic without necessarily considering the ethical or the ideological, because Austrian economics doesn't have to be integrated. It can be integrated with libertarianism, conservatism, all sorts. You can be a socialist and a praxeologist. It just probably would mean that you think the ideal society is that of the tribe or some super primitive economy where we have no wealth. Well, so let's apply this thinking to something that all human beings are concerned about, which is relationships, whether that be dating or marriage or parenting or even just friendships. What would Mises say about these kind of relationships and contrast that maybe with what Thomas Aquinas would say about personal relationships from a Catholic perspective? Can the two meet in the middle in any way or are they just a separate way of looking at the world? So I think it's more about compatibility. What Mises does throughout human action, throughout his writings, he assumes, he takes as given things that human beings seem to all want, peace, prosperity, et cetera. And he says, okay, I'm not gonna talk about whether those things actually should or should not be taken as a good society. I'm just gonna assume that what Aquinas does, of course, he gives us an ultimate end, supreme happiness, face-to-face vision with God. But when we go further into that, the highest virtue for Aquinas is agonist love, that is tree, doing good into the other. And so if we want to talk about Mises and Aquinas, I think where Mises leaves a empty formalism, formal term of ultimate end, we can take Aquinas and put them in there. We can also talk about Aquinas and Praxeology does give us a way to think about the gifts as well. And so we can have the situation in relationships where husband and wife, the husband and wife want to achieve the good for each other.  Like, what does well-being mean for the other person? What means do I use to achieve their well-being? And vice versa. In a sense, there's a bit of entrepreneurial activity where you have to actually speculate and say what would be good for this person and how will they react to it? Someone who's in a coma, it's pretty simple. You give them nutrition and hope they wake up, right? Someone who's lying in a ditch, it's pretty clear, pick them up, bring them to the end. Someone who's active and also a conscious actor, you actually have to think about how they interpret the thing you're giving them or the gift, so which can result in asymmetric interpretation. You can have the situation, different cuts, two people from two different cultures meet each other. Maybe the guy gives a gift, one person gives a gift and that gift is understood to be completely heinous. So there is that subjectivity component that's very important in praxeology. We do, we understand the world through the way we've been raised and all the ideologies and ideas that we've learned and we apply it to understand which ends are appropriate in given situations. And so sure we want to give a gift. We want to act lovingly. We want to give the other person their due. However, we actually have to interpret that. We have to understand what is their good and how will they interpret that? Well, I like what you said earlier, which is that any sort of ideological worldview could adopt Austrian economics in terms of just viewing means and ends and understanding the relationship between them, but I wanna go a little farther here on Aquinas, a lot of religious folks don't much like free market economics. In other words, they say that religion, especially Christianity, naturally presupposes a degree of caring about our fellow man that somehow this ultra-rationalist economics shuns to the side and that G. Wiz, Michael, really hardcore Austrian or free market economics is not all that compatible with Christianity. So give us your thoughts on this, give us a rebuttal. So my way of handling this isn't going, a lot of economists will go and see economics produces massive prosperity, but it's a dynamic economy which can result in instability for families and that's not good. So what I prefer to do is actually, let's start from the beginning. Let's go to the foundations of praxeology, the foundations of Thomism for the Catholics, for the Christians who like Thomism or even the agnostics and atheists who like realism because Thomism is also known as realism. So let's talk about what are the elements of human action into Aquinas? What are the elements of praxeology? What we see is rationality, rationality is in both. That is reason, ability to understand cause and effect to use means to achieve ends. We also talk about free will. Free will is also both in praxeology and Aquinas. We have for Aquinas, Aquinas usually talks about free will in terms of we live in a world of particulars. And this is also high, this is praxeology is high. We live in a world where we have, when we have to choose particular ends and we have to use particular means. And so we have choice about what hierarchy of ends and what means we will achieve to use those ends. Of course there's not so much free will regarding the ultimate end that we're always trying to alleviate uneasiness. As particular finite beings, we're always in a state of uneasiness. We're always, non-action is not really a choice. Teleological, human action in praxeology is teleological.  Oh, what else? Oh, it's non-satiation, we can never be satiated in all our desires. We cannot achieve all the ends we wish to achieve. We're in constant pursuit of ends and we're in constant pursuit of that ultimate end happiness. Both in Aquinas and both in Mises. Transitivity, okay, so transitivity is the idea. I prefer A to B, I prefer B to C, therefore I prefer A to C. Mises dismisses that idea of transitivity which most neoclassical mainstream microeconomists use as the fact that action takes place through time. We have many, we don't make all our actions at once. We go through time. Aquinas deals attacks transitivity by saying we live in a world of uncertainty. That's Rothbard, that's Mises. Same thing, we are uncertain about the future. And the other thing to consider is that Rothbard, he conserves himself a realist or a thomist. You can see his article that's article on extreme a priorism. Another great economist, not economist, he's a philosopher, he's the president of the Action Institute, Gabriel Zamotti, Action Institute in Argentina. He has all these articles in Spanish after we learned Spanish to read those articles, but there's so many English that you can access where he discusses how thomism and praxeology can be combined. And I'm really building off that work in that paper. I mean, that's 50 pages right now. It's probably gonna be three or four articles. I have to somehow split it up, but I'm going at the foundations. Are the foundations compatible? And where I see is not the, not are they only compatible, they're complementary or they're even identical. And so then we can ask, don't the really the only place where they're not identical is that ultimate end. What is happiness? What is the good of which we're in pursuit? But Mises is something very simple. He says, that's not praxeology doesn't tell us what the ultimate end is, Aquinas does. And so we just take it and fill it, fill it in that formal placeholder. Right, so to understand this very sort of thin definition of happiness that Mises proposes, you talk about his quote from human action. We call a man happy who has succeeded in attaining his end. So that's pretty thin. In other words, a crack addict may say, I really want to get high and go obtain some crack and get high, but they have probably haven't done themselves any favors in the long run. So that's sort of outside the Misesian paradigm. But so I want to expand upon this idea of rationality, that there's some connection between means and ends. It actually works. Even Austrians aren't, don't fully accept this, the idea, call it Mill's idea of Homo economicus. In other words, there's bounded rationality. We have limited knowledge at our fingertips, even with computers. And so not only is morality perhaps outside of Mises's analysis, but also perfect knowledge. We don't always have all the information to make perfectly rational means and calculations. And we don't have perfect knowledge. We don't know what's gonna happen in the future.  I don't know what your future preferences are gonna be. And I don't know what the heck's gonna happen in the world in the future, because we don't have that. There's all sorts of asymmetries in the world, which of course entrepreneurship is supposed to deal with. Every asymmetry is a profit opportunity in some way. You can get the property rights, right? So Rothbard, yes, Rothbard defines in man accounting and say, psychology, ethics, praxeology history, right? And one of the definitions he gives for ethics is how we arrange our ends. How should we should arrange our ends, right? What ends should we achieve in and how should arrange them? Of course also the relationship between me, which means would also be proper to those ends. I would add that as well. And so as long as the foundations of human action between two ethical theories are the same, which I think what you have in Aquinas and Mises, you can get to integrating them and synthesizing them.  You don't have to buy into, say, Rothbard's natural law to be an Austrian economist. You can be a conservative, you can be from all these different backgrounds, and you can use Austrian economics. I mean, what's one of the best examples of Austrian economics applied? It's Ordo Liberalism, Bill Humrupke. I read Bill Humrupke, and I'm thinking, I don't agree all the time with what he actually, everything in a humane economy, but in general, I prefer that than what we have now. And so if Austrian economics can be more influential, I see that as a step in the right direction. As for homoeconomics, that's very true. Austrian economics does not need the homoeconomics. We don't need the assumption that human beings are trying to maximize pleasure or maximize profit. That's totally not required because profit and loss determine what survives and what doesn't. The profit just rewards productivity. Losses reward on productivity is more of an evolutionary situation. The term that so where Christians or folks who are very concerned about morals and ethics go wrong is when they assume that we assume all behavior has to be selfish and profit maximizing. That's not a requirement. And Mises does in Robert talks about that, but it seems that those quotes go often unrecognized. The other thing to remember is that Austrian economists of themselves have come from different foundational backgrounds. So you have Manger, Morris, Erzateeling with Brantano, Mises, Conte and utilitarian Rothbard, Realist and Natural Law. And so there's that variety, which a lot of folks who look at Austrian economics don't even recognize. They think we're all utilitarians and we're all the homoeconomics. Well, I wanna talk about the realm of human action that's not necessarily voluntary or is even forced. And I'm thinking, let's say warfare when the marketplace breaks down and I'm thinking in terms of slavery, give us how can praxeology approach involuntary human action and what would Aquinas have to say about it? So let's do slavery. Slavery, you have a property in someone else. Property are what means. Use property to achieve ends. So human beings are trying to achieve ends. So when you treat a human being as a slave and human beings have free will and then we can act and we choose ends, we're not treating the human person as what they are. We're forcing them to do things that we want them to do. So on slavery, that issue that we're treating someone as property when they themselves have, in a sense, they're their own property in some way. When we treat a person as a means, we misunderstand the nature of that person. Every person is an ends and up themselves. And so we have to have, then that's what, so property should serve people. People should not be property required to serve others. In terms of war, I'm not sure if I can go there. So you'd be talking more about the issues with the draft. Well, the idea is that war isn't humans interacting voluntarily, it's more akin to the fisticuffs you alluded to earlier in our conversation. It's aggression. Yeah, so also there's not much market exchange in the military. There's a command and order and there's an end you want to achieve. The end is not, and also the profit loss mechanism doesn't necessarily apply in the same way. Military wants to defeat its enemy per se or defeat a country or get, grab some land. That's its end. And it's going to use the resources of the economy as its means to do that and people's means to do that. So it's a completely different way. It's not the market. It's much more closer to autistic action. I haven't thought this question out through it really enough. But it's, yeah, it's more command control. It's not spontaneous order. It's one of the most more essentially planned situations. And perhaps, I'll admit there are probably some situations where we may want to engage in that, say if you're being invaded by the Nazis, you probably want to engage in a war. Well, the fact that this is all so complex and that logic and philosophy and economics are so interrelated, I think shows that Mises was on the right path in writing a book as broad and sweeping as human action. But I think it also shows that there's not too many economists who are necessarily equipped to do that themselves. So that said, Michael, thank you again for your time. Ladies and gentlemen, have a great weekend.


Welcome to MOOC Interactomics course. Hello students and participants. This course is essentially going to give you a more detailed understanding of how various type of biomolecular interactions work. While the major focus of the course will be to look at different type of technologies which measures protein-protein and protein other biomolecular interactions. But today what I thought I am going to talk to you about a journey of entire proteomics field and different technologies which are part of the field of proteomics. This is going to give you a much broader overview of how different technologies are playing an important role to study the very complex proteome almost all the proteins which constitutes any organism. And of course to understand this we need much more breadth of the technologies as well as the depth of the technologies. So we cannot just achieve all the questions by looking at single technology platform. So in today's lecture outline will be first I am going to give you an overview of proteomic technologies. And then I am going to talk to you about various opportunities which big data sciences offers to us and which are the challenges which are part of this field. As you are aware the central dogma starts from the basic biomolecular workflow starting from the genes to the transcripts and the protein. The entire field which aims to study all the genes of a given organism is known as genomics and all the genes which constitute the organism is known as genome. And likewise all the transcripts we study with transcriptome in the field of transcriptomics and all the proteins we study in the field of proteomics. As you can see in the slide that genome provides only the snapshot of the total information which is encoded in a cell but the biological processes are very dynamic and very complex and they are governed along with the transcripts and the proteins which makes the changes in response to the dynamic behaviour of the cell. When it comes for the medical field we cannot just simply rely on one set of technologies and looking at only single type of biomolecule of interest because the human physiology or any other organism physiology depends on the intricate relationship of various biomolecules. So, if we are looking at the complex physiological systems we have to look at all the possibilities of different type of biomolecules. For example, if we are trying to capture the actual physiological consequence then we should look at the genome, the transcriptome, the proteome as well as the clinical information and the phenotyping of these individuals. All of this comes under the field of omics. So, for what we are doing in the field of medicine is essentially we are generalizing the medicine and giving the same treatment to all the patients. But the field of personalized medicine aims to look at each individual as a unique entity. So, can we look at a given individual and all of its biomolecule and try to make an educated sense of that what should be the best drug given to this individual? How to monitor the response of a given drug or therapy? What should be the best way of diagnosing the disease for the individual? How multi marker risk assessment could be analyzed? How one could eventually offer the therapies which are very, very effective for a given individual based on our comprehensive understanding of the omics system of that individual. All of these things are revolutionary changes which are happening in the field of biology. I think it is good idea through this course you will be exposed for many of these technologies which will be interesting for all the life systems including human. So, the entire field of omics started from the very ambitious and very comprehensive human genome project along with many other genome projects which actually laid the foundation of the whole field of omics. It was not only that 15 years of effort in a very disciplined manner which laid first time the draft genome map of human, but also it also gives us an ability that we have now gone in the field of life sciences and biology towards paradigm shift where we can start looking at all the possible genes of a given system. So, genomics which was you know the studies published in Nature and Science in 2001-2002 those really laid the foundation for the whole omics field including the field of proteomics which got inspiration from the field of genomics. As soon as people unraveled all the possibilities of the genes which constitutes human and other organism, they realize that biological systems are very complex, but the number of genes which are present they are not that many and they are not very different in different organism. So, the overall gene range was around 20,000 to 30,000 for different model organism including human. So, what makes human different, what makes different organism different and what gives them the unique ability to cope up with the environment to cope up with the especially physiological context and that is where the whole field shifted towards more functional molecules more towards transcriptomics and proteomics. Here I am depicting you the proteomics journey which is started from 1970s and started with simple experiment of how to separate the complex protein mixture using two different properties their molecular weight an isoelectric point which made the two-dimensional electrophoresis. And then along with that there were different developments are happening in the field of mass spectrometry and various type of sequencing projects ESTs and various type of nucleotide sequencing they were all also happening parallel and together all of those made huge progress in the field of omics around 1990 to 2000. When many of these things started getting more mature on the side we see the advent of electro spray ionization and milding as the ionization sources for the mass spectrometers. So, all of them were really giving the foundation for the new field of proteomics to start but only after the completion of the human genome project scientists started moving towards the entire field of proteomics and thought now we can start understanding the functional molecules which are the proteins. Different other technologies including various intractomics based technologies protein microarrays various type of immunoprecipitation followed by mass spectrometers and label free biosensors all of them started coming together and then started making the field of functional proteomics and intractomics. Further there were many constraints were there to validate the biomarkets validate the proteins. So, new field of target proteomics emerged around 2012-2014 and then as we moved along then first time the scientists have started realizing the full potential of mass spectrometers and the first draft of human proteome maps are published. So, what you see here that the cover page images of the nature which first time showed 2014 the human proteome map the draft was actually published while the whole proteome was not unraveled but still majority of the proteins were discovered and then idea was that first time we have now the evidence that the genes make the protein and we are now able to measure those peptides using mass spectrometers. So, two different independent groups published these studies first time and showed the draft human proteome maps. Additionally another group worked on the antibody based approaches and they also showed first time in 2015 a study published in science which was the tissue based map of the human proteome. First time they showed that where the proteins are localized and there is evidence that proteins not only exist but how it is localized and they also provided evidence at the tissue level and looking at the various type of immunohistochemistry and tissue arrays based approaches. So, there are various facets of proteomics one could think about gel based proteomics which essentially looks for variety of you know gels to separate the proteins, gel free proteomics which essentially could be mass spectrometry or even various type of intractomic technologies. Functional proteomics which again involves various technologies which looks at the biomolecule interactions look at the functional consequence of these proteins, how they interact, how they are part of the different networks and pathways and targeted proteomics which essentially looks at validating the protein targets. So, broadly we can divide the whole field into two broad categories one is discovery proteomics and second could be functional proteomics. Different technologies which are part of the gel based proteomics as well as different type of mass spectrometers could be covered under discovery proteomics. Various technologies which are more high throughput in nature essentially protein microarrays label free biosensors which constitute the intractome analysis they are more part of the functional proteomics. So, I am going to give you an overview of different technologies in a very nutshell because the focus of this whole course will be mainly on intractomics. But it is good idea for you to appreciate that where intractomics field fits into the broad field of proteomics and which are other technologies which are all going to help each other to understand the complex biomolecules. So, gel based proteomics as the image conveys that you can separate the proteins based on either only their molecular weight which you do in the standard SDS page electrophoresis or you can also separate them based on the isoelectric point in the isoelectric focusing units or you can combine the two and then separate proteins in the two-dimensional which is known as two-dimensional electrophoresis or you can make it more advanced you can do some quantitative proteomics when you can add the dyes to your control samples i3 dye your treatment samples i5 dyes and then you mix even the various control and treatment samples in one tube label them as i3 dye mix all of them together now separate them in the first dimension with the isoelectric focusing second dimension with the molecular weight SDS page and then now you are able to get not only that you know the protein separation in two-dimension but also the protein expression changes you can start monitoring. So, this whole thing is itself is very vast and heavily used which we will talk in a different course in different platform but I just thought to give you a reminder again that there are many technologies which are equally important which one need to appreciate in the whole field of proteomics then come the field of shotgun proteomics which is essentially driven with the mass spectrometry the complex protein sample you can make the lysates you can digest them using enzyme electrophysin due reduction alkylation and digestion followed by the cleanup of the peptides. So, from the complex proteins now you have made the peptides which could be cleanup and now these peptides can be separated using mass spectrometers for MS or MS-MS analysis which is going to provide as the peptide sequences and the protein information using the different database search. But what if you want to quantify the proteins using shotgun approaches where you do not want to separate them on different gels but directly you want to separate in the mass spectrometers and also you want to look at their expression changes. So, therefore the quantitative proteomics using mass spectrometers have become very attractive way of proteome analysis where all the proteins which are not digested in the peptide forms you want to label those peptides on the N-terminal sequence and the different type of reporter ions there are different type of tags available which could be used to label these peptides and that could be used for the field of quantitative proteomics. I have shown you here one example of using isobaric tags which are known as itrack or isobaric tag for relative and absolute quantification. Let us take a simple situation when we have control samples along with three different grades of disease and we want to analyze that what are the changes happening for each protein or each peptide from the healthy individual to the patients which are having the low grade of any cancer middle grade and the high grade. So, then we are labelling all the four different conditions using four different type of these itrack labels which gives you the numbers which are reporter ions from 114 to 117 and these reporter ions you are going to analyze and look at in the mass spectrometer which will be further used for the peptide quantification and protein quantification. This whole field of mass spectrometry based proteomics is really evolving very rapidly and while we will have much more detail of this whole field in different codes but it is good idea for you to appreciate the lot of advancements are happening in the mass spectrometry based proteomics. For example, I have shown on the slide different approaches both based on the label based and label free analysis using mass spectrometry for looking at quantitative proteome changes. For example, one could use like itrack even the TMT tags which are another isobaric tag known as tandem mass tags and one could do 6 plex 10 plex or even 14 plex analysis now with the recent tandem mass tags which are available or you want to not label your proteins and you still want to look at their quantification in the mass spectrometers that is known as label free quantification and in this middle part which you see what you are looking at you are looking at the spectral count of peptide. So, idea is that if a given let us say you know we are comparing control with a disease for a given protein if that protein expression is very high in the disease sample as compared to the control then many peptides will be formed for that particular protein and then the spectral count will be much more or different numbers will be obtained for the same protein. So, can we measure those spectral count and then that information we can use for the label free quantification. Additional approaches to also look at data independent acquisition where each of the window of your mass to charge separation you want to really obtain a very high resolution image of that and analyze the protein using label free manner. All of these things are very recent advancement of course I am not talking in detail right now because that is not the focus of the whole course right now but you should appreciate lot of advancement which has happened in the field of proteomics is starting from the gel based moving to the mass spectrometers and various type of quantitative proteomics methods. The whole scientific community has been struggling how to validate our targets. We have identified many proteins whether using microarrays or using the mass spectrometers or gels but now if you want to validate your proteins then you are relying simply on the antibody based assays either western blots or immuno assays some way you need some antibody or some biochemical assay to confirm that what you identified is correct. Therefore a new field of mass spectrometry based validation strategy has emerged which is known as target proteomics. Here idea is you have identified set of peptides which are differentially expressed from your control versus disease. Now you want to validate these peptides using triple quadrupole based mass spectrometers you will set up the various parameters so that software and hardware will only recognize the specific set of peptides and their transitions and you are only going to monitor the intensity of these peptides to find out are they really upregulated or downregulated when you compare the expression changes in different samples. So you can now set up this assay in more high throughput manner and if you really want to go even to look at more accurate quantification of the protein so you can synthesize the synthetic peptides which could be heavy labeled and now first you run in your mass spectrometers only synthetic peptides and look at their MS-MS spectrum pattern then you run your unknown sample and if it is matching then you are very confident and you can also compare the intensity with the heavy labels. So this is known as accurate quantification of the protein in this way you are able to validate your targets and also you are able to measure that what will be the concentration of these peptides in the given sample. So while variety of these technologies have been emerging, scientists have been still exploring are there ways more high throughput manner where we can with very small volume of the sample with very you know a lot of clinical samples are precious so whether drug molecules are precious lot of biological samples are very precious your protein and peptides which you extract from the samples are very precious how best we can conserve the sample and still obtain the meaningful information in a very high throughput manner. And also most of the previous technique which I talked are able to give you lot of leads but how to now determine the functional consequence of these proteins how to assign some function to these proteins. As a result different feed has emerged known as intractomics which relies on protein microarrays and different type of label free biosensors. I have shown you here one of the workflows for using protein microarrays for auto antibody screening in the human patient samples. So let us assume that you know the patient's blood sample followed by serum has been separated and now we are looking at which are the auto antibodies produced in the serum and can we detect that at the proteome wide manner. So in this case on the protein ships the very small volume of the patient serum which is diluted with the buffer will be floated and now then with a very small volume you know let us say we are talking about 50 to 100 microlitre of the volume of the sample which is sufficient to cover your glass slide which is already having the entire proteome of a given organism. Let us say all the 20,000 proteins of human you are able to screen with a very very small volume of the given sample. And wherever you see the signal which you can detect using secondary antibodies which is anti-human IgGs labeled with Psi 3 or Psi 5 then you can find out the potential biomarker candidate or you take another context you want to look for the an interactor of a given protein and you have the entire chip which is having all the 20,000 human proteins. So this protein which is your target protein of interest how this protein goes and binds with the many other proteins in the entire chip that will give you idea for the potential interactors of these proteins and likewise there are many applications which can be done using protein microarrays. But what if if you want to determine that you know the not only that the protein X bind to the protein Y but also what is the strength of this binding what is the on rate of binding what is the off rate of binding how strongly these are associated and how strongly or weakly they are dissociated. So then you have to move on to a label free biosensor platform and surface plasma resonance or SPR is one of the very strong platform to offer this kind of approach. So as you can see in the slide here now we have a gold slide on which the antibodies are printed. When you flow through your protein of interest the right antigen will bind to the right antibody pair and likewise you can have even the you know various other type of molecules printed on the gold slide and then if a small molecule is coming then protein is binding to the small molecule of the drug and you are looking at protein drug binding interactions. So many type of interaction studies could be planned and designed using these kind of label free biosensors and what you obtain is essentially the sensorogram which gives an idea for the association and dissociation rate of these binding and eventually you can also calculate the capital K capital D which is the dissociation constant and the kinetics of this binding which is happening in the system. So there are many novel label free methods are currently being used of course I have only showed one of them but many of them are relying on different type of physical principles to measure the proteins and different biomolecules with the physical principles without having any tag which is associated with the these molecules to be detected. So you are not adding any kind of fluorescent tag or radioactive tags to measure the binding interaction but you are simply looking at the change in the reflectance or looking at the conductance change various type of you know even interferometry based change those are all going to help us to determine what is the binding happening in the label free manner. So this is probably most you know natural most closest thing which can happen without giving any artifact signal. So even our own group and others have also started using some of these technologies for even some very interesting applications which are beyond the standard workflow of these you know instruments. For example even SPR we have used to measure the protein concentration from the serum sample if there are some serum biomarkers of interest rather than relying on the ELISA kit can we measure their binding on the gold chip by passing the complex serum samples and looking at how unique this binding interaction is happening and one could actually start developing these assays much faster. And we have also come up with some very innovative ways of measuring these interactions using CFCA which is calibration free concentration analysis of different type of serum protein measurement. As you go along in the course you will be exposed to many technologies but you should also keep in mind how best to integrate different technologies and make best use of each of the technology platform. For example SPR could be used to identify the interactors of interest let us say you have an antibody and you are passing a complex lysate. Now many some many proteins will be there in the complex lysate which will probably bind to the antibody. So antibody will bind to an antigen of interest an antigen may have even the complex which is having many other proteins. So now if you enrich this particular interaction by running the same reaction in multiple wells multiple channels and now you can elute out all the potential interactors which have identified from these reactions followed by if you can digest these proteins make a peptide form and run in the mass spectrometer so you can actually identify now the potential interactors. So in many ways you are now able to look for the new potential interactors which was not known but you have to apply coupling the different technology platform. So we have utilized here surface plasmon resonance with mass spectrometry to identify some new interactors. So in a nutshell if you look at the slide I try to convey you the idea that there are many technologies which are a part of the field of proteomics starting from the gel based proteomics to mass spectrometry based platform target proteomics interactomics. Of course each field itself is very vast and deserve a full course on its own. So we have dedicated this particular course on the field of interactomics which is much more functional understanding functional consequence of the proteins to understand and that is probably much more relevant for lot of you know biologist and people working in the field of you know life sciences. When it comes the looking at any kind of complex biological problem I think you have to be very unbiased and think about any technology which can answer the question in a more efficient manner. So therefore even when people are looking at the biomarkers for a given disease it is good idea to look at various biomolecules and then try to integrate the information. As you can see the complex system biology the complex central dogma of the life is starting from DNA RNA mRNA protein and then followed by metabolites all of this offers you know the various type of novel molecules and you never know that the most of the clues are coming from which type of biomolecule. If our resources our time if our technologies are available and we could apply that it is actually good idea to take the same samples and try to understand different level of information starting from the genome transcriptome proteome and metabolome and then try to integrate the information to find the most meaningful dataset. This is what is conveyed in the slide that the actual physiological context is very complex we have to build the layers of information for the same organism for any organism we have to start looking at the entire cascade of the events happening from the genes to transcripts to the proteins to metabolites and along with their phenotype the environmental effect all of them is only going to give us the complete picture. And if we are looking at more on the human biology or medicine then we have to add the clinical details and clinical information to make it more comprehensive and then only I think we should be able to get some very very right answer for the question which we wanted to address. So, all of this technologies which we talked starting from the genomics and of course genomics have really taken the big leaves with the next generation sequencing technology platforms and a lot of things are still you know very revolutionary happening in the field of genomics and then field also progressed toward transceptomics and proteomics and metabolomics all of them are generating the big dataset along with lot of medical information the radiology immunohistochemistry and different type of other datasets in life sciences. If you think about now the life science field and omics field is generating huge amount of data and that data is going to be very very revolutionary nature very is going to transform our understanding of biology. I showed a slide here for a given disease which we were looking at in the colloquial cancer. How different type of data set when we started analyzing them in the system network level what we identified was that you know set of proteins were quite distinct in the colloquial cancer patients as compared to the you know the air peritimoral regions or the normal regions. And then some of these associated candidate proteins we try to map them as a part of the interaction analysis and finally with the system network analysis we found that most of these are actually mapping towards certain nodes and which are those nodes how they are going to regulate the complex disease this is kind of excitement which one could try to obtain by looking at this is a big data set and then try to understand that there are many changes happening but what can be the root cause of many of the changes which are the major nodes which are governing all of these associated protein changes. So then I think you know you looking at data in much more comprehensive manner and then trying to identify the system level analysis is going to be very powerful. However the big data also offers a lot of challenges. How are we going to link the big data which we obtain from variety of these technologies I discussed to the real life innovation and the new knowledge and ultimately how it can really transform the societal development. These are the questions in front of us and of course the big data which is coming very rapidly also opens many questions what are the guidelines of the good data set which we are obtaining for the big data and of course field is evolving for each of the kind of instrument and technology. Now there are set of guidelines coming forward which are of you should keep in mind and keep an eye on those papers which defines the guideline for what is accepted as the best data set. Next question comes where the data should be stored. So much data terabytes and terabytes of data every day we are generating. How are you going to store the data which can also be retrieved easily from the other users? Data accessibility are we going to just simply share with everybody or we are going to have at least you know some way of coding the data some way of coding the patient information and then start utilizing that and share that information in more meaningful way. What could be the ethical consequence of sharing this data? There are many things which are a part of the regulations and guidelines of the big data set. I must appreciate that Department of Biotechnology Government of India has recently taken up this matter seriously and they have also now defined the certain guidelines and regulations for the big data and likewise the whole omics field is also keep deliberating what should be the best guideline of looking at the big data and sharing the big data sets. All right, just to summarize the lecture, I hope I conveyed you that there are many technologies, many promising technologies and approaches are part of the field of omics and specifically I spoke about the proteomics and variety of platforms are available which you can utilize to address different type of biological questions. Each of these technologies have a lot of potential, but you need not to get influenced with these technologies. Rather you think about what is the biological question you want to address and accordingly you can choose the right type of technology for your application type. I hope this sets up a good platform for you to appreciate the vast field of proteomics and several excitement and challenges which are part of this field and we are going to focus more on specifically on interactomics from the subsequent lectures. But I am going to leave you with this video which you will watch on journey of proteomics field and how the proteomics can translate the code of life. So we made this documentary and please watch this. Thank you very much for attending the lecture and finish this video and then we will be ready to interact with you in the next lecture on interactomics. Thank you. Most important, most wondrous map ever produced by humankind.  I wonder, perhaps time has now come to appreciate the significance of omic technologies such as proteomics. Proteins and proteomics are central to connect genomes with phenotypes with normal biological processes. There is no way to predict from the genome itself the crucial features of proteins. The real key here is that genomics can only tell you of the snapshot of the organism whereas proteomics will enable us to look at the organism dynamically. Proteomics is a science where we want to know about all the proteins that are coded for by the genome. Proteomics addresses whole systems and uses a broad unbiased approach to derive new findings. What are the technologies driving proteomics? Several techniques like mass spectrometry, protein microarrays and label free biosensors have been used in proteomics to elucidate the expression, localization and interaction of proteins. Unraveling the proteome is a very onerous and complex journey that demands the efforts and insights of an interdisciplinary team of scientists. Fourteen years after the release of the first draft of the human genome, scientists are working on the map of the human proteome using high-resolution mass spectrometry combined with high-springency informatics and other methods including antibody-based protein microarrays. In addition to mass spectrometry-based protein maps, scientists used tissue array platforms to spatially locate proteins in human cells and tissues and map their expression levels in various biological tissues. One major effort in this area has resulted in the human protein atlas. Despite this progress, there exist missing proteins which are yet to be annotated. Scientists hypothesize that the missing proteome could be a result of relative low abundance, tissue type, development or stress-specific expression of proteins. With the road ahead requiring comprehensive proteogenomic analysis and consideration of many additional sources of biological information. Researchers are using novel approaches for biomarker discovery by studying the differential expression of alternatively spliced isoforms in cancer, low abundance blood-based biomarkers for disease diagnosis, studying cell secretome, analyzing human immune response to develop vaccines, improving proteomic methods to study the host-pathogen interactions and so on. Hopefully some of the biomarkers that we discover will be useful for identifying disease early for helping doctors identify the best prognosis for patients. Proteomics has the unique ability to analyze protein production, degradation and post-translational modifications which are highly pertinent for biomarker discovery and translation. In glycosylation specific form of protein will give us a more specific and sensitive approach to detect the disease in tissue and the body food. Several novel techniques such as multiplex protein microarrays and beat-based arrays are also contributing to biomarker discovery. Protein microarray contains thousands of proteins in one chip which allow us to explore protein functions with a certain molecular probe. A recent innovation in this field is the development of ultra-high-density peptide arrays comprising of millions of peptides thereby allowing large-scale probing of biological samples. Present diagnostic as well as therapeutic strategies that are existing in clinical scenario clinical setting are all a result of the biology discoveries that have gone on over the last 50 years. The proteomics community has kept up its expectation of being a complementary approach alongside genomics thus contributing to diagnostic assays in clinics. How can proteomics cater to the needs of precision medicine? What if figuring out the right dose of medicine was as simple as taking our temperature and that's the promise of precision medicine? The precision medicine means one patient one drug at a time and therefore if you can detect specific target for any given cancer type we can then develop treatment based on the target information and therefore the precision medicine is going to be the future of oncology. My opinion that to be able to figure out precision medicine both at the diagnostic level as well as to be able to determine the the pathways in which drugs should be targeted to you you have to be able to really quantify well all of the proteome. How was proteomics moving towards targeted validation? One of the challenges facing proteomics is being able to get from the discovery all the way to validated markers that are useful in the clinic. So that's why targeted proteomics is so important by sacrificing a little bit in terms of depth of coverage but selecting analytes that we care very deeply about we can design proteomic assays that will be able to measure analytes whether they're proteins or peptides each and every time that we do an experiment. More specifically this essay have been now widely accepted in the community as an SRM or a performant triple quadruple instrument. And I think it's become very clear that those methods including multiple reaction monitoring and PRM and allied methods are going to have a major impact on the development of clinical diagnostics and then ultimately on patients health. Innovations in technologies like targeted proteomics have allowed researchers to validate interesting targets overcoming the bottleneck of availability of reliable analytical reagents. The transplanting pyrolysis has given us the capability to analyze proteomics in an unprecedented manner but that has also given us the capability to understand which proteins are being expressed which proteins can be analyzed by mass spectrometry and we can utilize that in a targeted approach. I've been developing SCOIN for the past eight years and it's grown incredibly it's in use around the world and it's exciting to see that proteomics researchers really seem to feel that it helps them with their research. SWATMS method was to expand the SRM type approach to much larger number of proteins potentially hundreds to thousands of proteins. The advent of new age techniques like SWAT with ability to identify and quantify proteins or peptides over a dynamic range and powerful tools like Skyline and SRM Atlas have added to the arsenal of targeted proteomics. How is the global proteomics community poised? The human proteome organization is an international scientific organization that promotes international scientific cooperation, educational training and technology breakthroughs in proteomics. HUPO was started in February 2001 in Versailles, France around the time when the first draft of the human genome was released. The human proteome project was launched nearly 10 years later in Sydney at our annual World Congress. HUPO coordinates its flagship scientific project that's called the human proteome project and that is broken down into three main thrusts a chromosomal effort, a biology effort and a disease effort and it has three supporting technology pillars bioinformatics mass spectrometry and antibodies that support those thrusts. It must take a comprehensive proteome wide view of cell functions connect the genome proteome and the phenotypes. Whether you are an early researcher or a seasoned veteran seeking to make important new discoveries I encourage you to engage in this vibrant and really exciting project. The journey of proteomics has been promising. My belief is that the proteomics community can contribute immensely to functional biology and decipher innovative solutions. Persistent efforts from communities like HUPO has helped proteomics establish an undeniable international presence. This has established the broader theme of research directions thus translating the code of life.


From the CUBE studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is a CUBE Conversation. Hello everyone, this is Dave Vellante. Welcome to this CUBE Conversation. And there's a company that we've been following now for the last couple of years and a trend in robotic process automation. And then automation specifically. It's a company in an area that we really like. We've been researching this and publishing and Daniel Dines is here. He's the CEO of UiPath. Daniel, it's great to see you again. Thanks for coming on. Thank you for inviting me, David. That's our pleasure. So let's get an update on your business. You know, COVID obviously sent everybody for a loop. We had been and have been following you guys quite closely. How are things going for UiPath? How has the pandemic affected your business? We've designed this company from day one to work in a hybrid mode, local, and obviously working from anywhere. And the transition to working from anywhere model was really fast to implement for us. So COVID in itself didn't affect the way we work. On the business side, I would say that we are seeing, you know, a mix of events. Some industries that were mostly affected by COVID were putting their budgets on hold while other industries were increasing 10 times. What I can tell you that in a nutshell, the numbers for us were really good. We are able to hitting and beating the targets that we set pre-COVID. And we focus quite a lot on helping our customers, navigating through these market waters. We have quite a lot of involvement in healthcare and federal business. We worked with a few hospitals to help with accelerating COVID tests. In one case, we were able to save two hours a day for every nurse. So instead of filling up paperwork, they were able to focus on their patient. And that's not one isolated instance. We've done tremendous work across the globe. And, you know, that we raised our last round in June, end of June, and that was a recognition of our accelerated business. Right, yes. I mean, you raised it in the middle of the pandemic. You know, I've been saying that, I mean, everybody of course says that COVID has accelerated a number of trends. And I've been saying there's now an increased mandate for automation. I think there was before, but yeah, maybe there was some complacency, although you didn't see it in your numbers. You guys obviously growing very, very fast. You mentioned healthcare, I would think of banking and financial as well, which of course was a stronghold. When you think about in the U.S. anyway, the Payroll Protection Act and the number of loans that had to be processed, you know, bankers would talk to me and say, our volume increased towards the magnitude. We had no way to do it.  So I've said that there is an automation mandate. And I think there has been because of the productivity gap, particularly in the U.S. and Europe, you don't see it so much and of course in China, but certainly the U.S. in the last couple of decades has declined in terms of productivity. So, you know, people are not going to be able to solve the world's biggest problems without automation. How are you thinking about that in this post COVID world? David, as you said, the awareness that we have to automate has increased 10 times compared to pre-COVID days. I would not say that yet automation is number one priority on this company's leaders agenda, not in the same way as conferencing and video conferencing and all this directly affected, positively affected the software industries. But I believe that while automation is slower to adapt and it requires a lot more investment to adapt, it's gonna dominate the agenda post COVID in the sense that people will have to recoup, you know, all the losses that they had in the COVID. They learn their lessons. And, you know, for instance, I talked with a few CEOs of large, you know, Fortune 500 businesses and they are telling me, Daniel, I wish that we have started earlier. So now we are seeing, you know, an adoption that is more top-down, an adoption that is starting from the C-level suite, even the CEO of large enterprises. Yeah, I mean, it seems to me that if a customer has tasted, you know, the benefits of RPA and automation and realizes what it can do for their business, they're going to maybe double download, especially in a time when revenues might be under pressure and you're not hiring. I know a lot of people have put, you know, hiring freezes on a number of head layoffs. So you got to do more with less. Guys, I wonder if you could bring up this chart. I want to share this and get Daniel's reaction. We're talking about land and expand. So this is ETR data and what it does is it asks customers where they're at, do they know about a vendor? In this case, it's UI path is on the left and automation anywhere and then some others.  Are you planning to implement it? And this chart shows those respondents that said, yes, we are a customer and we plan to expand our usage and you can see over the last three surveys, the yellow is even, you know, an uptick. And so people, this is essentially the takeaway here is that once people taste it, that you land and then they expand and find new use cases. Are you seeing that in your business and maybe you could give us some high-level examples? We're seeing it quite a lot. We have today more than 60 customers with over a million dollars in spending with us. There are more than 800 customers that spend more than 100K with us. And our net expansion rate is more than 140% consistently over many past quarters. That shows a very solid expansion desire from our customers and it shows that our technology is very well suitable for large-case automation, deployments, enterprise-wide, especially with our program, a robot for every person, we are seeing huge interest and way bigger deals. We are able to land upfront, work to upscale our existing customers. You know, in a way, I don't believe that in five years from now on, we will ever have people just to minusly move data from one screen to the other. I think this is a thing of the past as much as plowing the fields as a thing of the past. So I wonder if you could talk a little bit about where you've come from as a company. So I mean, you started in 2005.  And my sense is you started as a product company but recently you guys announced this end-to-end platform for what you call, or maybe Gartner calls, I don't know if it's their term of hyper-automation but you've gone from a product company to a platform company. I wonder if you could talk about how you think about that transition and the platform generally. To become a platform requires a certain maturity level and it's in a way a harder business to promote to an enterprise customer. They are very likely to test water with the product but when betting everything, automation, wide on a platform, it's a different game. So this is why we had to go from the steps of products, product then like a couple of products and then putting everything together into a platform. The power of the platform in this particular instance comes from the integration of all pieces in a platform. And an automation wide platform will have different sets of product that play from the discovery of the processes that you automate to the implementation and maintenance of the process into the analytics that helps you track your progress and also you have technologies that addresses to different persona in an enterprise from software engineers, RPA developers into the citizen developers. So it's a huge offering and what is really important for us is that we give a full-fledged platform. So an enterprise customer knows we will be able to build everything on the top of this platform and they will offer best-in-class where it matters. And we believe that best-in-class matters in few important areas like RPA, like process mining, like analytics, while they will offer good enough where they will offer integration with best-in-class products where it's not so important in the grand scheme of deploying automation. The integration is tremendously important piece. Put yourself in the shoes of a big enterprise instead of buying 20 different products, different licensing agreement, different maintenance staff, different teams to support them. You just have one and you have the guarantee they work very well together. It's a very big proposition and it requires maturity of the platform and we are making big strides into having the credibility that is required to have such a big investment. Well, I have to bring that up, I have to ask you. So you guys are obviously RPA and automation specialists building out a platform, very focused on that. And we always talk about this best-of-breed versus integrated suites and you're sort of talking about integration. Of course, we saw Microsoft come out and as well as others, IBM, I think SAP have announced what I would consider 1.0 products, not nearly as robust as you and some of your leading competition. But how do you think about that in terms of staying ahead of that? I mean, we all know Microsoft, you used to work there, they come out with a 1.0 and then the 2.0 and then eventually they get it right. So you have to move fast. Yes, absolutely. And we proved that we can move fast. We've built this company from zero, five years ago to, you know, we are almost half a billion dollar in ARR today. So we are fast. This is one of the core tenets of our culture, be fast. But speaking about the strategy, I believe that the space of low-code, no-code business application development and the hyper-automation space will converge into one single space. A company like Microsoft started with a simple product like EFTTT and that was dedicated only to cities and developers to build very, you know, small and quick integrations. Like if you look at Power Automate use cases, you'll see one of the most common use cases to set an alarm for myself. Well, I understand the value of such use case, but it's a far cry from setting an alarm and to automating, you know, end-to-end procure to pay or order to cash for a big enterprise or COVID testing. And basically we are coming from two different angles. We are coming from the RPA angle that is putting computer vision at the center of the technology and they are coming from quick API integration.  My belief, I believe that we have an advantage here in a sense that RPA is a technology that can produce immediate returns at a large scale. While the other type of technologies, first of all, traditional automation and then all this new type of API economy, API integrations and a largely failed to show scalability within big enterprises. They are nice to have, but they are not essential. When you are choosing a platform, my take is that you are choosing a platform based on what you need the most. This is where you choose the best in class and you need the certainty that you partner with the vendor that invest the most. Well, this is our bread and butter, this is where we start. And of course we are offering every piece that the other are doing where they are also getting into our world. But our advantage being cloud agnostic, being ERP agnostic, being CRM agnostic and having started from the most sensitive technology that offer you the most savings and the best productivity increases, it's a tremendous advantage. And of course you know I'm excited about this opportunity and I've talked to a number of your customers and so to me that's the proof in the pudding. But you mentioned you're incurring revenue approaching half a billion. So I got to add and you know as well that in my breaking analysis, we took a look at the total available market for RPA. And then I think, well we've extended that. I think we kind of missed the broader automation agenda and the platform thinking and we've updated those figures. I mean it's hundreds of billions of dollars of an opportunity at least. And so the reason I bring this up is of course last week we saw the hottest software IPO in history and Snowflake. There's a company with $400 million ARR growing at 120%. The company went from early this year, $15 billion valuation went up to 20, went up to 30. They launched at 33 billion within five minutes it was worth 80 billion. Of course it's settling down now in the 60 billion but unbelievable. And I would argue that your total available market is perhaps even larger. I would say it is larger because it has a deeper business impact than say a Snowflake. And of course people who watch my programs know that I'm a very high on that company. So my question is, what do you think about that IPO? How are you thinking about your own IPO? It would seem that UiPath is in a great position to at some point become a public company. We are first of all, if you are speaking about the time where nobody would argue that our time is not higher than Snowflake time. We can argue that their market is maybe more consolidated. Everybody understands data market in a way and our market might be way more scattered across different use cases. But in a way it's the market of data versus the market of all data versus all processes in the world. It's there way more people are tasked today with processes than to analyzing and working with data. And so we are going after a very large problem that we have to solve. And we have to empower people of doing what they are naturally built to do, like talking to other people, socially interact, being creative, making decisions instead of doing this numbing part of their daily jobs that are required by this state of the industry. So our time we talked with different bankers and I've seen various figures from like 200 billion to weigh into like two trillions for something that it's maybe. So no, I think time is a problem. It's the way we, I think we, what we wanna build, it's a durable business and it's a durable growth while in the same time being cash flow positive. And we are very close to achieve these goals. And that will, I believe that will be a very compelling proposition for our own IPO. I don't know if we can get snowflake multiples or not, but this is definitely not the biggest thing on my agenda. My agenda is to build a long-term sustainable, durable business. I am looking to next five to 10 years of this business. An IPO is just the fundraising event in, you know, after all. Great, so yeah, that's good. I wanted to ask you kind of what the parameters are. And I think you answered it, is you're not rushing to get in, to draft off of some event that you had no control over. That notion of cash flow positive is really interesting to me. I said about the snowflake IPO, there are plenty of TAM just like you guys and I agreed somewhere between 200 billion and three trillion, that's about right. But I think that what I said about snowflakes IPO is that I'm not worried about their lack of profitability right now. At some point, I'm really going to be focused on their operating cash flow. And if you can come out with the large TAM, your growth that you're at, the large ARR and cash flow positive, I can't wait to see that IPO, Daniel. That's going to be super exciting. So we'll be patient. But Daniel, thank you so much for coming back in theCUBE. You're always a great guest. Really appreciate the update on your business. Thank you so much, Dave. Really appreciate the invitation. Thanks. You're welcome. And keep it right there, everybody. We'll be back with our next guest right after this short break. This is Dave Vellante for theCUBE.


Hello, my name is Aaron Reeder, I'm the Vice President for Student Services. On behalf of Highline College, we'd like to send a huge thank you to all of our students for your patience and perseverance. While we are going through some very unprecedented times, we want you to know that we're going to get through this together. We still offer a full array of classes, virtual and online this fall quarter to help you reach your educational goals. Students will reopen as reasonably as possible with their safety in mind first and foremost. We continue to provide extensive and robust student support services through Zoom and email.  You're most likely going to be at home anyway on your computer, so why not save money and come to Highline? Fall quarter starts September 28th and we look forward to having you a part of our Highline community. Register today. Go T-Berts!


All right, here we go. Let's start again. Hey, everyone. My name is Akshay Parthasarathy. I'm a technical marketing engineer at NetApps data fabric group. I'm here to talk to you today about NetApps OpenStack integrations and a test drive that we have with OpenStack and Cloud on top. NetApps vision is to let customers like you have control of your data no matter where it's located. This is enabled by solutions such as Cloud on Tap, OpenStack integrations, Alta Vault, NetApp Private Storage, and other products. All right, what are we going to cover? We're first going to go through an overview of what this test drive is about. We'll then get into the architecture. What are the different instances and the different networks that are being deployed in order for you to take advantage of this test drive? The different tools you will use, these include on-command system manager, OpenStack dashboard, and OpenStack CLI, OpenStack CLI being the most important one as you go through the test drive. I'll then show you a couple of short demos. These are totally taken from the test drive. This is exactly what you'll do if you were to go and take the test drive on your own. So what are the different technologies that we use? The major ones are OpenStack, of course, and NetApps Cloud on Tap. Many of you all may already be aware. OpenStack allows you to control large pools of compute, network against storage data in your resident cloud. The next tool we'll use is Cloud on Tap. Cloud on Tap is basically NetApp's version of data on tap running on Amazon Web Services. You get the same advantages you get by running Cloud on Tap as you would on data on tap. Plus, you have a pay-as-you-go model. You can spin up Cloud on Tap and tear it down as you desire. So as I mentioned before, the whole test drive is set up on Amazon Web Services. So basically, you click to launch the test drive. You're going to have a few hours to play around with it, and then it'll tear it down for you. So here are the different networks and instances that you will use. The two most important ones for you are the Jump Host and the OpenStack Instance. The Jump Host is what you use to log into System Manager, which is NetApp's version of a GUI tool to control Cloud on Tap. The OpenStack Instance allows you to log into a version of OpenStack that is kilo-based, actually. It's not liberty-based. It's kilo-based.  NetApp Cloud on Tap, of course, gives you the storage operating system that you're going to leverage for all your exercises. In addition to that, we have a private network and a public network that have been set up for you. And this basically allows you to do the test drive. So on command system manager, this is one of the tools that you use. Using System Manager, you're going to configure your NetApps on Tap storage that is already built for you on AWS Elastic Block Storage and Elastic Compute Instances, Elastic Compute Cloud. To get to System Manager, you're going to log into your Jump Host using the IP address and credentials in your email. Then you're going to navigate to the Cloud on Tap IP address on your using the IP addresses, again, indicated on your email. So basically, you're going to use a Jump Host to log into System Manager. The next tool that you will use is OpenSack Horizon. You're going to log into your Jump Host again using the credentials in your email. Following that, you'll be provided with the IP address and credentials for you to use your web browser, Internet Explorer, in this case, to log into Horizon. The third tool that you will use, and this is the most important one, is OpenSack CLI. In order to log into the CLI, you're going to receive an email, the same email that you receive in the other two cases. You log into the IP address using the credentials that have been provided for you, and you conduct your exercises that way. So as I mentioned before, there are a couple of demos I want to show you. These demos walk you through a couple of different values you get by using NetApp Cloud on Tap with OpenSack. So some of these values include storage efficiency, unified architecture, data protection, seamless scaling, and others. So we're going to go through a couple of these. The first one is continuous operation with OpenSack Manila and the second one is storage efficiency using OpenSack Lens. So continuous operations basically is very important in any enterprise or small business for that matter. You want to keep your infrastructure running continuously. So this demo shows that using OpenSack Manila, which is the file share service. So we're first seeing what are the different file shares that have already been created. There's nothing created right now. We go ahead and create one using the Manila Create command. We're creating an NFS share in this case, and it's of size 1 gigabyte. So let's just make sure the share was created successfully. It is. We have a share that is available. So one of the things with Manila is that once you create the share, you should also specify an access policy to allow access, similar to an IP tables firewall, things along that nature. It's an access list. So we just created a 1GB NFS share. So what we want to do is we want to try to log into the share. And in order to do that, I've already created a couple of VMs. One of them is an Ubuntu VM that can be an NFS client. So at this point in time, we're logged into the Ubuntu VM. So let's create a directory and mount the share. So in order to mount the share, you need the export location. So in order to get the export location, we can switch back to the Ubuntu VM and basically copy and paste the output of the ManilaList command. So right now I've switched back to the OpenStack instance, basically another window, copying the export path from the ManilaList command, and then I'm gonna paste it over here into the Ubuntu VM. Okay, so now we've successfully mounted the share. We see here the output of the DF command, the share is mounted into slash MNT slash Manila underscore NFS. So let's place a file in the share. The reason I do this is because we want to check for continuous operations. So what I'm gonna do after we place a file in the shares, I'm gonna execute a script that checks for continuous operations. So I've just named it check mount in this case. So we're gonna run check mount. So now we've started the script and what this does is it continuously does an alas. It does a directory listing. So what we're gonna do next is we're gonna actually move the back end, which is the disk pool, as this script for continuous operations is running. So let's say, I mean, this can be useful in a number of situations. Let's say you wanna move from a regular SAS disk to an SSD disk, that type of thing. So that is our share that we just created and we can actually verify it by comparing it to the output of the Manila list command. So we go there, we go into system manager, we notice what disk pool it is in. And in this case, it's in a disk pool called aggr2. We're gonna move it to aggr1, which is another disk pool. So at this point in time, we've moved the disk pool. We just wait a couple of seconds for the share to be moved. And what we notice is that it's now in aggr1. So we can go back to the script that we had to check for continuous operations and see whether it's still executing. As you can see, the script is still running, indicating that you had continuous access. Yeah, so basically we moved disk pools and we had a script that was running in the background as we moved. There was no disruption in service. That's how we proved that. You had continuous availability of the share. So let me now take you to the next demo. So this demo is slightly different. It takes you through storage efficiency. And in order to demonstrate storage efficiency, we go through an exercise with glance. So at this point in time, what we're doing is we're looking at the different images in our glance repository. So these images will be created for you when you do the test drive. So here are the different images. We've got a few images, trustee, utopic, vivid, and so on. So let's go back to NetApps on command system manager again and basically do a storage efficiency exercise using the glance image repository. So at this point in time, we're looking at the different flexible volumes that are available in on command system manager. We see that we have a glance image repository, 20 gigabytes in size. In order to look at storage efficiency, we switch tabs to storage efficiency and this allows us to see the savings you get from deduplication and compression. All right, so we're in the storage efficiency tab and it looks like we only have 58% of storage efficiency at this time. So this basically means that you're only saving 58% of capacity on your glance image repository. So the storage efficiency processes are running in the background. So we can just kick it off manually and that's what I've just done. So I've just kicked it off in order to speed up the process for the demo. We see that the process is actively running. Let's wait approximately two minutes for the deduplication and compression to complete on this glance image repository. So after two minutes, let's go back and check. So as you can see, you get a storage efficiency of over 80% in this case. Now it is possible, we have in customer deployments, we've seen this is actually a common number, we see 80%, 90% storage efficiency. So if you're looking at 80%, what that means is if you have a 10 terabyte glance image repository, you're gonna potentially reduce it to only two terabytes of capacity that is required. What this means is you're gonna say it tremendously on the CapEx and OpEx to manage your storage. So that's storage efficiency for you. So these were two different exercises in the test drive. There are other exercises for you to try out and even go ahead and try these different ones. In order to take the test drive, go to netapp.github.io and click on the icon that's displayed over here. If you do have any feedback or questions, feel free to reach me. My email is as displayed, akshayatnetapp.com. So here are some other related sessions. There's the Manila Big Data session that just concluded actually. Feel free to check that out on YouTube later. There's another session that is about FlexPod. This is another popular product that we have from NetApp. Manila Design Sessions, as you see over here, there are also additional sessions about Cinder Design. So feel free to attend those as well. So that's all I have for this open stack on Cloud On Tap demo and demo with the test drive. So if you have any questions, I can take them at this time. All right, thank you. Thank you for your time.