Okay, so what we're going to imagine here is that you guys have gone through the analysis. You've been able to do some identification of either interesting pathways, interesting molecules, and at this stage you'd like to try and relate what you're doing with metabolomics to some of the other possible omics systems or other conditions and situations. Remember, metabolomics is only one part of that pyramid and that ultimately if we want to get some greater understanding we'd like to connect proteomics and genomics. So we've seen how we went from the metabolite lists that we learned how to do yesterday to get some information pathways. That's biology. That connects metabolites to proteins and proteins to genes. And if we want to use pathways, we try and work with the databases, some of which we've already talked about last time. So we saw how metabolologists is connected to K and to SMIT DB. And we talked as well the other day about SMIT DB and the number and types of pathway databases and what's in them and how you can use it. And as I say, this was a database designed for metabolomics. Unfortunately it's only for humans so it doesn't cover everything that everyone's interested in. And we saw how you could do things like mapping concentrations and mapping positions and cellular locations and that allows you to generate things. The next step beyond the pathway stuff and that was also the METPA work and you guys saw that as well was to going from pathways and lists to models. This is another element of systems biology and this is an aspect of simulation and this is crunching equations and one that also metabolomics I think has been sort of unique and has been leading the way. So has anyone heard of Bernie Paulson? One. Anyways, his group in San Diego has been developing reconstructions, metabolic reconstructions of entire organisms.  And to do a reconstruction they have to connect the genes, the transcripts, the proteins, all the reactions and all the compounds together. So it's non-trivial. And so their first efforts were on smaller organisms like mycoplasma where there's like 500 genes but then they moved up to E. coli where there's 1,000 plus genes. And they, as says, progressed now to humans and then originally it was just a single compartment, E. coli just has a single compartment but humans and more complex organisms have multiple compartments. And the main pathways that they generated for the Paulson reconstruction was 98 or just under 100 different pathways. We call it, Smith DB has 90. What people call a pathway is sort of arbitrary but anyways somewhere between 80 and 100 is where the metabolic pathways are known. Anyways, that information is compiled from resources like the HMDB also from the literature. And that information can be used to create what are called flux balance analyses. So there's the genes, there's the proteins, the metabolites, they have to drive the equations. They might do some testing on subsets to make sure that everything works. They have to deal with balanced equations. So this is chemistry where you have to actually worry about where electrons and protons are involved. You also have to have a charge balance with all of these. And they will have constraints. They have to know roughly how much of the starting compound is there, how much is produced. And each time they run these tests they'll find failures, they adjust and tweak. So this is many hours. But that's got genes, proteins, metabolites, reactions and then they can start doing these flux balance analyses. And this is essentially a way of saying given a starting state of how many metabolites and composition is this a sustainable metabolic reaction. If we in silico knock out a gene or mutate a gene or move a bunch of genes, will the system find an alternative way to create it or will it die? Will the system grow? This is not a dynamic simulation. What it is actually is a whole bunch of linear algebra equations.  So this is an optimization calculation where time, these are time independent equations and you're essentially working with it. How much of a given product, which is one set of metabolites, and how many inputs have I got? And will these maximize out in a certain way? And are they solvable? So again, if you remember a little bit from linear algebra, sometimes you come up with equations that are not solvable, divide by zero. Others you'll find things zeroing out. So in this way, they can predict whether a given system is functional or whether a given system will thrive. So you can predict essential and nonessential genes, essential and nonessential metabolites, essential and nonessential proteins. You can predict a phenotype. Will this organism or this human grow? So in the case of humans, we know certainly metabolic mutations, phenylketonuria or PKU, knock out this gene. What happens? What's going to accumulate over time? And does this match with what we observe in human studies? You could do this for E. coli, you could do this for yeast. So this is flux balance analysis and you're balancing the flux in and out. So it's a classical set of studies that chemical engineers do and Bernie Paulson is a chemical engineer. Another approach that is not a steady state analysis, but a dynamic analysis is something that we've been trying. It's a tool called SIM cell or simulating a cell. And you can try solving time dependent partial differential equations and dealing with spatial diffusion and stochastic events. And it gets really, really complicated. Or you can do a simple thing. I don't know if anyone has played the games like the Sims or Sim City, some of these old ones where these are called cellular automata or agent based games. And some of these are probably very similar to war games now. If you're battling enemies that the computer creates, it creates automata or agents that have simulated properties like a game player. So you can do agent based models where you're creating enzymes that have functions, genes that have functions, metabolites that have functions and connections to each other. And if a particular enzyme recognizes a particular substrate, you tell it that rule. And now you just tell the enzyme to just bounce around inside a cell until it runs into a substrate. And if it looks at the substrate and says, is this a good substrate or not? And if it is, then you tell it a rule, convert that substrate to its product. And then you can tell it a rule that convert that substrate to that product at this rate or with this frequency or with this speed. So in that regard, you can, and if you've played a game, these Sim type games from Sim City to these war games, they behave pretty realistically. Sometimes it's pretty hard to beat the Sim agent. But you can do the same sort of thing again with cells. And now you create essentially a time dependent, spatially dependent process where you can see events happening over time as signaling happens, as drug metabolism happens, as interactions will happen. Now this is not something that you can measure over eons. These are interactions that are measured over seconds to minutes. Whereas the flux balance analysis is something that says, you know, after a few days, months or years, this is what we'd expect to happen. So you can download Sim cell and you can run it instead of a sketch system where you can draw pathways and cell systems and then run them. So this is one where we created a compartmental model actually of drug metabolism. And we had, I think there's the liver and the heart or kidneys and the heart and then a cell system. And so you could actually show where systems were moving around. So it's not just cells. It could be compartments that allow you to do this. And you can take an existing pathway and then sketch it out onto the Sim cell. And all this is a schematic which tells the computer what it has to do. And then from there the computer actually starts calculating concentrations as they climb up and fall and changes in the appearance of products, some disappearing completely, some rising. And in fact this is a picture of a time dependent change. Actually in oscillation you can make cells oscillate fairly sequentially if you set up the right parameters. And the people may be seeing these chemical reactions which oscillate. So you can do the same thing with some genetic manipulation. And this is one that we had done. And this was the simulation. And people have seen this oscillation in cells. So the system works. And we've played around, this is a couple years ago, trying to get this simulation to go on in the computer and then to take that information that we were mapping and then to put it onto a virtual human. And this is where we're looking at some drug metabolism and where it happens and how things would distribute. And we can match what's been measured metabolically in different compartments. It's still a long ways from being I think something routine. But it's an example where you can take this list of metabolites and actually translate it into something that's biologically meaningful and measurable. So we've seen this picture before as well in terms of, again, you've got your list of metabolites. What can you do with it? And so these are the applications. And so some of them inspire the list of metabolites. But then once you've got your list of metabolites, you need to work with the people who were asking you to do this and tell them what it means to translate into decisions, to translate into protocols, to translate into biomarkers. So about half of the applications I listed there were in clinical medicine. And about half the people here are probably involved in some aspects of clinical or medical research. So I'll talk about some of those applications. One application I guess is something that you guys did in your tutorial yesterday, which was trying to learn a little bit about inborn errors of metabolism or IEMs. So imagine if you had your spectrum, it could be NMR, GCMS, LCMS, but in some case it generates a list of metabolites. What were the ones that could have been a bunch of peaks? You didn't know what the peaks were. The peaks then tell you what the metabolites are. And then you want to find out what do these metabolites do and what are they relevant for. And so we used the example of, here's another list of mass spec feeding it in, you get your list, and what does it tell you? So what you guys would have gone through in that tutorial was from these lists of compounds that were up or down regulated, you started saying that there was obviously some effects in whether it was diseases or by metabolic pathways that these are associated with phenylalanine metabolism and that they're also associated with a disease, PKU. So if you hadn't known anything about PKU and it started measuring some of this stuff, this would have led you to it. There are other applications beyond just sort of using the databases we've talked about. These are some interesting papers, which I'm going to show, where people have used metabolomics to identify in some cases the causes, in some cases probable biomarkers that help understand the etiology of certain diseases. This one, and actually it's not type two, it's type one diabetes, but this is the diabetes, this is juvenile diabetes is what used to be called. And this is a disease that strikes children and essentially wipes out their pancreas. Pancreas doesn't function and they're on diet and on insulin for the rest of their lives. And what's been known is that most type one diabetes cases, I mean there's many causes for it, but the ones that seem to be most fundamental are antibodies to this enzyme. And I'm trying to remember was this, it's GAD, it's a dehydrogenase, but it's sort of a glutamate dehydrogenase. And they wondered why. I mean, what's important about this GAD enzyme and why are there antibodies to the GAD enzyme? It has nothing to do with insulin and it has really nothing to do with sugar metabolism and so what's the deal? Anyways, in Finland they started doing a study where they were taking and monitoring kids who seemed to be at higher risk where parents had diabetes, where grandparents had diabetes, it was okay, what's going on? So this is a young girl where they were tracking her over every three months or nine years collecting blood samples and measuring some of the compounds that were in her blood. And one of the things that they found, this is the case of enzymes, this is the case of some of the amino acids. So one of the things they found was this child was born with a 13-fold higher level of glutamic acid than normal children in their blood. And as the glutamic acid started trending towards normal, another trend happened by a year or two where a metabolite of glutamic acid, gamma, gamma-aminobutyric acid also spiked and then gradually normalized. As these compounds are sure way high, really high, started to normalize, they started to see some of these enzymes occurring, trying to deal with this high dose, these high levels of glutamine. And it wasn't too long after these enzymes started peaking to deal with this huge level of glutamine that diabetes started appearing. So it argues that in fact, at least in this case and perhaps many other cases of tycoin diabetes, that it's, yes, it's an autoimmune response, but it's triggered by high levels of amino acids, a dysfunction amino acid production. The result is the body tries to normalize the amino acids, but to do that, it produces an enzyme. That enzyme becomes too high and then the body tries to normalize the enzyme by creating an antibody to reduce the enzyme action. That antibody unfortunately starts targeting the source of the enzyme, which is the pancreas. So it's one leads to another, leads to another, but the perspective has said, oh, it's, you know, it's some kind of autoimmune disease. No, it's a metabolic disorder that the body's trying to normalize and it has unintended consequences. So that's one example and this is where you can start looking up on some of these compounds and some of the interactions again with the HMDB where there's a fairly lengthy connection of high and low levels for certain metabolites and what they mean and how they can be interpreted. Another example was how to identify pathogens by metabolomics. And this is one of the very early applications. These are from 1995, 2001. Tabloids didn't even have a name. It was only on two papers and tabloids played this time. This is some other ones, but these are examples where people are trying to say, can you use what typically bacteria will produce or kick out as they grow or in culture to identify them? Because most bacteria under microscope look pretty much the same. So if you wanted to use metabolomics to identify urinary tract infections, which is one of the classic examples of a bacterial infection, and there are different sources of bacteria. Some are E. coli ones, some are Femona-based, some are this Urabalus one, Eruginosa. Each of them can cause UTIs or urinary tract infections, but you can actually look at the substrates that these bacteria may be producing and some cases by adding appropriate substrates. So some, so this is to urine, but you can add a substrate. And depending on how the case of lactose metabolism is very specific metabolites can be produced. Glycerol, it's very specific to pneumonia. Nicotinic acid is very specific to pseudomonas. And then methionine is very specific to the Morabalus species. So you can probe by adding substrates and identify specific bacteria that may be in urine that are causing or resulting in a specific UTI infection. When you look at biomarkers, and these weren't quite exactly biomarker studies, but there are certain classes of biomarkers that we're interested in medicine. UTI one, we're looking at diagnostic. Someone's complaining of difficulty with urinating.  And you try and identify the source, you figured it out. So it's whether you have a disease or condition. They're prognostic biomarkers which are basically designed to say, if you have the disease, how will you cope with it? How long will you live with it? How what's your likely resulting condition? The one that I think most people are excited about are, can you get a predictive biomarker? So we've read out your blood, we've read out your urine, and 10 years from now, we predict you're going to develop, you know, prostate cancer or you're going to develop diabetes. And if you know that, you can potentially do something about it. And in the case of genetic biomarkers, the BRCA1 gene is an example. If someone has these mutations, people get prophylactic mastectomies. If you go to 23andMe for $200, you can take a, you know, a genetic test and it'll give some snips and they'll give you some readouts about what your potential disease risks are. So those are the things about getting odds of a given disease. And then there's other ones, markers of response, markers of exposure. So how can we tell if you've been working in a, you know, chloroform formaldehyde plant? Well, we can look for certain compounds that might be in there. How can we tell you if you've been eating bad food or fatty food? Well, we can look at, you know, cholesterol levels and that might be an indication of what's in your body and what you've been eating too much of. We do this for drugs. And this is why metabolomics is of such interest to the drug companies because they are interested in drug toxicity and adverse drug reactions. So those ones, the last two are particularly interest to the pharma world, to epidemiology and environmental science. The first three are particularly interested to physicians. One of the things about biomarkers is that there are different technologies that produce different numbers of biomarkers. Using high throughput DNA sequencing, it's now possible to get up to 2,000 different genetic tests. You can go to a variety of labs around North America and they can do very specific genetic tests because they know certain mutations. So the BRCA1, BRCA2 genetic tests, but they're genetic tests for down syndrome, they're genetic tests for cannabis disease and lesion of hands and all kinds of ones and because they know the mutations. Some of them are like, you know, 600 different tests for cystic fibrosis. So that kind of adds it up. But, you know, some are unique. The problem partly with this is that these are lots of, this is an example of how we're translating genomics into, you know, real tests. But the tests only address about 5% of the disease, of the disease burden because BRCA1 type breast cancer is rare. Cystic fibrosis is rare. A lot of these monogenetic diseases are very, very rare, but we can sure find them. Now, protea or transcriptomics is another one that's been around for 20 years. We've been doing gene chips for a long time. We can use gene chips and there's thousands of publications that come out from gene chips every year. But as yet, there's been very few that have actually translated into tests that are used clinically. There's actually five that have been approved. Maybe two or three that are still available. One that's sort of famous is called the MAMA print, which is a gene chip-based test for predicting or essentially characterizing breast cancer. But there hasn't been a lot of uptake. Proteomics, again, has been around as long as transcriptomics. And there's proteomics labs all around Toronto and everywhere. Despite all of the work they have done and all the facilities and equipment they have, as yet there is no approved proteomic test for any medical disease. Now, there are protein tests, but the protein tests are all done using ELISIS. They don't use proteomics technology. They don't use mass spec. They don't use any of the things that you've learned about proteomics. So there's a big funnel. Genomics, lots of tests, but not many diseases. Transcriptomics for some important diseases like breast cancer, but not many. And then proteomics, zero. Metabolomics, although it's not been around as long, technically has probably produced more approved tests in part because of the fact that newborn screening and inborn areas of metabolism are technically metabolomic studies. They use mass spec to screen dozens of compounds at a time. And they will take blood spots from newborns and look through those and look at about 25 to 30 different compounds and make characterization assessments of whether they are going to get or already have a metabolic disorder. And for research labs in some of the bigger centers, especially in the U.S., they will have some of these tests, a new test added every couple of months as they find out a new compound. And we'll translate that into chemistry. So these are some numbers that I think are surprising to many people, particularly when we think about the huge investments that are going into this. And frankly, many funders, including the NIH, are giving up, feeling that these approaches are not leading to useful medical translations. They're just not. They tell us a lot about biology. They're really cool tools, but they're not translating into human health. What's that? The transcriptomics and proteomics. So here's some more examples of biomarkers where people have been looking at metabolomics. One was the discovery of the University of Alberta with some of our collaborators that you could use urine to diagnose pneumonia. So pneumonia is a lung disease, and everyone was rather surprised that urine can actually tell you something. But there's a close connection between what goes on in the lung and how the body fights infection and what actually happens in the gut microflora and the gut microflora intimately connected to what's in the urine. So they were able to identify a number of markers in urine, and the advantage of working with urine is it's fast, and the analysis is cheap, and it's easy to get urine. Whereas with a normal course, if someone presents with coughing, persistent coughing, they might say, well, come back in a week's time. If you're still coughing on your deathbed, okay, maybe we'll take an X-ray, and then it'll take a while for the X-ray to develop, and then they're in the wrong position. Okay, let's take another X-ray. And then, by the way, we're going to do a sputum culture, and then they have to wait and wait. And about three days later, you might see bacteria, but in about half the cases, nothing grows because the pneumonia isn't bacterial, it's viral. And again, they might still take an X-ray. And by then, because they can't do anything, you either have to get better or you're dead. So that's a pretty lousy protocol for diagnosing one of the more common diseases. And it's one of the reasons why pneumonia is essentially the number one killer of very elderly people.  If they die from Alzheimer's, it's actually pneumonia. If they die from almost anything, broken hip, it's pneumonia. So it's the ultimate cause of death for most chronic diseases. So how do you diagnose it? In this case, they were trying to look at healthy people. It was pretty easy to separate from someone with pneumonia. But TB presents with many of the same symptoms as pneumonia. And so they were looking at patients, and they also looked at people with viral pneumonia and bacterial pneumonia. And then that result was that they were able to distinguish quite consistently between those with pneumonia and TB, those with viral pneumonia and those with bacterial pneumonia, and of course, those with pneumonia and those who are healthy. And this is just illustrating, this is an NMR spectrum, but it could have been a G-C-M-S, L-C-M-S. And you can kind of look at between the TB patient, the pneumonia patient, and the healthy person. There's a big difference right around here. There are other amino acids, other organic acids that are quite substantially different. And so this differences are statistically significant. So these suggest that these could be very useful biomarkers for distinguishing and diagnosing pneumonia in a non-invasive way. Yeah, quite a few actually. So another idea of diagnosis is a case of organ transplant monitoring. Kidney transplants are the most common transplant operation. Thousands are done each year. But whenever a person gets an organ transplant, typically they have to go to have a needle biopsy.  And they'll do this every few months for as long as you live. And it's not fun, it's invasive, it's unpleasant, and it's also not very accurate. Because what they're trying to do is monitor a situation of are, is the organ rejecting or not? And if it's not rejecting, you're fine. If it's rejecting, they have to change your doses of medicine. In some cases, they have to take the organ out. So sticking needles into your back to take out tissue, costly, invasive, and accurate, is there a simple way of doing it? So people have tried to develop blood tests, they've been trying to develop microwave tests, but the microwave tests still require taking the tissue out. So it's invasive. Ideally, you'd like to have a blood or urine test. And if you think about it, the kidneys produce urine. So we've been looking at this one. This is identifying patients who were found by biopsy to be rejecting their kidneys. And we looked at the compounds in urine that were substantially changed. And there are quite a few. So this is called the VIP plot that you guys learned about. And this is a list of metabolites, carnitines, carnitines, or carnitines, or carnitines, mucos, or carnitines. These are the ones that had changed substantially. So we use PCA, POSDA, everything. But this is the thing that was our list of these in the substantially. Why carnitines? Well, acylcarnitines are produced by white blood cells. And when white blood cells are going through beta-oxidation, and when blood cells are dying, they release the acylcarnitines, red blood cells don't do this. And red blood white blood cells should not be in the kidneys. But if they are, it means they think they're fighting some kind of infection. And they're fighting really hard, especially because they're dying. And so we're seeing the waste products of dying white blood cells. And this is showing up, and seems to show up even before a lot of the symptoms of rejection. And so you can use that set of compounds. This is a relatively small cohort, but it's been done for I think a larger one. These are the normals, people who are not rejecting. And these are the ones that are going through rejection. And there's quite a distinct difference. And you can use a level of significance, and you can measure the sensitivity, specificity from this. This is quite a bit better than what they can do by histopathology. So it's a non-invasive, quick urine test looking for a few compounds that tells you whether there's rejection or not. Last year, there was a couple of papers that came out in nature, nature medicine, about metabolomics. And one that was particularly interesting was metabolomics and cardiovascular disease, particularly atherosclerosis. So all of us, if we're over about 20 or 25, probably have arteries that look like this. These are plaques that are building up, and they build up over a lifetime.  Others can pass by fatty food just smelling it, and their arteries suddenly changes to that. And that's one of the puzzles they've had, is why are people more prone to certain atherosclerotic events. So this one tied things together quite interestingly. So this was published last year.  But there's also a connection they've noticed between individuals and between gut microflora. And what they found was that choline, bt, and trimethylamine oxide were three compounds that were changed people having, and in rats, with atherosclerosis. Choline is a product of phosphatidylcholine. That's the fat that you get in eggs and fatty foods, less of them. This is cleaved off when the fats come into the large intestine. Bacteria start chewing on this stuff and convert the choline to betaine, and then the betaine trimethylamine oxide. This ends up in the blood. It ends up in the urine. This ends up in the blood. This also ends up in the blood. Now, choline in itself isn't bad, but this one is bad. High levels of trimethylamine oxide actually induce atherosclerosis. They cause plaques to form. So it's a plaque-inducing chemical. Turns out some people have bacteria that produce lots of this, and some people have bacteria in the gut that don't produce a lot of it. So there's some people who could use much fatty food as they want, and they'll never get atherosclerosis because there's not too much TMAO. And other people who have certain types of bacteria produce lots of TMAO, and they're going to be at higher risk. So your gut microflora partly determine whether you're going to get atherosclerosis, but your diet also. So this is an interesting connection, but it's also, I think, an interesting fact about this poison TMAO, which the body actually needs, and it's used to help a number of things with protein folding and stabilization. So it's a vital chemical, but too much of it can lead to plaque formation. And so this is this diagram that they show where fatty food, fatty acids, fatty acids are broken down, choline ends up in the gut microflora, cholines transform to tri-methylamine, and then eventually the tri-methylamine oxide, and TMAO causes atherosclerosis, which leads to heart attack, stroke, and death. Another publication that came out last year, same time, Nature Medicine, a group been in Harvard and at the General Hospital, was looking at the Framingham Heart Study. This is a 50-year-old study where they've been collecting data on patients and their heart health, and they know everything about them medically, and they have huge banks of data and samples, and so they can monitor things about what people do, what they eat, physiological features that put people at risk. And so this is how they've learned that, you know, too much fatty food increases heart risk. And if you go to the Framingham website, they will give you all these predictors where you can type in your heart risk. This has been determined from many, many studies, so it's a hugely important study. It defines what we think about heart disease. But you can also identify people who have diabetes and Parkinson's, and they have all of this because this is a large population. So they looked at the group that was actually developing diabetes, and they took basically healthy people, middle-aged, well, maybe in their 30s, and basically said, um, okay, what's their metabolic profile? Let's take, we'll start with control for people about, you know, height, weight, control for people that are basically healthy. We'll also track a record that they have a family history of diabetes and a few other things. So they got basically two groups that looked to be about the same, but they knew that one group eventually developed diabetes. One didn't. So physiologically they are identical. Um, height, weight, body mass index, age, smoking, drinking preferences, all those things that, you know, think you would put you at risk or reduce the risk, whatever.  What they did notice is that in many of the people that developed diabetes after 12 years when they presented it as normal, is they had very high levels of branched chain amino acids and very high levels of phenylalanine and tyrosine. So they were puzzled by this, and so they replicated the study using a Swedish cohort. And then there's other work that's been done that's been done on much larger ones, and they may not have found all of these amino acids, but they either find loose seen or they find phenylalanine over and over and over again. As being elevated in people at risk, not just immediate risk, but 10, 15 years from now for diabetes. So they present as healthy, they present as fit. And this has to do with understanding of certainly the branched chain amino acids that typically found in very nutrient rich food, but they also play a role in, in altering the activity of mTOR, sort of a master regulator. And branched chain amino acids are insulin analogs, especially leucine. So this chronic dosing of leucine, or release of leucine, we're not sure what, essentially is like having a, well, it's like someone playing loud music all the time, eventually you get resistant to it. So this leucine creates insulin resistance, which is type two diabetes. So, some of you might be aware of very extensive genetic studies that people have been doing on type two diabetes because people have really thought, still do believe, most people believe that diabetes is a genetic disease, that it comes from, that there is a genetic dependency, people with a family history of diabetes develop diabetes. And they've done millions and millions of dollars, tens of thousands of people with GWAS studies, four diabetes. And the risk increase that they've been able to identify from all the known mutations and polymorphisms can increase your risk by about roughly 25%. This one study, which may have cost, I don't know, $100,000 to do, identified these five compounds in terms of risk factors, they have a 100% increased risk. And then they found another compound more recently that doubles this again. So, yes, there's a genetic component to diabetes, but no, there's a metabolic underlying change, whether it's dietary, whether it's gut microflora, whether it's a physiological thing with how your organs connect and function, but it's driven by these particular amino acids that seems to prime it. And again, remember that type one diabetes study, whereas glutamic acid, which eventually led to the autoantibodies that led to progress. So the triggers seem to be metabolites. And this makes them very good predictive biomarkers. So metabolomics offers, I think, a great opportunity to find biomarkers. And these are some examples. Will these things get into the clinic? Probably not, because a lot of people just don't know how to handle or find or produce biomarkers. So I wanted to show you a little bit about how to do that. Something that Jeff and I have been working on is trying to take lists of metabolites or genes or proteins, and something about their concentrations or relative concentrations, and to look at case control or diseased or healthy or whatever you want. And the website is called Marker Maker. Jeff is working another variation called Rocket, which will do fewer things. But anyways, this one is the idea is to take the data that you've got, and maybe you guys have done some of this, and you may have some, and to try and find a collection of compounds that can serve as very specific or accurate biomarkers for, let's say, a disease. It could be predictive or diagnostic or prognostic. When we talk about biomarkers, we don't talk about PCA or PLSDA. We talk about receiver operator characteristic curves, or ROC curves. And what it is, it's a graph or plot of sort of sensitivity versus specificity or true positives versus false positives.  How many times are you wrong? And you can plot this out. If you've done a hundred tests, then you can figure out what your performance is. The best test would look like this. Zero false positives, 100% true positives. That most tests kind of look like this. And it's getting to the point that the PSA test is almost like this, which is flipping a coin or better than, I don't know, better than guessing. So some tests work, some don't. But you really want to see a curve that looks like this. And you can calculate it based on the components, the markers that you've worked on. So disease and no disease. So that's a binary or two-state system. So this is something that was developed originally when World War II, when they were trying to figure out whether they were detecting enemy objects and hitting them. And there was a hit or a miss when they were bombing them. And so this is how they would plot their performance. And so these curves evolved from World War II to be essentially used in medicine to figure out whether you've got good or bad biomarkers. So typically, as I said, an RSE curve should have this sort of log characteristic shape. Poor RSE curve is a straight line. And then the area under the curve is a better measure of the quality of the biomarker. So the maximum area for a maximum biomarker is one. A random one is 0.5. And so if you can find an AUC of better than about 0.8, it is a good indication of a good biomarker. Most medical tests are below 0.8. So these are just some examples of AUCs. So this blue one is a terrible one. That's 0.5. Green one is not very good. But once you get up to the red and the turquoise and the purple one, those are very high ROCs. And so if these were diagnostic tests, these would be very helpful. So Marker Maker, we've given you the URL. This is what it looks like. You guys can go there if you want. It's sort of similar in its structure to what you saw with Metaboanalyst. You can take a set of, well, you can either do ROC analysis of single biomarkers, or you can do collection of a whole bunch of biomarkers and see what is available. You can do regression analysis. You can take those models that someone has described or you generated and see how they perform with a new validation set. From there, you can then select a data set the same way similar to what you did with Metaboanalyst. You can browse the data set. You can click on the data set. It'll upload it. Once it's there, you know, we can press upload and move forward. It'll do the same data integrity check that you saw before just to make sure your data sets. These are CSV formatted files just like with Metaboanalyst. And then you can do some data processing. You can remove some low quality, estimate missing values, try and reduce the quantity of data. And then you can now plot out your marker. So this is a set of Metaboanalysts from the data there that we had. And this is for a disease called preeclampsia. So pregnant women will develop high blood pressure and sometimes this can lead to complications. It's actually the leading cause of maternal mortality in the industrial world. So we'd like to find predictive biomarkers to identify people who will eventually develop preeclampsia. These are taken from mothers who had not yet developed preeclampsia but later did. And we were looking at those at another cohort of mothers who didn't develop preeclampsia. And this is the list of biomarkers ranked in terms of their AUC. So we clicked on here with Colleen and this identifies an AUC with this one marker of about 0.75. I think that glycerol and acetate is somewhere up around 0.9. And it produces an example. And this is one of the things I wanted to show is that, yes, they're different, but what was most intriguing is we found about 10 people. This is the control group that had incredibly high levels of Colleen. And you might recall this connection between Colleen-Vetain and TMAO and heart disease and CBD. We don't think these people actually were at risk. They were younger mothers but they were taking Colleen as supplements. And it turns out the Colleen supplements give you huge amounts, 100 times physiological levels, which may actually be harmful in the long run. So this one is an example of not a real biomarker but a bogus biomarker because we were dealing with supplements. And so when you're collecting from patients, you need to know something about what they eat or consume. Otherwise, you end up with sort of bizarre results. So that was an example for one biomarker. You could also then click on biomarker selection for multiple biomarkers. And so you could create a model where you use not just one but two or five or ten biomarkers to come up with a biomarker profile. And these are some examples. So with that preeclampsia, using a slightly different modeling system, there's a list of about 10 or 12 compounds that if we use them and combine them all together, we get this AUC of about 98%. So that's very predictive. And it actually could be very useful for kind of diagnosis. There's early preeclampsia. There's late preeclampsia. This one is not so much of a problem. You usually get it in like your 33rd week. But if you can predict those who might get it, then yeah, it certainly comes to the pregnancy. There's autism. There's a whole bunch actually on this particular page called tricorder.ca. ROC curves that we calculated using metabolic profiles that are from the literature that we've measured. And as I say, anything you can get above 0.8 is generally better than what's currently offered as a medical test. Yes. Yeah. These are biomarker panels. So you have a list of several hundred. So you're not sure which ones are going to come out. So these are the ones that came out to your best biomarkers. Is it just linear combinations of these things? No, it's somewhat more complicated. So these ones are, this one is a SVM, a singular value, or not singular value, but a support vector machine analysis using a Gaussian kernel where it's gone through a whole bunch of the components and tried to identify those. A marker maker itself uses a linear kernel for the SVMs, but it will also use random forests. You've got, what's that? PLSDA. You'll use PLSDA. You also use decision trees. And the classic way that people generally think of markers is decision trees. If colline is above 0.2 and betaine is below 0.6 and if glucose is above seven, then this. And if it's in this category and then phenylalanine is below 0.2, then it's in this category. And those are things that a doctor can understand, something we can understand. So one of the ideas was to try and come up with algorithms or linear combinations where you could actually write an equation or an algorithm that converts the readings to a diagnosis. So that's done for a couple of them. Can't do it for an SVM, but it does produce the algorithm for it. So you can actually download the algorithm and you can use that as your model. So it parameterizes it. And that's been a problem is that people publish markers and they'll say, yeah, I got this beautiful curve and you can't, there's no description of how they got the curve. There's no formula. So you could go off and measure the same or another cohort and they'll tell you all these markers. But now you've got to recalculate it. And now you're using a different model because they didn't publish their model. So you don't know whether their model will work. And that's the fundamental reason why you have, you know, markers at zero and four for transcriptomics and then proteomics. And if we're ever going to translate omics science into medical applications, we have to make the models available, the formulas available, and we have to make the measurements quantitative. So are these routinely tested now? What's that? Are these routinely tested? No. When for autism is in the states, there's a couple labs that are doing it. But yeah, I mean, this literature is filled with papers of great biomarkers and that's as far as they go. So it's just sort of like publishing to yourself. Okay. So that's applications in clinical medicine, pharmaceutical research, I think I'd mentioned before. Drug discovery. Drug industry has been very interested in it. Drug discovery is a huge gamble. It's expensive. It takes 12 years to develop a drug now. To do clinical trials, it's up to 6,000 patient volunteers. Only one in a thousand compounds that the labs discover actually make it to phase one. Only one in five of the phase one drugs actually make it to FDA approval. And only one in two drugs that are making it past phase three actually are on the market after a few years. The costs, as I say, are close to a billion. Time is lengthy. And so different phases, they'll use different techniques. Chemistry's important. Genomics can be important. Proteomics can be important. But metabolomics actually can be used all the way through. And that's one of the reasons why the drug industry is so keen on it. And metabolomics can be used to help in some aspects of discovery. Talk screening, trying to look for efficacy markers, trying to do safety biomarkers in the early phases. And then clinical safety biomarkers and clinical efficacy markers in phase two and phase three. So every point in the drug development pipeline can be used. And metabolomics can be used. So one of the examples was how metabolomics could be used in drug discovery. And this was a paper that came out a couple years ago about metabolic profiles in sarcosine for cancer progression. And then another one with two hydroxyglutarate for another type of cancer. For glioma in particular. In the case of sarcosine, this is an amino acid, methylated form of glycine. But what they found, and this is metabolon, measured tissues and identified people with metastatic prostate cancer. And high levels of sarcosine are seen to be indicative for metastatic prostate cancer. And that was a complete surprise because no one had ever seen or measured part of sarcosine and didn't expect it. But they went on and they did lots of samples, lots of tissues. They looked in blood and urine as well. And they found it significantly higher in the metastatic cancers and in the early prostate cancers. And the cancer-free ones had no detectable level. The other thing is that when they started doing cool, does it mean anything? So this is when they started doing knockdowns. And then when they did a knockdown of the enzyme glycine and methyl transferase, they found that it attenuated prostate cancer invasion. And then when they did a knockdown of sarcosine dehydrogenase, it induced invasive cancer. So the connection between the metabolite and the protein and the gene was reiterated, in fact, to suggest that sarcosine is probably a pretty strong indicator of metastatic prostate cancer. There's been some debates. Some people have had challenges in reproducing this data. But part of it, I think, has to do with the methods that other people have been using. You can also think about metabolomics in terms of drug discovery. And there's an approach that people have heard of genome-wide association studies. But you can also do metabolome-wide association studies. So in genome-wide association studies, like for diabetes, they look at huge cohorts, people that develop disease, they sequence them thoroughly, and they take normals and also sequence them. And they've started looking at very complex diseases, things that are polygenic that have high heritability, Crohn's disease, Alzheimer's disease Parkinson's, adverse drug reactions, autism. Once they identify those mutations or SNPs, then they can try and see how much these genetic mutations affect disease response. This approach is very valuable. It has allowed us to identify hundreds of genes that are associated with dozens of diseases. But it hasn't actually led to any new drugs. And the reason is sort of the following. You'll do a study. From the study, you'll identify your genes. And from those, you have to find identifiable drug-able genes. Then from the genes, you have to clone a target. From the target protein, then you have to test a whole bunch of drugs. Large chemical library. With that chemical library, if you find a hit, then you have to start going into the preclinical and clinical and phase one, two, and three trials. And then from there, you have to go to the drug marketing phase. So that's the model that every drug company uses right now for drug discovery. So you can do some calculations in terms of the cost and the time and the probability of success. So not every GWAS study actually gives you an answer. Only about 20-25% identify some very clear, useful trend. Something that's statistically significant. Many GWAS studies contradict each other. That's been a real problem. So let's say you found your genes. Then you're pretty confident it's been confirmed by two other GWAS studies. Now you've got to clone your genes. Even if it is a gene and not all of these GWAS studies identify genes, you can't always clone them. Some are unclonable, they're too big, too difficult. And so from the cloning, then you have to try and produce them again. Not every gene that's clonable can be returned into a protein for testing or an assay for testing. Once you've got this, then the hard stuff begins. Because then you've got to do your screening. And this is where libraries and compounds, teams of medicinal chemists screen and test and screen, and they refine the molecules and try again and test. And as they do finally find a couple hits, remember you only have a one in five chance of actually going from this to this, then from this one, from the preclinical set to the full clinical one, one in 500 or one in a thousand. And then once you've actually got your drug, even if it's the best one out there, if there's a competing one, you have maybe one in two chance that the drug will actually work. So multiply your odds, you know, 20% by 50%, by 50%, by 20%, by 0.5%, by 50%, what does it come to? The odds of producing a drug are 0.001%. Total cost, 1 billion. The total number of years right now is 20%. That's why the drug pipeline has dried up. And every drug company will admit to that. The other thing to remember is that not all diseases are genetic. Even the most genetic diseases, like cystic fibrosis, can have a huge phenotypic variability. There are people with cystic fibrosis who run marathons. There are people with cystic fibrosis who die at age 10. What's the difference? They may have had exactly the same mutation. Many major disease-only account for disease genes account for 1%. So the BRCA1 gene, the one that's in the news all the time, it's 1% to 2% that have it. Alzheimer's, the A beta gene, 1% to 2%. The other 98% are spontaneous. ALS, 1% to 2% have a mutation in SOD1. The polygenic diseases, when we've done twin studies, the ones that are highly heritable, like autism, like Crohn's disease, heritability values come in at 50%. For those really well-studied diseases, when we do the GWAS studies and extensive sequencing, we only get a heritability of 20%. So either the twin studies are seriously over-estimating it, or we're seriously misidentifying the genes. Cancer, like the number one genetic disease, somewhere between 20% and 40% of all cancers are actually caused by bacteria or viruses. This is well-known, but not widely discussed. And so when you look at all the numbers, about 80 to 90% of diseases have either an infectious origin or are acquired or wise from a cumulative exposure from the environment. So this has given an rise to this idea of tabillite, today's the drug discovery. So doing metabolic screening to look for these conditions, just like this autism, or preeclampsia, or cancer, or diabetes, or organ rejection, and to look at those compounds that have gone up or down. And those biomarkers we're listing, not all of them were up, not all of them were down, but it's a combination that produces it. But we can go and use the tools you guys just used a few hours ago, look at those pathways and see where these things sit. And in many cases, because we know metabolism so well, you can often find activators or inhibitors for very specific enzymes in these metabolic pathways. In some case, if the thing is down, say if you're short on glycine, and that's causing the, or at least, part of the character of the disease, eat more glycine. I mean, that's a pretty simple solution. So there are some things that you can do either by looking at existing drugs, existing activators inhibitors, or existing metabolites that allow you to actually address this step without having to go through the phase one, two, three billion dollar trials, and actually go to the point where the therapy is being used, and because you can do metabolomics, you can also monitor whether this is actually having an impact. It's also cheap. Metabolomic studies that we've been doing for quite a number of diseases, actually, success rate is about 80%. The analysis, as you guys have seen, is pretty short, even if you're just learning the software. To go from here to here is pretty fast. We haven't had time to show you guys how to go from this to some of these pathways, but it's pretty quick too. It's one of the reasons why we developed Drug Bank, to be able to search with compounds. And if you can identify either that there's a shortage, so if we told you the reason or that you test very high for leucine, isoleucine, phenylalanine, tyrosine in your blood, what does that tell you? You're at high risk for diabetes. What are you going to do? Change your diet. And what's the major source for leucine, isoleucine, phenylalanine, and tyrosine? Well, there are certain foods that are very rich in those. And one thing is typically avoid red meat. Another thing is avoid foods that are rich in phenylalanine and tyrosine. This is what PKU patients have to do, so you follow the PKU diet. And okay, this is a treatment. We didn't have to come up with a drug for you. You could also go to another thing called metformin, which is given to diabetics, but it is something that actually does change the metabolism as well. So that's another drug that you would have found looking from here that alters the metabolism. So you repurpose the drug. So again, no new drug discovery. Just go to your doctor and ask for some metformin. And so now we can monitor. Is your phenylalanine leucine isoleucine level? Is it dropping? Is this regulating? And if it is, okay, what I'm doing is working. If it's not, let's try another therapy or another approach. So this is another root, one that potentially is cheaper, faster and more successful to drugs. Have we not, not with metabolomics? Well, I think there is roots for, I think everyone understands that there is, once someone is diagnosed with Alzheimer's disease or Parkinson's disease, the damage is already too serious to fix. You can't reverse it, but if you can prevent it, that's the way to look at it. And I think there are a number of compounds that they are finding that do seem to reduce the risks. So in the case of Parkinson's, smoking, I think actually reduces it. Coffee drinking reduces it. Chocolate, coffee. With Alzheimer's, they, there's very good evidence now that people who have diets that are rich in curries have substantially lower risks for Alzheimer's. So there are compounds that have been identified and even labs show these effects. And if you go to the sort of the gray literature, find all kinds of people saying, try this, this is my diet and it's helped save my grandmother or whatever. But it's, I think it is arguing. I mean, we're entering an era where drugs aren't being found. It's too costly to develop drugs. And we've gone past that apex of the drug era where new drugs were coming up for everything. So we're going to have to find something else. Sometimes it's useful just to look to what's already there. So this is some examples of what we could and can do and have done. If we lived 200 years ago and we had metabolomics available, we would have been able to find all these sailors coming home with rotting teeth and staggering tissue problems. We would have been able to do a metabolomic study and found that they had very low ascorbic acid.  That's why we have limies or sailors. And this is what allowed England to conquer the world. It allowed their sailors to go to sea longer than any other sailors because of this trick of eating vitamin C. More recently, we found that low folate levels in mothers, expected mothers, or mothers planning on having children lead to neural tube defects. So now we have folate supplementation everywhere, almost too much. If you lived in the 1930s and 1940s, these were very common. Many people died or suffered because of with rickets, plegbra, pernicious enumia. It was widespread. It was incredibly widespread. So people didn't die of cancer, heart disease. They died of bees. And correction was to basically they identified low vitamin B, low niacin, low vitamin D. We've supplemented now. And now you do not see these diseases except in some parts of the developing world. Very easy fixes. Another disease that was very common in the 1930s. Now we have iodine salt. The bees again would have been picked up through metabolomics. This is screened now. You can detect this. And now you can use the low phenylalanine diet. And this saves children. Epilepsy. Right now the best treatment for epilepsy actually is a ketogenic diet. It's not the drugs. And essentially what you typically find are at very low levels of ketone bodies. There are other genetic diseases and again there's some very simple treatments that can be done to handle them. And they are metabolic disorders you can read. And some of them manifest not at birth but sometimes later in life. There are other very common diseases from goat, fatty liver disease, hypertension. These are the chronic ones that many of us will have to deal with as we get older. And in these cases there are very specific compounds and metabolites that are very characteristic of these diseases. And there are some very simple treatments to either prevent or help with the treatment of those diseases. And it's again you're just looking at what the metabolites are. Are they high or low? Can you modify them? The ones that are low bring them up. The ones that are high bring them down using mostly off-the-shelf stuff. Some of them are off-the-shelf pharmaceuticals. Some of them are off-the-shelf supplements or foods. Yeah. I think we're just we're not too far from finishing up here. So there's other applications. People do not only in terms of drug discovery but also measures of absorption, distribution, metabolism and excretion. This is how we test drugs. We look at how they're absorbed and modeled. We can measure them through different fluid compartments.  You can actually disample them and you can reuse the animals. You can watch them temporally. And this is where Jeremy Nicholson got started in metabolomics. And they looked at different liver toxins and kidney toxins and they were able to identify very specific markers for toxicity. And this is a graph you guys have seen but this was actually collected on on animals that were treated with drugs or drug like compounds that caused kidney and liver disease. So you can look for very specific markers and not only can you say it's a kidney problem but you can tell exactly which part of the kidney is being damaged based on those biomarkers. So again you don't have to open the animal up. You don't have to do a biopsy or necropsy. This is something that you can get readouts. So these are some of the markers that you'll typically see with whether it's lactate or phenylalanine or various amino acids that lead to damage. There's the TMAO and kineremic acid and xantheremic acid also associated with damage to tubules. Markers for liver toxicity. Again these have been worked out for animals and you can help. With drug usage when they're doing phase two and phase three clinical monitoring people are supposed to take the drug at the same time every day if they don't sometimes the clinical trial fails. So they like to make sure that the people are following what they said and so in this case they can say here's our drug here's the drug oh you didn't take it here's the drug here's the drug and then go back and say you know maybe we should exclude this person from the trial or maybe it explains their unusual behavior. Likewise when you're on drug trials you're supposed to go alcohol free not supposed to take other drugs again you can see this individual but then you can see on day three they drank a lot and it's showing up and so in fact that may affect the drug efficacy. These are things that people don't tell you but the metabolone does. Their applications in in food and nutrient analysis people are looking at foods and trying to decipher what's in them. Of course we have you know the cereal box lists and other things but that only gives you information on macronutrients there's some very specific micronutrients that are important. So there are some simple foods like Gatorade in the case of beef there's about 6,800 compounds in a slice of steak or hamburger. Meat is different than fruit on very very different sets of kinds of compounds and so understanding what's in these foods is part of what's in food DB. Some of the food you get hasn't been treated correctly. There are cases where people will modify grape juice apple juice they can adulterate with corn syrup they can adulterate with sucrose and you can actually detect these things and there are a number of cases where people have identified foods that have been a poisoned or be changed and sold at very high market value when in fact they were basically fake. You can see if there's beet sugar or corn syrup again you can distinguish between the two different sweeteners and there's some strict regulations about what can be added to food but no way of easily testing them but Metabolomics gives you that possibility. So not only can you figure out what's in food or what shouldn't be in food or should be in food but you can also see how food changes our bodies and the types of foods that we eat and what it might do to phenotype. So fruit vegetables the vegans are thin and skinny people eating meat are supposed to be lean and muscular and people eating fried food are supposed to be fat but in fact you can sometimes get completely the opposite. Why? Well there's some issues we'll explain but there are certain markers so normally we try and take diet questionnaires and say what did you eat today and one of the things they found is that extremely obese people under report what they eat by a factor of two and this is something that completely puzzled and completely messed up food epidemiologists for 20 years. Likewise let me ask what do you remember eating and what do you remember eating for the last week most people can't. So if you could actually get a readout of what people are eating by looking for markers in blood or urine then you can actually get a pretty good idea a very precise idea of what they've been exposed to what they're eating how much they're eating and also how their body's processing it. So some people can have just a you know half a cup of coffee and they're wired for the entire day others can drink six cups of coffee at midnight and fall asleep at 12 30. That's part of your metabolism that's also how your body interacts with food and that's not something that you can read from a diet questionnaire. What we eat also influences what is in our gut and that chronic exposure can obviously change the types of things that are in the gut and will change particularly what we'll see in urine and so again one of the big worries is obesity and diabetes and since 2006 and there's considerably more data now but there's association with the gut microbial and the types of bacteria that you have and whether that will lead to a life of obesity or a life of being largely lean and that set of bacteria actually is determined in the first year of life and it sticks with you largely. It can be changed sometimes dramatically where people have had very intensive doses of antibiotics but apparently breast milk is designed primarily to has all kinds of sugars that your body can't consume but they are sugars that certain types of bacteria consume so they're intended to colonize certain classes of bacteria so people when they've noticed this that were raised on formula have a generally higher incidence of obesity than those who were on breast milk and that's because the formula lacks those special sugars which lead to the good bacteria from inhabiting the gut so they've identified certain nutritional phenotypes lane homes and where they've distinguished phenotypes in China and Japan and England and in the U.S. you can see that England is basically the same as the U.S. but there's very distinct separation between Japan and China these correspond to the foods that they eat and the types of compounds you would see in their urine this is another one that was done in Italy where they looked at a whole bunch of people and found that yes there's considerable variability in across the spectrum of people but individually our metabolotype doesn't change a whole lot so this is collected I think over six months of urine and they're comparing them and they were 25 people they all separate it as individuals but so they know none of them overlapped and they were all in terms of the variability within their metabolomes were much smaller than the variation across the metabolomes so it's a fingerprint it sticks with you for life what's in your blood what's in urine what's in saliva stays with you and that is as unique as your fingerprint on your fingers so what are we looking for and ahead in terms of metabolomics this is some speculation but there's some things that are already happening one of the most exciting parts about metabolomics is the idea of metabolic imaging we normally have done microscopy with staining but the idea of essentially doing chemical composition and imaging both at a macroscopic and microscopic view is really exciting so with magnetic resonance imaging and magnetic resonance chemical shift spectroscopy we can wipe out about a dozen metabolites in the brain and certain tissues pet scanning also was a metabolic imaging technique typically with glucose or a few other small molecules you can also take tissue samples and you can do histology using moldy imaging and if you layer tissues out you can do moldy and collect mass spectra over certain regions and specific cells and then read out the mass spectra and identify certain populations of metabolites so instead of coloring tissue by by a stain you're coloring a tissue by its metabolic composition so this is the way that moldy chemical shift imaging is done just to take your tissue sample raster out where your laser is going to hit collect the spectra at each raster point calculate some of those spectra and then map those spectra and the metabolites over the tissue sample and then you might superpose a picture of the tissue sample on top of it and then you can actually see where these metabolites are and now you're generated not just a single picture often it's a dozen or a hundred pictures of different concentrations shifting around in the tissue said metabolomics is often expensive thing we use big gcms lcms nmr instruments that's one of the things that makes it sort of inaccessible to people physicians don't want to figure out how to operate an lcms or an nmr but if you could give them something this size they would it would be something that physicians and clinicians would use and in fact there is a technique a tool called the i-stat which uses microfluidics and tests about 10 or 15 compounds now it's not metabolomics a lot of the stuff is you know blood gases and sodium and a couple other things but it conceptually is is what people would like to see i think people have also heard stories about how you can use breath to sometimes diagnose diseases so these are things called electronic noses these are also handheld devices and they detect volatile compounds they don't identify and quantify yet but they're getting there and this is something that is quite compelling so this is another version of the enos and again they're using sort of headspace chromatography and there's certain patterns that they can detect that seem to be characteristic of acetone or chloroform or benzene so they are used in industrial centers where toxic chemicals may be around and they help monitor whether it's a toxic release but they're not quite at the stage where someone could breathe on it and you would get diagnosis so to wrap up and we're almost on time or two minutes ahead of schedule trying to integrate metabolomics with genomics proteomics bioinformatics is critical i think what we've seen is that metabolomics is also a very good tool very good technique for modeling modeling cells modeling metabolism we've seen it with the flux balance models that poulsen has done they're they're actually world famous i think we're seeing a lot of the applications metabolomics being used in many areas of life science i've been focusing on medical applications but there's obviously many others you guys have seen it where you're looking at cows there's examples of plants there's lots of work with microbes every field of life science can be impacted by metabolomics i think the other thing to remember is good data comes from good experiments garbage in equals garbage out if you are attentive to the quality of the data tended to doing qa and qc if you understand some elements of the statistics and principles which i try to pass on to you then you can do good experiments in terms of the emerging trends the last points we're seeing i think greater levels of of imaging pushes to imaging and greater levels of automation and miniaturization and i think these are important and if we want to be able to sort of match the throughput that you can achieve with proteomics and genomics that's where metabolomics needs to go um a lot would call the volatilome or volatone this is something that's a new area of metabolomics so detecting those volatile compounds that give something it's it's aroma it's smell um the food or perfumes but also the things that are critical sometimes for diagnosing what's going on in the lungs i've pushed this and i'll push it again quantitative metabolomics is something that many of the leaders in the field are are really advocating so i'm not alone in this it's a difference and it's one of the things that could differentiate metabolomics from the other omics which have struggled because they were not quantitative micro rays are not quantitative proteomics is not quantitative except now in victoria but it's the case where because they weren't measuring absolute concentrations people could take it to the clinic because all the experiment was good for was that platform alone and if that platform is not moved to another building or instrument or someone else is using a different platform they can't replicate it the other hand if you can quantify precisely and everyone's agreed that it's micromolar and we all agree out of measure micromolar um anyone can use it any country any lab any facility smaller kits uh devices portable systems that's also emerging i've talked about the biocrities kits i've mentioned the i-stat these are things that are coming online and the last part i think the integration into more diagnostic prognostic and predictive applications given you some examples that have been published some hints and some tantalizing data and so perhaps the task is for you to see if you can take some of those ideas and translate them to practice so metabolomics is the youngest of all the omics sciences and well genomics still continues to go strong it's now a commodity so we don't have to do a whole lot of technology development we just say let's sequence so there's lots of data to work on but really the the challenge in genomics is bioinformatics it's not the genomics proteomics is still developing but we're also getting that you know understood more my prediction is the metabolomics because it's still the younger one there's still lots to do and technology development still lots to do in bioinformatics there's still lots to discover there's 200,000 compounds that we have yet to measure that could have a role to play in all kinds of things in in cells plants insects mammals disease and etiology um in processes so i think the prospects are bright and there's still a lot to be done so if you're just entering into metabolomics metabolomics i think you've got a long and prosperous career ahead of you so with that i want to thank some people Jeff for helping TA and prepare some of the slides and Michelle for setting this up and keeping us on schedule and feeding us and some of the funders like Genome Canada which is paying and subsidizing some of this and you saw some of the other sponsors as well and then some of the work we're doing has been supported by Alberta Innovates including i think Jeff's scholarships historically uh so thank you very much


Everybody forced here rip in 2022 tops museum collection baseball hobby 12 bucks full case number seven This is a tiered random team style break. I'll show the results on screen for you. You can always find a late in sports cards.com The live on tic-tac now. Let's get started That's what I meant to hop Like losing to the Jags at any other years not like that bad or the Irmire Jags. Oh, yeah Finn's bills cults And it makes sense the bills doesn't make sense the other teams do yeah That was probably when the dolphin started off like oh and seven they were like ice-cold They always beat the cults for some reason and like Culture is the Teen that had a ton of potential and should they should have made the playoff they should have yeah There were a lot of people's like Trendy pics because they were like playing wealth time. Yep. All right. Here we go everybody Both case and museum collection So we've only got two breaks left for tonight's here on Bundesliga random team case and a random division select football full case Got a league football release in tomorrow Matt's got more baseball breaks at 10 a.m. Eastern start time guys on the baseball channel We got plenty of space for personal tonight John Kirby Marsh Durand got quadra like George Springer blue jays 82 of 99 Blue jays of the first day in blue for the Padres Blake Snell 133 of 150 What is a Sunday night game this week? Gotta know what I'm gonna be watching Like The only storyline is yeah, I get it Do a relic auto Padres Eric Osmer numbered 82 of 300 Blue Shane Bieber 10 of 150 for Cleveland's just says loading One second Do a relic auto for the Braves Matt Olson nice hit Braves numbered one ninety eight of one ninety nine And copper Willie Mays Willie Mays Hayes here Oh the third the first Thursday night game is always like the Sunday night game war because it's a kickoff So they'll have like the Sunday night crew there. Yeah, we're gonna get that Collins or a slide, you know Quad Jersey blue jays Springer Hernandez Bichette and Vlad 30 of 99 or Vladdy, I should say Now here's a guy copper Ernie Banks, I don't hate Collins some people hate Collins I don't hate him if you're a good broadcaster for long enough people start to hate you if you're not John Madden Like the color guy people will start to hate Tony remote sometime guarantee it. Oh, yeah for sure Joe Buck is objectively fairly decent. I mean over the course of his career. I just he's had some I'm indifferent on Joe buck But every call I watch like Red Sox World Series is him. So it's like some part of me is like, okay cool Joe buck You know Quad Jersey Cardinals Jack Flaherty 14 to 75 and copper Raphael Devers. Oh I think he's got a great Robo just brought something different that a lot of people. Yeah We just haven't seen before on like a big network Yeah, exactly Steven Every time to Thomas quad Jersey or quad relic Red Sox both arts Martinez Devers and for do go 42 of 75 And copper bregman for the Astros Oh Dan house is the worst for sure by a mile Jersey card brewers Corbin burns 33 of 50 copper chipper Jones. We need to have Charles Barkley in chat call the game Or at least be like commentary for one of the legends who's like the best NBA Not like color, but like but my play-by-play God canvas Braves Dale Murphy God triple relic Otto White Sox Eloy Jimenez number 90 of 100 and purple Devers 10 of 99 for the Red Sox Now I didn't think that Chris Good point on that. Yeah, Troy Akin kind of does Bob Costas is great Bob Costas. I think most people can go on board with liking Bob Costas. Yeah, he has that He's like the closest thing you'll get to Vince Scully today Yeah, he has that very like classic Like I don't know cadence and voice of everything man Costas is great A's archival autograph 105 125 like the radio voice or like if someone wasn't actually watching you could like Yeah, feel it better. I guess copper Shane Bieber cost us for sure He only does certain things for NBC though. He doesn't do a lot Got an archival autograph Ted Simmons Cardinals number through a 163 of 299 in the copper Freddy Freeman Who's on TV right now guys? Who's this thing? All right, got patch card Orioles Carrick in jr. 9 of 35 copper Bobby White jr. Rookie Royals Had some rolls good to Timer not old time Canvas Ichiro got Jersey Cardinke's Aaron judge 17 of 50 and Reggie Jackson copper Dual Jersey auto Reese Hoskins Phillies Number 22 of 50 Phillies on the copper parallel copper poohals Elliot Ramos got a rip Merrifield dual relic auto for the Royals on the Whitt Merrifield 34346 of 399 and Blue Soto for the Nats 83 of 150 Artist proof canvas Shohei Otani nice one angels Limited to 50 on the artist proof you got a copper Ballinger Got canvas strawberry for the Mets Quad Jersey card here Kershaw Verlander DeGrom and Cole. That's Dodgers Astros Mets and Yankees Let's see Dodgers James a Astros Brian Oh Mets Brian and that's three different already. Yeah, so that'll be random at the end It's four different ones. Yeah, no one has over 50% you need three quarters of this one That majority random at the end on that one and copper rookie Julia Rodriguez Mariners Al Michaels is great. Al Michaels is fantastic. Remember. I remember Michaels and Madden after some rolling Madden You know who I miss a lot is Doc Emmerich for hot. Yeah. Oh my god. Yeah, he's classic Doc Emmerich Got quad relic Will Myers 16 of 99 for the Padres couple of camo jerseys there Got copper Babe Ruth Canvas Mets Jacob DeGrom Patch card Cubs Alfonso Soriano 9 of 35 on the three color patch they copper Miguel Cabrera Yeah, man, he had some really big years the Cubs and Yankees I'll see you play Got a frame here. Oh, no, we got a book. What is that? Oh boy? What do we got here? Oh Oh Copper Nolan Aronado J rod rookie All right book one of one Momentous material dual jumbo patch auto relic book card Astros Alex Bregman. You're done Alvarez Nasty logo patch on the Alvarez side congrats Houston Beautiful patches man, especially that that logo good Lord Brian oh with the Astros that is freaking gorgeous Momentous material dual jumbo patch auto relic book card one of one Good Lord, man. That is freaking awesome. Love the books and museum collection Regmen in the Ordon and congrats again Brian Brian.  We got a Jorge Solaire auto for the Braves six of 50 nice hit Braves And blue Clemente 31 of 150 for the Pirates. I wouldn't steal that. Yeah Magus That's cool. Yeah, that's why I think these these don't Yeah, yeah, not a big fan of this I guess just for normal. Yeah, we have 360 mags of man. I'll use it. Yeah, that's awesome Yeah, these are to open easier to handle We'll just put a sticker over before you send it to Canvas Peter Lonzo Mets you've got a very nice reds quad patch card Jesse winker 3 of 25 Got two three color patches and a couple two color patches a good-looking card Got George Brett copper all off season. They were worried about Matt Stafford's arms in this season good to go Torkelson rookie hunter green got patch card Mets dick of the ground 14 of 25 Copy at the orizo St. Sick came acres of the buffalo. Well, it's too late for me. I guess it's not because it hasn't started yet I also don't have any better options for like a flex in my true honesty. You start three flexes. So Triple relic auto raise Wade Boggs Nice hit raise 169 to 275 Purple for the Giants Buster Posey 53 of 99 and a copper Acunia It's that or it's like the quarter girl pattern like Christian Kirk or something Rookie out of White Sox Jake Berger 149 of 299 and a copper Chris Bryant. All right halfway through Pretty solid hits. They're really nice Astros book So I got a framed card coming up if we can see a sketch dude, please the sketch map holder There was three things that while you saw I didn't see it No, I gotta go after this break. I'm gonna run. Oh, this is weird. First time I've seen the mini boxes upside down The Twilight show not supposed to be like this It's got canvas Bo Jackson Royals do a relic auto White Sox Jose abray you he's a free agent. I keep forgetting that 85 one that he's older But I'm probably cash in still need a first baseman on a shorter deal He still got it man. Got copper hobby bias 31 year old rookie, right? Rookie the year 31 Cubs out of Aramis Ramirez 158 of 299. There he is red Garrett Cole 6 of 50 Yankees Got Andrew Jones auto Braves 156 at 299 Quad Jersey Padres Eric Osmer 21 of 75 and that is a blue Frank Robinson for the Orioles 133 of 150 Got free jersey card John Smoltz 12 of 50 and a copper playing kershaw. So we did have an extra hit in that in that box Which which one Jesse there's a few different kinds like the book sketches there should be the regular size sketches That's awesome though That's gotta be like five favorite Pulling sketches is awesome. It hits it's different does when you'll cruise rookie Brindella cruise Cubs dual Jersey auto Ian Hap Cubbies now to 18 of 399 and copper rookie Cal Raleigh Mariners big dumper Bradley it's all caps guy Philly Zach we were 35 and 50 on the jersey card and blue Ricky Ray Shane Bosz 113 of 150 Oh, just like the man. The man. He was freaking awesome, dude So it's the fold Quad Jersey Astros Yuli Gurriel 90 of 99 Copper Tony Gwynn Yeah, there could be multiple sketches of the players, but that'll be the only like foldable But they could have the other sets as well, they don't have checklists for that because obviously it's Not something that they totally have control of Canvas Astros Nolan Ryan What happened the artist proof canvas Bo Jackson? Alright guys Anything else Quad Jersey Astros Alvarez Bregman Tucker and Altuve 99 of 99 on the bookend Angels red Anthony Rendon 15 of 50 Yeah, one and 11 One to go on select football a random divisions full case and 11 to go on the tier one boonies League of Ranitine case That's it for tonight everybody plenty of space for personals elite football releases tomorrow and Matt's live at 10 a.m ESA for more baseball breaks on the baseball channel patch card Dodgers Cody Belanger 21 of 35 Copper Piazza canvas for the Cardinals Nolan Aronato Triple relic Auto Reds Barry Larkin Nice Larkin copper parallel number to one of 25 Copper rookie Twins Royce Lewis Wander Franco rookie base. We got a redemption Got blew upon Rodriguez 32 of 150 for the Marlins and the redemption to try a slow roller for you guys if you to receive archival autograph of Julio Rod Reggae's Mariners kaboom Congrats Seattle first J-Rod that I've seen us pull This one's going to Ronald a nice hit. They're not sure it has archival will be numbered to I do not know for certain sick hit Mariners Congrats on that one And that archival should be on card. I think all the archivals are on card the relic autos have stickers, but Just the autos would be on card sick hit marriage Congrats on that That's right Vaughn something in probably a little more just considering it's kickoff There's gonna be much other shenanigans going on a bit nonsense Bunch of nonsense Probably have football about 45 minutes here Maybe kickoff in a commercial So I'm gonna go three now then commercial Nice one here blue J's Vladimir Guerrero, Jr. Superstar show pieces autograph 15 of 25 Some nice hits and back-to-back boxes now Congrats on the blue J's. That's Craig G. Go get that magged Got quad relic angels big show trout Rendon and Walsh 30 of 75 You go angels and copper trout You got tonight Vaughn or franca rookie base. You've got a rookie auto Louise Heel 281 of 299 for the Yankees copper Nolan Ryan Astros Vaughn Vaughn's just happy for both that Vaughn's just the fan of the game Shreddy for the Patsy the Dolphins weekend those will lose tonight boom first place because you know the deaths are getting smacked by the Ravens first place, baby dual jersey auto twins Max Kepler Week 1 week 17 first place Patriots 313 to 399. Yeah, shootout would be pretty awesome blue rookie Bryson's thought Billy's 46 of 150 Canvas tuts. He's Padres. We've got Jersey Card Yankees Garrett Cole for a 50 and copper Vlad All right, two more boxes here. Good luck everybody. We got a full recap coming up after also There was It was near the beginning of that There's the four pitchers it was like Cole and Verlander Got quad jersey Giants Brandon Crawford numbered 67 to 75 Since you apologize, I'll only yell at you for a bit. Okay, got copper Mets be Alonzo Silver Frames card coming right? Canvas soda for the Nets we got bat barrel One-of-one Padres Eric Osmer Congrats Padres that is a sick-looking bat Got that authentication sticker right on it. That'll tell you what game. It's from I think as well Got the hard maple right there congrats Padre. That's freaking awesome Eric R with the Eric Osmer Congrats on that jumbo lumber bat nameplate rally one-of-one That's awesome Red Sox legend Eric Osmer. That's right Purple Blakes now 74 and 99 for the Padres Actual Royal easily a Royals legend do a relic auto braves Gansby Swanson Number 126 of 199 copper rookie Torquilson Tigers Okay, I mean a good case a lot of big hits Great case We'll have one more box after this one, too Brewers auto like archival auto 8 of 250 Josh Hader and copper rookie hunter green Reds Last one got optic basketball up next everybody there sold out the only two breaks we have left tonight There's one spot left in the slight football random divisions full case And there are 11 left on the tier one boondits league of soccer at the end of the night Canvas for the Yankees there is Garrick quad relic Twins Suno Polanco Correa and Urshella 11 of 75 Sam's boy Gio purple Salvador Perez 47 and 99 Royals It was it was on card it probably makes sense like that then yeah Kirby Marsh got Jersey card raised Austin Meadows 40 of 50 copper bachette I Could be something got a copper ohtani Wow, that is freaking insane Jumbo base relic auto one Soto Two of five nationals oh my goodness Game used base relic Haha, Andrew s Dude Andrew's having a night yeah Andrews having a hell of a night Good lord that is freaking insane.  Yeah, man six Soto the go-to Got some dirt on it as well a sticker has something up with it. Yeah, there he is Stickers got something up with it, but the numbers intact strange Maybe they're trying to push you harder or something like I don't know that's that's awesome, dude I'll love that. That's a that's a sick card. I saw those on the checklist. I was wondering where they were like That's pretty good the number That's awesome, dude And then the same box has that has the frames car we get frame card the very last and it's gold anticipation I Man what a last couple mini boxes here. All right. There's a copper Jram base cards here Duran Aaron and the Griffey and the gold framed auto is a John Smoltz nine of ten Atlanta a nice hit Atlanta. They had a pretty good break, too What a box that was at the end I was a really good break. Give me one sec I will do a quick random for this relic here, dude. The dust in this product is very yeah Like I don't know what it is, but my eyes and my my nose are like going crazy right now It's like worse than like a prism or something honestly That's what got my eye earlier Dodgers Astros Yankees Okay Dodgers Astros Mets and Yankees on the quad relic here Kershaw Verlander to Grom and Cole to have 99 Yeah, I've got to get all those draft Kings commercials in but then suspend Calvin Ridley Like a mr. Krabs. I like money Three and two five times Whoever's up top gets the card one Two three four and five Yankees up top Yankees get the card to the recap now So this one's going Yankees show that again first of the recap really cool stuff This is what you what's what you get in museum collection for those cases like this Quad relic twins here Sunno Polanco Correa and Urshella that is numbered 11 of 75 Jersey Card Ray's Austin Meadows 40 of 50 quad Jersey Crocker for the Giants 67 of 75 Jersey Card Yankees Garrett Cole for a 50 Quad relic Angels Otani trout Rendon and Walsh 30 of 75 Got patch card Dodgers Ballinger 21 of 35 Quad relic Astros Alvarez Bregman Tucker and Altuve bookend 99 of 99 quad Jersey Gurdiel 90 of 99 Astros Philly's Jersey card Zach Miller 35 of 50 Braves John Smoltz 12 of 50 Thank you, Josh Allen rushing. I'm gonna say I think I'm gonna go off the board here I'm gonna say Gerald Everett gets the first touchdown the quad Jersey Padres Eric Osmer 21 of 75 Patch card Mets to Grom 14 of 25 quad patch Reds Jesse Winker 3 of 25 I'm a sorry honor for the Cubs 9 of 35 Aaron Donald for the map monster payout. Yeah, that'd be huge. Will Myers 15 of 99 Padres Jersey card judge 17 of 50 the Yankees Ripken 9 of 35 Orioles Burns Brewers 33 of 50 quad relic for the Red Sox Bogart Martinez Deverson Verdugo numbered 42 of 75 quad Jersey Cardinals Jack Blair to 75 Blue Jays quad Springer Hernandez Bichette and Vladdy 30 of 99 and George Springer quad 82 99 the blue Jays Yeah, I'm going Gerald I brought to heck with it. I mean the gold frame Braves John Smoltz auto 9 of 10 Haters Hater Brewers Josh Hader 8 of 250 on the auto Dan's be dual relic auto Braves Number 126 of 199 twins dual Jersey auto Max Kepler 313 of 399 Yankees rookie auto Louise Heal numbered on the front there 281 of 299 triple relic Barry Larkin auto here Cincinnati 125 dual Jersey auto Cubs Ian Hap numbered 283 of 399 Braves auto Andrew Jones 156 of 299 Cubs Aramis Ramirez 158 of 299 Dual relic auto White Sox Jose and Bray you 85 of 199 White Sox rookie auto Jake Berger 149 of 299 Rage triple relic auto Wade Boggs numbered 169 of 275 God Braves on the Soler auto 6 of 50 dual relic auto Royals went Merrifield 346 of 399 dual Jersey auto Phillies Reese Hoskins number 22 of 50 Cardinals auto Ted Simmons 163 of 299 triple relic auto White Sox Eloy Jimenez 9100 A's auto Dennis Eckersley 105 of 125 Braves Matt Olson dual bat relic auto 198 of 199 and dual relic auto Eric Osmer Padres 82 of 300 and then everything else is pretty big here. They've lad Superstars show pieces out of 15 and 25 blue jays the archival auto redemption of the Mariners Phenom rookie Julio Rodriguez Nice hit Mariners jumbo based relic auto Nationals Juan Soto numbered 205 We had a jumbo lumber bat barrel nameplate relic one of one of the Padres Eric Osmer Congrats on now. We got the sticker to see which game it was from and then we had a one of one Momentous material dual jumbo patch auto relic book card for the Astros It was Alex Bregman and New York on Alvarez a three-color Bregman number or maybe Probably number patch and then you got the Astros a logo patch on the Alvarez side of things that is a one-of-one Thanks again, everybody. We've got basketball coming up next


Here's a thing. I told you all about when it came in the mail a few weeks ago and I've been wanting to get to this for some time now.  Namely a dinosaur board game. I Have not opened this envelope. I do not know what's inside it. Oh I'm also doing something that I've been wanting to do for some time namely. I've got two cameras here I've got one sitting on the table right here next to me for close-ups Which I'll be doing plenty of while I open this first of all Take a look at the back of it. There's nothing there, but I want you to notice This envelope is not a decorative Inner envelope. This is the mailing envelope. This has the postage stamped on it. This has the address written on it This is obviously the previous owner or one of the previous owners The postage date here looks like it says 1989 specifically June 1989 take a look. It looks like June 1989. I believe that's 1989 I would have thought that this was much much older, but anyway, there it is 89 He's got the classic Stegosaurus the erupting volcano The Pterodactyl up there the comsignathus it looks like down here All the obligatory stuff and it has the the art has a texture to it Like it was painted on a canvas. It's very nice actually now Let's open this thing up Here's all the color Chef Hoy already Dinosaur adventure game kit each kit contains the dinosaur adventure game board one number die and one picture die Four moving pieces 20 dinosaur picture cards five scenic backdrop and four model dinosaurs now Now we're getting somewhere. Is this the entire board? Yeah, that's the board Adventure begins In the lower right start here airplane landing strip Place photos here. Yeah, this is copyright 1988 Copyright 1988 Where's the end of the game? I don't see an obvious end of the game. Maybe the objective is in the gameplay Here is a Painted diorama this is from the this is the color version of Obviously what was on the envelope What I thought was an ornithomimus before was a Brontosaurus of some kind. I guess this is supposed to stand up like this and Let's look at the Contents see we've got a triceratops Stegosaurus, I guess these are standees. Yeah, okay, we've got four dinosaurs and I Guess these are the moving pieces All right, here's our four moving pieces are ethnically mixed buddies here Right and these are the die I guess folding paper die Scenic backdrop and four model dinosaurs. Okay. That's what these are the Tyrannosaurus the Patosaurus good they call it a patosaurus not Brontosaurus Although I guess Brontosaurus is back these days Let's put together one of these one of these model dinosaurs as they call it. I'll only put together one of these I don't want to do too much folding because I'd rather preserve the thing. I'd rather preserve the set than Try to make it perfectly usable All right, there we go There's a standing stegosaurus We could do the same with the triceratops and all of All the dinosaur pals, but I'll skip that for now log bridge The log bridge move two spaces. I guess this would go somewhere on the yeah, it would go right here on the game board and then some sort of bamboo structure that would go here and a Stone bridge of some kind that folds and goes right here. It looks like Okay, a word about dinosaurs A patosaurus is the correct name for Brontosaurus Boy, oh boy So these are the instructions for the game just this stuff in the middle I've just got the game to be the explorer who gets all five photos of the different dinosaurs on the game board squares and Gets back to the airstrip first. Okay, so you you get back to the airstrip first. I almost forgot all the Dinosaur photo cards these have never been separated That's nice. There's a sorrow of us. He doesn't have his own standee.  Let's get to the instruction how to play okay It looks like there's just four instructions begin by determining the order of play our all players roll the number die to see who goes first Place your character on the landing strip Roll the number died to determine number of spaces you move you may move in either direction Follow the instructions on game board. Okay. That's the game. I guess Whenever you land on dinosaur photo square roll the picture die to see if you can take a photo Okay So this has a lot of a lot in common with that With that Pokemon snap game So the the goals is to take pictures of dinosaurs There's no killing. There's no hunting. There's no violence. That's great. I Like this I approve Players should keep their photos face up players may not collect doubles of any picture They already have do not roll the picture die if you land on a photo square picture you already have If you land on a quick question square, you may not repeat an answer already given Hint for answer clues study the dinosaur models. Ah, so the dinosaur models Serve a gameplay purpose. It's not just for decoration. If you answer a question correctly, you may move one square in either direction Here are some recommendations for what to do with the backdrop in the dinosaur models arrange on your dresser as a Display for school project. You can add sticks grass and stones and other materials hanging a mobile Number five is use other action figures number four is what gets me Play hide the dino Okay, we'll skip that Anyway, there it is Looks like simple gameplay the idea of taking pictures of dinosaurs as You're navigating the board is a neat idea That was a little bit before it's time. I think this is really cute Shift boy rd dinosaur game