So I'm Lincoln Stein. I'm Director of Informatics and Biocomputing here at OACR. And I work on problems involving curated pathway databases. You'll hear a lot today about one of the projects I work on. The Reactome Database of Biological Pathways and Reactions. And Robin Haw who you know well from the past few days is our outreach coordinator for Reactome. And we'll be leading you through an exercise after this lecture. So I have to make it move. Okay, there we go. So yesterday you learned about gene over-representation analysis. We're now going to go into a little more detail on more advanced topics in pathway and network analysis. And as I think should be clear to you now, by now, the reason people do pathway analysis is to do dimensionality reduction. You're studying a type of cancer, you're looking at mutated genes or hypermethylated or hypomethylated regions and microarray exchanges which affect hundreds or thousands of genes. And in order to make sense of this large number of genes, you need to reduce them. One problem in identifying driver genes among all the noise in a cancer genome is the number of hypotheses. If you're looking at thousands of genes, you have multiple hypotheses and your chances of identifying any single gene as being a driver gene rather than a passenger gene suffers from a loss of statistical power. So by reducing the number from hundreds of genes to dozens of pathways, you can increase the statistical power. You can find meaning in that long tail of rare cancer mutations that almost all of us see. And you can take a small number of pathways and turn them into biological stories to generate hypotheses that you can test. So pathway network analysis is a very broad term. It's applied to almost everything. It's any analytic technique that makes use of biological pathway information to gain insight into a tumor or other biological system. Very rapidly evolving. There are a huge number of approaches that various groups have taken. What I've done in the Wiki is to give you a link to a review article that was co-written by myself and members of the International Cancer Genome Consortium's Mutation Consequences Working Group where we sort of do a review of all the pathway and network analysis techniques out there. I encourage you to take a look at this. This manuscript is under review so please don't distribute it. It's unpublished data. And a lot of the slides I'm showing you are taken out of that review article. So first, let's discuss what the difference is between a pathway and a network.  They are the biochemical pathways that you learn in introductory biochem. In undergrad, they are the signaling pathways. They are the kinase cascades. They are pathway. They are mitotic spindle assembly. They are a very detailed description of a series of directed interactions between macromolecules, proteins, DNA, RNA, lipids, and so forth. And so here is a simplified example of the EGFR, the EGF receptor pathway, showing some of the initial steps. EGF receptor and its ligand EGF which associate with each other to form a multimer. There's actually an EGFR dimer and the EGF monomer. This is a negative regulator here, LRG1, which inhibits EGFR dimerization. This complex is enzymatically active, converts ATP into ADP to give a phosphorylated product which then activates downstream steps. So ultimately leading to cell growth. And there is another, there's a positive activator of this event, SARC1.  So you can take a, and there are a variety of network types or directed networks, undirected networks. I think Gary discussed those yesterday. You had that? You have yet to do cytoscape. Oh, you have yet to do cytoscape. Well, you have connection, you have, you have undirected connections between two molecules such as an interaction where they're peers and then you have directed ones where one is doing something to the other. So a protease cleavage reaction, there's the protease and there's its target and that would be represented in a network by an arrow to indicate information, directed information flow. We can represent the EGFR pathway as an EGFR network. And so what we've done here is we've taken all the green round corner rectangles here, converted them into directed interactions. We have EGF that's interacting with EGFR. It's being inhibited by LRG1 that's interacting with SHC1. And then we're also able to bring in other things that we know less well about. Let's say we've done a proteomics pull down of the active EGFR, EGF receptor complex. And we found a number of other molecules that are co-immunoprecipitated, co-pulled down. And we can say, okay, well, these are interacting. We don't know exactly how they're undirected interactions. And the advantage of the pathway view is that you understand the mechanism, but it is typically the product of human curation, coming going into the literature and extracting these pathways, putting them down in the diagram. And they typically they'll only cover a portion of the genome space. So the best pathway databases cover maybe a third of the well annotated genes in the genome. In contrast to networks, they actually tell you less about mechanism, but they're more expansive. They can bring in less well characterized proteins such as yeast to hybrid screens, microarray based co-expression, literature binding for things that are known to interact in some way.  So they tell you about more genes, but there's less information about how they're actually interacting with each other. So in this I know you've covered. In any pathway or network analysis, you need two basic ingredients. You need first a list of altered genes, proteins, and RNAs. This is what you provide from the experiments you've performed. So up-regulated RNAs, proteins which are mutated, series of changes in ubiquinolation in a tumor cell line that's been inhibited by an SH RNA, you name it. And number two is you need a database or a source of the pathway or pathways or networks that you're going to apply this information to in order to interpret the list in the context of the pathway or the network. So pathway data, so we're going to talk about where you get the second ingredient. So basically you get them from databases. In a pathway database, typically these are curated databases where biological experts, Ph.D., have gone into the literature and extracted the well-known biological knowledge from the literature. They provide a biochemical view of biological processes. They typically come with pretty pictures which you can intuit easily. They capture cause and effect, and they're human interpretable. Disadvantages of pathway databases that they don't cover the genome very well. And because it's a subjective human-driven process, different databases disagree on the boundaries of pathways. So if you go to Keg and you go to Reactome and you ask for the EGFR pathway, you'll get the same core pathway, but you'll get different things at the branches where people disagree where EGFR receptor stops and another pathway, say, growth regulation starts. So we're going to talk about a few well-known pathways here. The most well-known is the Kyoto Encyclopedia of Genes and Genomes, Keg. It has human-curated pathways in both human, many different vertebrates, many, many, many prokaryotes. It's large, it's quite comprehensive. It gives you pathway diagrams like this one and provides services that allow you to find what pathways your gene list is overrepresented in using the tools that Quaid and Gary talked about. And it will then give you colorized views of those pathways in which it has lit up the genes on your list on the pathway diagram so that you can start to understand what's going on. The reactive database that Robin and I work on is a human-centric curated database of pathways. All the work is done on human pathways taken from the primary literature. It's quite comprehensive, probably for human. It's the most comprehensive of the pathway databases, very high standards for curation, very low error rate. And like Keg, it provides these nice, browsable pathways. This is actually a Google Maps view. You can click on individual molecules or reactions or the connections between them and you'll get a little summary down here which gives you information and you can actually dig into this in great detail and do analyses right in the diagram, including overlaying your own data on top of the map so you can see your data in the context of the map, doing overrepresentation analysis. Robin will take you through some of the things you can do with Reacto. So I've already told you most of this. Currently, as of the most current release, we're covering 1,500 pathways in human, 7,327 human proteins. And this is very detailed information. If we say that there's been a phosphorylation event on a protein, we indicate which amino acid is affected. We indicate if there are multiple RNA isoforms that are expressed in different tissue types, we identify which isoform is relevant. We provide a Google-style reaction diagram. You can do some basic overrepresentation analyses on the site. And we also have a service in which we take the heavily curated human pathways and project them onto other species to produce automatically generated pathway diagrams using orthology information. And if you're looking at other vertebrates, this is pretty good inference. If you're going down to invertebrates or yeast or prokaryotes, it becomes increasingly dicey, as you can imagine. And Reactoom is open access and free to use. So now we turn to network databases. So networks can cover less well-understood relationships. It can be used for genetic interactions where you know that there's a suppression or enhancement or an association, but you don't know what the physical basis for that is. Physical interactions from high throughput screens. Regulatory relationships such as co-expression, you know from a large number of microarrays that two genes are always linked when one is highly expressed. The other is highly expressed, but you don't know the mechanism for that, but you can express it in a network. Things like genontology, term sharing, and in fact adjacency in pathways. If you know that there's a molecule that forms a multimer, you can say that the components of the multimer interact with each other are connected by a network arc. Network databases can be built completely automatically or via curation. Typically, they're a combination of the two. They provide more extensive curation, more extensive coverage of biology, although the underlying evidence is more tentative. If a pathway database says that two molecules are interacting with each other, there's usually very good evidence that that's occurred. If a network database says that, you should need to take that with a grain of salt and look for the underlying evidence. Here are some popular curated networks, our BioGrid that's based here in Toronto. It's curated interactions of literature. 529,000 genes, obviously not all from human, they're from multiple species, 167,000 interactions total. In-tax database, which is a European project, is again curated interactions in literature, 60,000 genes and 200,000 to 3,000 interactions. And the Mint database based in Italy, 31,000 genes and 83,000 interactions. Respectively, one of the interesting things looking at this are the different ratios between genes and interactions, and it indicates differences in the curation and automatic inference algorithms used by that network database, rather than differences in biology. In addition to those three or four that I gave you, those three that I gave you, there are another 900 or so network and pathway databases. There is, which can be of course very confusing, which one do you choose? I want to point you to a nice resource for looking at collecting information from network and pathway databases called the Pathway Commons. A number of years ago, the largest network and pathway databases got together and agreed on a common language to describe pathways and networks, and a language called Biopax, and then the databases have all submitted their holdings to this resource called Pathway Commons, which allows you to browse networks and pathways from multiple databases. There's also a resource called Wiki Pathways, which is a community-hosted pathways database, which is also accessible through Pathway Commons. Okay, so now we've got the two ingredients. You have your gene list, and we have information on networks and or pathways. What are you going to do with this? So this is another figure from that review article. There are basically three broad classes of analysis you can perform. The first is gene enrichment studies, which you've already covered intensively. This takes this rich network and pathway information that you've gotten throws away most of the information and divides genes into a series of buckets or bags, named after gene ontology processes or pathways or subcellular macromolecules such as the ribosome. It depends that you can slice them in many different ways. And then using statistical tests on your gene list asks, are any of these buckets, are the genes in my gene list, gene or protein list, over-represented, statistically over-represented in any of those buckets? And so what it will give you is a series of over-represented gene or protein buckets, which will tell you something about the underlying biological process. The second class of analysis uses more of the topological information in the network or pathway. It's actually mostly used for network analysis. Using the entire network, you overlay the information from your gene list, and you ask, are there any topologically unlikely groupings of the genes in my gene list? Are there subsets of the genes in my gene list which are interacting with each other more than you would expect by chance from a randomly selected set of proteins? And then from that you can extract clusters of genes, little subnetworks, which are altered in your cancer or other biological system. And this is called de novo subnetwork construction and clustering. The advantage of this is it allows you to discover new relationships among genes, proteins, and other macromolecules, which were not previously put into a bag. The most sophisticated type of analysis is pathway-based modeling. Here you keep all the detailed regulatory actions, and you operate directly on the pathway level, and you try to build models of how the altered genes in your system are working with each other to change biological activities. So you may have 15 different mutations. You're going to try to integrate them together and say, what effect is the aggregate of this going to have on EGFR signaling? So I might have an EGFR amplification and a K-RAS activation. They're on the same pathway. Are they synergistic with each other or are they canceling each other out? This will help tell you this. So what are these three things used for? So gene enrichment is typically used to find out what biological processes are altered in my cancer or other biological system. It won't tell you exactly how they're altered or what the effect is, but it gives you a very strong head start on coming up with hypotheses. With de novo subnetwork construction and clustering, you can actually do discovery of new pathways. You find a group of genes which are interacting and are also altered in your cancer and you ask, well, what is this? This is not a biological process we've seen before. What exactly is it significant? It also is very useful for identifying clinically relevant tumor subtypes. So you can see different patterns of active subnetworks in different subsets of tumors, for example, and try to relate those to clinical characteristics such as response to chemotherapy or patient disease-free survival. The third one gives you the most detailed information. It's also not coincidentally the hardest type of analysis to perform. How are the pathway activities altered in a particular patient or tumor? What is the integrated effect of multiple mutations, copy number changes, methylation changes? And it also helps you make predictions about targetable pathways in a particular patient or tumor type. So in Richmond-affixed gene sets, you've now been covered. I'm not going to go through them in any detail, although Robin will have an exercise using Reactome for gene set enrichment. By far and away, the most popular form of pathway in network analysis, because it's easy to perform, there are lots of good end-user tools, and statistical models are very well worked out at this point. Disadvantages are there are many possible gene sets, and typically when you do one of the analyses, you'll get many processes which are interrelated with each other that are up-regulated or down-regulated. And you need to sort through those lists, and that's what gives rise to things like the enrichment map that Gary talked about, where you take multiple go processes and you try to put them back together into uber processes. And the bags of genes metaphor is getting rid of regulatory relationships among the genes, so you lose some of that information. That's all I'm going to say about gene set enrichment. Now we're going to look in more detail on subnetwork construction and clustering. So in this class of analysis, you start with a network, you apply your list of altered molecular entities, and you identify topologically unlikely configurations, such as all the genes which are up-regulated in your system are clustering together in a tiny little corner of the network. You can then extract those clusters of unlikely configurations and then apply biological annotation to them to understand what their role is. So here is an example of doing this with reactome, and Robin will, if there's time, will take you through an example of using cytoscape to do this sort of thing. Reactome has created a network representation of the pathway database called the Reactome Functional Interaction Network. What we did here is we took our curated pathways. I'm having trouble finding my mouse here. Curated pathway databases. We added a lot of uncurated interaction information, these two hybrids, co-expression gene, literature mining for co-mention of genes in the same paragraph and so on. And put that together with a little bit of machine learning to reduce false positives, and ended up with a network of 11,000 proteins and 270,000 interactions, which, if you remember back to the network databases, got a very high ratio of interactions to proteins, you know, indicating how much richness there is in the underlying pathway databases. We then have a system that runs in cytoscape for extracting and clustering altered genes from your gene list to give a series of modules, altered modules, and typically there will be 10 to 30 of them, and then you can annotate them, look at them in more detail. So this is what, this is actually a previous version of the network when it was just a little bit under 11,000. So this is a little corner of that network showing that there's a lot of non-random clustering just in the network itself, because of course, every time you have a subcellular organelle such as ribosome, it ends up being a little cluster of highly interacting proteins or genes. We cover about half of the named genome and we've tuned things that the false positive rate is kept to below 1%. And I'm showing just 5% of the network here, it's much larger than that. So here's an example of using the FI network to discover novel biology. So this is a slide from, I guess, back in January of this year. We've been doing pancreatic cancer whole genome sequencing and have identified these identified mutated genes in the coding regions of 52 pancreatic cancers. And what you're looking at here are the top 50 or so genes that we've identified, there are hundreds of them, and their frequencies. And this is pretty typical for a cancer mutation spectrum that there are a couple of genes, KRAS in P53, which are very frequently mutated. KRAS is mutated 95% of the time. P53 is 50 to 60% of the time mutated. And then there's this long, long, long tail that goes way off the screen of genes which are infrequently mutated some of them are passenger mutations some of them are rear driver mutations which are contributing to the disease but we don't know which ones are which. None of them meet the boundaries meet the threshold for statistical significance just on the basis of recurrence. If we apply this, if we apply this as a simple extraction and clustering system to that list, however, you see very non-random association of these genes. And in fact, we see multiple modules coming out. We have the largest module involves KRAS, as you would expect, the size, the area of each node corresponds to the number of patients who had a mutation in that gene. And you see a penumbra of genes that interact with KRAS or with each other in that long tail. And it involves genes drawn from or EGFR signaling, or B signaling, FGF signaling, and axon guidance interestingly. Yes. Please, the last question is more significant. Sure. Yeah, yeah, yeah. Sure. So you have to distinguish between whether it's biologically significant or statistically significant. To do statistical significance, there's simple procedures to take multiple random draws from the genome of a series of genes of the same size distribution and recluster them and ask, how often do I see one of these clusters coming up by chance? And if you do that, there are some pathways which are false positives. So module 7, which is extracellular matrix, focal adhesion, integrin signaling, which you'd think is very biologically significant, turns out there's a large number of... there's a big number of large, frequently mutated passenger genes in that set, which also interact with each other, and they come up all the time. So you end up xing that out. And the cytoscape tools gives you... cytoscape gives you tools for doing that analysis and removing them. In terms of biological significance, that's up to you. We can't do it on a single patient, but the interesting thing about this type of clustering is that even when we had only 10 patients, we were essentially getting the same list of modules that we got at 50, and now we're up to about 80, and the map hasn't changed. It fills in a little bit, but the basic structure hasn't changed. So it's actually really, really powerful. And we've done experiments in which we took the full set of genes and then we started introducing false negatives. We just dropped genes out at random to simulate what happens when you're looking at low-coverage sequencing or low-cellularity tumors. And up to... the map remains cohesive and essentially unchanged until you get down to about 60 to 70%, at which point the modules start falling apart. So it's actually very tolerant of a high-false negative rate. Okay? Yeah? Just in terms of being able to also be differentially expressed genes... Absolutely. It's just a gene list. It can be two-fold up-regulated genes. It could be genes which are near hypometallated regions. Any gene list that you can give it or it can be a list of proteins from a proteomics experiment? No. So this method doesn't use a ranking. You select the threshold and give it the list. Now, I'll talk about hotnet in a moment which allows you to attach values to genes such so that you can distinguish between the two-fold up-regulated genes and the two-fold up-regulated ones. Okay. Okay. Have any questions? Okay. And the fascinating thing about this type of clustering is that it can reveal subtypes within the tumor. If I were to take this list of 200 recurrently mutated genes and just do a hierarchical clustering of them, I would see no substructure at all because most of the genes only occur... Most. The vast majority of genes are mutated only a couple of times in the whole set and they don't cluster together. I wouldn't be able to distinguish different subtypes of the tumor. However, if I've reduced... I've reduced this to the 10 or so modules and cluster on the basis of what modules are mutated in each tumor, actually a very striking pattern emerges. What we're looking at here is a hierarchical clustering of the modules going across and we've color-coded the modules to indicate how many genes in each module are mutated in any given patient, in any given donor. Then we have the patient samples running along the y-axis. You can see four clear apparent tumor subtypes here, one of which is, say, KRAS module minus P53 module minus and when we went back to those samples, it turned out not to be pancreatic doftal carcinoma. There was ampullary carcinomas that had been misdiagnosed, so we picked that up. That's nice. We have one here where two of them are ampullary and one of them is still under debate about what it is. It may be a rare KRAS negative doftal carcinoma. Type two is KRAS positive P53 positive, PIC3 kinase positive. Type three is a different combination of those. Of course, we immediately went and looked for histopathological differences aside from the ampullary carcinomas that didn't seem to be any differences. We looked for survival differences and didn't see that. You win some, you lose some, but in another case, when we did the same thing in breast cancer, and this is actually an RNA expression differences. Now, we did exactly the same thing with mRNA expression arrays in estrogen receptor positive breast cancer. We actually found a module that involves a cell cycle and a rorobikinase signaling which is a strong prognostic indicator of poor outcome. This is a Kaplan-Meier curve showing disease-free survival in these patients. Estrogen receptor positive patients typically have a good prognosis and are expected to live a long time. However, in the subset of patients who had high expression in this module, they actually had a much more rapid recurrence rate so much so that they have the same prognosis as triple negative patients. That would be a patient population that you might want to pull out and treat more aggressively. Here are some popular network clustering algorithms. Gene Mania, which Quaid talked about. Can we introduce it when it's coming up? It's coming up in the future. Gene Mania is a website which has several hundred different networks all integrated together and allows you a very nice interface in which you enter a few genes from your gene list and it finds other genes which are highly related to those, pulls those clusters out, displays them, lets you annotate them and explore them. It's most useful for finding genes that are related to your process that you might have missed experimentally. The hotnet system from Ben Raphael's lab at Brown University uses a sophisticated clustering algorithm which avoids a major... designed to avoid a major artifact. If you consider a gene like P53, P53 has been studied very extensively for two decades and has a lot more interactions than most other genes do just because it's been more heavily studied. And if you do a naive clustering, P53 module will typically come out just because it has so many interactions. What hotnet does is it models the network like a lattice of wire, metallic wires. And then you introduce your mutated genes or your up-regulated genes as a series of hotspots on the lattice and the heat diffuses out using a physical model. And a gene like P53 which is so heavily interconnected has many connections leading out and so it loses heat more rapidly than other genes. And so it compensates for the differences in annotation. So it does a better job at finding high-quality modules which are less prone to these artifacts. And in fact, after the publication of that algorithm, we swapped out the Reactome functional interaction networks clustering algorithm. We added as an option to run hotnet for clustering and it does a better job. There is another site escape app I want to draw your attention to called Hyper Modules written by a post-doctoral fellow in Gary Bader's lab which is designed specifically for finding network clusters that correlate with clinical characteristics. So that if you have experimental data sets and arrays or sequencing data you can present that to the network along with clinical characteristics you're interested in, such as response to Adria Mycin. And it will try to find clusters that not only are statistically unlikely in your set but which are more strongly correlated with the clinical characteristics that you're looking for. And then there's the Reactome functional interaction network site escape app. It has a long name which Robin will be talking to you about and it offers multiple clustering and correlation algorithms including some of the more sophisticated ones that I'll talk about next. So pathway based modeling is the last topic and this is both the most sophisticated and the most inaccessible. The hardest for you to use. It is in this case you're applying a list of altered molecules to biological pathways, not to networks. It preserves the biological relationships, functional relationships among them and in particular these algorithms are designed to allow you to integrate multiple different types of molecular alteration into the same model so that if you have copy number variations and expression changes and single point mutations you can put those all into the same model and get a prediction of what their effect is. And this shades very quickly into systems biology. So the types of pathway based modeling there are typically pathway based modeling is very specialized for a particular biological question. So for biochemical systems such as metabolomics where you have yeast growing in a fermenter and you want to make predictions on what will happen when you increase the amount of maltose this is classically described using partial differential equations and Boolean models. I'm picking one of the most easily accessible tools out one called CellNet Analyzer from University of Padua. And this will up to systems of about a dozen genes. This will make predictions on what happens on the metabolic side. Once it gets larger than that these models become intractable to solve and you need a lot of detailed information you need the KMs for each reaction you need the binding coefficients all the very detailed biophysical measures. So in cancer typically these are off the board you can't use them. For the studying signaling cascades and particularly kinase cascades there are network flow models in which it uses information messaging theory to study what happens to a piece of information signal as it goes through the cascade and uses models that were developed for communications networks. And so the ones, two examples are net forest and network kin for studying phosphorylation cascades if you're doing proteomics and you're studying kinases this is the tool you should turn to. If you're working on expression arrays and you're interested in decoding or deconvoluting the transcriptional regulatory network to find say master regulators or master switches which are altered in your disease and which are affecting a series of genes underneath it you should turn to the transcriptional network-based reconstruction methods I recommend Andrea Califano's arachne software and what this does is it uses information theory to sort primary from secondary effects and then construct a hierarchy of transcription factors so it solves the problem that you have one transcriptional regulator and you have 15 targets in your system of interest both the regulator and all 15 targets go up simultaneously and you don't know which one's regulating which it's actually able to use information theory to sort that out and find what the master regulator is and lastly and most recently there are probabilistic graph models or PGMs which are a very general form of pathway modeling for cancer analysis and I'm going to go into more detail on this because this is probably the most relevant for work in cancer biology and an algorithm called Paradigm is the paradigm for this whole class of models so the way Paradigm works it comes out of Josh Stewart's lab at UC Santa Cruz published about four years ago three and a half years ago starts with individual pathways taken out of a database I'm showing a very simple example here of P53 it's MDM2 repressor it's actually a eupyconolation reaction and P53 leads by a whole series of other pathway steps we're not showing here two apoptosis Paradigm takes this biological model and turns it into a probabilistic graph model using the central dogma so the MDM this single MDM2 node here actually expands into four different things in the model the MDM2 gene, the DNA the MDM2 RNA the MDM2 protein via translation and the MDM2 active protein via a post-translational modification each of these has a series of weights attached to it which indicates how the changes in activity of the gene will change the activity of the RNA so for example if you have an amplification of the gene you expect the RNA to go up by some level and then the weight can be a correlation coefficient same thing with P53, P53 gene P53 RNA, P53 protein and the active protein and then there's one interaction between these two pathways MDM2 eupyconolates P53 and will end up inhibiting the P53 active protein the weight in this case will be a negative weight alright, so that's a nice little model and it follows the central dog that makes sense you can then paradigm does this across all annotated pathways and then you can apply your gene list to it and your gene list can be a series of mutations that affect the DNA a series of RNA fold changes that affect the RNA copy number changes that affect the genes maybe you've got some mass spec data on the protein indicating the protein has gone up or down or has changed or has had a post translational modification and then it will integrate all these information together and it will give you a readout on the pathway activity has the combination of all these alterations caused apoptosis activity to go up, to go down or to remain the same and this technique actually works quite well in the general case here it has been applied this is in the 2010 paper from the Stuart lab has been applied to glioblastoma multiforme from the TCGA where they had they had RNAs, they had CNV changes and they had they had point mutations and methylation and what we're looking at here are individual patient samples and members of the different pathways that went into paradigm and the prediction of the changes in pathway activity so where it's showing that there is a group of patients here group number three which have very strong predicted inhibition of the GATA transcriptional factor pathway here and increase in the EGFR pathways and so on and so forth group number two has a different pattern so we're representing the effects on each patient as a series of pathway activity changes and you can then go into this and use this to do what if experiments for example you can take the model and say okay I have a patient who has predicted increase of activity in EGFR let me go through the list of 2000 or so clinically approved drugs and that have known drug targets applied them to the model and see if I can reduce EGFR activity to normal or maybe I want to knock out an essential pathway in this particular tumor in a synthetic lethal fashion so you can ask that of the model and actually find in some cases drug targets that are specific to that tumor to facilitate precision medicine so unfortunately there's good and bad news the bad news is that it's although it's open source it's distributed in source code it's hard to compile the thing they don't provide you with any pre-formatted pathway models there's no documentation they'll see to run it and even if you get it up and running it takes weeks to run one of these simulations on a cluster it's not actually the thought experiment that I told you of doing a virtual screen for targeted drugs you can't actually do that in practice it would take years to do it there's also some good news recently the software developers on Reaktum have incorporated the paradigm algorithm into the Sight Escape app and we've introduced performance increases it increased the performance about 50 fold so you can now start to do those experiments and you can do analyses in real-time and we've pre-populated it with Reaktum-based pathway models it's in alpha testing now it's not really ready to demo at this point but it looks very promising and will be available within a few months I believe so that's the end of the talk you have ended the talk which you have copies of with a series of URLs and background reading on each of the three classes of algorithm and we'll take some questions and maybe have a quick break and then Robin is going to give you a practical exercise of using some of the tools and techniques I talked about


I'm talking about this money doesn't just buy you a better life better food better cars better education also makes you a better person. Hey guys my name is Boris I'm a physician assistant and this video is about how I got $200,000 from the government in order to pay for my schooling for my post back for my PA school for my housing and for my living expenses during post back NPA school and I was able to graduate as a physician assistant completely debt free that sounds good to you stay tuned watch the rest of the video. Okay so how did I get the GI bill the US government to pay for $200,000 worth of education so worth of tuition fees living expenses all of that how did I make that happen using the post 9-11 GI bill I'm going to share my screen and show you exactly how I did it okay so I actually kind of prefabricated this spreadsheet already but I'm going to put these numbers in new so that you could see exactly how I got to that number of $200,000 provided by the GI bill for my post back and for my PA school education so I ended up graduating as a physician assistant completely debt free okay so the first thing I'm going to do because the first thing that I did was my post back at Cornell University so because this was in 2017 at this point that's like five six years ago using Cornell's current tuition which I think is like 70 or even $80,000 I'm not sure would not be accurate so I actually found this from you know news.cornell.edu so it's definitely a reputable source and in 2017 the tuition was as you can see $52,600 that's just tuition that's not books that's not fees that's not living expenses housing gas none of that it's straight up just tuition so let's populate that so tuition post back it was just those two semesters it was about $52,000 for that year for those two semesters okay so that's what this is next I'm going to do I'm just going to do uh tuition first right so if you go to any PA programs website you should be able to find the cost to attend the whole program so the problem with this is a lot of the cost is estimated so here you can see the grand total for attending LeMoyne where I went physician assistant program is about $162,000 $167,000 for the two years it's a 24 month program the reason this is not necessarily accurate is it includes all kinds of other fees that are estimates and also things that may or may not be paid by you so for instance the pants is about $500 it's the exam that you have to take after graduating from PA school in order to get your license assuming you pass so the GI bill actually paid for that for me which is awesome but people who don't have the GI bill you know you'd be paying for that state licensing fees depending on the state and also the people that you work for your employer if you get a job right out of school they might pay this for you your DEA license in order to prescribe medications again your employer might pay that for you mine certainly did um AAPA membership fees NYSSPA that's the New York State PA Association all things that you should be a member of if you work in New York State but again your employer might be paying those things so either way the point here is this $167,000 is not necessarily accurate so I am only going to go based on these two things tuition fall spring and summer the whole year and here fall spring and summer for year one and for year two so as you can see here about $46,000 per year $47,000 per year for the tuition so I'm going only off of that so for PA school tuition year one it said $46,000 tuition year two $47,000 now other stuff here you could see in the breakdown books equipment room and board you know utilities personal transportation a computer you know $1500 is definitely not what my computer cost that I went through PA school with it was much cheaper than that parking health insurance you know all this stuff that may or may not apply to you so what I'm going to do as far as living expenses is much simpler than this and I'll show you exactly how all right so our spreadsheet so far just has tuition books and supplies the GI bill gave I want to say about a thousand bucks for the year so a thousand bucks for a post back and $2,000 for PA school because one year here and two years there so it was about a thousand dollars a year that the GI bill gave you for books and supplies now as far as all the miscellaneous ancillary expenses those are hard to calculate because they're so different but what I'm going to do is calculate just based on the housing allowance so you may or may not know the post 9-11 GI bill pays for your tuition in full and it also gives you a housing allowance so the way you calculate that is you go right here to the defense travel website and you are you calculate the housing allowance that you get per month depending on the area that you live in so for instance I'm going to do PA school first and I went to PA school in 2019 and 2020 and 2021 I guess and I know that the zip code around Syracuse is this and when you use the post 9-11 GI bill you actually get E5 basic housing allowance with dependents so if you see that and you allow pop-ups you see right here all right so for Syracuse for 2019 what I actually received was 1500 and 15 dollars per month for so let's just say it's about the same for both years I think it goes up every year a little bit but not all that much so my housing allowance for the 24 months of the program was equals 24 times about you know 1500 a month give or take right so about 36 000 during my PA school time the GI bill paid me that for the housing allowance which of course I used for housing but also for food for transportation for everything else I basically lived off of that 1500 dollars per month plus my savings that I had from the military now because oops I actually need to look up the zip code for Ithaca because my post back was at Cornell University that was in Ithaca New York which has a totally different cost of living and because of that it has a totally different basic allowance for housing so I'm going to say Ithaca New York zip code and you can do this with anywhere in the world that you have school right so if you happen to have school in like Los Angeles New York City your basic allowance for housing is going to be much much higher than what I got in Syracuse so 14853 let's go with that right so 14853 and this was actually in 2017 that I went to post back so it might be a little lower and you'll see right here 1692 is about what I got per month for housing allowance in Ithaca so this is going to be equals and it was only nine months because it was just two semesters so nine months I think it might have even been less than that because Christmas break didn't count so I want to say like eight months right eight months times about 1700 bucks per month is what I received so based on these calculations you see that my post back tuition was about $52,000 my PA school tuition total was a little over $90,000 books and supplies were about a thousand dollars a year and then my housing allowance total during my two semesters of post back was about $13,000 and then my housing allowance total during the two years of PA school was $36,000 of course I supplemented that a little bit with my savings that I saved up during the military because it's kind of difficult to live on this housing allowance but that's not to say that it's not a tremendously generous benefit and I super appreciated the VA paying for me to go to school and also to live and have housing so the way we're going to calculate this is equals some parentheses all of this good stuff right here that gives us a grand total of $197,600 let's bold that for emphasis look at that almost $200,000 that the Department of Veterans Affairs the VA the post 9-11 GI bill in addition to the yellow ribbon program paid in order for me to go to school to go to post back to get my GPA up in order to get into PA school and then finally to get through PA school completely debt free so let me actually stop sharing my screen now that we have this uh this figure of about $200,000 a year that I ended up getting from the GI bill okay now you can see my face all right so just a couple more things here because if you are a veteran you might be thinking wow that is absolutely amazing I want the GI bill to pay $200,000 for my schooling in order for me to graduate debt free with a great career that pays good money and not have to pay back student loans right so first off I'm going to talk to the veterans the vets out there who want to use their GI bill very very good benefit definitely tremendous but one thing I want you to think about and one thing that is not talked about frequently enough is this if you are using your GI bill and you do not graduate or even if you fail a class you have to pay that money back okay that's something that a lot of people don't think about when they're signing up for classes right you sign up for a class you think I'm going to pass this class you don't think I'm going to fail this class but it's something that you need to keep in consideration that if you fail a class the GI bill is not going to pay for that they're going to make you pay them back if you fail out of the whole program for instance if I had failed out of the PA program I would have had to pay all of that money back if I would have failed classes in my post back I would have had to pay all of that money back so worst case scenario let's say I get into PA school I'm stoked I want to be a PA it's awesome I've been working for 10 years in my life to finally get here and then I fail out of the program well not only am I now not a PA I also have to pay the government all that money back that they paid for me to try to go to PA school and not only that but I'm not a PA so I'm not making any money how am I supposed to pay $200,000 back to the government and if you know anything about the government they're very slow to give you money and they're very very fast to garnish your wages or any possible way you have of making money and take that back so the government takes money much faster than it doles money out trust me you know being in the military being a veteran relying on the VA at some point in my life I know that fact the government does not like to give out money and they do very efficiently take money from you so careful so basically just kind of a message of warning I guess if you are a veteran you're going to use your GI bill understand that if for whatever reason any reason whatsoever you fail out of whatever program you're doing you're paying all that money back okay so that's one now talking to the civilians out there that are not veterans or maybe not veterans yet uh one to say don't be jealous of veterans with their GI bill because if they happen to you know fail out of the program they're paying all that money back so it's not exactly you know carte blanche to do whatever you want with education you got to pass you got to do well so that's one two don't be jealous of veterans because yes it is a tremendously generous benefit and I know all veterans really appreciate the post 9-11 GI bill so that we can go to school for free and graduate debt free but it's not exactly free man I mean we gave up years of our lives to get this benefit and some veterans were much less lucky than I was and they give up a whole whole lot more than just a few years of their life some people gave up limbs arms legs hands feet some people gave up you know their mental health PTSD anxiety some people gave up you know other health areas I think every veteran has something wrong with their health due to their service you know so yeah the GI bill is tremendous but it's not free we paid for it in years at the very best and at worst with you know our lives or somebody's life or our injuries or our health be it physical or mental or both okay so that's just a message to anybody who might think like oh why do veterans get this benefit I think as a veteran and also now as a taxpayer it's the least we can do it's the least we can do for people who put their life on the line for their country for our freedoms the GI bill letting people get ahead in life get educated is the least we can do for our veterans okay so yeah that's one thing also to the civilians watching this who might think like man I do not want to pay you know thousands and thousands of dollars and graduate with a hundred two hundred three hundred five hundred thousand dollars in debt and this GI bill thing sounds amazing and the GI bill is not the only way to get the government to pay for your schooling if you give them some time you know the VA has options ROTC has options the GI bill of course if you enlist or even if you go in as an officer and then you get out of the military this is what I used that's always an option so if you happen to be a civilian and this sounds good to you you know while you're in school only focusing on academics not being financially tremendously stressed but only having to focus on academics and if you pass you graduate debt free you know so if that sounds good to you the military GI bill other things in the military that can pay for schooling are tremendously good options and I actually have a whole video I made about that I'm going to link right here I'm sorry my friends here are going to make me some dinner so sorry that dang just distracted me but anyway and also just a little rant about tuition prices like man they're going up like crazy like the cost of school now is absolutely freaking ludicrous and if I was advising any young person I would say definitely get on this path man try to get the government try to get some way to someone to have somebody pay for school whether it's an academic or an athletic scholarship if those things are not an option for you like they are not for most people the military option the GI bill anything like that is a great great option and if anyone has questions about that post that below and I will be happy to answer those all right guys so I hope you enjoyed the video how I got $200,000 from the government to pay for my post back and also my PA school and graduated debt free


So my name is Alfred Bayer. I'm working for AVL software and functions and these days we are here to present our our IUNIC which is based on an AGL based Linux distribution. What is IUNIC? Yeah, IUNIC is our others control unit. That means you can use this this unit to blacken it to the car can be used from rapid prototyping till SOP and it provides all typical interfaces you need for an others control unit. And for our demo here we provided three different applications because we wanted to show some kind of a virtualization concept which shows on the left side our highway pilot and on the same hand we have in the middle our cloud connectivity that means we can read out data from the IUNIC, pull it into the cloud and so that we can simulate that we could collect data from a complete fleet and with artificial intelligence we can then do some kind of predictive diagnostics for example. Do you do a lot of business in the automotive market? Exactly. For a long time? That's our main target. Our company is based in Regensburg. It's AVL software and functions and we are also doing projects towards electrifications, inverter control, battery management systems or DCDC and things like this but we are also now in the others area very strong and we build up this control unit to have a perfect starter kit for our customers. Cool. Do you want to introduce your friends around here? Yeah. So here I'm Harish. Hello, I'm Harish. I'm the software engineer at digitalization at AVL software and functions. So here the third application which is running on this IUNIC device is the emotion-based music player. You could also try it live. It basically works. It recognizes the emotions of the person and it plays the different music based on their emotions in the car. So I can also share a demo. So it's a busy show. You can start. You can, you can. Now there's music somewhere. Yeah. Here there is an happy. So happy. So he's getting one kind of music. If he will be on the side, he will get another set of music. If he's angry. So based on the emotions, it plays a different sort of music which makes the driver very comfortable. Is it the copyrighted music? It's... I hope they won't tag my YouTube video. Is it copyrighted? No. No? Ah, okay. It's open. Yeah. Cool. All right. So it can detect if you're happy. Not only the happy, it can detect different sort of emotions. So it's trained basically with seven different types of emotions. Sad, happy, fear, angry. Surprise. Disgust. Now I'm sad. Look at this. Oh, he's sad. It detects that I'm sad. You're angry. No. Yeah. That's the thing is done by autistic intelligence, right? Yes. And Harish developed here the learning model and with that, the driver, you can give a driver another kind of music so that his mood will change and he can drive properly further. Yes. So that's the main idea behind this project.


Carl Antolotti haf ydy'r rôl rôl yng Nghymru yw Everton Football Club dros yma, Rhael Madrid. Everton's 21-22 Premier League seision mae nifer fan nid yw'r gwaith. Rafa Benitez haf oedd yn ymwneud ymwneud ymwneud Ymwneud. Rafa Benitez yw'r gwaith, rhaid i mewn ffannol. Nid yw'r gwybod rhaid i weld am y cyfwyr. Rafa Benitez efo'r cyfwyr yn dda'r gwaith. And it was probably too late. My grandpa is funny ever since, I was generally excited to see you far and come through the door. I just remembered the heroes welcome and the치를 of the tides with the fans. But the tides with the fans. For the first time in a generation we were threatened with releguy что with sackings in the manager's department and a boardroom level. The club has split, but ended in unity.


Hello, and welcome to this presentation of the STM32WB Low Power Timer, or LP-TIM. It covers the features of this peripheral, which offers a set of timing features and can generate waveforms even in low power modes. The Low Power Timer peripheral, embedded in the STM32WB microcontroller, provides a 16-bit timer that is able to run even in low power modes. This is made possible thanks to a flexible clocking scheme. The Low Power Timer peripheral provides basic general-purpose timer functions. One major function of the Low Power Timer is its capability to keep running even when no internal clock source is active when configured in asynchronous counting mode. The Low Power Timer's main feature is its ability to keep running even in low power mode, when almost all clock sources are turned off. The Low Power Timer has a very flexible clocking scheme. It can be clocked from on-chip clock sources, LSE, LSI, PCK-L1, PLL2P, PLL3R, and PERCK clock. Or it can be clocked from an external clock source over the Low Power Timer's LP-TIM in one input. This latter feature is used for building pulse counter applications and is a key function for metering applications like gas meters, etc. The Low Power Timer features up to eight external trigger sources with configurable polarity. External trigger inputs feature digital filters to cancel out faulty triggers that could be raised in noisy operating environments. The Low Power Timer can be configured to run either in continuous or one-shot mode. One-shot mode is used for generating pulse waveforms, while continuous mode is used to generate PWM waveforms. The Low Power Timer is a peripheral with two clock domains. The APB clock domain contains the peripheral's APB interface. The kernel clock domain contains the Low Power Timer peripheral's core functions. The kernel clock domain can be clocked by internal clock sources from an external clock source through the timer's LP-TIM in one input. The Low Power Timer peripheral embeds a 16-bit counter that is fed through a power of two prescaler. The Low Power Timer peripheral features a 16-bit auto-reload register and a 16-bit compare register that are used to set the period and duty cycle respectively for a PWM waveform signal output on the timer's LP-TIM out output. The Low Power Timer features an encoder mode function that can be used to interface with incremental quadrature encoder sensors using the peripheral's IP-TIM in one MUX and LP-TIM in two MUX inputs. Both inputs feature glitch-filting circuitry. The LP-TIM CMP and LP-TIM ARR registers, in conjunction with the bitfield's wave from the LP-TIM CFGR register and SNG-STRT from the LP-TIM CR register, are used to control the output waveform. The output waveform is either a typical PWM signal with its period and duty cycle controlled by the LP-TIM ARR and LP-TIM CMP registers respectively or it is a single pulse with the last output state defined by the configured waveform. If the last output state is the same as the one at the waveform's beginning, then one pulse mode is configured. If not, then set once mode is configured. The Low Power Timer's output polarity is controlled through the WAV-POL bitfield in the LP-TIM CFGR register. The Low Power Timer features a counter reset function used to reset to zero the contents of the LP-TIM CNT register.  A synchronous counter reset is performed by setting the count RST bit. Due to the synchronous nature of this reset, it only takes place after a synchronization delay of three LP-TIM kernel clock cycles. When the RST-ARE bit is set, an asynchronous counter reset is performed on the next APB read access to the LP-TIM CNT register. The Low Power Timer features an encoder mode function that can interface with the incremental quadrature encoder sensors using the peripheral's input 1 and input 2 inputs. Both inputs feature glitch filtering circuitry. The encoder function is similar to the one embedded in the general purpose timers. In order to use the encoder mode function, the Low Power Timer must be running in continuous mode. One important thing to note is that only Low Power Timers 1 and 2 embed the encoder mode function. The Low Power Timer peripheral features seven interrupt sources. The Compare Match Interrupt is raised once the content of Counter Register LP-TIM CNT matches or is greater than the Compare Register LP-TIM-CMP content. The Auto Reload Match Interrupt is raised when the Counter Register's content matches the Auto Reload Register's content. The External Trigger Event Interrupt is raised when a valid External Trigger is detected. The Auto Reload Register Right Completed and the Compare Register Right Completed Interrupts are raised when the transfer of the content of the LP-TIM ARR register and LP-TIM CNP register, respectively, is completed from the peripheral's APB interface logic into the peripheral's core logic which are contained by two different clock domains. These two interrupts are useful in mitigating the overhead of polling on the status of writing to these two registers when the peripheral core clock is too slow compared to the APB interface clock. The up and down direction change interrupts are raised when the encoder mode function is enabled and the counting direction is changed from up to down or vice versa. The counting direction of the Low Power Timer's counter reflects the rotation direction of the quadrature's sensor. The Low Power Timer peripheral is active in run, sleep, and stop power modes. The Low Power Timer is able to wake up the microcontroller from either sleep or stop modes. For more details, please refer to the following documentation available on our website.