Invited Keynote: Investigating Epistatic and Pleiotropic Genetic Architectures in Bioenergy and Human Health
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Jacobson, Dan
The new CoMet application consists of implementations of the 2-way and 3-way Proportional Similarity metric and Custom Correlation Coefficient using native or adapted GEMM kernels optimized for GPU architectures, and received the 2018 Gordon Bell Prize. Nearly 300 quadrillion element comparisons per second and over 2.3 mixed precision ExaOps are reached on Summit by use of Tensor Core hardware on the Nvidia Volta GPUs. These similarity metrics form the major parts of largescale Genome-Wide Epistasis Studies (GWES) and pleiotropy studies. These efforts seek to identify genetic variants that contribute to individual phenotypes, including susceptibility (or robustness) to disease. The team is using CoMet to investigate the genetic architectures underlying complex traits in applications from bioenergy to human clinical genomics.
 Good morning. My name is Jack Wells. I'm from the Oak Ridge leadership computing facility and it's my pleasure and honor to chair the morning keynote and introduce my colleague Dan Jacobson. Dan is the chief computational scientist in the biosciences division at the Oak Ridge National Laboratory. Dan's group focuses on the development and subsequent application of mathematical, statistical, and computational methods to biological data sets in order to yield new insights into complex biological systems. Fundamentally, he's trying to discover and understand the higher order combinatorial interactions among these molecules and cellular components as they lead to emergent phenomena in organisms i.e. the phenotypes and how they interact with their environment. Dan and his team, including my colleague at OLCF, Wayne Joubert, one of the two Gordon Bell Prizes awarded at the Supercomputing 18 last year together with the team from Lawrence Berkeley Laboratory studying climate data sets. Dan's group's contribution was the first to break the exascale barrier at 2.4 exoflops for a 16-bit arithmetic and their knowledge has performed, to their knowledge, has performed the largest and fastest scientific calculations ever performed. So this morning, his title, his topic is Investigating Epistatic and Pliotropic Genetic Architectures and Bioenergy and in Human Health. So I welcome Dan to the keynote this morning. Thanks, Jack. And thanks to the, and of course, the VPN strikes again. Thanks to the organizing committee for this wonderful invitation to speak to you today. And I'd really like to take you on a little bit of an adventure through our view of systems biology and how we're applying supercomputing to really solve, we think, new and interesting biological problems. The broad context we think about is on a planet where we have a effectively largely expanding human population, we have major challenges we need to address in the future. I'm having to do a sustainable energy and our approach to that is with bioenergy applications. Obviously we need to feed this population without actually destroying the planet that we live on. So we're very interested in solutions for precision agriculture and of course we're going to have to cure diseases of this population as we continue to grow and do better and better medicine and in this case precision medicine. We tend to think about biology on population scale. Biology for many, many decades was a discipline that focused on individuals of a species, individuals in a population. The seed change really since the past decade or so has been to really start to think about populations and variation across populations in the molecular components of organisms and how that leads to these emergent properties. So ranging from studying trees for bioenergy through humans for biomedicine and many organisms in between, we're collecting data sets that are very high dimensional data sets. So on the genome variation side we have ranging from sort of 10 to 85 million variants in the genome that we're studying in populations. In some of our populations we've already collected about 180,000 going on 200,000 phenotypes measurements of populations and collecting data across all what we call the omic layers. So gene expression and protein expression and metabolite expression. In fact the microbiome of an organism is really important. You've probably heard a lot about the importance of microbiome for human health as really a new frontier in medicine. The same thing is really true across all you care about. Plants are also dependent on their microbiome. So the more we can understand the interaction between microbiome and host and how the host controls its microbiome, whether they're friendly microbes or pathogens, is of great importance. So we're collecting all of these sort of different data layers, not for the joy of data collection, but really to try to build systems biology models of an organism and understand how it functions and interacts with its environment. The overall vision is to take all of these different omic layers, capture the interactions within and across them, often in the form of network models of various sorts. We now have the ability to fairly accurately predict the three-dimensional structure of the majority of the proteins in a proteome. Five years ago that was probably science fiction, now we have good methods coming out of various groups across the world, and we have a number of these pipelines up and running on summit and are showing through and creating three-dimensional models of most proteins and then working on docking them together so that we can reassemble in three dimensions the molecular machinery of the cell. One of the reasons we're doing this is we also have compound libraries of about 28 million small molecules that we want to dock against that with the idea that if you have proteins of interest for a specific function or disease that you want to stabilize or destabilize, these become intervention targets. So this becomes a large discovery engine that we can apply for various sides of biology. One of the workhorses as a community we've used for quite a while are genome-wide association studies which you can really look as a matrix problem. The idea of having genome variants that vary across the population, so every row is an individual in a population, every column is how an element of the genome varies across that population. And typically in genome-wide association studies you're just using mixed linear models to look for associations between these vectors of genome variants and phenotypes, properties of an organism that you've also measured across that same population. Now this has been a powerful method but also presumes on a really horrific precept that we know is not true and that all of these genome variants or SNPs are measured independently. You do an individual statistical test for every variant and that assumes independence in a biological system which we know is simply wrong. Bialogical systems are highly interconnected but this is where the community has been coming from. It is powerful but it also has the shortcomings. Typically you often for a single phenotype you view the results as what's called the Manhattan plot. This is just a negative log transformer, the p-values for each one of those independent tests and you see these, you do the magical p-value statistical game, you set an FDR threshold and you declare everything above that threshold to be significant. That ignores this sort of balance between type 1 and type 2 error. In being so worried about false discovery rates for false positives, you're also embracing lots of false negatives. So a lot of what we're trying to do is also dig much deeper in the signal and use other tools to figure out the biology that we've been historically ignoring. But irrespective of that, this is sort of one visualization if you're measuring one phenotype. But if you have 180,000 phenotypes, do you want to look at 180,000 Manhattan plots? Mostly that leads to blindness. I can testify. We've tried. And that turns out to be just this really lousy way to integrate information. So we're capturing often this information as networks. So in this case, the node in the middle is the phenotype that's been measured across the population. The surrounding nodes are SNPs, genome variants or genes that contain those genome variants and the edges are simply representing a statistically significant association. When you do that with several hundred thousand phenotypes, you get very dense networks that are starting to represent the complexity of the biological system. And you can start to ask questions of that. As good and skeptical scientists, though, any experimental result, you should not believe. Right? It's a result out of any experiment is really just the first step of a hypothesis. And typically from one result, especially G.O.A.'s result, you go and design a whole series of other orthogonal independent experiments to support that high support or refute that hypothesis coming out of the first one. What we realized along the way, often we do this with very painful wet lab experiments that can take years, we realize in all the data that we have collected across these populations and in orthogonal experiments on single genotypes as well as population scale that we've already have multiple lines of evidence built into these data sets. So in representing them as different network layers, representing the different experiments and the different types of data that are associating, we've written network algorithms that can go through and take a question and the question can be very specific or very vague. So in the in bioenergy research, one of the things we're very interested in is the cell wall of a plant. That's where all the sugars are. Cellulose and hemicellulose are all pumped in the cell wall. And that's where the energy is in bioenergy. So we're very interested in that as a field of study and how they're constructed and deconstructed. So in our first example of this algorithm, which we call a lines of evidence approach, we simply asked the question, show me new genes that are involved in cell wall biosynthesis or regulation that have not been associated with that before. Very vague, vague question. We started the query with genes that are known to be involved in cell wall biosynthesis and some metabolites that are known to be involved and ran it through the algorithm. And it gives us a rank order list of every gene in the genome in its likelihood. It's the number of lines of evidence it has to answer the question that you're asking. In this case, out of this came an immediate discovery of a gene regulatory network transcription factor circuit that controls how genes are expressed, that controls in this case how the organism, how the plant decides to pump carbon under different stress conditions. So in this case, we found the circuit that controls how the plant is responding to drought stress. When it's under dry conditions, it has choices as to how it uses its carbon and energy, whether it puts it in the cell wall or how it deals with abiotic stress. In bioenergy, we're always looking to deploy crops into marginal lands, lands currently not used for agriculture. So understanding the balance between how it responds to drought and how it produces things that we can later use for energy is very important. And we actually found this two-member switch that tells us exactly how it partitions carbon. So now we can use this to design a new genotype that can be adapted to drought stress and maximize our energy output, all from this network query. We're doing this over and over again. You can ask really any set of questions and pump out these subnetworks that are ready for further study or even genome editing to create new organisms. So a lot of this work has been, as I mentioned, based on traditional genome-wide association, very powerful methods, but they're also kind of crummy methods. And that we know that they often only explain a tiny bit of the genetic signal called heritability. So how well do you inherit a trait from your predecessor? Often a very good GWAS hit, a very, very good one, will explain maybe 5% or 10% of the genetic signal. So that's clearly suboptimal. There are multiple reasons for that. There are rare variant issues. We know that the variations, the mutations that are very infrequent in the population often have big effects on phenotypes, but for missing data reasons, they're sort of statistically inconvenient so people simply throw them away. And that's part of missing heritability. So pan-genome, I'll talk about in more detail. There's environmental impact. All of biological systems are plastic. They're responding to their environment. So phenotype is conditional on environment, and that's usually not included in GWAS approaches. And finally, epistasis, which I'll talk about in some detail. So we're trying to transition from this very traditional approach to finding the minority of the genetic signal to approaches that are going to capture, hopefully, the majority of the genetic signal of a system. So I mentioned rare variants. I won't talk about it today, but we are developing new methods to capture this side of the signal. And very importantly, we're going after the environmental impact, what we call the exposome. So we have from genome to exposome, the environment that an organism is reacting to. So to do this, we have to think about now this sort of signal, both in temporal and geospatial terms. An organism evolves across a wide range of environments, across its native habitat. We have to capture that geospatial range. It's also conditional on seasonality. We know that there's not just one temperature in the year unless you're living in the tropics. An organism is adapting to all sorts of fluctuations during the year. We have to have methods that can capture both geospatial and temporal patterns. We put together data sets of pretty much all the climate and environmental data that we can get our hands on around the world to start to capture everything from soil up through light spectral quality, things that are going to affect organisms. Often we get this at monthly resolutions. We're interpolating down to daily resolution. As you do this across these different environmental factors, what we're calling climat types, you'll see different seasonal patterns to different climat types across the year. And that allows us then to do the sort of association testing, a genome against climat type, for every day of the year. And we would expect that the adaptations are happening specific times of the year as you have different climate conditions changing throughout the seasons. And this is in fact exactly what we see. So on the top here is a heat map representation of the climate type, in this case, eridity, how dry an environment is. And if we look in the early part of the year, there appears to be no genetic adaptation to these climate conditions. But popping up in the middle of the year, we have this bimodal distribution. So what we're seeing here is the negative log transform of a p-value of a single mutation, a single SNP in the genome. And how it's adapting to this climate type condition at a specific time of the year. So if we had taken the mean average of the year, which is what's been done historically, we probably would have completely missed this association. If we'd taken it at other times of the year, we would have missed it. So again, capturing geospatial and temporal information. So every row in this heat map here represents the GPS coordinates of where a genotype came from to begin with, the environment that it evolved in. And we're finding specific associations across the population. In this case, in a gene involved in cuticle formation, how waxy the surface of a leaf is. So that's how a plant controls itself in a dry environment, how many layers of wax it builds up. So we're actually finding the gene responsible for that. In this case, we're looking at a different climate type. So this is wind speed. And here we see a different signature in a different gene popping up in the spring. And this gene happens to be involved in leaf development. And you can imagine as your tender little shoots are coming out in the spring, wind stress can do a lot of damage to the nascent leaves as they're just coming out of their buds. So adapting leaf development and timing of that becomes a very important adaptation. If we look at this exact same climate type, wind speed again, and we look at signatures on other genes, here we're finding a very different pattern and a very different time of year. So now popping up in the early winter in a cell wall-related gene. So this is probably trunk stress. How likely is the tree going to be blown over during the heavy winds of the winter? And so a completely different function, a completely different part of the plant showing up at a completely different time of the year. Again, we would have missed this entirely if we'd just taken a yearly average or not accounted for the spatial variation. So we've done this, we've calculated these for about 28,000 different climate types across the year, sort of running across as many components that are going to be relevant to biological organisms as we can think of. And of course, done all these associations and are capturing these as another network layer to help us interpret the biology of how an organism is adapting to its environment and what are the genetic controls for that. Pan genomes have been becoming a major focus in biological studies because they're representing a new type of genetic signal. Historically in genomics, we've taken a single individual from a population in deeply sequenced genome and assembled it, and that's become the representation of the entire species. And detecting genome variants has all been mapped against that one single individual of a species. Now, it turns out that that's sort of a lousy representation of all the genetic material in a species. There's all sorts of things in the rest of the population that are not in the reference. So we've built a series of pipelines running in OCF to take very large data sets that this one is Popula Strika Carpas, the first one of these we've done in eukaryotes. It's 70 terabases of DNA sequence. We're trying to assemble all together across the entire population. And when we do that, we find the baseline number of genes in the poplar reference genome is about 39,000 genes. We're finding a new 25,000 genes that are not in the reference at all. So there's a massive missing genetic signal that's present across the population that we've historically simply been ignoring. We can, of course, then create this into a matrix problem. You can represent the presence and absence of those genes across the population and use that for association testing across phenotypes. When we do that, again, in this case, metabolomics, so metabolites, how they're expressed across the individuals in the population, and some members of the microbiome, we see these patterns. So largely up at the top here, we're seeing the metabolomic associations, genes controlling metabolites, largely down at the bottom. We're seeing the microbiome, the bacterial associations with the host genome. When we zoom in on the bacterial association, so this is what we're all of a sudden starting to see, is a map of the plant defense system. Its immune system, how it's associating with different members of the microbiome across the population. So one of the real challenges for organisms are that they have a phenite number of genes, but nearly an infinite number of bacteria and fungi and archaea, they're going to be exposed to in the environment. So they have to come up with combinatorial responses to get multiple signals and use multiple genes to modulate their response to the different types of the different bacterial species, fungal species they're going to see in the environment. And here, we're starting to see across the population in different regions, how they've actually created new genes, or selected for new genes, to adapt to either friendly microbes or pathogens in the environment across the native range. Now we can look at the intersect between metabolites and bacteria, because we also know that another mechanism besides just the immune system that plants use to shape their microbiome is actually their own metabolites, their chemotype, their chemical environment they can use to shape their microbiome. These happen to be higher order salicylates, which is a very clever mechanism a plant can use because some of them are toxic to microbes and some of them are food for other microbes. So it's a great way to shape your neighborhood if you can feed your friends and poison your enemies. So we're not suggesting that as a human strategy, but plants use this very effectively. And so here we have selected out genes that are associating both with a higher order salicylate and specific microbial taxa. So this is actually what I call a PhD thesis in waiting. This is a set of hypotheses a student can run with and determine exactly those mechanisms and validate those mechanisms of how it uses these compounds specifically for different parts of the microbiome. So I've talked about this side of the sort of workflow and I'll focus quite a bit on epistasis now. We know epistasis is a major mechanism within inheritance, within genetics of biological organisms. We can see it through years and years of painstaking studies at very small scales. For human diseases, we think it's actually the rule, not the exception. We're seeing that really across the board in all organisms. And you can imagine the concept that a single mutation in a protein complex may not have a major effect on the complex. So the definition of epistasis is that you will see in association only when you look at multiple variants at the same time. If you measure them and test them together against the end phenotype, you won't see the signal looking at them one at a time. And biologically, we think this is because a single mutation doesn't destabilize the complex. You have to have multiple mutations to destabilize the complex or the binding sites. This can be protein complexes. This can be any interacting components of a biological system are going to have this sort of signature. Now we know it's there. The difficulties it's tough to look for because the combinatorics of a single genome of a eukaryotic cell is pretty large. It's on the order of 10 to the 170. Silence. For context, we think there are roughly 10 to the 82 atoms in the visible universe. So the complexity of a single cell is way beyond that. So I can tease my astrophysical colleagues to say if we talk about a big problem, we should stop saying, oh my god, that's astronomical. We should say, oh, hell, that's biological. The complexity is outstanding. And there's no amount of supercomputing we can brute force our way through numbers like 10 to the 170. But we still desperately need the supercomputing, but we need more clever algorithms to explore this space. Now the good news is, is that biological systems are under chemical, physical, and probably most importantly evolutionary constraints. So one, there have not been enough generations of any species that's ever lived on the planet to explore 10 to the 170. This has not been enough time. But more importantly, we see all the mutations we see in a population are viewed through the lens of selective pressure. So you have a mutation, and it will stay in a population or not based on if it's beneficial or deleterious to that organism. And therefore, we're selecting four patterns. We're selecting for these correlations between genome variants by the very mechanisms of evolution. So the true combinatorial space is not 10 to the 170, it's probably 10 to the 40 or 10 to the 50, still really big numbers. But with clever algorithms and the data sets that are getting large enough in biology are allowing us to represent the combinatorics that are truly there and then find ways to discover it. So the first, our first approach to this has been a co-evolution approach. This was an algorithm originally developed by Charlie Clymer, who's somewhere in the audience at the time at WashU now at Missouri St. Louis. And the underlying hypothesis here is that one mutation in an organism itself becomes a selective pressure for a compensatory mutation. Again, imagine that protein complex. You mutate one part of it. If it's falling apart, you're going to select for something that brings it back together. We know this is a phenomenon. We've seen this many, many times at smaller scales across all species. So we know it's there. The question is how do you look for it? So in collaboration with Wayne Jo Baer, we took this algorithm and ported it initially to Titan and then to Summit. And the very first step in this algorithm, depending on the species, is on the range of 10 to the 15th to 10 to the 16th vector comparisons you have to do. The vectors we did last year were in the order of 600,000 elements long. And that's just the first step. And you want to actually do this probably thousands of times to account for population structure in a population. We apply incredibly stringent thresholds. We only save one out of every three million comparisons that meet these thresholds. And then you can store the results as a network. So the edges are genome variants, SNPs in this case, and sorry, the nodes are genome variants, and the edges are the significant correlative relationship between them. And the topology that now is network is showing us the biology of how information is stored and related to each other within the genome. So we know that SNPs very close to each other on the genome are in linkage disequilibrium. This is the idea that they're simply co-inherited together all the time because there's no recombination between them. That's a well-known genetic phenomenon, and we capture that. These little blobs of tightly connected SNPs are a representation of local LD. What we're interested for the co-evolution hypothesis are these much longer edges marching across the entire genome that are representing this co-evolution signature of things that are one becomes the selective pressure for the other, and therefore is representing epistasis. It was mentioned, we managed to get this running as part of the early science program, thank you, Jack, on Summit last year, and Wayne was able to figure out how to port this algorithm to use the new tensor core features on the invidia GPUs. And that gave us this tremendous boost in performance that allowed us to cross the exascale barrier last summer in incremental performance since then. We're now up to 2.41 exaflops at 16-bit precision. I think our first reported result out of this was about 1.3 or 1.8 exaflops, and so in going up to 2.3 and 2.4, that's only a minor five or 600 petaflop incremental performance. Was at one point hoping we would get to 2.5 and say, okay, 2.4 is fantastic, and my post talk sort of slapped me and said, are you kidding me? Your incremental performance is more than twice the size of the normal capacity of Summit, as a 200 petaflop machine on the normal GPUs. So this sort of architecture is allowing us to really go way beyond where we thought we could get, but more importantly, it's allowing us to do new science, new biology that was literally impossible to do a year ago. We've been dreaming about these sorts of analyses for well over a decade, and last year it became possible for the first time. Compared to a competitive algorithm on another suit computer, this one was only 15,000 times faster than the state of the art. Again, new science all of a sudden becomes possible. Scaling behavior of this has been incredible. The melanox backplane with the adaptive routing means we don't have to worry about the backplane topology, so strong scaling as you saw was linear. Re-scaling is absolutely flat across these algorithms. The architecture is really, really helping us. And Wayne has done a fantastic job of designing the load balancing that makes this all possible. So we can take these resulting networks, use different network metrics, so you can do breadth first searches, you can do Markov clustering, you can do spectral clustering, and to create sets out of those networks that represents the underlying biology and use those sets for now epistatic discovery, epistatic testing, finding a lot of this missing heritability of the interacting components of a system. That gives us effectively a linear view of epistasis. We also know that there are nonlinear relationships, and for that we're turning to explainable AI approaches. So of course, AI has been in the news a lot. Traditional AI, machine learning, and deep learning historically have been great classification engines, but effectively black boxes. And many of them have been focused on problems that aren't necessarily near and dear to the heart of science. Picking out puppies in cats in pictures on Facebook is not a great scientific research challenge. But we don't want just classification, we want to understand the patterns that are leading that classification. That's the science, that's the biology. Just those common tutorial patterns we're actually searching for. So in collaboration with Ben Brown's lab at the Berkeley National Lab, and these efforts led in my lab by Jonathan Romero and Ashley Cliff, who are also in the audience, we're using iterative random forest that we have scaled and up and running on summit as a different take to explainable machine learning. So random forest have been a classification workhorse in machine learning for a long time, but they're effectively stochastic. You get slightly different results every time you run them on the same data set because of the random nature. So the beauty of iterative random forest is that you run a whole series of them. So random forest is a collection of decision trees, weak learners individually, strong learners collectively. You do one random forest, take the results of that, they become the weights for the input of the next random forest, and so on and so forth. So after about six or seven rounds of this, typically you start to get convergence. Your answer becomes consistent. You get the same answer every time. More importantly, in running all these random forest, you have all these different decision trees. And it turns out that your interaction space is in these pods in the splits of the decision trees. So with a different algorithm called random intersection trees, you can find these pods that occur over and over and over again as predictive, in this case, of the phenotype. And that turns out to be the epistatic space. So this is another way, really, to break that curse of dimensionality and find the combinatorial space that is represented in the data sets. So instead of doing individual tests at a time, we're now finding phenotypes that are conditional on all possible combinations of the genome variants in a population. This is a representation of doing this across all the metabolites across the population, so you get both an epistatic and a pleiotropic network, but genes are responsible for multiple functions. If we zoom in on this, we see the epistatic architecture around a single metabolite, which has traditionally been thought to be involved in the Lignin biosynthesis pathway. In this case, we actually find it's not involved in Lignin, it's actually been turned into a defense compound. It's another metabolite the plant is using to shape its neighborhood. And we see the genes associating with it are all screaming immune system, plant immune system. With the idea that we can now design new crops based on how we're understanding phenotypes in environmental associations, again, we're using random forests or iterative random forests to give us response surface representations of the combinations of genetic elements and how they can be optimized together to lead to phenotypes, to output of a system. We can also build in multiple layers of this, so in fact, in addition to the phenotypes, we can build in environmental layers and optimize an organism for both its productivity as well as its adaption to specific environments. So this is an IRF-driven genome selection algorithm. We can simulate the results in silica and pick the parents to actually breed a species into a targeted environment. Now that we can figure out the environment, wouldn't it be nice if we knew what all the possible environments were? So we did this trivial little task of taking every point of dry land on the planet at square kilometer resolution. So that's 156 million places on the planet. The tensors representing all these environmental conditions, it's about 414,000 elements long, and then compared them all to each other, again, on summit. Set a threshold and you can store the result as a network. In this case, it turns into a network with 156 million nodes and something in the order of 25 trillion edges that are representing the climate similarity, the environmental similarity of every point of land on the planet. We can start to view these networks in lots of different ways. This is a view of Markov clustering at low resolution. You start to break things out at different layers of resolution. Keep in mind, historically, the climate type maps have had 17 different environments, 17 clusters to represent the entire planet. Our lowest resolution clustering gives us 68 as we amp up the clustering. We start to see very different biographic patterns start to fall out across the planet. This gives us the ability to then choose areas that you want to design organisms to deploy for. At 715 clusters, we frankly run out of colors to represent them. It's a visualization. I think the real power of this approach is in the underlying network. You can start anywhere on the planet and say, what is similar to this? This has lots of applications in different areas. We've done it globally. We're doing it regionally in different parts of the globe. Again, higher resolution clustering, breaking out different environments that you can use for biotechnology, ecology, a whole range of other studies, even biomedicine. To our knowledge, this is the largest climate type map ever predicted. It has lots of different applications. Imagine using GWTS and using the genome selection algorithms. You can pick apart on the planet and design organisms specifically to thrive there. Very briefly, I'll run you through a little bit of the precision healthcare. Opioids addiction is a major, major issue in North America. Running in the cost of trillions of dollars. The CDC estimates that roughly 10% of people who are prescribed opioids will go on to develop a full-blown addiction. 10% from a morbidity point of view, from a clinical point of view, is a horrific number. From a biological point of view, it's a fascinating number. Why only 10%? Why are we not all addicted? Opioids are actually the result of an evolutionary arms race between insects and plants. Opioids are our second every metabolite of plants that have been selected for because it's a very clever strategy. If you have a caterpillar chewing on your leaves, if you get it stoned and happy and it falls off and stares at the stun, it's not eating you anymore. We are simply collateral damage of that evolutionary arms race because it's targeting fundamental neural mechanisms that are shared by all bilateral organisms. Pre-Cambrian evolution are these methods that are being selected for. I can show you sea slugs that will be stoned and happy and hallucinating on opioids. These are fundamental mechanisms. They're very difficult to understand historically because we think they're epistatic. This is gene by gene again and environment. This falls into methods that we know how to do now. We're fortunate to have this collaboration with the Veterans Administration where we have the clinical records for 23 million people going back 20 years sitting at Oak Ridge. We have 600,000 genotypes for them. That number is headed towards 2 million over the next few years. We have a great collaborator named Justice to study this problem. We can use these sorts of approaches, including the machine learning approaches, to discover the data-driven phenotypes associated with opioid addiction. We also need to study them in model organisms because the E component. When you're looking at historical electronic health records, you don't really have a great way to capture environment. Organisms usually complain if you experiment on them. IRBs are not fond of that. These are retrospective studies. We need model organisms that we can apply stress to. We're actually using Drosophila as an insect, the original target of opioids, as our discovery engine. It's been used for decades for biomedical research. We figured out very high-throughput ways to collect behavioral phenotypes across Drosophila genetic variant populations. We figured we can phenotype about 7 million flies in a five-year period. Really high-throughput phenotyping. Again, start to pump it through these discovery pipelines so that we can understand the epistatic architectures in humans and in insects and how they're conserved mechanisms into this sort of discovery pipeline so we can look for therapeutic outcomes, ways to address addiction, ways to predict it, and ways to prevent it. All that is great, but we're greedy. We want more. We know that there are also relationships between the phenotypes, not just the genomic elements. So we want algorithms that can look for all possible interactions of phenotypes, conditional and all possible interaction of genomic variants. Of course, as you go across all these omics and phonomics layers, you can represent each one of those as a matrix. You want all possible associations within and across matrices. As we add in all the environmental information, these very quickly become data cubes and in fact, arbitrary polytopes. To our knowledge, there's not an existing algorithm that can handle arbitrary polytopic space. So of course, we're creating one. Jonathan has a prototype of a method we're calling TERF that will start to be able to explore this arbitrary polytopic space and find these biological relationships within and across all polytopes representing a biological system. These sorts of approaches have applications to lots of different areas. I've talked a lot about explainable machine learning. We're also tackling deep learning and trying to make that explainable as well. The sort of things I've mentioned for eukaryotes, we're also doing a lot of work to make these methods amenable to microbes. In the interest of time, I'll sort of blaze through this. This is just a quick preliminary result of how in a collaboration with a group of the National Renewable Energy Lab, Greg Beckham's group, they came up with a gene that was important for their function of interest, but they had no idea what it was. It's my favorite gene annotation ever. Hypothetical protein of unknown function. But we threw it into our systems biology networks and saw that everything around it was an outer membrane porn, something that looks like a channel through a membrane. We predicted the 3D structure and, in fact, it's a beta-barrel channel. So when they've been confused about what this protein was doing for half a year, within a week we had experimental results and telling them exactly where to look for function. In fact, could explain the mutations they were seeing that were loss of function, how they're actually affecting the key components of the beta-barrel channel. So if you want to talk about multi-scale modeling, I would contend that we're going from single nucleotide resolution in genomes up through ecosystem interactions and all the way up to spectral light quality, which is coming from our nearest star. So that's from a nucleotide to an astrophysical phenotype. I think we're stepping across a few levels of scale here. The overall grand picture is this whole combination of all these representations and environmental data pumping through lots of different ensemble approaches of algorithms for the sort of unified vision that then leads to more and more hypotheses to generate more data in experimental systems that you can pump back in. We're able to do a lot of this work because of the fantastic resources at the OLCF and start to think about these crazy projects that a year ago would have been 2 billion hours untitled. So Jack wouldn't allow us to apply for that. Now that's only, well, that would be 340,000 years on a laptop. I'm not going to live that long. But that's 17 days on summit, which is starting to be a reasonable thing to ask for. Big shout-outs to all the collaborators. This is an incomplete list. A big shout-out to Charlie Clymer in the audience for the origination of the CCC algorithm for co-evolution, funding sources, student programs, et cetera. Ashley and Jonathan are in the audience and have posters here. Please talk to them. A big thank to Jack for all the support across the years in the introduction to Wayne. Peter Thornton and ORL is our climate scientist on the climate type clustering projects. I'm fortunate to lead a really, really fantastic group of students and postdocs and staff and faculty. The students decided we had to have a group acronym, so they came up with the Jacobson Advanced Institute for Learning, or JAIL. So when you join the group, you become an inmate and are incarcerated in science, and we have some fun with that. If you're interested in a broad range of topics, please talk to us. We're really, really collaborative. We love working with other groups to solve complex problems. If you're a student, we have full-time PhD programs. We have visiting, collaborative visits for PhD students, internships. A lot of postdoc slots open now and opportunities for sabbaticals and collaborative visits from faculty as well. And with that, I can take questions.
 Good morning.  My name is Jack Wells.  I'm from the Oak Ridge leadership computing facility and it's my pleasure and honor to  chair the morning keynote and introduce my colleague Dan Jacobson.  Dan is the chief computational scientist in the biosciences division at the Oak Ridge  National Laboratory.  Dan's group focuses on the development and subsequent application of mathematical, statistical,  and computational methods to biological data sets in order to yield new insights into complex  biological systems.  Fundamentally, he's trying to discover and understand the higher order combinatorial interactions  among these molecules and cellular components as they lead to emergent phenomena in organisms  i.e. the phenotypes and how they interact with their environment.  Dan and his team, including my colleague at OLCF, Wayne Joubert, one of the two Gordon  Bell Prizes awarded at the Supercomputing 18 last year together with the team from Lawrence  Berkeley Laboratory studying climate data sets.  Dan's group's contribution was the first to break the exascale barrier at 2.4 exoflops  for a 16-bit arithmetic and their knowledge has performed, to their knowledge, has performed  the largest and fastest scientific calculations ever performed.  So this morning, his title, his topic is Investigating Epistatic and Pliotropic Genetic Architectures  and Bioenergy and in Human Health.  So I welcome Dan to the keynote this morning.  Thanks, Jack.  And thanks to the, and of course, the VPN strikes again.  Thanks to the organizing committee for this wonderful invitation to speak to you today.  And I'd really like to take you on a little bit of an adventure through our view of systems  biology and how we're applying supercomputing to really solve, we think, new and interesting  biological problems.  The broad context we think about is on a planet where we have a effectively largely expanding  human population, we have major challenges we need to address in the future.  I'm having to do a sustainable energy and our approach to that is with bioenergy applications.  Obviously we need to feed this population without actually destroying the planet that  we live on.  So we're very interested in solutions for precision agriculture and of course we're  going to have to cure diseases of this population as we continue to grow and do better and better  medicine and in this case precision medicine.  We tend to think about biology on population scale.  Biology for many, many decades was a discipline that focused on individuals of a species,  individuals in a population.  The seed change really since the past decade or so has been to really start to think about  populations and variation across populations in the molecular components of organisms  and how that leads to these emergent properties.  So ranging from studying trees for bioenergy through humans for biomedicine and many organisms  in between, we're collecting data sets that are very high dimensional data sets.  So on the genome variation side we have ranging from sort of 10 to 85 million variants in  the genome that we're studying in populations.  In some of our populations we've already collected about 180,000 going on 200,000 phenotypes  measurements of populations and collecting data across all what we call the omic layers.  So gene expression and protein expression and metabolite expression.  In fact the microbiome of an organism is really important.  You've probably heard a lot about the importance of microbiome for human health as really a  new frontier in medicine.  The same thing is really true across all you care about.  Plants are also dependent on their microbiome.  So the more we can understand the interaction between microbiome and host and how the host  controls its microbiome, whether they're friendly microbes or pathogens, is of great importance.  So we're collecting all of these sort of different data layers, not for the joy of data collection,  but really to try to build systems biology models of an organism and understand how it  functions and interacts with its environment.  The overall vision is to take all of these different omic layers, capture the interactions  within and across them, often in the form of network models of various sorts.  We now have the ability to fairly accurately predict the three-dimensional structure of  the majority of the proteins in a proteome.  Five years ago that was probably science fiction, now we have good methods coming out of various  groups across the world, and we have a number of these pipelines up and running on summit  and are showing through and creating three-dimensional models of most proteins and then working on  docking them together so that we can reassemble in three dimensions the molecular machinery  of the cell.  One of the reasons we're doing this is we also have compound libraries of about 28 million  small molecules that we want to dock against that with the idea that if you have proteins  of interest for a specific function or disease that you want to stabilize or destabilize,  these become intervention targets.  So this becomes a large discovery engine that we can apply for various sides of biology.  One of the workhorses as a community we've used for quite a while are genome-wide association  studies which you can really look as a matrix problem.  The idea of having genome variants that vary across the population, so every row is an  individual in a population, every column is how an element of the genome varies across  that population.  And typically in genome-wide association studies you're just using mixed linear models to look  for associations between these vectors of genome variants and phenotypes, properties  of an organism that you've also measured across that same population.  Now this has been a powerful method but also presumes on a really horrific precept that  we know is not true and that all of these genome variants or SNPs are measured independently.  You do an individual statistical test for every variant and that assumes independence in a  biological system which we know is simply wrong.  Bialogical systems are highly interconnected but this is where the community has been coming  from.  It is powerful but it also has the shortcomings.  Typically you often for a single phenotype you view the results as what's called the Manhattan  plot.  This is just a negative log transformer, the p-values for each one of those independent  tests and you see these, you do the magical p-value statistical game, you set an FDR threshold  and you declare everything above that threshold to be significant.  That ignores this sort of balance between type 1 and type 2 error.  In being so worried about false discovery rates for false positives, you're also embracing  lots of false negatives.  So a lot of what we're trying to do is also dig much deeper in the signal and use other  tools to figure out the biology that we've been historically ignoring.  But irrespective of that, this is sort of one visualization if you're measuring one  phenotype.  But if you have 180,000 phenotypes, do you want to look at 180,000 Manhattan plots?  Mostly that leads to blindness.  I can testify.  We've tried.  And that turns out to be just this really lousy way to integrate information.  So we're capturing often this information as networks.  So in this case, the node in the middle is the phenotype that's been measured across  the population.  The surrounding nodes are SNPs, genome variants or genes that contain those genome variants  and the edges are simply representing a statistically significant association.  When you do that with several hundred thousand phenotypes, you get very dense networks that  are starting to represent the complexity of the biological system.  And you can start to ask questions of that.  As good and skeptical scientists, though, any experimental result, you should not believe.  Right?  It's a result out of any experiment is really just the first step of a hypothesis.  And typically from one result, especially G.O.A.'s result, you go and design a whole  series of other orthogonal independent experiments to support that high support or refute that  hypothesis coming out of the first one.  What we realized along the way, often we do this with very painful wet lab experiments  that can take years, we realize in all the data that we have collected across these populations  and in orthogonal experiments on single genotypes as well as population scale that we've already  have multiple lines of evidence built into these data sets.  So in representing them as different network layers, representing the different experiments  and the different types of data that are associating, we've written network algorithms  that can go through and take a question and the question can be very specific or very  vague.  So in the in bioenergy research, one of the things we're very interested in is the cell  wall of a plant.  That's where all the sugars are.  Cellulose and hemicellulose are all pumped in the cell wall.  And that's where the energy is in bioenergy.  So we're very interested in that as a field of study and how they're constructed and  deconstructed.  So in our first example of this algorithm, which we call a lines of evidence approach,  we simply asked the question, show me new genes that are involved in cell wall biosynthesis  or regulation that have not been associated with that before.  Very vague, vague question.  We started the query with genes that are known to be involved in cell wall biosynthesis  and some metabolites that are known to be involved and ran it through the algorithm.  And it gives us a rank order list of every gene in the genome in its likelihood.  It's the number of lines of evidence it has to answer the question that you're asking.  In this case, out of this came an immediate discovery of a gene regulatory network transcription  factor circuit that controls how genes are expressed, that controls in this case how  the organism, how the plant decides to pump carbon under different stress conditions.  So in this case, we found the circuit that controls how the plant is responding to drought  stress.  When it's under dry conditions, it has choices as to how it uses its carbon and energy, whether  it puts it in the cell wall or how it deals with abiotic stress.  In bioenergy, we're always looking to deploy crops into marginal lands, lands currently  not used for agriculture.  So understanding the balance between how it responds to drought and how it produces things  that we can later use for energy is very important.  And we actually found this two-member switch that tells us exactly how it partitions carbon.  So now we can use this to design a new genotype that can be adapted to drought stress and  maximize our energy output, all from this network query.  We're doing this over and over again.  You can ask really any set of questions and pump out these subnetworks that are ready  for further study or even genome editing to create new organisms.  So a lot of this work has been, as I mentioned, based on traditional genome-wide association,  very powerful methods, but they're also kind of crummy methods.  And that we know that they often only explain a tiny bit of the genetic signal called heritability.  So how well do you inherit a trait from your predecessor?  Often a very good GWAS hit, a very, very good one, will explain maybe 5% or 10% of the genetic  signal.  So that's clearly suboptimal.  There are multiple reasons for that.  There are rare variant issues.  We know that the variations, the mutations that are very infrequent in the population  often have big effects on phenotypes, but for missing data reasons, they're sort of statistically  inconvenient so people simply throw them away.  And that's part of missing heritability.  So pan-genome, I'll talk about in more detail.  There's environmental impact.  All of biological systems are plastic.  They're responding to their environment.  So phenotype is conditional on environment, and that's usually not included in GWAS approaches.  And finally, epistasis, which I'll talk about in some detail.  So we're trying to transition from this very traditional approach to finding the minority  of the genetic signal to approaches that are going to capture, hopefully, the majority  of the genetic signal of a system.  So I mentioned rare variants.  I won't talk about it today, but we are developing new methods to capture this side of the signal.  And very importantly, we're going after the environmental impact, what we call the exposome.  So we have from genome to exposome, the environment that an organism is reacting to.  So to do this, we have to think about now this sort of signal, both in temporal and geospatial  terms.  An organism evolves across a wide range of environments, across its native habitat.  We have to capture that geospatial range.  It's also conditional on seasonality.  We know that there's not just one temperature in the year unless you're living in the tropics.  An organism is adapting to all sorts of fluctuations during the year.  We have to have methods that can capture both geospatial and temporal patterns.  We put together data sets of pretty much all the climate and environmental data that we  can get our hands on around the world to start to capture everything from soil up through  light spectral quality, things that are going to affect organisms.  Often we get this at monthly resolutions.  We're interpolating down to daily resolution.  As you do this across these different environmental factors, what we're calling climat types,  you'll see different seasonal patterns to different climat types across the year.  And that allows us then to do the sort of association testing, a genome against climat  type, for every day of the year.  And we would expect that the adaptations are happening specific times of the year as you  have different climate conditions changing throughout the seasons.  And this is in fact exactly what we see.  So on the top here is a heat map representation of the climate type, in this case, eridity,  how dry an environment is.  And if we look in the early part of the year, there appears to be no genetic adaptation  to these climate conditions.  But popping up in the middle of the year, we have this bimodal distribution.  So what we're seeing here is the negative log transform of a p-value of a single mutation,  a single SNP in the genome.  And how it's adapting to this climate type condition at a specific time of the year.  So if we had taken the mean average of the year, which is what's been done historically,  we probably would have completely missed this association.  If we'd taken it at other times of the year, we would have missed it.  So again, capturing geospatial and temporal information.  So every row in this heat map here represents the GPS coordinates of where a genotype came  from to begin with, the environment that it evolved in.  And we're finding specific associations across the population.  In this case, in a gene involved in cuticle formation, how waxy the surface of a leaf  is.  So that's how a plant controls itself in a dry environment, how many layers of wax it  builds up.  So we're actually finding the gene responsible for that.  In this case, we're looking at a different climate type.  So this is wind speed.  And here we see a different signature in a different gene popping up in the spring.  And this gene happens to be involved in leaf development.  And you can imagine as your tender little shoots are coming out in the spring, wind  stress can do a lot of damage to the nascent leaves as they're just coming out of their  buds.  So adapting leaf development and timing of that becomes a very important adaptation.  If we look at this exact same climate type, wind speed again, and we look at signatures  on other genes, here we're finding a very different pattern and a very different time  of year.  So now popping up in the early winter in a cell wall-related gene.  So this is probably trunk stress.  How likely is the tree going to be blown over during the heavy winds of the winter?  And so a completely different function, a completely different part of the plant showing  up at a completely different time of the year.  Again, we would have missed this entirely if we'd just taken a yearly average or not  accounted for the spatial variation.  So we've done this, we've calculated these for about 28,000 different climate types  across the year, sort of running across as many components that are going to be relevant  to biological organisms as we can think of.  And of course, done all these associations and are capturing these as another network  layer to help us interpret the biology of how an organism is adapting to its environment  and what are the genetic controls for that.  Pan genomes have been becoming a major focus in biological studies because they're representing  a new type of genetic signal.  Historically in genomics, we've taken a single individual from a population in deeply sequenced  genome and assembled it, and that's become the representation of the entire species.  And detecting genome variants has all been mapped against that one single individual  of a species.  Now, it turns out that that's sort of a lousy representation of all the genetic material  in a species.  There's all sorts of things in the rest of the population that are not in the reference.  So we've built a series of pipelines running in OCF to take very large data sets that this  one is Popula Strika Carpas, the first one of these we've done in eukaryotes.  It's 70 terabases of DNA sequence.  We're trying to assemble all together across the entire population.  And when we do that, we find the baseline number of genes in the poplar reference genome  is about 39,000 genes.  We're finding a new 25,000 genes that are not in the reference at all.  So there's a massive missing genetic signal that's present across the population that  we've historically simply been ignoring.  We can, of course, then create this into a matrix problem.  You can represent the presence and absence of those genes across the population and use  that for association testing across phenotypes.  When we do that, again, in this case, metabolomics, so metabolites, how they're expressed across  the individuals in the population, and some members of the microbiome, we see these patterns.  So largely up at the top here, we're seeing the metabolomic associations, genes controlling  metabolites, largely down at the bottom.  We're seeing the microbiome, the bacterial associations with the host genome.  When we zoom in on the bacterial association, so this is what we're all of a sudden starting  to see, is a map of the plant defense system.  Its immune system, how it's associating with different members of the microbiome across  the population.  So one of the real challenges for organisms are that they have a phenite number of genes,  but nearly an infinite number of bacteria and fungi and archaea, they're going to be  exposed to in the environment.  So they have to come up with combinatorial responses to get multiple signals and use  multiple genes to modulate their response to the different types of the different bacterial  species, fungal species they're going to see in the environment.  And here, we're starting to see across the population in different regions, how they've  actually created new genes, or selected for new genes, to adapt to either friendly microbes  or pathogens in the environment across the native range.  Now we can look at the intersect between metabolites and bacteria, because we also know that another  mechanism besides just the immune system that plants use to shape their microbiome is actually  their own metabolites, their chemotype, their chemical environment they can use to shape  their microbiome.  These happen to be higher order salicylates, which is a very clever mechanism a plant can  use because some of them are toxic to microbes and some of them are food for other microbes.  So it's a great way to shape your neighborhood if you can feed your friends and poison your  enemies.  So we're not suggesting that as a human strategy, but plants use this very effectively.  And so here we have selected out genes that are associating both with a higher order salicylate  and specific microbial taxa.  So this is actually what I call a PhD thesis in waiting.  This is a set of hypotheses a student can run with and determine exactly those mechanisms  and validate those mechanisms of how it uses these compounds specifically for different  parts of the microbiome.  So I've talked about this side of the sort of workflow and I'll focus quite a bit on  epistasis now.  We know epistasis is a major mechanism within inheritance, within genetics of biological  organisms.  We can see it through years and years of painstaking studies at very small scales.  For human diseases, we think it's actually the rule, not the exception.  We're seeing that really across the board in all organisms.  And you can imagine the concept that a single mutation in a protein complex may not have  a major effect on the complex.  So the definition of epistasis is that you will see in association only when you look  at multiple variants at the same time.  If you measure them and test them together against the end phenotype, you won't see the  signal looking at them one at a time.  And biologically, we think this is because a single mutation doesn't destabilize the  complex.  You have to have multiple mutations to destabilize the complex or the binding sites.  This can be protein complexes.  This can be any interacting components of a biological system are going to have this  sort of signature.  Now we know it's there.  The difficulties it's tough to look for because the combinatorics of a single genome of a  eukaryotic cell is pretty large.  It's on the order of 10 to the 170.  Silence.  For context, we think there are roughly 10 to the 82 atoms in the visible universe.  So the complexity of a single cell is way beyond that.  So I can tease my astrophysical colleagues to say if we talk about a big problem, we  should stop saying, oh my god, that's astronomical.  We should say, oh, hell, that's biological.  The complexity is outstanding.  And there's no amount of supercomputing we can brute force our way through numbers like  10 to the 170.  But we still desperately need the supercomputing, but we need more clever algorithms to explore  this space.  Now the good news is, is that biological systems are under chemical, physical, and probably  most importantly evolutionary constraints.  So one, there have not been enough generations of any species that's ever lived on the planet  to explore 10 to the 170.  This has not been enough time.  But more importantly, we see all the mutations we see in a population are viewed through  the lens of selective pressure.  So you have a mutation, and it will stay in a population or not based on if it's beneficial  or deleterious to that organism.  And therefore, we're selecting four patterns.  We're selecting for these correlations between genome variants by the very mechanisms of  evolution.  So the true combinatorial space is not 10 to the 170, it's probably 10 to the 40 or  10 to the 50, still really big numbers.  But with clever algorithms and the data sets that are getting large enough in biology are  allowing us to represent the combinatorics that are truly there and then find ways to  discover it.  So the first, our first approach to this has been a co-evolution approach.  This was an algorithm originally developed by Charlie Clymer, who's somewhere in the  audience at the time at WashU now at Missouri St. Louis.  And the underlying hypothesis here is that one mutation in an organism itself becomes  a selective pressure for a compensatory mutation.  Again, imagine that protein complex.  You mutate one part of it.  If it's falling apart, you're going to select for something that brings it back together.  We know this is a phenomenon.  We've seen this many, many times at smaller scales across all species.  So we know it's there.  The question is how do you look for it?  So in collaboration with Wayne Jo Baer, we took this algorithm and ported it initially  to Titan and then to Summit.  And the very first step in this algorithm, depending on the species, is on the range  of 10 to the 15th to 10 to the 16th vector comparisons you have to do.  The vectors we did last year were in the order of 600,000 elements long.  And that's just the first step.  And you want to actually do this probably thousands of times to account for population  structure in a population.  We apply incredibly stringent thresholds.  We only save one out of every three million comparisons that meet these thresholds.  And then you can store the results as a network.  So the edges are genome variants, SNPs in this case, and sorry, the nodes are genome  variants, and the edges are the significant correlative relationship between them.  And the topology that now is network is showing us the biology of how information is stored  and related to each other within the genome.  So we know that SNPs very close to each other on the genome are in linkage disequilibrium.  This is the idea that they're simply co-inherited together all the time because there's no  recombination between them.  That's a well-known genetic phenomenon, and we capture that.  These little blobs of tightly connected SNPs are a representation of local LD.  What we're interested for the co-evolution hypothesis are these much longer edges marching  across the entire genome that are representing this co-evolution signature of things that  are one becomes the selective pressure for the other, and therefore is representing epistasis.  It was mentioned, we managed to get this running as part of the early science program, thank  you, Jack, on Summit last year, and Wayne was able to figure out how to port this algorithm  to use the new tensor core features on the invidia GPUs.  And that gave us this tremendous boost in performance that allowed us to cross the  exascale barrier last summer in incremental performance since then.  We're now up to 2.41 exaflops at 16-bit precision.  I think our first reported result out of this was about 1.3 or 1.8 exaflops, and so in going  up to 2.3 and 2.4, that's only a minor five or 600 petaflop incremental performance.  Was at one point hoping we would get to 2.5 and say, okay, 2.4 is fantastic, and my post  talk sort of slapped me and said, are you kidding me?  Your incremental performance is more than twice the size of the normal capacity of Summit,  as a 200 petaflop machine on the normal GPUs.  So this sort of architecture is allowing us to really go way beyond where we thought we  could get, but more importantly, it's allowing us to do new science, new biology that was  literally impossible to do a year ago.  We've been dreaming about these sorts of analyses for well over a decade, and last  year it became possible for the first time.  Compared to a competitive algorithm on another suit computer, this one was only 15,000 times  faster than the state of the art.  Again, new science all of a sudden becomes possible.  Scaling behavior of this has been incredible.  The melanox backplane with the adaptive routing means we don't have to worry about the backplane  topology, so strong scaling as you saw was linear.  Re-scaling is absolutely flat across these algorithms.  The architecture is really, really helping us.  And Wayne has done a fantastic job of designing the load balancing that makes this all possible.  So we can take these resulting networks, use different network metrics, so you can do  breadth first searches, you can do Markov clustering, you can do spectral clustering, and to create  sets out of those networks that represents the underlying biology and use those sets for  now epistatic discovery, epistatic testing, finding a lot of this missing heritability  of the interacting components of a system.  That gives us effectively a linear view of epistasis.  We also know that there are nonlinear relationships, and for that we're turning to explainable  AI approaches.  So of course, AI has been in the news a lot.  Traditional AI, machine learning, and deep learning historically have been great classification  engines, but effectively black boxes.  And many of them have been focused on problems that aren't necessarily near and dear to the  heart of science.  Picking out puppies in cats in pictures on Facebook is not a great scientific research  challenge.  But we don't want just classification, we want to understand the patterns that are leading  that classification.  That's the science, that's the biology.  Just those common tutorial patterns we're actually searching for.  So in collaboration with Ben Brown's lab at the Berkeley National Lab, and these efforts  led in my lab by Jonathan Romero and Ashley Cliff, who are also in the audience, we're  using iterative random forest that we have scaled and up and running on summit as a different  take to explainable machine learning.  So random forest have been a classification workhorse in machine learning for a long time,  but they're effectively stochastic.  You get slightly different results every time you run them on the same data set because  of the random nature.  So the beauty of iterative random forest is that you run a whole series of them.  So random forest is a collection of decision trees, weak learners individually, strong  learners collectively.  You do one random forest, take the results of that, they become the weights for the input  of the next random forest, and so on and so forth.  So after about six or seven rounds of this, typically you start to get convergence.  Your answer becomes consistent.  You get the same answer every time.  More importantly, in running all these random forest, you have all these different decision  trees.  And it turns out that your interaction space is in these pods in the splits of the decision  trees.  So with a different algorithm called random intersection trees, you can find these pods  that occur over and over and over again as predictive, in this case, of the phenotype.  And that turns out to be the epistatic space.  So this is another way, really, to break that curse of dimensionality and find the combinatorial  space that is represented in the data sets.  So instead of doing individual tests at a time, we're now finding phenotypes that are  conditional on all possible combinations of the genome variants in a population.  This is a representation of doing this across all the metabolites across the population,  so you get both an epistatic and a pleiotropic network, but genes are responsible for multiple  functions.  If we zoom in on this, we see the epistatic architecture around a single metabolite, which  has traditionally been thought to be involved in the Lignin biosynthesis pathway.  In this case, we actually find it's not involved in Lignin, it's actually been turned into  a defense compound.  It's another metabolite the plant is using to shape its neighborhood.  And we see the genes associating with it are all screaming immune system, plant immune  system.  With the idea that we can now design new crops based on how we're understanding phenotypes  in environmental associations, again, we're using random forests or iterative random forests  to give us response surface representations of the combinations of genetic elements and  how they can be optimized together to lead to phenotypes, to output of a system.  We can also build in multiple layers of this, so in fact, in addition to the phenotypes,  we can build in environmental layers and optimize an organism for both its productivity as well  as its adaption to specific environments.  So this is an IRF-driven genome selection algorithm.  We can simulate the results in silica and pick the parents to actually breed a species  into a targeted environment.  Now that we can figure out the environment, wouldn't it be nice if we knew what all the  possible environments were?  So we did this trivial little task of taking every point of dry land on the planet at square  kilometer resolution.  So that's 156 million places on the planet.  The tensors representing all these environmental conditions, it's about 414,000 elements long,  and then compared them all to each other, again, on summit.  Set a threshold and you can store the result as a network.  In this case, it turns into a network with 156 million nodes and something in the order  of 25 trillion edges that are representing the climate similarity, the environmental similarity  of every point of land on the planet.  We can start to view these networks in lots of different ways.  This is a view of Markov clustering at low resolution.  You start to break things out at different layers of resolution.  Keep in mind, historically, the climate type maps have had 17 different environments, 17  clusters to represent the entire planet.  Our lowest resolution clustering gives us 68 as we amp up the clustering.  We start to see very different biographic patterns start to fall out across the planet.  This gives us the ability to then choose areas that you want to design organisms to deploy  for.  At 715 clusters, we frankly run out of colors to represent them.  It's a visualization.  I think the real power of this approach is in the underlying network.  You can start anywhere on the planet and say, what is similar to this?  This has lots of applications in different areas.  We've done it globally.  We're doing it regionally in different parts of the globe.  Again, higher resolution clustering, breaking out different environments that you can use  for biotechnology, ecology, a whole range of other studies, even biomedicine.  To our knowledge, this is the largest climate type map ever predicted.  It has lots of different applications.  Imagine using GWTS and using the genome selection algorithms.  You can pick apart on the planet and design organisms specifically to thrive there.  Very briefly, I'll run you through a little bit of the precision healthcare.  Opioids addiction is a major, major issue in North America.  Running in the cost of trillions of dollars.  The CDC estimates that roughly 10% of people who are prescribed opioids will go on to develop  a full-blown addiction.  10% from a morbidity point of view, from a clinical point of view, is a horrific number.  From a biological point of view, it's a fascinating number.  Why only 10%?  Why are we not all addicted?  Opioids are actually the result of an evolutionary arms race between insects and plants.  Opioids are our second every metabolite of plants that have been selected for because  it's a very clever strategy.  If you have a caterpillar chewing on your leaves, if you get it stoned and happy and  it falls off and stares at the stun, it's not eating you anymore.  We are simply collateral damage of that evolutionary arms race because it's targeting fundamental  neural mechanisms that are shared by all bilateral organisms.  Pre-Cambrian evolution are these methods that are being selected for.  I can show you sea slugs that will be stoned and happy and hallucinating on opioids.  These are fundamental mechanisms.  They're very difficult to understand historically because we think they're epistatic.  This is gene by gene again and environment.  This falls into methods that we know how to do now.  We're fortunate to have this collaboration with the Veterans Administration where we  have the clinical records for 23 million people going back 20 years sitting at Oak Ridge.  We have 600,000 genotypes for them.  That number is headed towards 2 million over the next few years.  We have a great collaborator named Justice to study this problem.  We can use these sorts of approaches, including the machine learning approaches, to discover  the data-driven phenotypes associated with opioid addiction.  We also need to study them in model organisms because the E component.  When you're looking at historical electronic health records, you don't really have a great  way to capture environment.  Organisms usually complain if you experiment on them.  IRBs are not fond of that.  These are retrospective studies.  We need model organisms that we can apply stress to.  We're actually using Drosophila as an insect, the original target of opioids, as our discovery  engine.  It's been used for decades for biomedical research.  We figured out very high-throughput ways to collect behavioral phenotypes across Drosophila  genetic variant populations.  We figured we can phenotype about 7 million flies in a five-year period.  Really high-throughput phenotyping.  Again, start to pump it through these discovery pipelines so that we can understand the epistatic  architectures in humans and in insects and how they're conserved mechanisms into this  sort of discovery pipeline so we can look for therapeutic outcomes, ways to address  addiction, ways to predict it, and ways to prevent it.  All that is great, but we're greedy.  We want more.  We know that there are also relationships between the phenotypes, not just the genomic  elements.  So we want algorithms that can look for all possible interactions of phenotypes, conditional  and all possible interaction of genomic variants.  Of course, as you go across all these omics and phonomics layers, you can represent each  one of those as a matrix.  You want all possible associations within and across matrices.  As we add in all the environmental information, these very quickly become data cubes and  in fact, arbitrary polytopes.  To our knowledge, there's not an existing algorithm that can handle arbitrary polytopic  space.  So of course, we're creating one.  Jonathan has a prototype of a method we're calling TERF that will start to be able to  explore this arbitrary polytopic space and find these biological relationships within  and across all polytopes representing a biological system.  These sorts of approaches have applications to lots of different areas.  I've talked a lot about explainable machine learning.  We're also tackling deep learning and trying to make that explainable as well.  The sort of things I've mentioned for eukaryotes, we're also doing a lot of work to make these  methods amenable to microbes.  In the interest of time, I'll sort of blaze through this.  This is just a quick preliminary result of how in a collaboration with a group of the  National Renewable Energy Lab, Greg Beckham's group, they came up with a gene that was important  for their function of interest, but they had no idea what it was.  It's my favorite gene annotation ever.  Hypothetical protein of unknown function.  But we threw it into our systems biology networks and saw that everything around it was an outer  membrane porn, something that looks like a channel through a membrane.  We predicted the 3D structure and, in fact, it's a beta-barrel channel.  So when they've been confused about what this protein was doing for half a year, within  a week we had experimental results and telling them exactly where to look for function.  In fact, could explain the mutations they were seeing that were loss of function, how  they're actually affecting the key components of the beta-barrel channel.  So if you want to talk about multi-scale modeling, I would contend that we're going from single  nucleotide resolution in genomes up through ecosystem interactions and all the way up  to spectral light quality, which is coming from our nearest star.  So that's from a nucleotide to an astrophysical phenotype.  I think we're stepping across a few levels of scale here.  The overall grand picture is this whole combination of all these representations and environmental  data pumping through lots of different ensemble approaches of algorithms for the sort of unified  vision that then leads to more and more hypotheses to generate more data in experimental systems  that you can pump back in.  We're able to do a lot of this work because of the fantastic resources at the OLCF and  start to think about these crazy projects that a year ago would have been 2 billion  hours untitled.  So Jack wouldn't allow us to apply for that.  Now that's only, well, that would be 340,000 years on a laptop.  I'm not going to live that long.  But that's 17 days on summit, which is starting to be a reasonable thing to ask for.  Big shout-outs to all the collaborators.  This is an incomplete list.  A big shout-out to Charlie Clymer in the audience for the origination of the CCC algorithm  for co-evolution, funding sources, student programs, et cetera.  Ashley and Jonathan are in the audience and have posters here.  Please talk to them.  A big thank to Jack for all the support across the years in the introduction to Wayne.  Peter Thornton and ORL is our climate scientist on the climate type clustering projects.  I'm fortunate to lead a really, really fantastic group of students and postdocs and staff and  faculty.  The students decided we had to have a group acronym, so they came up with the Jacobson  Advanced Institute for Learning, or JAIL.  So when you join the group, you become an inmate and are incarcerated in science, and  we have some fun with that.  If you're interested in a broad range of topics, please talk to us.  We're really, really collaborative.  We love working with other groups to solve complex problems.  If you're a student, we have full-time PhD programs.  We have visiting, collaborative visits for PhD students, internships.  A lot of postdoc slots open now and opportunities for sabbaticals and collaborative visits  from faculty as well.  And with that, I can take questions.
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