 Great so welcome everybody so in about less than one minute we will be able to start again. So let's just wait for all the participants to come back from the breakout rooms and we'll start. I think everybody's back so it's my great pleasure to introduce the next lecturer, Alvaro Sanchez. Alvaro is a professor at the Department of Ecology and Evolutionary Biology at Yale University and these work focuses on predicting microbial dynamics and microbial evolution. And in today's in actually this cycle of lectures, he will talk about the assembly and evolution of microbial community and today is giving the first of three lectures so thank you very much Alvaro for being with us and please share your screen. Can you hear me okay? Yes, perfect. All right wonderful well thank you everybody for taking the time to be here today. So I'm going to give three lectures on the topic of microbial community assembly. And the three are very interconnected. So and I'm thinking of them as very I think relaxed and the idea is that you can get you know I have three hours to explain the how at least I see this problem. And I'm hopeful that that you know if you have a questions or or anything you can interrupt or let me know at any point. So, as I said we work on this very fascinating problems of understanding how microbial communities form and how they evolve and I think this is a very important problem for many reasons but perhaps the main one is because they have colonized all of the, all of the sources of the earth. You find them in the bodies of all animals and plants you find them in the soils and the oceans and absolutely everywhere. And the, they, in all of this habit that they carry fundamental and critical functions, they are responsible for the vast majority of nitrogen fixation for instance in the atmosphere, they directly impact the health of their hosts with animal implants. But that reason understanding the composition of these communities is so important. And for those of you who are not necessarily having paying attention to the entire field of microbial ecology I'm sure most of you have heard about the human microbiome. This is a, a, the realization has been, I mean, that is more, more acute in the past couple of decades that we share our bodies with an enormous number of microorganisms that have colonized basically all of the surfaces of our bodies that are connected with the outside world. We have microbes in our skin, in our gut, in our mouth, our stomach, and essentially anywhere where they could colonize from the outside world they have. And in all of these areas of our bodies they form these very dense and complex ecological communities containing a large number of species that interact with each other in really interesting ways. And, and a, the outcome of these interactions are the assembly of an ecological community of microbial community, who, which as I said before can have important effects for our health. And given the importance of these communities, a critical question is whether we can predict the assembly of these communities in a way that will allow us to manipulate them and to steer these communities towards states that are favorable and away from those that are not. And, and this is a question that has been very close to, to the heart of my life. This is one of the ones perhaps the main thing we're interested in. And if we want to develop a predictive theory of microbial community assembly, one that can tell you, okay, if I manipulate my this, if I can intervene in a microwave in this way, I'm going to observe that response. If we want to walk towards having such a theory, there's even more fundamental question that we need to understand first, which is how reproducible microbial community assembly is. And the reason for this is that the only way you can predict something is if, if it is reproducible, right, like if, for instance, imagine that you did an experiment 10 times and every time you got a different result. There wouldn't be anything you could predict, right. By the other hand, if you get to do the same example, experiment multiple times and every time you get the same result and there's some signal there's some phenomenon that you can aspire to predict. So, so that's a very fundamental question, which is how predictable is the process itself. And as I will discuss in the next two lectures. This is the answer is very interesting because it depends on the level of organization at which you're looking at it. I'm going to start by giving you a specific example and a paper that I, that I really liked that came out a few years ago by Stanos Loka and Michael Dovely. And this is a team of researchers that we're studying the assembly of the microbial communities that form within the foliage of bromeliad plants. So as you can see here, at the base of the of the foliage, bromeliads have a little, a little hole and the cavity that fills up with water. This water is steaming with microorganisms. We have bacteria, archaea, you have protists, you have, you know, all kinds of other, other microbes. And it is believed that the composition of those communities can be very important for the plant as it helps them fix nitrogen and some other things. So this is also a very interesting model system for understanding this question of how reproducible community assembly is because one could go to the same geographical location. These, these plants, I believe this study in particular was in Brazil, bromeliads are a tropical plant. And you can look at a set of plants that are in close proximity to one another and examine the communities that are assembled in them, right. And these are plants that I said, you know, those, the habitats in which those communities form are very similar, the plants are from the same species, they're experiencing very similar temperature humidity, and potentially also you should be, there should be colonized but very similar bacteria that are in their environment. So it's a very nice natural system to investigate how reproducible microbial community assembly is. And just because I wanted to, to walk you through what kind of data they collected and because the data that we collect ourselves look very similar, I wanted to just to take some time to explain to you what they observed. They looked at a, what, I think a few dozen plants that were in close proximity to one another and they measure the microbiome on each one of those plants. And the data looks more or less like this, this is the proportion of the microbiome that is made up by different, by different taxa, this operational taxonomic units, which is to think of these as the lowest level of resolution like a species level for instance. And, and they're looking at the composition of the microbiome of one particular plant and its color here represents a different, a different to you. And the width of this, of its, of this color represents the abundance of that OTE of that species in the community right so if, if the, for instance, this, this green, or took whatever color here. The width is very small means that the abundance is small, whereas this brown color here means that it's the width is larger, it means that that species was more abundant. They looked at the composition of plants that the communities associated to different plants they found that they were very variable. Less than 1% of all the species were found in all of their other plants. And as you can see here, each column represents a different plant, and, and most of the plants are contained a very different, very different community. And intriguingly what they found, however, is that when instead of looking at the, like, the taxonomic composition of the plants like they did here. They looked at the, the, the look at the metagenome of, of its community they look at with the, the, the, the abundance of the abundance of genes involved in different metabolic pathways. They found that if you do that, you know, the result for a given plant looks something like this right in brown here they're looking at the abundance of genes involved in fermentation in blue genes involved in reduction respiration, a current fixation and a bunch of other metabolic functions. And when you look at that level of resolution community assembly was much more predictable and when you look at the metagenome of different plants you find that they're quite convergent and similar to one another. And this idea very similar results have been reported in other systems as well. For instance, in studies of the microbial communities associated to micro algae, which I can find that community assembly. When you looked at it at the level of function and the metagenome, they found communities that were very similar to one another when they look at different individuals, yet when they look at the taxonomic composition, it was much more variable. And the same has been observed, for instance, for various body parts in the human microbiome. Again, taxonomic composition, even at the final level could be quite variable for, for different subjects in the same body part. Yet when you look across different subjects but look at the level of metabolic pathways that you find the different fractions of the metagenome that are devoted to different metabolic functions. You find that different body parts have distinct microbiomes and that they are much more similar from host to host. There's still variation, but it's much less. And that has been proposed as little the proposition that of a paradigm for the organization of micro communities. And this is again by Steno's local colleagues, where they, they propose that similar environments should promote similar microbial committee function while allowing for taxonomic variation within individual functional groups. So you could have, within a functional group, you could have that different taxa can differ in which, and different habitats can differ in which taxa they contain, yet when you look at them at the level of function, they are much more similar to one another. Right, so this is pointing to the existence of a generic organization of micro communities. And this question that was at the beginning of how reproducible micro communities assembly is, then it seems like there are the answer will depend on what level of organization you are studying. So, but even though there seems to be a rule that emerges when you look at a large number of different habitats, it's still very clear where it comes from, right, and what is the origin of this principle that seems to be found so often. So to understand this better, it's worth thinking a little bit about the forces that shape the assembly of microbial communities, or ecological communities in general, right. Ecological communities are the outcome of both deterministic and stochastic processes. So on the on the deterministic side you have selection, right, and here the idea is that in a given habitat, you're going to have a some taxa that grow better than others, right, and that are going to have higher fitness and others taxa. And this is going to lead to more convergence. If you have two, two different habitats that are very similar to one another, the selective pressures they will experience are going to be also more similar. And that can be a force that will help to generate convergence in committee assembly. On the other hand, there's also a wide range of ecological processes that are stochastic in nature, right, and will lead to more variable committee assembly. You have, for instance, dispersal, it's clear that you can only find in one habitat those taxa that were able to colonize the habitat. And the process of dispersal, and this is very true in microbial communities, is going to be highly stochastic. For instance, think of the bromeliad tanks that I was telling you about before. It is clear that if a specific mosquito arrives and lays an egg into one water tank but not another, then the bacteria that it carries will colonize that habitat but not the other, right. So that is going to be a force that will lead to variability. Of course, micro populations, even though they're very large, they're still made up by individual cells that have to divide and have to die. So you have births and deaths and all those are stochastic processes too. So finally, you have mutations. Evolution will always be a force and microbes evolve fast. They have relatively large mutation rates and large population sizes, so mutants emerge quite rapidly. And then, of course, mutations occur randomly in the genome, so that's another force that will generate variation, could generate variation in community assembly. And finally, one that is very interesting is historical contingency, right. We, one of the very salient features of microbes is that as they grow, they cause traumatic modifications in their environment. I don't know if anyone here has had experience with growing bacteria in the lab, even for other reasons than doing microbial ecology, but if you have, even for cloning or whatever, you probably took a colony, say, of E. coli and grew it in medium that, for instance, could contain glucose as the main carbon source, you grew it overnight and you harvested the cells the next day. So if you've ever done that exercise, you should know that in the process of just growing from one small colony to a very large number of cells, the environment that is left out after you harvest your cells is completely different from the one at the beginning. In just 24 hours, these bacteria have turned an environment that may have contained glucose as the only carbon source, for instance, into an environment that will contain a large number of other nutrients. That's the only way, right, in which microbes can affect the environment. Micros can engage in microbial warfare with one another, they can release antibiotics and colicins and other toxins that can kill other bacteria, they release them to the environment as they grow. They also modify the spatial structure of their other habitat by forming biofilms, and they also secrete enzymes to the outside world that can break down complex, the physical structure of the environment, where it all comes complex polymers around them. And the collective modification of the environment that microbes cause as they grow leads to another ecological force, if you will, that is a combination of both stochastic and determinism, which is historical contingency. And we can visualize historical contingency quite easily, right, imagine that you have now two different taxa arriving in an environment, right, and only this one experiences can grow in that environment, so you have very strong fitness differences between them. But as this taxon grows in the environment, it'll change it, right, and as it changes that environment, then the other taxon now might be able to grow. Maybe even a taxon that could not grow in the original environment could potentially grow after the first one has been growing in it through the environmental modifications that it applies. And that will mean, of course, that if you have two habitats that are regionally very similar from one another, and they get colonized by different taxa, because as we said, that first part is quite random. Each one of those taxa will modify the environment in a different way, and now those environments won't be similar anymore, and they will present different selective pressures that will then impact further colonists that will arrive later, as well as the overall physical and chemical structure of those habitats. So, what's important here is that some of those forces, like selection, can lead to reproducibility across habitats that are identical, right, and of course, not habitats that are not, right, but at least when habitats are very similar, selection will, it's a force that will lead to more similar communities, under some conditions, whereas sense and historical contingency can push you away from similarity and may cause more dissimilar communities. And the complications, of course, that all of these psychological forces are acting in nature at the same time, and they're very difficult to disentangle mechanistically in the wild. So, one of the questions we are trying to, one of the approaches we're trying to follow in my lab is to see if it is possible to recreate the process of microcommunity assembly under conditions where we can study it, and we can disentangle and control the levels of variation in the laboratory. And our idea would be that if you want to ask reproducible microcommunity assembly is, it would be very helpful to be able to study this in a system where we know, for instance, if our habitats are at least initially all identical from one another or not. This is very difficult to do in the wild, right, even in the case of the bromeliad plants that I said before, you don't really know that all those plants are really identical. You know they're similar, right, but you don't really know what the selective pressures are in those habitats, and you don't know how different they are from another, really. And, moreover, we wanted to see if it would be possible to have these habitats being as controlled as possible so that we could know what the selective pressures are, or at least have a very good idea of what they might be. For instance, if we control the number and identity and concentration of all nutrients, if we can control the temperature in the pH and other physical parameters, such as the geometry of the vessel with the bacteria grow, so we can control all of those things and make them equal across habitats, then we would be able to ask this question of how reproducible community assembly is in a much more controlled manner. In addition to that, what we seek is a system, an experimental system, where we can also control a lot of the ecological processes that are very difficult to know, even in natural habitats. For instance, in the case of the bromeliads, again, we don't have access to the entire colonization history of those habitats. We do not know if some habitats were colonized by different set of bacteria than others, but this is very difficult to really make that inference. But under laboratory conditions, we can inoculate migrants at known time intervals and we can also know how many cells are arriving and with what frequency, from which regional pulse of species, so all of those things are things we have under control. We can also control how connected the habitats are, if there's migration within them or not, if there are any populations, we can impose them. So we wanted to have, in other words, an experimental system where we can study this question of how reproducible community assembly is, under conditions where we can eliminate a lot of the sources of variation that would occur in nature across habitats, so that we can really get to the bottom of what are the intrinsic processes that lead to variation at the community assembly level in an experimental system. So if we had a system like that, the question is if we could predict a community assembly. So there is this question, and to give you a sense of what is the experimental pipeline in our lab, we start, we use a technique that microbiologists call enrichment cultures and we do this high throughput. We start from natural samples, for instance, we can take a leaf of a plant, and then we can stick that leaf into a test tube. At this stage, we are treating our samples with antifungals and other drugs that will eliminate the growth or inhibit the growth of eukaryotes, for no other reason that we wanted to, these are the very first experiments we did. So we wanted to make them as simple as possible and be left only with the bacteria. With that, we can take all of the bacteria that live here, we can filter all of these plant particles, and we are left with essentially only the bacterial component of the community that we had on that leaf. And now we can do is we could take that large initial pool of species and sample from it and put it into a bioreactor, which is a very small, you can think of it as being a very small test tube. And we can let them, we can add some migrants into bioreactor that contains a synthetic medium. This is a growth medium that we have created in the lab, where we know exactly what all of the nutrients are, and we know exactly the amounts that we're providing. And, and then this is for those of you who know more, this is this in this experience I want to tell you about, this is M9 minimal media. And I'm going to start by using glucose as the only carbon source, although in the future, you'll see all the other nutrients as well. But say that you have a minimal media where this is limited by carbon, and it has a single source of carbon here, right? So this is the simplest environment you can think of, this is a liquid environment, there's no special structure to begin with. And we are at the single growth limiting factor, which is in this case glucose. And now once we colonize that habitat from the community, we let the cells grow. We typically let them grow for 48 hours. And after 48 hours, what we do is we take this, so some cells will grow, some others will not. But then those that grow, we will take a small sample from here, right? And we'll inoculate another habitat just identical to the one we had on the first day. And so we again, we have another little test tube when we put fresh concentration of all the nutrients, the glucose and all the mineral salts and everything else we're adding. And then we let that grow again. And then after 24 hours, 48 hours, we'll take a small aliquot and put it into a new test tube where we replenish all nutrients, and we can keep doing it again and again. This is called serial passaging. And it's a mechanism, you can think of it as a seasonal environment with seasonal bottlenecks as well as addition of nutrients. And then at the end of every 48 hour period, we use 16S community level sequencing to take a census of what cells are present, what species are present, and what their abundance is. So again, we do this at the end of every 48 hour period, right? On the grown community before we replant the water. So I guess the first question you could ask is if you do this simple experiment and again you're looking at the assembly of a community in an environment that contains a single growth-limiting research, you might not think that more than one species might survive, right? The first question is will you get a community if you do this? Or are you going to get a single species that will outcompete everybody else? Well, let me give you an example. I mean, the answer is no, we always get communities, even though we only have a single growth-limiting research. But the way that the dynamics look like, right? On day zero, we have a very large community. And I will show you some examples of what this community looked like in a minute. Again, each color represents a different genus. And the width of its band represents the abundance of that genus in the community. All this gray stuff you see over here, these are species that represent such low abundance that we cannot really show you here all of the bars that you would need to in order to see them. And now what we see is that after 48 hours, community has changed, as you would expect. Some of these tags that could grow, but the other ones could not. And then as a function of time, every single time we do one of these 48-hour growth periods, community composition starts changing. And after about eight to 12 transfers, you see that community composition has reached the state where every day after every 48-hour period, the communities look very much like they did the day before. So they reach a state of, we call this a state of equilibrium, understanding that of course there might be even slower time scales here that we're not capturing. But we have actually done experiments now where we have propagated communities for longer time. But for the rest of this talk and the next two, let's just assume that after once the community is stabilized here, that this will be a state of equilibrium. And as I will show you, we have probably the last lecture, we have solid evidence to believe that this is actually very close to an actual dynamic equilibrium. All right, so this is the system we have. Let's go back to the question we're asking, how reproducible is my community assembly in this habit that are so well controlled? And I think here the idea is, okay, so a system is going to be reproducible if every time we do an experiment, we're going to get the same result. And if every time we did the same experiment, we've got different results, then we would not be able to predict community assembly, right? So this is what we did, right? We take from the same species pool, we inoculate eight replicate populations, eight replicate habitats that are identical to one another. And we are going to propagate them in the exact same manner as I showed you before. And I'm going to show you the community assembly we observed after about 80 generations or 12 of these transfers. Again, so this is the outcome, right? So if you look at community composition of the species level, you find that even though these eight habitats are identical to one another, as identical as you can make them. Even though they're all colonized from the same regional pool at the same time, right? So even though we're eliminated all sorts of variation to the maximum extent we could. I mean, of course, you still have to factor in human error and these are experiments like nothing is perfect. You know, this is, this gotta be much more reproducible. For instance, the bromelia tanks I showed you before, right? This is well controlled laboratory study. And as you can see here, each of these times you did the experiment, you basically got a different outcome, right? Here we're showing species, in fact, genus level, it doesn't matter. And again, different colors represent different ESVs. You could see here, I don't know if you can resolve this here, but these are two different shades of red, right? Three different shades of red. So anyway, you can see that communities are not the same, right? Even though you did this experiment multiple times. However, we also noticed that if instead of grouping all the, all the, the, the, the reason we were sequencing by species, we group them by the taxonomic family that these belong to. What we find is that when we do that, then community assembly becomes much more reproducible. And, and that all of these eight replicates contain ratios of the same two dominant families. In blue, this is the center of active Asia. This is the family to which the famous bacteria belongs to. And in red here, this is pseudomonasia. This is another very common, in this case, a very common environmental bacteria. You find them in the soils and plants and also in the human body. So members of the family can be also a pathogen. All right, so you see that when you do the experiment eight times, the results that you observe are very reproducible at the family level, but not reproducible at all at the species level. Okay, so, so this is if you use the same inoculum, right? But what if you use different species pools inoculating its habitats, right? How reproducible would things be then? I mean, I think there's all kind of reasons why you would imagine that this should lead to less reproducibility, right? Because its habitat now is being colonized by different group of bacteria. So if anything, you would expect things to be less reproducible than they were before, where all these habitats were colonized by the same bacteria, by the same inoculum. We went and collected samples, environmental samples from soil plants, aquatic communities, like soccer field, and various other soil plant and aquatic environments around, you know, 20 miles radius of our lab. And if you look at, this is at the, if you can see this here, the different communities were quite different from each other, right? I'm talking about the soil inocula. Even when you look at this at the other level, you find that there are, you know, they contain very different species and abundances and taxonomic groups. All right, so now when we repeat the experiment that we did before, but now using this, these 12 regional species pools that again are very different from one another. And we process them to the same pipeline, we inoculate them into a different, different test tube and all of them contain glucose as in a carbon source that they grow on, and you incubate them for 80 generations just as we did before. What you find is that you have that each of these inocula lead to different communities, right? And at the species level, which is, you know, not surprising because we are using different pools of species, right? Yet when you do the same thing with it again and look at assembly at the family level, now you find that community assembly are still very reproducible, even though the inocula was used in each one of these wells is different from each other. So, this is painting this picture that community assembly is actually very reproducible, right, at the, at the family level, if conditions among wells are the same, right? Even when you're putting different sense of taxa on each one of those wells. And this is another way to visualize this data, right? At the start, each of these communities have very different fractions of these two dominant families, Interactive Asia, Summoner Asia, as well as other families. But at the end, they have converged to a very similar location on this, on the simplex highlighting the river productivity that one sees in our experiments. Right, so all of these that told you about prompts three questions, which are going to be the subject of the three lectures I'm giving. The first is going to be the rest of this talk, I'm going to be talking about this first point of why so many species coexist on a single limiting resource. Tomorrow, I will be talking about why community assembly is so convergent on the family level, what does that mean that high levels of taxonomy community assembly is more reproducible. And finally, the last day, I will be telling you more about why is community assembly so variable at the species and genus level, right? And this is going to try to give you explanations for these three kind of interesting results will reveal, right? First, many taxa can actually coexist when used to play a single limiting resource. And second, that the composition of those communities that will form are going to be very similar to family level and very variable at the species level. All right, so today I just wanted to devote the rest of this lecture to this first question, which is why do so many species coexist on a single growth limiting resource. So, the kind of the reason why these might be so much surprising is that is the competitive exclusion principle, right? We are providing a single limiting resource in the case that occupies in the data I showed you before, it's glucose, and this is a carbon limited environment, right? So you might imagine that, or at least in fact that's what I did when I first did these experiments back in the day that I was going to see just a single taxon that would have compete everyone else. And if, you know, very simple yet profound ecological theory tells you that's more or less what you should expect, at least that coexistence should be difficult. There are other conditions where you have a single supply nutrient, there are, of course, ways around it, but at least kind of your first knowledge expectation would be that coexistence is going to be hard. And in particular, that you really shouldn't have to be expecting to have more species than there are limiting resources, right? So here we are having one. So, the solution really, or the idea that we had was that this might be caused by a phenomenon that is very widespread in microbial communities, which is metabolic risk feeding. And I gave you some examples before about how when microorganisms grow in an environment, they can fundamentally alter that environment, right? And perhaps the best way to illustrate how powerful this can be is to tell you a little bit about this one experiment that is one of my favorite experiments of all time. It's a beautiful, beautiful result. So this is an analysis of a previous experiment that was done in the lab of Julian Adams where they had evolved a single clonal population of the bacterium E. coli in a chemostat. So for those of you who are not familiar with what a chemostat is, so you think it's basically a continuous culture device. You have a vessel where it's a growth chamber where you would have, for instance, a population of E. coli growing. And then you are constantly feeding nutrients from a medium reservoir. And on the other hand, you are, it's wrong with my mouse, I don't know why. Okay, and here you are also taking out media, right, as well as cells. So you are keeping, for instance, the volume constant, right, of your vessel. So now the faster you flow, the faster you take out matter too, right? So you are in a chemostat, you can control the feeding rate, which is the same as the flow rate through this system. And you can also control the content of the medium that you're supplying and so on and so forth. So in this case, there was a chemostat that contained a single clonal population, originally clonal population of E. coli. And it was adapting, it was growing this environment for over 700 generations. And as E. coli grew in this environment, again, this is an isogenic population. What it does is that as it optics glucose, it will metabolize the glucose and E. coli has a form of glucose metabolism that is a respirofermented, right? So to grow fast on E. coli, it needs to grow fast on glucose, E. coli needs to partially ferment it. And what it will do is that it will metabolize the glucose and produce part of that goes through the entire TCA cycle, but some part is just driving into overflow metabolism. And when that results into the production of relative quantities of fermentation by products like acetate, glycerol, succinate, pyruvate, and other organic acids that are produced as E. coli grows on glucose. So while it grows on glucose, it releases other nutrients into the environment. And as it does that, it changes the biochemical and nutritional composition of that environment. As E. coli grows in this glucose in the chemostat, it's transforming an environment where it has a single growth limiting resource into an environment that contains other niches as well. So that leads to, in their study, in a relatively short time, about 750 generations, the originally isogenic population of E. coli had diversified into an ecosystem. And so on the one hand, they observed that E. coli had, there was a strain of E. coli that had evolved a stronger growth rate in glucose and a larger optic rate of glucose. But an avoidable consequence of growing faster in glucose is that you're going to have to be more wasteful, right? And then you're going to have to create more acetate and more glycerol and release it to the environment. And that enhanced concentration of acetate and glycerol, that is where the cells are basically stewing on, led to the creation of niches that were occupied by other derived strains that had adapted to consuming acetate and glycerol. So in other words, in less than a thousand generations, E. coli had diversified from a single clonal population into a small ecosystem consisting of three cross feeding strains that coexisted with each other. And again, this is the power of cross feeding, right? You can give them bacteria single growth limiting research, but they're going to change the environment and create other resources that provide the substrate for other cells to grow, potentially leading to more diversity that you would expect, naively. So to get a sense of what a cross feeding could be a ecological force of importance in our habitats. We did experiments where we took, in this case, one of our communities and we isolated some of the dominant members and what I used to hear each of these circles here are a colony of a different species. And you can see that here that you have different morphologies of these colonies and that is indicative of different species present in a community. Each species contains its own, when it forms a colony, the colony has a different shape, right? So we're able to separate and to isolate the members of, for instance, this community here, which contained three coexisting taxes of the family interactoricaeusia, intervacter, rautelans, intervacter, as well as one taxon of the genus pseudomonas. And one, which is the stenotrophomonas, which we were not able to isolate because it doesn't grow on its own. It requires the presence of its ecological partners in order to grow in this habitat. Okay, so first of all, we find that all of these species can grow. There's four that we were able to isolate are able to grow on glucose as the only carbon source. Now, the question we asked then is, can they also grow on the metabolic secretions of the metabolites released by the other species? So we did a very simple experiment. We took each of the isolates and grew them on glucose for 48 hours. And after those 48 hours, we took the medium that was left over and used it as the substrate on which the other taxa were put. So we would take another species and see if it could grow on an environment that contained glucose no more. There was no glucose left. It was completely consumed, but contained all of the metabolic secretions by the former, right? And we grew all the taxon metabolic secretions of each other. And the result was quite what's striking, right? Here I'm showing one example. This is Citrobacter, one of the four members of this community, growing in the secretions of enterovacter. This is another of the members. And on gray, here, show you the growth of Citrobacter in glucose. You see that it has this very sudden rapid growth, which is caused by the overflow metabolism. When Citrobacter grows, it uses this fermentative pathway. And during that time, it grows really, really fast. And then it reaches this first plateau, which it reaches when glucose runs out. And then it starts, and I'm talking now about the gray curve here. Citrobacter will start growing on its own secretions. But if you grow it entirely on the secretions of enterovacter, you will see that the growth rate will never be as large. I mean, the slope of this growth curve, which is here in black, will never be as large as the one in glucose. But it can still grow quite robustly and it can reach high yields, right? What this shows is that there's still even after glucose is consumed, and very large growth potential in this bacteria on the byproducts, right? That cross-feeding can be quite substantial in our communities. And we repeated this experiment for every possible parent in this four, and we noticed that all four of these stacks could grow on all of them could grow on glucose, but they could all grow on each other's metabolic secretions. And I'm going to tell you more about this in the next lecture tomorrow. But before I move on to the next, I just wanted to highlight how important cross-feeding can be even at the level of the whole community. Here we are plotting the biomass of the community as a function of time, and I am plotting it as well as the amount of glucose in the environment. So we measure the amounts of glucose present at time zero, which is there was a 0.2% glucose in the environment. And then after 24 hours, when we took the next measurement, we quantified the growth, the total amount of growth of 95 different communities that had spontaneously assembled in glucose environments. There's other 95 dependent experiments from different inocula. And we noticed that, of course, as you would expect, there's a lot of cells, right? Cells have grown on glucose. But after 24 hours, glucose was completely gone. It has been exhausted, yet growth continued strong for another 24 hours. And in fact, now we know that growth had that glucose has been exhausted even before those 24 hours, depending on the community typically takes between 12 and 18 hours for glucose to be exhausted. So the total amount of growth on the byproducts, now we are quantifying this to be about the same as the total amount of growth that occurs in glucose, right? Total amount of biomass. And what we can do then is when we did this collaboration with my colleague, Bankat Mekta, and what we were trying to do is see if we could incorporate this cross feeding into the MacArthur consumer resource models. And the ones that will tell you that you should only expect to see a single species in a single growth limiting resource. And indeed, the simulations recapitulate our findings. So the only difference really that we're adding is this term here, which incorporates the metabolite production through cross feeding. And the rest is exactly the same as the MacArthur model. And, you know, again, we observe that when we set the cross feeding to zero in an environment where we supply a single growth limiting resource, there's a single species that can survive the ones are competitively excluded. But when we assume that the cross feed, despite using random matrices that capture the amount of cross feeding, we find that there's generically multiple species coexisting. And the number of tax you see is about half, typically on average, the total number of resources that can be supplied through cross feeding. All right, so I've given you some some evidence that cross feeding is very important that it that the environment is no longer a one research community after even a few hours of growth, and that that is very likely giving us the multiple species being able to coexist. But there are other potential factors right that could lead to coexistence even without cross feeding. And one of them is spatial structure, right. And you might find that different taxa are occupying different, different niches within a spatial niches within our habitats. So all the experiments that I've told you about we're done in stable liquid environments, right, so it's liquid environments that are still we're not shaking them or anything like that. And when that is the case and you may imagine that different bacteria might be able to be occupying different strata on the on the on the either some some tax some tax that could be for instance living in a water interface, others could be living in the in the solid liquid interface on the bottom. And so once we wanted to get a sense of to what degree this would be important. So what we did is that we repeated the experiment by shaking our vessels growth vessels vigorously. And therefore we're moving as much of of spatial structure as we could. And I just have to say also we had no evidence of biofilm formation, at least suggesting that that that also was not an issue. And every single time we've done it we've observed coexistence of multiple taxa right so even when you remove spatial structure to the best of your ability, then multiple tasks I can still coexist together on a single supply resource. Another potential possibility is temporal niches, and that that could also lead to coexistence. And to understand why let's go back to this community that we have been talking about that we have kind of dissected before that contains this for taxon factor, sit back to enter back to zoom on as I will tell you know one possibility could be that that even if cross feeding was not playing an important role. As taxa grow glucose, it might be that different taxa have a growth advantage over others at different concentrations of glucose. For instance, it could be that sit back there had an advantage at very high glucose concentration. But then as the as the as the media as the tech as the community starts depleting the amount of glucose. It might be at the level of the level where it has an advantage and it might be that later on, enter a factor in this period here. At this level of glucose concentration enter a back to has an edge over the others a competitive advantage, or it could be that then later should want us later on. Right so simply by the beating the amount of glucose as the cells grow. That's another way with bacteria are modifying the environment, and that could potentially to go existence in fact, in some case it has been observed that that is a mechanism that is important, there's some some lists are better at growing at high glucose concentrations others are better at going out lower glucose concentrations right so so that could lead potential to potentially to a stabilization a stabilization mechanism. So we, we wanted to test this, this idea so what we did is that we took those four taxa, and then we grew them at different glucose concentrations and measure the growth rates at those concentrations. What we did then is we parameterized the growth rate as a function glucose for each one of these four taxa and using a monote model, and then we just incorporated a very basic consumer resource model that contains this parameterization and simulated the growth of our communities again assuming this periodic supply of glucose every 48 hours, followed by a dilution issue so we parameterize this and ask whether in under the condition of our experiments, you would expect this for taxa to coexist, and there's no right there, you can even hint just qualitatively looking at the data that there really isn't a very serious crossover right sit back there tends to grow glucose better than than the other system, the only one that crosses over but enterobactors will want to roll Tela their growth curves are very parallel to one another right so that there is not like they're occupying different concentration reaches that with the position of glucose over time might lead to coexistence. Another possibility is acidification again it's it's another way in which bacteria can change the environment without necessarily invoking any cross feeding. So, as I was telling you before the production of acetate and other organic acids and lactic acid in particular will bring the pH down right. And if you look at the, the various members of this community and they're back to roll tell us it's back to the seven teresia which are fermentative. And they all produce different organic acids and when they do the pH of the environment comes down right. So one possibility was that as these cells are growing the pH is dropping right and if the pH is dropping it might be that some types are better than others at different pH right so this is another mechanism in this case that is not needed by cross feeding simply is lowering the increase in the acidity of the environment slowly, and it could be the different types that have a growth advantage of different pH values. However, what we find is that even though each one of these taxine isolation will lower the pH of the community of the of the habitat, the community itself does not. So that when you look at the, the pH over time for a community and here are some examples. The pH rarely drops below six and in most cases even it remains very, very similar over time to what it was in the beginning again that the individual species themselves do acidify the environment but the community itself. That's not at least 20 appreciable degree. And at any rate we also measure the growth rates of these taxa at pH ranging from six to seven which is where the pH lies and over the top 48 hour duration time, and there's really no crossover point either for that. And finally, what we've been also looking into is cell death right so one possible mechanism that is not cross feeding but could also lead to coexistence is a cell death. And there's two different ways which you could see this. For instance, you could think of, well we're growing these communities under serial growth conditions so we're growing them for 48 hours. And in that time cells have time to grow, but also they would have time to die right. So, if cells are coexisting with each other is going to be true that they have the same fitness right so basically every every species that is coexisting in stably in these environments will have to double exactly 6.7 times every 48 hours, if they want to coexist with each other. And it is possible that that is which that is done because some cells actually grow better in glucose than others but then they die. And then the two cuts up at the end right so that's one possibility. But another possibility also has to do with environmental modifications as cells die. They will also spew out their contents out into the environment right so that can also create niches. So we call that cross feeding right it's not really cross feeding when you die. So, at any rate, we wanted to explore this question and we asked how much death we observed in our in our habitats as a function of time. And there are various experimental techniques you could use to monitor cell death in culture and there are both dies that will stain to the price membranes that memorize the prices you you see an unviable cell, but also when cells lies they leave this this kind of corpses behind that you can also detect by face contrast microscopy. So we looked at micrograph after micrograph of these communities to try to quantify the, the number of cells that were either the price or dead lies after a different time points. And we observed this for two different communities that the fraction of that cells is relatively small, and we don't really see. In one case we saw as a little bit of a change over time but in the other we really didn't. So, it doesn't seem like cell death is going to be a major contributor to to coexistence in our communities we're not seeing anything massive. At least. All right, so in this first lecture, I introduce you to the problem that we're going to be talking about for the next two. And I've been also focusing on these three questions that we wanted to to address. And I've been focusing on this one right and why so many species coexist on a single limited resource. The, the answer that we are finding is that the by and large the main reason is metabolic crossfeeding. There's many other contributors and I'm not claiming that a certification or that other factors could not contribute as well in fact that the first sense I mean if cells died that will release the content even though they're not many cells. And that is part of the environment right so basically everything the cells are doing affect the environment will affect the coexistence. But crossfeeding is a very, very large factor right like about half of the total biomass that we observe in our communities is is originates from molecules that have been released to the environment for nutrients have been released to the environment by the bacteria that grew on the supplied research right so it's it's a from what we have been able to tell the dominant factor in our communities. The summary of this first lecture is is simple right first we have observed that very large number of taxa can coexist in Sierra Lea passes environments with a single single supply limiting research. We find metabolic crossfeeding being a primary responsible for that coexistence. And we also find that communities that are assembled in simple habitats can exhibit reproducible assembly at high levels of taxonomic organization a family or higher, but they're very variable at lower level soft taxonomy. And this behavior is is reminiscent of this metabolic versus convergence versus our functional convergence despite taxonomic diversions that people have reported in nature but we have a better sense of understanding mechanistically, because these communities have been assembled in habits that we understand. And this is something that will explain tomorrow and on Wednesday, and we'll get to any more depth. So, I just wanted to close by thinking everybody my lab, and who are the people have been doing this work. And, and yeah, that's, that's all I have to say if you have any any questions I'm happy to take them. Thank you so much for the fantastic lecture. So there are a few questions from the chat that I can start reading from you. And in the meanwhile, if anyone wants to ask a question please use the raise and the tool of zoom. So there is Leo, who is asking about the cross feeding, whether you have major metabolomics in the spent medium to determine which metabolites are key mediators and that it might and he comments that might be interesting to compare the number of species in the community and the number of such key cross feeding mediators. Yeah, that's that's a really good question. So we have a lot of work metabolomics for some of these communities. And so the dominant by products that we observe our, our acetate, succinate. Pyruvate and lactate. And then we see a large number of other by other by products at relatively low abundances in the business is doing LCMS mass spectrometry. There are of course a large number of resources right in the, in the mass that are, you know, detected through my spec. But as I said, the key here is not just the number of resources you have but also the abundance right because many are very relatively rare. Now in simulations we've done. It is true that even just that even resources that are relatively rare can be critical to stabilize communities right so in principle you could have more. So you could have just a few a handful of resources that have abundance and a large number of resources the law abundance and those law abundance resources, even though they might seem an important can actually be quite critical at least the simulations for coexistence of fairly decent number of species in our habitats. So I completely, I think it is a very interesting question is, is how many resources do you need in order to have coexistence. And, but we've done also experiments in our lab where we went from just having one resource to two or three. And what we find is that that the richness of our communities doesn't grow. It barely does right so even though you're doubling or tripling the number of resources that the number of taxa you see barely grows at all does a little bit but only statistical sense and it's in most cases. So there's, I suspect that there's other factors that limit diversity in our in our communities and we still trying to and we have some ideas of why this is but we don't have any any actual evidence at the moment. Thank you. Thank you. That's fantastic answer. Okay. Great. So there is one question from Lorenzo. Hi, I wanted to ask you a bit more about the eight communities developed from the same sample on the same resources. And I wanted to ask about if you have any data on temporal stability so the stability of the composition in time instead of on different samples. Right. So, yeah, we do. Here I'm showing one I mean we have I have other plots I could show you, but this is quite, this is quite general right so this is I have maybe I maybe I can bring a slide for next next time and show it at the beginning. Yeah, I know we've done it and in fact, in fact it's in the paper if you if you if you go to the supplementary material, where we show data like this for 12 color inocular that we find is that there's this initial axis quite remarkable we tend to see an initial increase in the abundance of interactive Asia, but then as function of time it stabilizes and after about 12 transfers remains constant. And we have another paper we've done this for 18 transfers to 12. And, and yes, once you once they reach about it depends on composition but between seven and 12 transfers, the communities will stabilize and remain constant for the remaining of the experiment. And we finally have an experiment that we just did it that we propagate 12 communities for a year. And we're still processing the data so so yes we have data on it and it's in the supplement of the of this paper which I don't know why I'm sorry this is from an old slide. It's 2018 in science and you can see the in the supplement you can see the results that look exactly like this, but for other communities and they look very similar. Thank you very much. Great, there is a question from Martina. I have a question regarding the special structure, and I agree that you can have coexistence without special structure but since you have different results when you shake or you don't shake do you think that the stratification might be important, especially because you have some, I don't know by products of species that maybe are doing an aerobic metabolism that maybe are at the bottom and right. So we, that's a good question and it here. This is their color differently but but this is so sorry for that but this is, this is central bacteria is yeah. And this is the monads. So that at the family level that the results are not that different right they're very similar, whether you shake or not that's just the coloring is different but sorry about that. But but we find that actually they're flipped here blue is where it should be blue. But but yeah, no, it's, it's, we see the same as this interactive is yeah. We have now done experiments in in deeper wells, where we're a different geometry and when you do it then that you do find that the respires have a hard time and I think it's because of oxygen depletion that cannot be supplied fast enough right so if oxygen cannot get through, then the respire is going to have a hard time because they don't have like receptors and fermentative metabolism is going to be favored. But at least in this experiment I'm showing here. And like for instance when you do this in 24 what plates or in on in not very deep 96 what plates, then the results are very consistent and doesn't matter whether you shake or not. The outcome is very similar but your point is well taken and I think if you, if, if you have a deeper well where they can be an oxygen gradient, then by all means I think that spatial structure can be quite important. Yeah, thank you. Maybe can I ask you just a quick question. The metabolomics were targeted or untargeted. So we've done targeted. And we've also done more recently and targeted to. Thank you. The next question is from Kisa. Oh, hello. Hi, I just put the questions in the chat but do you like, do you have the comparison between the effect of serial passages. If you do a lot of serial passing then it is putting selection pressure on on the microbes that prefer the carbon, the single carbon source. I don't think I could understand the question. Okay, because in your experiments you do like a lot of serial packaging, more than 80 generations. What, what would the effect of those serial passages be on the final final constitution of these microbial communities because I think those those procedures would have would impose selection on the microbes. Right, right. If you didn't do the serial passages, even if you if we had done a single batch, right and never. Right, no, that's a very, that's a really interesting question, right, because one alternative and with something we're exploring now will be to just don't a single batch, right. And then let cells, basically stew in there, right, and die and whatever right. That could that is something we're exploring now because I think by, in fact, you know that experiments with to see your passing every so you could think of that experiment as a, as a limit right when when t tends to infinity right of procedure time t, right. And now I can tell you that if you do the, we've done experiments where we included only for 24 hours instead of 48. And the pseudomonas are gone. We only see interactivity and diversity plummets right. If you go from 24 to 48, then you see pseudomonas appearing right. So monos are the primary consumers, this is kind of spoiling tomorrow's lecture but it's okay. That's the monos are the primary consumers of the products in these communities and they grow primarily on the second half of the 48 hour right. So I suspect that if you leave it longer and longer, right, then you're going to start catching up others, other other species that may grow more slowly. Right, but that that may be, maybe they won't die right and they will, they will, I would expect to start seeing cell death if you let the cells, therefore very long periods of time, and they will have a succession of bacteria. Now the complication though is that bacteria can start mutating very fast. They can enter disgust state and start mutating really fast when they start right. And there's other other issues that could occur when cells are starving from profit induction to evolution will become a more important role, play a more important role I think in our communities. So, so yeah, it's a really interesting question is one that we want to explore in the future. And we started by looking at just going in the other direction first looking at what happens if we cut short incubation time. My expectation is that as you increase the incubation time diversity is going to go up. And you're going to have more taxa together but it's just a hypothesis we haven't done the experiment yet. I have another question. So, if, like, when you are doing 16 s RNA sequencing. Is there a way to rule out the dead cells in the tube from being detected as live cells. I'm having some trouble with my nose. Okay. Can you repeat the question. Yeah. Yeah, when you're doing the applicant sequencing would there be a message to rule out the dead cells. Right. That would be difficult just with 16 s we for the experiments that I've shown you today we did we did do this right we we measure we did microscopy to get an estimate of how much cell death there was and how much that was contributing to our committee assembly and we found that we should have a very small contribution right there's there's very few cells that are in a, either dead dead or in a metabolically arrested state that likely would be that like if you just like that staining. So, so yes but I agree that if you did this, I think the only the only thing I could think of is this is will be just a microscopy in conjunction with 16 s to get a sense of how important cell death is. At least my cell viability may be more than that that is always difficult to to harder than it seems but at least viability or you can see a fuse might also be another way to to do it. Okay, thank you so much. Welcome. There are actually more questions but in, we are sort of out of time but I think that there will be time for them in either tomorrow or west or on the next lectures. So, I asked everybody was question to keep them and ensure that since the lectures are very similar topics they could be asked in the next lecture. So, thank you very much, Alvaro for these very nice overview, and we'll take a break of five minutes and we'll start again at five p.m. Italian time with the last lecture by the next day. And we'll meet again with tomorrow. So thank you very much. Everybody. Okay, so we are going to be back in less than one minute where people are going to join back from the breakout rooms. In the meanwhile, I want to remind everyone, if you are following from YouTube you can ask a question by typing them in the chat. And, as you know, you can ask questions on zoom by writing in the chat or using the raise and tool of zoom. So, as soon as everyone joins back from the break out rooms we'll be able to start. Okay, I guess everybody's back so we can start again with the lecture by Daniel Segre. So please, Daniel, if you could share the slide. But first thanks to be here. Great. Thanks. Okay, thank you. Okay, should I start? Yes, yes, please. Okay. Great. So hi everyone again. We're going to continue talking about dynamical modeling of communities. And if you remember, our third part is going to be on special temporal modeling and long term history of metabolism. But where we stop last time before we can actually talk in detail about this, I want to remind you one of the issues that we saw arises when you start when you try to build models, flux balance based models of communities based on this multi compartmental multi compartmental approach where you have different cells containing different metabolites and the metabolites have just different labels based on the compartment they're in. And we said that this would require some assumptions about the ecosystem level objective, and it have issues such as, you know, we then really allow you to predict the abundance of different species in the community. And we also cannot accommodate for predictions of this special special temporal dynamics of these communities. And we started showing this figure which I'm going to now discuss in detail, which as you'll see will solve all with all of this problem at once in a in a way that is opens up a number of possibilities. And this is something that is called dynamic flux balance analysis. It's an extension of flux balance analysis that adds back the temporal aspect of this in a way that is a little bit different than what you would see when you build a standard kinetic model. And the idea is the following. So imagine right to remember that when you have a metabolic network with definition of the boundary conditions, you know, the molecules that come come in. And are described by inequalities that tells you what is available and by how you know what to rate. So if you have the biomass function by mass reaction that defines growth of the cell, you can solve the flux balance problem which will give you a prediction of all the flaxes that allow the cell say to produce in an optimal efficient way, its own biomass. The outcome of the simulations right are is a vector of fluxes which include the rate of uptake of each nutrient, the rate of production of biomass and the rate of production of each of the byproducts. Now imagine that building a this dynamic flux balance modeling as a stepwise approximation of the growth curve. So you start from an initial amount of nutrients in the environment so this blue curve is environmental nutrients available. And you start with a very small amount of biomass. So in your first step of a flux balance analysis if you saw flux balance analysis under those conditions and we'll talk in a second how we translate nutrient availability into an uptake rate which is what you need for flux balance. But if you infer the fluxes for the nutrient uptake and the biomass production these are essentially the slopes will give you the slopes of this curve right will tell you how fast the organ is, organ is grows at this instant in time. And all you do is assume that this is that it's reasonable to extend this initial slope for a certain amount of time and interval delta T. So you will update the new biomass after this time delta T. In a similar way, you can predict how much nutrient is being consumed by just multiplying the rate of consumption of this nutrient by this interval in time to have prediction of the new updated nutrient abundance. And you can keep doing this again and again so you're here you'll solve a flux balance problem again, and you'll end up having a new level of the consumer nutrients and the biomass increases, and so on and so forth. And what you can also see happening here is that some point, there may be no byproduct present at the beginning, but as the organs grow there is a secretion of this byproducts of you update the amount of the byproduct in the environment this will start increasing. So by doing this dynamic FBA, you will have a piecewise linear approximation of the growth curve in green and of the abundance of each different nutrients. For example, this initially available nutrients could be glucose and a byproduct acetate in the example of the college that we saw before. So, one, one, one thing I want to mention right away is why is this helpful for modeling ecosystem so this, you know, you can imagine this being useful for modeling the abundance of a certain organism in a given environment. So this is very similar to flux balance analysis, but it adds this temporal component, but what is additionally very important about this is that imagine you put in the same simulation, a second organism that has a biomass, which we call biomass prime so it's a different organism that has a similar resource allocation problem, but imagine that the second organism only grows on this pink byproduct cannot grow on the initial nutrient. So what could happen here is that this organism that could not grow at the beginning because it didn't have its preferred substrate can now grow on this pink byproduct this new metabolite that is being secreted. And this process now becomes an emergent phenomenon right we didn't know a priori whether the second organs could grow or not. It could only grow after this byproduct is being accumulated because of the activity of the first organism. And what is most interesting here is that there is no assumption about an ecosystem level objective, each organism is trying to maximize its own fitness its own growth capacity. But as an outcome of this process, we can still see as shown in this example, the emergence of cross feeding right one organ is secreting a product that an other organs can use as food source. So you can see how powerful this part of them can be because it allows you to model communities and exchange and competition right this to organize might compete for the same nutrient so that nutrient will run down faster without having to assume this explicitly. And this all depends on the intracellular circuits of each organism what is each organs can and cannot do. So it's really a way of observing and predicting the emergence of competition and cooperation based on exchange of metabolites straight from the organisms genomes. Now I hinted to the fact that this is requires some additional care in terms of predicting the uptake rates. And what is in the end nice about this this is a essentially a hybrid approach that uses some components of kinetics standard kinetics and some components of FBA for intracellular metabolism. And if you think about this right FBA will require the fluxes the incoming fluxes, but here we start from an initial concentration of the extracellular metabolites not the flux. So how do you convert the concentration to a flux. Well, the obvious ways that you use Michaelis meant the kinetic kinetics the classical saturation curve we mentioned before, where you can predict the uptake rate in this case what's going to be the upper bound to what the cell can take in as a function of the concentration. So you'll need to know the parameters that define this curve so these are the traditional Michaelis meant and constant in the cake hat for enzyme kinetics, but just for the boundary condition so you'll need to know this kinetic parameter is only for the reaction of uptake of the different nutrient from the environment. So, again, there is a kinetic component in the uptake rate, but then once the molecules are inside inside the cell you assume that the cell is at steady state, and you predict the intracellular fluxes and the growth rate as a function of the standard steady state approximations, but again with environmental condition dictated by the concentration of the metabolite rather than just arbitrary bounds on the fluxes. So this allows you to monitor the change in the environments and see how the environmental composition is modified and the presence of whatever microbes you have here and this in turn can affect the future of the community. So, when we develop this idea of using dynamic flux balance for studying microbial communities, we also wanted to embed in this the capacity to model the spatial structure of communities as well. So in addition to implementing this engine, this dynamic flux balance engine in a given region in space we added process of diffusion. Initially we model the pressure generated by cell when they grow and divide, potentially fluctuations in the environment, and we do this by looking at the local neighborhood in the end we do numerical solutions of the degrees for describing these processes and we end up having this discrete simulations in time and space where each region in space represents a certain average amount of the biomass of a given organism, and we can do the simulations of the amount of the amount is growing on a petri dish. In this case it's just one single organism, but of course you can do this for an arbitrary number of organisms, and again model the dynamics of communities in space and time which is why we call this a system, it's computation, we call it resistance in time and space. The first work presenting this was from 2014, but the platform has evolved significantly. Let me show you some example of how we first tested this platform. In the past, such as the Vmax and the KM for the uptake rates were taken from the literature. Similarly, we could implement a death rate that was known from previous measurements, metabolite diffusion, biomass diffusion, and there are other parameters that are essential for the system, a very limited number of parameters relative to what you would have again if you had to model the whole kinetics of the cell, so there are no internal kinetic parameters, internally everything is based on FBA so there is no internal kinetics. This is a snapshot of the simulation of simple organisms growing, and one first test was showing that the rate of growth of colonies on a surface is actually known to increase linearly and the growth rate obtained with comets with the simulations was very similar to the growth rate obtained experimentally in prior observation, and you can see that it strongly depends on the carbon source that is available highest with glucose and lower with lactate and acidate. So this was an initial, if you wish, testing or calibration of the model, and now the model has evolved into a much broader platform. So for those of you that are interested, this is what we call comets two is a much enhanced version, which is now available at this website run comets.org. And this is a collaboration between our lab and the lab of Keryl Korolev at the U and the lab of Alvaro Sanchez at Yale and Will Harcom at Minnesota. And what is nice this turned into, I mean it was initially and it still is an open source platform with the idea that different groups could add different modules and the hope is that people will be interested in using this platform, reporting if they find anything they would like to see or it's not working properly and also consider adding different modules. So this is written in Java and but we have now Python and MATLAB interfaces. Just to give you an idea of what this can do, right, you can predict as shown before the column, let's say column is going on a surface or on a petri dish, but at any given time you have all the variables that flux balance and dynamic flux balance analysis give you. So at any given time you could look for example that the growth rate you can see here, the colonies tend to grow on the sides, the perimeter of the colony, you can look at the amount of the metabolites left on the plate at any given time. So for example, glucose is being depleted and acetate is being produced. These are E. coli colonies. There are a number of other features we are adding now non-linear diffusion finite population effects so you can see this dendritic structure and sectoring happening and the nutrient dependency of collaring morphologies. Thanks to Alvaro's input comments now how the capacity to implement evolutionary processes and there are a number of other features that are being added, exocelular enzymes, secretion and functions and so on. So this is for now available as an archive preprint and again manual instructions should be all on the website. So this is the platform but let me show you a little bit more of what kinds of things we did early on and we're doing now to using this approach to really try and understand community dynamics and interactions. So when we first implemented this we were lucky to have an exciting collaboration with the group of Chris Marks and Will Harkin, who was the time was a postdoc in his group, had developed this very nice artificial community of two strains. One was E. coli strain that lacks the capacity to produce methionine. So this organism cannot grow on its own unless you provide methionine in the medium. And the other partner was a Salmonella strain except that the Salmonella could grow on acidity but not on lactose. So if you were to grow this system on lactose the Salmonella would not be able to grow but as you can already see because E. coli secretes acetate that can feed the Salmonella and if the Salmonella could produce methionine to help this E. coli then this could be a stable community of two obligatory synthetic bacteria. Turns out in addition to engineering this strain, Will had to evolve the system in order to make sure that the Salmonella could really produce the methionine to feed the E. coli and this ended up working beautifully. So what we did this was for us an opportunity as we wanted to test comets. We built incorporated the model of E. coli, of the E. coli mutant and the model of Salmonella and we asked whether the model would recapitulate the experimentally observed proportion of the two species. So this was the experimentally observed proportion of the two species. Interestingly, this was this proportion with a higher E. coli and Laura Salmonella was converged to irrespective of the initial conditions. So this was a stable composition reached from different initial conditions and comets recapitulated quite well those proportions. Now you could think that this is a little bit of an overkill and in fact you could imagine making much simpler models of this organism that would recapitulate based on the uptake and secretion of compounds, these the observations, but first of all, I mean it's in this case there is no tuning of internal parameters, the Michaelis-Menten parameters were taken from the literature so it's still quite nice to see this agreement and what was somehow then quite surprising is that this works also for a three-species community and this is again experimental work done in the Marx lab. In this case, in addition to these two organism, they added a third bacterium called methylobacterium extorquence. This is a bacterium that typically grown plants so it's interesting that this community is really a synthetic communities that is composed of organisms that do not come from the same biome. These are just have different origins but you can make them coexist and in this case the way methylobacterium was added to the system is by providing methylamine as the only nitrogen source and of course each of these organisms will need nitrogen so if they don't have ammonia in the medium they will need to get the nitrogen from the methylobacterium which can produce ammonia and in fact feed these two so this three-species community now is a community where each of the species need the other two, none of the individual organisms and none of the pair can grow on its own but the three species can grow together and again there was the experimentally observed proportion of the three species after a number of passages and this was recapitulated reasonably well by the Comet's simulations. Now what is also interesting here by the way is that in a somehow counterintuitive way methylobacterium who is the slowest growing organism was the most abundant in the population and this is because probably was not producing the needed nitrogen at smaller amounts so the only balance that the community would find was with a higher abundance of that organism. So this is promising and this is really the first indication that Comet's might be a valuable resource for modeling ecosystem level metabolism and you know more about this later but let me show you first some example of how one can use the spatial aspects of Comet's to also address questions about the spatial structure of community and interactions on a plate. Oh before actually going there one thing I want to highlight that is actually important is that you know remember we talked that some of these secretions are spontaneous secretions so for example the acid it produced by E. coli that fits the Salmonella is this natural production that E. coli will have to maximize its own growth rate so this is a one of the kind of the costless secretions we discussed last time but this other secretion the secretion of methionine is really something that was an evolved trait so it's somehow imposed even if it's a costly trait it's imposed by the necessary interaction between these two organisms were co evolved on plates in our simulations we had to impose this methionine secretion flux because a flux balance model could not naturally take into account the mutation that the Salmonella strain had to overproduce methionine in this case so this is something that is kind of a material for future research how to really and whether it's possible to take into account this evolutionary mutations that could produce give rise to the production of costly metabolite. There is a very nice paper by a former postdoc in the lab which I'm not going to discuss here in detail but looks exactly at how this costly secretion in dynamic FBA or FBA can be combined with game theory to address questions about stability of micro communities connected by leakage of metabolites. But let me go back to as I was saying earlier the spatial structure of these communities there is one simple experiment that we'll did with the two strains the Salmonella and the E. coli just growing them at different distances and as one might expect because they depend on this diffusible molecules the closer together the better they can help each other and therefore the better they grow faster they grow and this is recapitulated also in the comments experiments but this was somehow quite trivial but we'll have the idea of testing a slightly more complicated scenario and the idea was the following so I imagine you put to these two colonies on a dish you have an E. coli strained our methionine knockout strain in our Salmonella the evolved Salmonella strain on a plate so as shown before they will grow diffuse probably acetate and methionine there may be other metabolites involved but likely these two would be key and be able to grow but now the question is what happens if you put a second Salmonella strain in between these two and the expectation we had and one of the reasons we modeled this is that we expected what we called kind of an eclipse effect so we expected that somehow this Salmonella strain would take a lot of the nutrients the acetate secreted by E. coli and leave this initial Salmonella within the shadow without and not allow it to grow as efficiently or maybe at all as it did before so this was somehow the expectation we wanted to model this metabolic eclipse on a petri dish and we actually did the modeling first and what we found was quite surprising that is what we saw is that the this Salmonella and this is showing the growth of the distal we call the distal Salmonella so this colony in presence and in absence of this intermediate colony and what we saw from the model predictions that was that this Salmonella could grow faster in the presence of this eclipsing intermediate colony and this was somehow puzzling we weren't sure whether this was an artifact of the model but when we will did the experiment to confirm that this is also happening experimentally that is this Salmonella strain in the middle of the plate we ended up helping this distal colony rather than harming it and as you can probably imagine the reason for this is that even if this Salmonella is really potentially using some of the acetate and that the E. coli is secreting of course diffusion goes around and it turns out that what happens is that this Salmonella is closer to the E. coli so it will help this E. coli grow more efficiently produce more acetate and the net effect on this distal colony is that the growth rate of this colony is increased and helped by the extra acetate produced by E. coli more than it is reduced by the eclipsed effect of the intermediate colony so somehow the idea is that this intermediate colony which seemed originally or in our minds was going to be a competitor of this one ends up helping because it helps its partner so this is sorry just to clarify very quickly that intermediate one can also excrete methionine right? yes yes yes and we thank you for the question and we did do the control with non secreting Salmonella and in that case you do observe really this eclipsed effect thanks for the question so you know one could obviously explore different geometries there are some nice follow up studies to this but I think the main take home message from this example and for me was kind of quite revealing is right we tend to often think of interactions as being positive or negative but when you look at them in the spatial context things can get quite more complicated and I think that's something that is important to keep in mind and taking account when you look at community in spatial settings one thing I want to show you one can look because of the capability of comments you can look at any given time at different aspects of the simulations for example again these are the three colonies you can look at the intensity of the acetate secretion flux and as expected you can see the coli in blue is secreting acetate whereas the two Salmonella strains are using up the acetate and you can see that this changes as the colony grows and most interestingly recapitulating what we discussed early on at some point at the periphery of the colony right the E coli it's still secreting acetate but the internal component of the colony were probably the lactose which is the main carbon source here is running out so these E coli cells start to take up the acetate again that they secreted before and grow on that acetate so there is this phenotypic change that happens within a colony there are independent confirmation of this happening in E coli but it's interesting that this happens also in this case and again it shows you the potential insight that one can get by looking at these different layers of the metabolites in comet simulations so there is a few things that comets and its different applications can help with one thing we start doing and this is feasible through a network visualization software called Vizant developed by Junjun Hu and Charles Delizi we combine this with our comet simulations in order to map the outcome of the simulations onto a network where you can have both the individual organism again this represents Salmonella this E coli and this is not really immediately obvious but this represents in a way that is not really intelligible but represents the whole metabolic network of Salmonella and this represents the whole metabolic network of E coli and these are the metabolites that are being exchanged in red are the metabolites that are used by both organisms so these are sources of competition between the two organisms and there is the oxygen, nitrogen, sulfur and phosphorus sources there are metabolites that are produced by both such as CO2 and then there are metabolites that are being exchanged in grey such as the methionine and the acetate and for example here the model predicts that for some reason also galactose could be an exchange metabolite something that is a new prediction of the model but this is just to highlight that in addition to representing the simulations as dynamical graphs showing the change of abundance of different species one can start building ecological networks where you have both the species, the microbial species and the metabolites potentially getting insight into what aspects of the internal network are responsible for the exchange utilization of the different resources in the environment and in an early attempt which is not really representative of what happens probably in a real gut microbiome but we started doing simulations of some key taxa from gut microbial communities including the famous or infamous costridium difficile and you can see that there are a number of metabolites that are exchanged or that organisms can compete on and again this is just the tip of the iceberg of the kind of things that we and others are doing and can be done in the future to use these dynamical models to try and predict the structure of communities I will end by showing a couple of examples of how we're using comets for a number of other applications one is the use of communities for the use of flux balance modeling to try and predict what my environmental composition could give rise to a desired community structure so you saw this from Alvarez talk and we talked about this in the past that somehow there is a lot of interest in understanding how environmental composition affects microbial community structure and the question Alan Pacheco in the lab asked recently is whether we can use this dynamic flux balance modeling to try and induce a desired composition based on just the medium composition based on the molecules that you feed to the community so here what Alan did was to use a genetic algorithm to try and design communities with a specific proportion of taxa let me illustrate how we use the genetic algorithm in this case these squares represent the different nutrients given to the community in dynamic flux balance comets simulations so based on the set of nutrients you give it could be three, five and so on you get a certain dynamics for the community and you can rank or give scores or rank that the community is based on how close they are to a desired composition to a desired structure so for example if you want all species to be equally abundant at the end of the simulation this would be a very good simulation so this means that this nutrient set is a very valuable one this would also be quite good so you can select those two and do mutations and recombination of these genomes so to speak that represent the nutrient composition of the media you can obtain a new set of media which can then be fed to the algorithm again to provide a new round of the optimization process so this is essentially just an optimization process that is performed using a genetic algorithm based on the fitness calculations for the community that are obtained with comets and this is one example of the outcome of these simulations where you can ask for example for high abundance of one of the species in this case out of three this is the satelis if you ask for a high abundance of this species but for survival and so the other species do not disappear you'll get a certain set of nutrients and a certain structure of the community that will be predicted to achieve this composition and this will change of course if you change which the organism that you prefer to be the most abundant so there is a lot more than one could do and a lot more data that is in this bioarchive preprint but I want to quickly soon switch to something else I'll conclude this part by just by saying that I think one of the goals and one of the exciting part of using this dynamic flux balance modeling in comets is that one can start thinking about making predictions for natural communities and engineer communities and try to see whether we can gradually reach the capacity to design communities with specific properties and of course this will require extensive experimental testing we and others have started doing some of that and I think it will be exciting to see how this progresses one thing I want to highlight to conclude this part is that in addition to the kind of mechanistic modeling that we discussed where you try and predict the interaction networks in a community and the mechanism of interaction of exchange of metabolites starting from the genomes there is a lot of data set that come from metagenomic sequencing and predictions of co-currents networks and I think that one of the exciting endeavors in the future will be to try and understand more of the interplay between these two types of networks and it's clear that these co-currents networks do not necessarily mean actual interaction between these pieces but I think it will be very useful and interesting to try and understand what is the connection between these two because we'll be able to do more and more of this type of networks and there is already high abundance and there will be more of co-currents networks based on sequencing of multiple communities let me pause here for a second before we move to a very different topic if there is any question there is a question from Miguel Rodriguez Yes two very quick questions one is I saw you had in many of your plots either ever bands or ever bars even in the simulation is that derived from stochastic simulation or is it an actual measurement of error derived from the this was yeah that's a good question I think that the error bars I showed in the older models here yeah I think these are based on uncertainty in the parameters at that time we didn't do yet stochastic simulations so we could only vary the initial conditions or the parameters based on the uncertainty in the parameters but now we do have can put stochasticity in the simulation so we can also generate error bars based on the stochasticity of the stimulation themselves so I think yeah both are possible and early on we didn't have stochasticity now this is part of the model yes just a quick follow up there is one of the one of those your slides has a bunch of the different organisms for which you model the potential interactions from the gut and it was clear yeah this one it's clear from here that E. coli has the richest metabolism of all but obviously that's probably just because we know more about the coli than we know about the others is there is there a way to measure that the incompleteness the effect of the incompleteness of a model into into the simulation that's a great question and yes I absolutely agree I think the fact that we know much more about coli is what causes this abundance of different arrows I don't know I mean it's I'd have to think about this I don't know that there is a clear individual metric that can tells you how complete we expect the model to be there is certainly from the gut feeling algorithms one can estimate let's say how many kind of reactions were missing early on and how many have been added from the process of gut feeling so I could imagine that could give an idea of how much missing knowledge may be present in a given organism but I haven't seen anyone and we haven't done this I think it's a very good idea interesting point that would be helpful to have you know some standardized metrics I know there is actually a beautiful a approach called memo it's a suit of tools to analyze genome scale models built by a large group of people I think that that may have some quantifications of this uncertainty so it's worth checking this but that's a very good question I think it will be important to have metrics like this. Thank you. There is another question from Keith. Yes. Yes, it's a sort of extension from Miguel's question. But if I want to use this comet platform. Do I need to get the growth rate of each species and genome of species and have a great annotation. So we need three of those components. You do not need the growth rate you do need the annotated genome, or an already built stoichiometric model. So I think I don't have it here but if you look at some of the slides from the first presentation or the second presentation. There are pointers to their number of resources where you can download already built stoichiometric models. So there are there is a database called big the IGG that has a number of organs that's from Bernard Paulson's website. There are other groups that have their own models there is a number of publications with already built at the models and resources like K base that can build automatically draft models from genomes and do got feeling. So there are a number of tools to generate this model from genomes. Once you have that information, in addition to the stoichiometry itself in order to do the dynamic flux balance model you do need the kinetic parameters for the uptake rates. But actually, what I should say that, you know, in the simulation that showed before, for example, we assume standard KM and K cat for all metabolites based on glucose which is certainly not accurate, but it was good enough to give this predictions. Maybe because the main carbon sources were still sugars and organic acids. But yes, in principle, you do need those parameters. They're not very difficult to measure much easier than intracellar parameters but they're still necessary parameters for running dynamic flux balance analysis. You do not do the growth rate though the growth rate is an outcome of the simulation. And again for this kinetic parameters I think if you know nothing you can assume kind of some uniform parameters from the literature. But the more you put in of course the more accurate the predictions can be. So, when I, if I want to create this natural community, like how many species with this model take like, is there like a limit or threshold. Yeah, I think there it's, I think the problem, you know, you can put in certainly tens or I think hundreds of models easily I think I don't know exactly what the largest number we tried but believe it's in the hundreds. So I think the limitation is not so much. You can imagine I mean the simulation is will grow linearly with the number of species because you'll have to go with each model to do its own flux balance but it doesn't grow more than linearly with the number of species. The limiting factor for doing models of complex natural communities is actually the knowledge of the, you know, having good accurate stoichiometry reconstruction from the genomes, not the simulation engine itself. Oh, okay. And I should say, you know, we have tested the simulations on small communities. So, whether or not and how accurate this will be a more complex communities still unknown and I think it's an interesting question that we are interested in addressing. Thank you so much. Yep. Thank you. Great. I don't see any more. I'll move forward to just tell you a little bit on, you know, go to a slightly different approach which is related to something we mentioned before, which is the ecosystem, whole ecosystem level approach to metabolism. Somehow, it's something we applied to the study of the early evolution of metabolic networks. This is work done with Josh Goldford and Hyman Hartman, Temple Smith and Bobby Marslan. And the idea, the starting point for this is somehow the following. So we tend to think of fossils as something that you see in rocks, right? You can see something like this. This is actually the current fossils evidence of the first, some of the first multicellular organisms. This is from Canada. And we can also, we got used to the idea that sequences, right? The sequences are also somehow fossils of early life. They contain information about the ancient history of life. And the question one can ask is why not networks, right? Do metabolic networks, the same metabolic networks, or they contain in their structure, in their architecture, information about the ancient history of metabolism and of life itself. And the question is how can we tease out information about this. And this is related again to this idea we expressed early on that when you model a community, you can think of a community as a set of organisms, each of which has its own internal circuits. And they could exchange things and you can ask this question of whether you can predict ecological interaction based on the intracellular circuit of this organism. We can also raise the question of whether perhaps at some point we've maybe complexity of communities are complex enough and we know now from Alvaro's lecture and evidence in other contexts that like functions are so important in determining the fate of a community. It doesn't even matter which organism performs what function, we could treat the whole community of us as a soup of enzymes, right? And look at the set of all the reactions as if they belong to a single compartment. Now, when you study ancient life, there is a very special meaning to this concept. And this meaning is related to horizontal gene transfer. And we know that microbes can exchange enzymes with each other. And enzymes can be transferred from one organ to another. So metabolic networks that at a given time seem to be stable for and be associated with a given species. If you look at the long term history of life, you know, they can change and move from one organ to another. You can imagine this being a very plastic process where it makes more sense to think of ecosystem level metabolism as a property of the whole ecosystem and not of individual organisms. So when you think this way, you can start asking these questions of not just what an organism can do with its metabolism, but also what an ecosystem can do. What are the metabolic capabilities of an ecosystem? Except that now, when you ask questions about the ancient history of life, you can make hypothesis, sorry, about what was possible in the presence of a few individual specific molecules that might have been present on early Earth. So you can ask questions about the expansion of this metabolism from an early subset of metabolites. So it's as if you can try and get some historical record of the growth of metabolism starting from its early seed of possible compound. So in the way we did this, we applied this was by using an algorithm that has been developed a beautiful, simple but very powerful algorithm that was developed by Oliver Ebenhoch in Reiner Heinrich's group several years ago. And this is called the network expansion algorithm. And this is the following. I'm going to illustrate this on a very simple toy model of a network, but imagine this representing the huge network we saw before. So this is the network of all possible metabolites as circles and reactions with the arrows. Imagine now you start with a seed of possible compounds. For example, these two molecules are present. So you can ask simply, given that these two subsets are present, what possible new what's a new subset could be possibly appearing in our world, given that these reactions are possible. And of course, this reaction could in principle occur in these two new metabolites could be added to this network. So you can define as the scope the scope as the total set of metabolites that are being producible. This reaction cannot occur because you don't have this initial substrate. So the scope that you obtain is the set of these four metabolites. Of course, if you are to add this initial molecule in the seed, then you can have these two, but also by the second net reaction, and the whole network now becomes feasible. Now, in, in generating this network, we don't say anything about the presence of the enzyme that are needed to capitalize this reaction. Assume somehow that catalysts are present enzymes or proto enzymes are present. If we look at the early history of life, we can get we'll get back to this later. So I hope this is clear. Again, this is a very simple topological algorithm that allows you to know what portions of a network can be reached based on an initial set of compounds. You can play the same game for the real network by taking some initial compounds and ask which of the, you know, 10,000 or so metabolites present in the community can be reached. Is there a question. I think it was not a question. Oh, okay. Please raise. So, so one can ask what what space of this network can be reached based on the initial set of compounds. And I'll show you first the way we applied this algorithm a few years ago in work done with Jason Raymond, asking a question related again to one of the things we discussed early on in my first lecture which is the transition from an anoxic one oxy world remember about 2.2 billion years ago. The atmosphere started becoming from an oxy started to be filled with molecular oxygen. And this is due to the activity of bacteria. Now, oxygen can be very toxic, of course, and cause a lot of changes in metabolism. So we asked based on this network, what could happen to metabolism if it's initially does not involve oxygen. After some transition, it does involve oxygen. What changes would you expect could occur in metabolism because of the presence of oxygen. And there is a lot of aspect of this. So I'm just showing a snapshot of one of the outcomes of this analysis. In blue, you see the anoxic network. So this is, again, this is not a network of an individual organism, but it's the, again, the expanded network from an initial set of plausible early earth metabolites into an expanded network that is involves the reactions that are present in multiple different organisms. But I want to point out that what is striking is that there are these additional branches that become possible only when you add oxygen to the initial seed of the network. So when the network expands in presence of oxygen, there are also new molecules that become available. And interestingly, there are very little changes at the core of the network. So even if we know that of course there is oxygen has a role as a electron acceptor for oxidative phosphorylation for this more efficient metabolism that we discussed. But there are a lot of other roles that oxygen has, which are known, but they're kind of diffused through different pathways. And so you can here you can see the impact of oxygen availability as a molecular substrate that enables the production of molecules that are some of these more complex molecules such as flavonoids, sterols, which includes cholesterol and a lot of molecules that are involved in communication such as hormones, the terpenoid metabolism. So there are a lot of molecules that are in fact can be signatures of eukaryotic and multicellular life that are really associated with the rise of oxygen in the atmosphere. So this is one possible utilization of this network expansion algorithm, but I want to show you a more recent result, which was also obtained for the same algorithm addressing what is known as the phosphate problem in origin of life research. So this is a mineral called apatite, which contains phosphate. And then this is an example of what could happen to a marine ecosystem where phosphate is added in large amount in this case due to pollution. And this causes a huge rise in the amount in the growth of algae, photosynthetic bacteria. Now, what is interesting about these rocks is that these are the kind of rocks where you expect phosphate to be found, except that it's very poorly bioavailable. So it's difficult to extract phosphate from these rocks. In fact, it can be extracted by bacteria through the secretion of organic acids, but it's hard to imagine how phosphate could have been available for early metabolic processes. And this is a problem because we know, as we saw again the first time there are molecules, central molecules to life such as quinzam A and ATP that contain multiple phosphate atoms in orange here. And there is plenty more. In fact, one cannot really imagine a life without phosphate because DNA and RNA are phosphate containing molecules. So a life without phosphate would not have nucleic acid would be a life without genomes and without transcription and translation and without this energy currency that we discussed, ATP. So we started asking this question, though, whether it's possible that an early metabolism could have emerged prior to the availability of phosphate. And therefore, whether it's possible that perhaps living systems could have emerged as an early metabolic process that could have later on even rise to life as we know it today with Darwinian selection based on genomes and transcription translation. So we asked this question of whether, you know, if you start from a seed of metabolites that contain some carbon sources, sulfur, which is known to be present on early Earth, and nitrogen, different sources of nitrogen. So these are all carbon, carbon, nitrogen, sulfur, but there is no phosphate containing molecules. The question is, could you get any metabolism at all based on this phosphate free seed of metabolites. And our expectations when we were going to run this network expansion algorithm was that phosphate is so strongly embedded, it's present every ATP that drives reactions contains phosphate. There is phosphate everywhere in metabolism today, so we thought it would be pretty much impossible to have anything but small pieces of this network. But we were very surprised to find that instead there is a core network that counts 315 reactions, 260 metabolites that is fully connected, and that does not contain any phosphate at all. So this is this expanded network. It starts here, you can see CO2 and ammonia and some simple molecule. And as the iterations of this network expansion algorithm progress, you add more and more molecules. It turns out that you can add, I believe, 10 out of the 20 amino acids are part of this network and some of the precursors of nucleotides. And again, no phosphate at all. But there is this core network that is embedded in present day metabolism that is again not present necessarily any individual organism, but at an ecosystem level, this could be a snapshot of an early metabolism before phosphate become available. I don't have time to go into all the details of this, but I want to highlight first of all that this would be, so we don't really know, right, we're speculating about something that could have happened 3.8 billion years ago and we and nobody really has any idea of what exactly happened at those times. But what is interesting about this kind of net, you know, this network we show is that this really exists. I mean the network is there whether or not it tells us something about ancient history, we're not sure. But it is potentially a fossil of this early metabolism and one additional evidence for this and that could provide some cooperation to this idea that this could be a fossil of early life is the fact that. So those reactions would have been catalyzed by simple mineral surfaces and maybe small molecules, but there were no proteins at that time. So how could we get some insight into the possible catalysts at that time. And what one can do is look at the enzyme that catalyzed those reactions today those 350 reactions. The protein itself is a very complex structure that could have not been present early on, but this the co factors, some molecules that are in the active regions of this enzyme, some of these are very ancient molecules for example. A lot of the sense and contain this iron sulfur clusters that are minerals that are known to be associated with early earth environments, and we looked at how frequently this iron sulfur clusters appear in this core network in this network of phosphate independent metabolism versus the full expanded network. And it turns out that there is a very strong enrichment of this iron sulfur clusters in the, this core network relative to the complete network, indicating that perhaps really this network captures some of the early activity of metabolism on our planet. So this is the top work which I'm not going to have time to go into exploring in a much more systematic way how different assumptions about the early earth environment could give rise to different proto metabolism. So in terms of the thermodynamic feasibility of this reaction. So in this network expansion that I just described initially we did not think about into account thermodynamics, but you can imagine that in addition to the topological feasibility of this network you want to look at the thermodynamic feasibility and this will depend on pH and temperature in a way that is can be estimated based on the energy formation of each of these components. So we did this calculations and one can find again which networks can expand to the full to a large networks and which conditions do not give rise to expanded network of finding more parameters that seem to be conducive to initial metabolic network. And I will conclude just by saying that what we started doing which I think is also, you know, a seed of something that could be done more in the future, we started applying this flux balanced model to proto cell models to proto cell system so we took this network that were obtained from this network expansion algorithm, and we tried to model them with the same tool that we use now to study present day cells, and one can look at whether these proto cells could sustainably produce simple biomass, composed in this case just of much simpler molecules than the biomass of organisms today, such as simple lipids and keto acids that could be precursors of present day proteins. So I will stop here and just acknowledge my group and thank all of you for listening from all over the world. And I'm happy to take any questions. Thanks a lot Daniel for these three fantastic lectures. So, we have time for a few questions so so if anyone wants to ask please use the raise and feature or type it in the chat. I have a question myself so regarding the, the, the result on the network scoping and sort of reconstructing this primordial metabolism, and perhaps I missed that but is there, I mean, somehow you, you see that there is this backbone and you see that but what is the new and you are sort of using that to infer something that as you say that then three point two billion years ago and there is some somewhat growing process on top of this net right so is there a new model you can compare this is the result to so is this something somehow you couldn't expect by chat to happen by chance, in some sense. That's a that's a great question, I think there is so you can you can like one one example of this would be, you know, which I didn't go into in details but these are all the different conditions so you can add or remove different conditions and ask what happened if you didn't have this compound with this other compound, and you can see that many initial conditions do not result in an expanded network so it's not an obvious case that all possible conditions will lead to an expanded network where you need certain conditions to occur, and when you add the thermodynamics you can see that also you have ranges of temperature and free energy, and so on that, that give and not give rise to this network. What you're pointing out is, is a broader question which is, this is based on the chemistry we know today right and, and how do we know that we're not biased towards just, you know, this is based on all the reactions that are known to be present in today but there may have been other chemistries that, you know, appear that these appear throughout the history of life. And, you know, one, what could argue about this one thing that I think is very helpful is to do exercises going back to what I introduced at the very beginning this artificial chemistries and that that can give opportunities to ask to generate null hypothesis with arbitrarily complex chemistries to ask how likely it is to obtain an expanded network based on different assumptions on the nature of the chemistry and the amount of reactions that were lost throughout this process. I'm not sure this address your question. I think it's a it's a hard question to ask you know what is a null hypothesis when you talk about this early metabolic processes. Yes, I mean I guess it's why all the question about the origin supply and hard to answer. So there is another question from Matteo, who is asking whether you see a connection between your work is work and recent theoretical advances in stochastic thermodynamics of biochemical networks. I, I, yes, I mean the short answer is yes I think there is a lot of interesting connections. I think that right the way this flux balance model started early on there was no thermodynamics. But I think that adding the thermodynamics opens up a lot of new possibilities. In particular, yeah, one can look at the, not all this metabolic flux states that are feasible based on FBA are necessarily feasible thermodynamically in the classical examples you can have, you know, three reactions running in a circle that is balanced in terms of fluxes but of course it's infeasible thermodynamically. And there's been a number of studies trying to add thermodynamic constraints to flux balance modeling. And, and I think, you know, the works you're mentioning are I think are somehow complementary to the literature but I think there is a lot more work to be done in bringing these two pieces together. And I think there are also interesting questions of whether you know one can revise the objective functions based on thermodynamic principles. So I, you know, I think it's, it's an open area but I definitely think there is there are connections and I think a lot of these are still there to be explored. So, I don't see any other questions so let me thank again Daniel for these three great lectures. And now we are taking a five minute break into the breakout rooms before the next lecture by Justin. Thanks a lot. Thank you. Hello Justin. This is Antonio here. I will be chairing the session this last session of today. So we'll be ready in a couple of minutes. Okay. Excellent. Nice to meet you. Okay, welcome back everybody for the last lecture of today's session. It's a pleasure to have here once more Justin Nico, who will tell us more about Palio eCollege. Please Justin. Great. Thank you for having me. Italy looks a lot like my garage. So it's a pleasure to be here though. And let me just share screen. I'm not sure I can hear you. Okay, so can you hear me now. I can hear you well. Maybe it's on Antonio's side. Okay. Okay, so I assume that others can hear me. Okay, Antonio, you can hear me. Okay. And let me share my screen. And, okay, so you can see my slides. Okay. So, again, thank you for having me. Today I'm going to talk again about a bit of an overview of theoretical paleo paleo ecology. My highest overview of theoretical paleo ecology because I'm going to be speaking about things that I know something about, which I guess is a good place to start and that is with respect to reconstructing interactions in the past, and using reconstructed systems to say something about modern systems. So today I'm an assistant professor at the University of California Merced and started there in 2016, overlapped with Jacopo at Santa Fe Institute, very briefly. And you can see my, my Twitter handle and website are there in the bottom right. So I just wanted to give a little bit of a layout for where I was going over the next couple of days for the series of lectures that I'll be giving. Today I want to talk more about understanding extinct ecosystems and why understanding extinct ecosystems is important, as well as how we go about reconstructing past communities with tools from ecological theory. So, so my goal today is really a broad overview I'm going to be discussing work that a lot of work that other people have done some work that I've been doing. And hopefully convince you that that examining these extinct systems is important and relevant. Tomorrow, I'm going to really focus on a particular case study of ancient Egypt over the last 10,000 years or so to understand how the unraveling of that mammalian community over 10,000 years since the end of the Pleistocene can tell us something about how mammalian ecological systems work generally. And part of that is also discussing some mathematical techniques, generalized modeling that can be used to assess the dynamics of nonlinear systems when a lot of the system is unknown. A lot of the particulars of how organisms might be interacting with each other is unknown. So Thursday, changing gears quite a bit, focusing on energetic constraints at a much smaller scale and principles of ecological interactions at the scale of physiology really to see if we can say something about very, very large scales, in particular macro evolutionary processes such as the evolution of large body size. And then most of this work is focusing on mammalian systems except a lot of what I'm going to be talking about today. Okay, so what am I going to be talking about today. Why is understanding extinct ecosystems important. How do we go about reconstructing past communities with with modern tools. So we're going to be covering a lot of ground. First I'm going to focus on just reconstructing ancient communities and I'm going to try to follow somewhat time and some order of time from from the earliest life to life as influenced by the arrival of humans on the landscape. So part of this again is just focusing on on reconstructing the structure of interactions. But also one of the one of the advantages of looking into the past that looking into the past can give us is being able to see how communities were structured and organized before and after large mass extinctions. Of course, one of the big open questions in our world today is how will communities respond to climate change to anthropogenic disturbances. And we can gain a lot of clues by looking into the past by seeing how communities responded to large disturbances that are on record. I'm going to change gears a little bit part way through the talk and think about how organisms themselves have structured the biosphere. And what the role of these ancient ecosystems in sorry, what the role of these ancient ecosystem engineers might have been in structuring communities. And then finally focusing on food webs in the Anthropocene so how have humans more recently influenced the structure of interactions. And I'm open to questions being thrown out during the talk I don't know what the, I think you've been waiting till the end of talks and that's fine too, but I can't see my chat window so that's the only, that's the only thing. Okay, I'm not going to belabor the point because I think you've been talking about these concepts really for the last few weeks but of course species interactions reconstructing or understanding species interactions and paleo food webs presents some unique relative to understanding contemporary systems. Now of course, when we're thinking about consumer resource relationships where we're thinking specifically about the flow of biomass right from one species to another. And there's many different ways that we can measure this in in the past, of course observation is not currently available to us until the invention of some kind of time machine. We can observe in different ways. In some systems one of the systems I'll be discussing today that are very well preserved sometimes we can find gut contents we can actually use gut contents to reconstruct who is eating whom ratios of stable isotopes is another way that we can look into the past and reconstruct how the flow of biomass and this is really, this was my entrance into science I was, I worked in a stable isotope lab for large part of my PhD. In those types of situations we can use the chemical signature of bone and tissue, the ratios of different stable isotopes to track biomass flow. Because you are what you eat except what you excrete and that's kind of the rule of stable isotope ecology and, and it allows us because many state ratios of stable isotopes are preserved for long periods of time. In bone, sometimes fossils, you can go into the past and reconstruct just as you would for contemporary systems. And another big part of this is a lometry understanding how body size dictates who can eat whom in a system, or who can interact with whom in various ways. A lot of this is constrained by a lometry so we can use allometric principles derived from modern systems to essentially constrain how we understand how species may have interacted in the past. And of course, once we have these networks of interactions we can assess how structure has changed over long periods of time or not. And how that structure might impact or affect the resistance or resilience of a system to disturbance and other aspects of dynamics that we might be able to infer. And, you know, one of the benefits here is that the story has already been told, right so we can go into contemporary systems and assess measures of dynamics and try to postulate what that means for how that system changes in the future. When we look into the past the experiment has been run. So if we say something about dynamics so for example we might go into reconstructive food web and try to say something about the susceptibility of different species to extinction. Well, then we can see if that susceptibility actually results in extinction by looking forward in time so so we have this time this temporal flexibility that paleontology gives us and looking over long periods of time and some unique challenges as well. Okay, I think I said I'm not going to belabor the point but then I belabor the point. Okay, let's orient ourselves let's root ourselves in the history of life. Okay, so earth form 4.6 billion years ago, which is at the bottom of the spiral which looks like it came from a biology textbook because it did. And we started the bottom of the spiral and we start moving up forward in time. We have the earliest cells at about 3 billion years but for our interests we're really going to start in the, in the last full turn of the spiral so we're going to start at the Cambrian explosion. And that was about for 500 billion or sorry, 500 million years ago half a billion years ago that we can see I think I can use my pointer I think you can see my pointer. I hope. So this is the Cambrian explosion we start here, and then this last full turn of the spiral is really the evolution of complex ecosystems. That's that's the record that we have. And so we're going to start in the Cambrian explosion we're going to spend some time in the Permian towards the end of the Cretaceous which is the end of the rain of the non avian dinosaurs. So with that we're going to zip back to the Devonian the expansion of plants on on the terrestrial landscape, which is a really interesting time that I've been thinking a little bit more about. And then, and then we're going to, you know yo yo back up towards the more recent and think about how the, how humans have have impacted systems and the more recent past. So the Cambrian explosion. So this is work really beautiful work. I think done by Jennifer done at the Santa Fe Institute this is a plus biology paper published in 2008, where she and a team of paleontologists essentially reconstructed interactions of species in these beautifully preserved shell fauna. So the Burgess shale is in Canada and that's what's pictured at the top. The other shale is the Xing Zhang fauna I hope I'm probably not pronouncing it right in China dating to around the same period of time. So they were looking at two of these beautifully preserved shale faunas to reconstruct the interactions because they're so well preserved there's a lot of information about who was eating whom in the system. So they could reconstruct the interactions and then see if food web organization is in any way comparable to contemporary systems to get a sense of the scale of this question we really have to understand how alien, these systems were. And I want to spend a few minutes just just looking at the Burgess shale. Okay, so these are, first of all, half a billion years old. And I want you to notice how very well preserved they are. But then let's look at some of these species. Micromitra looks kind of like a today's, you know, urchin. And that is somewhat urgent like hallucinogenia just the names of some of these species illustrates how strange they are. We don't even know. Well I guess more recent when they found it they didn't know which was up or down I think they have a better sense of that now I still don't. So these are very strange and very different looking species than what we have in shallow intertidal systems today. The British shale was a shallow marine system. Here's a couple others. These, the bottom colored images are the more recent understandings of these two different species. Adontogryphus and Nectocaris, the above illustrations are how they were originally conceived. Some were better than others. It's a lot of work trying to reconstruct what was what, even in very well preserved shale fauna. Now, one of the original ideas that is very well described by Stephen Jay Gould in his book, Beautiful Life, or sorry, Wonderful Life that I showed on a previous slide. One of one of his original ideas and it was a popular idea for a while is that the Cambrian explosion was really this period of massive experimentation where these different life forms had a lot more morphological disparity compared to similar systems today. So you can see this middle image here is this Gould 1988 1989 interpretation where the X axis is documenting the morphological disparity of the system and time is moving from the bottom to the top. And so his idea was that, you know, these systems were were experimenting with very different shapes and very different modes of life. Many of which were not successful. And those that were successful, of course, gave way to life as we know it today. The more recent interpretation of this, however, is that there's about as much morphological disparity in the Cambrian explosion as there is today, maybe even a little less. And even these very strange species that don't look like they have any modern relative are. Oops. I think I skipped a slide. I was probably going to pop up later by accident. These, these two very strange looking species are actually early mollusks. And so, so even these kind of alien looking organisms that were in the British shale, or in the shell faunas are related to modern relatives. And, and perhaps the systems aren't quite as alien as we thought. So, taking advantage of this preservation allows paleontologists to go back and reconstruct who is eating whom. So this is a very simple illustration of the Burgess shale food web. We can reconstruct, of course, you know, the algae species and then those organisms that were specializing on grazing and filter feeding illustrated in the in the brighter green color. Those organisms that were scavenging. Those organisms that were active predators, and we can get a sense of the actual species interactions by observing stomach contents which I'm showing in the lower left here is that this is the stomach contents of one of the organisms in the Burgess shale fauna we can actually see inside the soft tissues were not preserved but left impressions in the shale. These species allow allow us to reconstruct interactions the bite marks can be matched to the mouth parts of predators. And of course, body size determines to large extent, who is capable of eating whom in terms of active predation. We accumulate all of these different lines of evidence we can and even wait the different lines of evidence with with our ability to say something about the interaction, you know, more certain interactions or less certain interactions, where we can reconstruct the food web. And this is what Jennifer done and her team did. So now that they're able to reconstruct the food webs of the shale faunas. What's one way that we can go about comparing whether you know the structure of these systems was different or similar to those today. The way that they went about this is looking at the cumulative link distributions. And so here I'm showing link distributions for, let's see 12345 modern food webs, you know, all of the, all of the ones that you've probably seen over and over a little Rock Lake, you've been an estuary so would park. And one of the, and then the link distributions is illustrated below it. One of the things that you can immediately see of course is that these link distributions are long tailed. So we have most species, tending to be specialists in other words they have fewer trophic links to other species, and relatively fewer species being generalists, where they're linked to many many other species in the systems in the system with trophic links. So one way that we can more visually compare these systems because they all different size and they all different link density is to divide to scale the cumulative cumulative link distributions by the average number of links per species. And this is what I'm showing on this slide so now we still have the cumulative distribution on the y axis but on the x axis we have the number of trophic links that that's scaled to the average number of links per species so we're scaling the distribution to the density of the network. And what we find is that all of the distributions fall on top of one another so all of these different systems do seem to be constrained by, or do do do appear to be to share similar constraints in terms of the distribution of specialists and generalists trophic specialists and trophic generalists in the system. And just one thing to note that the distribution tails fall off much more quickly than you'd predict from scale free networks so I've just you know put on top of this a power law relationship. Where, and we see these ecological systems are fall off very quickly. So, so generalism is not following a scale free power law relationship here. Okay, so, so what how does this relate to the Cambrian system. Well we can take these cumulative degree distributions and of modern systems and compare them directly to the Cambrian food webs because this is we have the same type of information. What I'm showing here, again, is the same x axis and same y axis as before the normalized number of trophic links on the x axis and the cumulative probability distribution on the y. The color squares are modern food webs from eight different sites, and the black and the gray circles are to Cambrian food webs, one from 505 million years ago and one from 520 million years ago. And the message is pretty clear here. They all fall on top of each other, which really kind of blew my mind, because, again, these are ecosystems at the very beginning of multi cellular complex life. These are some of the first ecosystems that we have record of assembling. And to think that they share such strong structural similarity to modern systems to me is very, very striking. It suggests that there is a fixed a fixedness to food webs that there are similar processes constraining interactions. And that these processes that constrain interactions are truly independent of taxa truly independent of location. I mean this these this is a shallow marine system on the shores of Pangea. So of course very different than than anything today. This is half a billion years ago, and independent of environment. So, you know these energetic constraints these interaction constraints are apparently they would appear to be very fixed over time and space. I guess I'll let the animation play out here we have a trilobite which bit the dust, bit the dust in the Permian, which we'll get to in a minute here. So one of the pointers one of the top predators in the British shale. I guess while, while this is playing I can see if I can see the chat window. Okay, you can see the pointer. Excellent. Okay, so because now yeah. So, so every nature now it's going to replace every nature video has to have a predation event that's like a law of the universe. Well, let's, let's move forward then and think about communities before and after mass extinction so we've looked back at the Cambrian so I've I've point. I'm showing that here so the Cambrian is about 500 million years ago that's where we've been move forward for you know quite a while. We don't pop up into the very end over here but let's move forward. It's been a couple hundred million years. And what I'm showing on this graph is the extinction intensity so so earth has been marked earth's life on earth has been marked by five large extinction events. It's very likely we are creating the sixth most the sixth mass extinction. But one of the largest the largest mass extinction event was at the end of the Permian. It's called the Permian promo triassic mass extinction. So we're going to visit this mass extinction, how are communities structured before the mass extinction, how are they structured after the mass extinction, can structure tell us anything about the dynamics or the robustness of the community. And then we're also, we're going, we're not going to the Cretaceous mass extinction, the asteroid impact unfortunately, but we're going to go to this event that occurred right before the asteroid impact. It's called the in Cretaceous restructuring period. Now this is when all the big famous dinosaurs were walking around the landscape and what we see towards the end of the Cretaceous is a very large decline in diversity. There's even a period where sauropods just disappear. They reappear later so they didn't go extinct at that point. So strange things were happening at the end of the Cretaceous in terms of restructuring dinosaur diversity, and this was happening right before the asteroid impact sealed their fate. So we're going to visit these two big events in the history of life and try to understand whether you know the changes in the structure of the food web can give us any insight into the robustness of the system before or after. Okay, so again, two big events, the Permian extinction and the incretaceous restructuring. Let's look at this in a little more detail. So the Permian extinction. This is also called the Great Dying. This was 251 million years ago. It's, you know, the causes of this are somewhat contentious. I would argue that there were asteroid impacts. It's certain that there was massive volcanism, resulting in these, you know, what one of the events resulted in the Siberian traps. And it's thought that this massive volcanism occurring over hundreds of millions of years actually triggered global climate change that just altered the landscape destroyed primary productivity and resulted in this, you know, cascade of extinction from primary productivity to primary consumers to to those consumers that are eating the primary consumers. Ultimately 70% of terrestrial vertebrates when extinct at this mass extinction event, 96% of marine species when extinct so this is, this is major major. At the end of the Cretaceous we have the incretaceous restructuring this was about 72 million years ago this was a little more subtle. There was a decrease in dinosaur richness. There's fewer endemic taxa. And one of the big questions has always been, were in Cretaceous systems less robust due to this restructuring event. Did this event set the stage for the KT extinction would dinosaurs have gone extinct, or non avian dinosaurs because of course birds are dinosaurs would non avian dinosaurs have gone extinct. If this restructuring didn't happen did this restructuring really erode the robustness of the system so that the asteroid impact had a larger effect than it would have otherwise. Both of these reconstructions I should mention by the way this is work done by root Narayan and Mitchell. They used a technique called guild level reconstructions where we really can't say to the species level, who was eating whom. Instead, what they went what they did was reconstruct guild level reconstructions of who was eating whom. So the guilds here are, you know, coated blue, and then the species are coated green on the inside so we might not be able to establish species to species interactions, but we can do that with confidence based on what we know about modern systems, which guilds were interacting with each other and once we have these guild level reconstructions of the system, then we can randomize species interactions within the guilds, and build a, you know, a set of food webs that we can analyze that represent likely, you know, interactions between species as a function of their of their guild interactions. And the question that they went about addressing once they were able once once they reconstructed these these guild level structures from which they could simulate many many different potential food webs of species interactions. They focused on this question of have large perturbations impacted food web structure or function. And they assess this by looking at the effects of primary extinction on the structure of the system. So for example if they go in to their simulated food webs and initiate a primary extinction would that result in a series of secondary extinctions. Do you remove if you remove the only resource of a consumer of course that consumer would be a secondary extinction that that is the result of the primary extinction applied to the resource. And then you can set different cut off levels for how sensitive you think that primary extinction, or how sensitive secondary extinction should be to primary extinctions. So, they initiated a number of primary extinctions as given by this perturbation magnitude so as they're increasing the perturbation magnitude, they're increasing the number of primary extinction, extinctions that are imposed on the system. And of course you would always expect that the number of secondary extinctions would increase with perturbation magnitude as you remove more species from the system. More species will secondarily go extinct as well. And the interpretation is that systems with a higher proportion of secondary extinctions are more fragile in other words they're less robust. And so this is the assumptions kind of going into this experiment, taking, taking food webs reconstructed before and after these large disturbances and assessing the effect of perturbations on secondary extinctions. So what do they find. Okay, so I'm showing two sets of results here one for the Permian extinction and the bottom for the incretaceous restructuring. What I'm showing up top is. In the first panel able to f again we have perturbation magnitude on the x axis and the number of primary secondary extinctions on the y. In the Permian before the mass extinction. We have this relatively tight sigmoidal relationship between the perturbation magnitude and the magnitude of secondary extinctions. So of course as you increase the perturbation perturbation magnitude you have an increase in the number of secondary extinctions. This is relatively, you know robust to the number of perturbations at the beginning. We don't have the sigmoidal increase this, this, this really sharp increase in secondary extinctions, until the perturbation magnitude is quite high. In comparison, if we look at Triassic systems, again, these are systems that were reconstructed, following the promo Triassic extinction. So you, you know, these are recovering systems. We find something very different we find that, well root nirine at all, found that, regardless of the perturbation magnitude there's many more secondary extinctions that that you'd expect by removing species. And so you still have this sigmoidal relationship. But the spread at lower when the perturbation magnitude is relatively lower is much greater in terms of the number of secondary extinctions. And this would suggest that Triassic systems are less robust are more fragile, following the promo Triassic extinction event. Similar message from the incretaceous restructuring although they're plotted on top of each other here so now blue is before the incretaceous restructuring and red is following the incretaceous restructuring. And this would, and so what we see here is, you know, still a sigmoidal relationship again this event is not nearly as dramatic as the promo Triassic extinction event, but we have these elevated secondary extinctions for any given magnitude relative to how how robust the system was before the incretaceous restructuring. So the message, the messages here then are that large perturbations appear to have left less robust communities. And that declines in robustness may exaggerate extinction events so it's very true, or it appears to be the case, I shouldn't say it's very true but it appears to be the case that that the system took some time to recover and that the Triassic system immediately following the promo Triassic was less robust. It's also very possible that the the incretaceous restructuring really set the stage for the effects of the asteroid. When it hit the planet 66 million years ago, signaling the end of non avian dinosaurs. Again by Jennifer Dunn focused on fall okay so so 66 million years ago the asteroid hits the planet. Non avian dinosaurs are wiped out and this really opens up niche space for mammals. And this is to a large extent why we're here. And one of the best faunas for these early mammalian communities is 18 million years after the asteroid impact in Germany the missile the missile fauna. So Dunn and colleagues reconstructed these incredibly highly resolved food webs one for a lake community and one for a near shore community, a forest community that was nearby this lake. Nearly 700 species are in these networks which challenges a lot of modern modern food webs contemporary food webs using again a cascade of different lines of evidence from functional morphology gut contents damage patterns body size copper lights etc. In these really highly resolved systems, and they show that 18 million years after the asteroid impact that the structure of these systems is indistinguishable from contemporary food webs. So a very similar message as the Burgess shale. But this suggests that even if systems are less robust following mass extinction events as root nirine pointed out following the promo Triassic or sorry, yeah, promo Triassic mass extinction event. So after the asteroid impact, given 18 million years the system had apparently reassembled to a structure that's no different from contemporary systems. Okay. I think one of the most interesting things when you look back into the paleo record and reconstruction of life on earth is the role that organisms played in essentially establishing the biosphere. In other words, the environment not only had a large impact on evolving communities evolving communities had a large impact on the environment. One of the most dramatic examples of this and this is this is via engineering these are changes levied upon the environment by evolving species changes to the atmosphere changes to the bedrock changes to rivers. So the more detail we uncover from the paleo record the more dramatic. We see species having an impact on a biotic on the biotic system. One of the most dramatic examples of this is the evolution of multicellular cyanobacteria and the oxygen crisis. Okay, so if we look back this is atmospheric oxygen that I'm showing on this graph before 2.4 billion years ago there was very little oxygen in the atmosphere. Okay, 3.2 billion years ago we have ox oxygenic photosynthesis beginning at around in the most recent estimates put this almost squarely around 2.4 billion years ago is the evolution of the first multicellular cyanobacteria. Immediately following the evolution of multicellular cyanobacteria and diversification of multicellular cyanobacteria, we have an explosion of oxygen into the atmosphere. And of course this is an inference but the timing suggests that the evolution of the atmosphere is due to the evolution of multicellular cyanobacteria. So these are the first global engineers, at least that we have record of that are operating on a on a grand scale, pumping oxygen into the atmosphere. So understanding these these feedbacks between the biotic and a biotic environment is really vital for understanding the evolution of early life on our planet. And of course understanding the future of of our planet. We are engineering on a similarly grand scale we are changing the climate in a much shorter period of time by the way. And we don't really know what the effects of this is going to be. So, understanding the roles of these global scale ecosystem engineers is important for understanding our role. It's also important for understanding the history of life. One of the recent projects that I've been involved with is trying to understand the role of engineers within complex ecological communities. So on the right I'm showing the cyanobacteria and forest ecosystems on the top, which which I'll get to the evolution of forests in a moment. In the middle here, this is a rock boring shipworm that was recently discovered so it actually digests rock, but it's an ecosystem. It's also an ecosystem engineer on a much smaller scale and paying paying tribute here to the rock eater from the Ending Story in the subset image for those of you who have seen the Never Ending Story. I'm also dating myself I get a little bit. But the rock boring shipworm bores through rock and near streams and it actually creates micro habitat for invertebrates that live within these rocks so it's creating habitat for other species. So it's also an ecosystem engineer on a smaller scale. And of course elephants are common examples of ecosystem engineers as they move about changing the landscape and opening habitat for smaller grazing organisms. But what is the role of engineers within complex ecological communities. There's been a lot of theory developed to examine the role of ecosystem engineers and systems, but most of it is within a smaller scale so understanding maybe one or two species how they might be impacted by by engineers. We wanted to understand how engineers might impact a community of species. And that really required us to think about how to integrate these abiotic interactions into the biotic interaction interactions that we characterize with food webs. And we wanted to really zoom away from just thinking about trophic interactions we wanted to think about interactions more generally so taking into account trophic interactions as well as mutualistic interactions. And so this is a little schematic that details our process of integrating these different aspects into into a species network, where we have three different types of interactions we have eat interactions need interactions and make interactions. And we have two different types of nodes in the network we have species, which are the colored circles, and we have modifiers which are the black, which are the black nodes. The modifiers represent the abiotic changes that are introduced by species. For example, the rock boring shipworm picture to the right creates opening a porous opening in the rock so the porous opening in the rock is the abiotic condition is the modification that species makes to the environment that other species might rely upon. So species can eat other species species can need other species and from these interactions they create trophic and mutualistic interactions species make modifiers and in that case they are ecosystem engineers, and then other species can eat or need those modifiers. And again the modifiers are somewhat abstract, so they're not, they're not specifically detailing any type of modification but a general modification that species make to the system. With these series of dependencies that species now have between each other, and with the abiotic modifiers, we can then establish a. An assembly process and a set of dynamics, a very simple set of dynamics that dictate how systems are put together and change over time. So on the panel D here I'm just illustrating a very simple food web where we have let's let's just kind of walk through this here. So these are consumers that both are eating this resource. The consumer is an ecosystem engineer and it makes this modifier this modifier this modification to the landscape is being consumed by this species. These two consumers are competing for this resource. The other species that my pointer is on is engaged in a mutualistic interaction with this lower trophic level species whereas the other consumer is just engaged in a trophic interaction there's no service dependency. And now we imagine that another species is colonizing so we have the colonization of this yellow species with kind of this black ring around it to indicate that it's just colonized into the system. Where this consumer is eating it and the modifier is needed by that species. The one of the key ingredients to this assembly framework that I'm understandably describing very quickly is that we allow species to have varying numbers of trophic interactions, but they need what they need. In other words, if they lose anything that they need any of the service interactions that they need, then they go extinct. However, they won't go extinct if they have at least one thing that they eat. So in other words, you have to eat at least one thing to stay in the system but you have to have all of the species or modifiers that you are engaged in service interactions with to stay in the system. So the assembly process then allows this colonizer to come in. And extinction works through primary extinctions work through competitive exclusion. So, we consider the fitness gains of species that are engaged in service interactions so species engaged in service interactions gain a fitness benefit. Whereas species that have multiple predators lose fitness because they're spending more of their time avoiding predators than they are trying to fulfill their own functions of life. Species that are generalists have a fitness disadvantage relative to species that are specialists. So those rules determine which species in the system are subject for primary extinction. And so if this consumer is goes extinct. If you see a cascade of secondary extinctions. This consumer that just colonized into the system is going to go extinct because it's losing the one thing that it eats. And this consumer is going to be subject for secondary constriction as well because it's lose it's losing the species with which it depends on with a service requirement so it has a need interaction with this species. So if the species disappears, it loses that need interaction, and it's also subject for extinction. So this this is my very quick introduction to this model. It sounds like there's a ton of ingredients but it's a relatively simple set of relationships. Okay, so what are some of the things that we found. As we increase the number of engineers in the system. There's a nonlinear effect on primary extinction and secondary extinction frequencies within the system. And again, this is a measure of our. This is one measure of robustness for these communities, and I'll come back in a second and relate it to, you know, the fossil record what does this have to do with fossil record. So as we increase the number of engineers we're also increasing the number of engineering dependencies, how many species depend on those engineers. And we find that when extinction when engineers are relatively rare so this is this this one here this is when engineers are rare. We find that there are higher rates of primary extinction coupled with lower rates of secondary extinction. And that means that extinctions are more common but they're of limited magnitudes such that disturbances are relatively compartmentalized. The reason for this is that there's stabilization of consumers in the system because there's redundant resources that that eventually increases the vulnerability of prey to predators and that's increasing the primary extinction frequency in this case. However, as we increase the number of engineers and with that the number of engineering dependencies, we find that both primary extinction and secondary extinction rates decline. And this core, this corresponds to increase persistence of species in the system. And this has to do with the expanding niche space that ecosystem engineers supply to the system. And when there's many more engineers in our systems there's also more engineering redundancies in the system. So you have multiple species that are engineering the same modifiers in other words they're changing the a biotic environment in the same way. It's similar to having multiple species of tree, which are pumping oxygen into the atmosphere if we lose one species of tree we're not losing oxygen in the atmosphere because so many other autotrophs are doing that. Okay, so, so there does seem to be a very important role. This is again a first pass but there does seem to be an important role of engineers in communities. How can we levy levy that to explore paleo systems. And I think I'm going to end on this note I had I think I have till 1015 am I going up to 1015 or guess you'll stop me at some point. Okay. So how could we levy this ecosystem engineering community model to say something about the past to try to understand past systems. One of the big questions that I would like to really explore is is the is the Devonian. Now in the Devonian. The evolution of early land plants at the beginning of the Devonian and at the end of the Devonian 60 million years ago, we have forests. Okay, so this is called the Devonian plant explosion. And obviously a really important period in Earth's history. We also have another oxygen bump associated with the evolution of forests. Early tree species these early plants that were living on the land were ecosystem engineers on massive scales. They created the soil. Okay, so we have soil generation. They began the process of weathering the soil. And this served to accumulate carbon into these silicate weathering products. And so it's sucking carbon dioxide out of the atmosphere, placing it into these silicate weathering products. These weathering products were being swept into the ocean and buried in marine sediments. It's thought that the series of events led to massive climate cooling and glaciation. And there's a large extinction at the end of the Devonian. And it's been theorized that it's tied to this plant explosion. And to this initiation of these terrestrial ecosystem engineers, changing both the soil, the atmosphere, and as a consequence, the marine system. And where massive extinctions were then occurred at the end of the Devonian during this 60 million year interlude. And so how would we love you a model like the one that I described. I'll be very quickly to explore this problem. So we can imagine, and I'm illustrating this on the on the bottom left here. So if we have a species or a set of species that are that are creating a modification to the environment, these might represent Cooksonia the early the earliest terrestrial plant that we have on record, pictured in the circle down here at the early Devonian in this evolutionary time. These plants diversify into a large clade of terrestrial land plants, eventually forming these early lipid and drawn forests that we have record of these these giant fern like forests towards the towards the late Devonian. So all of these forests are also contributed contributing modifications to the environment. And so I'm drawing a link here between, and I'm just picturing a single modifier here but we could imagine that might be a set of related modifiers. So now these modifications are are impacting other species other species now that are colonizing these forests are depending upon the modifications that these ecosystem engineers are making. And perhaps there's also direct exclusion of other species. So, whereas some species may be depending on the modifications that ecosystem engineers are making other species are being excluded from those environments. So exclusion is one thing that we haven't really investigated with this initial model of ecosystem engineering within a community context, but I think it's going to be a very important piece of the puzzle. Okay, can somebody tell me how much time that I have, I think, I think I should probably just stop here. The next kind of the next place I'm going and this is going to feed well into what I'm talking about tomorrow is the effects of humans on ecosystems. And I can just begin tomorrow here. That's great. Thank you. Thank you very much. I think we have several questions in the chat. You can read them out or you can invite directly the persons who asked the questions to speak for themselves. Okay, so I'll go back. So here's an earlier question. What's the reason or the theoretical argument behind this universal law of trophic links. Is it dependent? Is it independent of average biomass per individuals? Because it's so here's my answer. Because it seems to be independent of system. It seems to not be system specific. It's a general relationship that spans space and communities and types of communities and types of systems. It would seem to be relatively universal, or at least it hints at some universality. And then I would say it would likely be independent of average biomass per individuals because I would, unless that also might be relatively consistent. Although, you know, there's strong allometric relationships between the amount of biomass per individual in a system that also changes quite a bit from one system to another mammalian system. In mammalian systems we have damas law in ectothermic systems. Well, damas law corresponds to ectothermic systems as well. This might be one of the things that could be structuring some of those larger scale interaction patterns that we see. I don't think it's full. I think this is, I think this is an open question. I'm not sure if I have a good answer for why trophic links would be structured in that way. You know, one of the things that we've thought about exploring is, and we kind of get to this in our in our ecosystem engineering model is that, you know, specialists tend to have short term fitness benefits. Specialists tend to be better at capturing their prey because they have adaptations to capture specific prey. However, over long periods of time over large perturbations of events, generalists tend to have an advantage because they can adapt when things go bad. So this this ratcheting between generalism and specialism may very well lead to the types of patterns that we see in the link interaction distributions. So we have the survival of generalists after a mass extinction, like we see at the KT when the asteroid impact hits, you know, when the asteroid hits the earth. The organisms that small or that survive are small generalist mammals and small generalist organisms, regardless of whether they're mammals or not. So if it's if you have survival of these generalists after these mass catastrophes, but then selection towards specialization over shorter periods of time that very well might might explain some of that. Okay, what are guild level reconstructions or specifically what are guilds so guilds are organisms that share similar foods. So I might put pollen eating bats pollen eating birds into pollen eating insects into into the same guild. So they may be not closely related to each other phylogenetically but they share similar resources. And so the idea of reconstructing guilds is just finding organisms that that that share the same types of foods. And that's all inferred by the way that's just inferred from paleo reconstruction body size constraint. You know, do you have sharp teeth are you a carnivore, or are you obviously an herbivore, because of the shape of your teeth. So there's a lot of, you know, there's a lot of not guessing but I would say, there's different lines of evidence that go into that some of some of which might be good evidence and some of it might be somewhat shaky. So we have to be really careful about, you know, what we're assigning, what we're assigning and how certain we are of it. So I'm just kind of going down the list, it would be great if you can share some references on any math, any mathematical models about this topic. I assume you mean with respect to reconstructing food webs, you know, I haven't talked yet about dynamics in terms of, of building ODEs I'm going to talk about that a lot more tomorrow. So far, this true. So far, I've only really been talking about structural dynamics. So not imposing change change in biomass over time or change in populations over time but I will tomorrow for sure. Yeah, do trophic network summary features vary before and after large disturbances. There are some structural differences in the permeate promo triassic food webs that I was discussing so before the large extinction event at the promo triassic boundary. I don't, I don't have those in my brain at the moment, but they're in the root neuron paper. I don't know when I run out of time is cell network theory which is introduced. You know, I don't know I'm not familiar with that theory. So I would, I would be interested in. Is that something that was discussed during during this class. Well that would be interesting I'd be interested in learning more about it. Great. Yeah so so if there's time for any other questions I'm happy to do my best to answer them. Yeah, let's see if there's anyone who wants to ask a question they can raise their hand also and speak on the mic and see any of those. Okay, so if that's the case. Thank you very much Justin for this introductory lecture and we look forward to hear more about this in the following days. Sounds wonderful thanks for having me and I'll I'll see you tomorrow. Thank you. See you tomorrow. And bye everybody. Bye.