 Let's start again with our Friday session. The next lecture is Daniel Segre from University of Boston, with his second lecture about metabolism from its genomic skills to existence. Please, Daniel. Thank you. Slides work. Yes. Okay, great. Hi, everyone again. So we're going to continue talking about metabolism in microbial communities. So this is a perfect segue from the previous talk. And I just want to remind you in the previous time, we were talking about the logic of the cell, the metabolism as a resource allocation problem. And just to remind you briefly, the idea was to look at the complete metabolic network in an organism with knowledge of the nutrients that are coming in, the biomass components that are needed to produce a new cell and new biomass. And we introduced flux balance analysis where there are constraints on the concentration of each metabolite, not changing in time. So steady state constraint. There were assumptions of capacity limits for the nutrients that are coming in and for some reactions that may be known to be irreversible. And we showed that one can use optimization, for example, finding the state that is optimal for the cell to efficiently produce its biomass through maximization of the growth rate. And in general, one can actually write this problem in the following way where there is a steady state is expressed as this relationship between the vector of the fluxes multiplied by the stoichiometric matrix. We saw last time, and there are general capacity constraints in general, what can put upper and lower bound to each flux and optimize any linear combination of the fluxes using linear programming. We also showed the geometrical interpretation of this where there's a feasible space and we're really looking for an edge and vertex in this polyhedral cone, this convex structure that will represent the point that maximizes our objective function. Now we wanna dive straight into how we can apply this to the study of microbial consortia. And I will first show why metabolism really matters for microbial consortia. You saw some examples already, but I wanna walk you through the way we and others started thinking about this from the perspective of the complete knowledge of the metabolic capability of organisms. And one slide I always like to show about this is the following. So this is one of few instances, I think, where a lot of the interaction between different microbes are well characterized. So this is a picture from a review by Colin Brander and colleagues showing different shapes. These are different microbes that colonize, in this case, the human teeth. So this is part of the human oral microbiome. And what is stunning about this image is that really, a lot of these links are known metabolic interactions between microbes. Some of these are the early colonizers, and then there is this growing community that is, we tried to get rid of by brushing our teeth. And what is known about this interaction, some of these are known as contact interaction between different species. And what is known also is that some of these interactions are related to metabolic exchange. And as you heard before, this is probably a very common way in which microbes can cooperate with each other or exchange with each other material in many different ways. In this case, for example, a non-pathogen, peripheral monosytingivalis can exchange different metabolites with another microbe in this biofield. So this was, you know, this is interesting. The question is how common are these interactions between microbes based on metabolic exchange? Can we model them using flex balance modeling and so on? And I'll show you first how we early on tested this idea that metabolism really plays a role in the formation of biofilms and microbial communities using exactly this known structure of the biofilm. This was an idea from a former student in my lab, Barou Mazumdar, who took this map and asked the following question. So you can imagine having looking, we knew the genomes of all of these microbes. These were well characterized microbial strains. So we could look at each of these strains and their intracellular internal capabilities in terms of what metabolic functions they had. And at this stage, we weren't ready to do yet genome scale modeling, this flux balance analysis for each of these organisms. But we took a much simpler approach, which as I'll show you soon, was nevertheless quite insightful. And the idea was the following. For each pair of microbes, we could ask, we could compute a metabolic distance. So this is a pairwise metabolic distance just based on the profiles of which reactions each organism contained. So for example, if you have two organisms here, A and B, you know, simple version of this reaction vector, reaction content vector. So for example, organism A contains reaction one and reaction four. Organism B contains reactions three and four. And based on these strings that are just binary strings, which you can obtain from the annotated genomes of these organisms, you can compute a distance. We computed a jacquard distance to quantify how different metabolically these two organisms are. And of course you can do this for any pair of organisms and you'll have this matrix of similarity or dissimilarity based on the metabolic capabilities of these organisms. So what was done then was to compute the average metabolic distance for different kinds of paths through this community. So you can imagine taking paths that are we called order preserving. So paths that only go upwards in the biofilm in what is known to be the layer structure of the biofilm from the early colonizers to the late colonizers. So this would be an order preserving path because it only goes upwards. And of course there are many other random paths that do not preserve the order of colonization that can jump up and down between different species in the biofilm. So we could then compute the average distance between all pairs of organisms in both the random paths and the order preserving paths. And what we observed was that there was a clear difference between the distribution of metabolic, of average metabolic distances between the order preserving paths and the random paths. And in particular, the order preserving paths had a pairwise average pairwise distance between subsequent organism that was significantly smaller than the average pairwise metabolic distance for any possible random non-order-reserving path. So this was an interesting indication that somehow if you look at the correct order of colonization there is something unique about the species-species similarity in terms of metabolic functions. And as you may imagine, one possible interpretation of this is that organisms that are metabolically more similar to each other will have metabolites to share and will be able to gradually connect to each other metabolically building this biofilm. Now this somehow was an early indication that metabolism matters in microbial communities and in particular in this case in the order of colonization of the biofilm. We observed by the way that if you look at the same property of the pairwise average distance for non-metabolic genes, you don't see this clear distinction between the two distributions. And of course there are different possible interpretation of what was found, right? This could reflect really the fact that organisms build on top of each other but could also reflect at least partially an environmental gradient. For example, maybe there is a more anaerobic environment at the bottom of the biofilm and increasingly aerobic as the biofilm grows and in that case, perhaps the similarity and the similarity reflects adaptation to this gradient. But there was something else that came out of this which you felt was quite interesting. These kind of paradox, if you take this idea that metabolically similar organisms will tend to stay close to each other and build the biofilm, what would prevent this to collapse into an absolutely minimal distance boredom where all the organisms are really clustering in a structure by their similarity. Of course, in that situation, competition might dominate the capacity to exchange metabolites through synergy. But it raises this interesting question of whether there is an optimal metabolic distance for metabolic synergy between complete difference between two metabolic networks and complete similarity. And I don't think this is a resolved problem. I think there are interesting papers coming out recently on this question. I want to show you how we started addressing this early on by using a concept that is called elementary flux modes and I don't have time and I'm going to all the details of that but I'll just mention what is essential here which is this elementary flux modes are a way of enumerating all the possible pathways in a metabolic network, all the possible minimal unique pathways in a metabolic network. And you can take an organism and duplicate it and ask the following question. If I put two organisms together and just count the number of pathways that are possible when I have these two organisms together relative to the elementary modes that are present in organism one plus the number of elementary modes, the number of these pathways that are present in organism two. And if the two organisms are completely non-overlapping and you can build artificial metabolic network to engineer to have an arbitrary degree of overlap between these two networks. So if these two networks have zero overlap then if you compute this quantity the number of pathways of the system all together divided by the sum of each of the two individually then of course the sum of the pathways exactly the sum of the pathways present in each organism and this quantity is one. And at the opposite extreme if you take two organisms that are exactly the same have exactly the same metabolic capabilities when you compute this quantity each organism, the two organs are the same so the elementary modes of the junction between these two organisms is really the same of each organism alone and this quantity will have a value of half. And what is interesting and you cannot really quantify this unless you actually do the calculations and we did these calculations you can find that there is and this is almost like an analysis theorem right there is a maximum here as one might expect but it's interesting that you can find this sweet spot of how much metabolic overlap will lead to the maximal number of new metabolic pathways that are embedded in this combined systems system of organs 1 and 2 so this is just based on the topology it's a very simplistic calculation but it points to the possibility that there may be out there some sweet spot or some kind of ideal level of metabolic similarity that will lead to maximal enhancement of metabolic capabilities and of course this doesn't take into account competition it's just about how much new metabolism and tablets can be done by bringing two organisms together and as I mentioned last time I'm going to pause occasionally but feel free to interrupt and if I'm not monitoring the chat but there is a question just someone please stop me and I'm happy to pause and address that question so from this early very simple presence, absence analysis we really want to move to a stage where we can model computationally the dynamics of communities and as we saw in multiple talks including the previous one one can look at a community as a set of entities that can be represented between at different levels of description this would be more like a logical model where you have individual variables representing individual organism you can try and model the community as an ecological system in this way there is the possibility which is really what flux balance can do and what we'll focus on where you can think of the circuits within each organism and try to predict the interactions between different species based on what you know about the intracellular circuits of each organism and as we'll discuss probably next time there is also the possibility of thinking but I want to hint to this now there is the possibility of thinking of a community for a complex community what really matters is what functions are present overall in the community and we can ask the question of whether or not compartmentalization matters so is it important to know which functions are performed by which organism or is it possible and useful to think of a community as this overall conglomerate of metabolic functions so for now we will focus on this type of modeling we really know the intracellular wiring we know the environment and we try to predict ecological interactions and the dynamics and the structure of the community and we'll also talk a little bit about design of the community and I see there is something in the chat if it's a question yeah do you want to ask this question yeah thank you so I type in the box also my question is that for the first example of the order of the conglomerate in the bulk room the result is that the bacteria in the other path has smaller metabolic distance so my question is that does that mean that if there are two types of the effects which is one is the environmental gradients another one is the ecological interactions could change the dynamics so would that result mean that the environmental gradients dominate over the ecological interactions because in my mind maybe I'm wrong just think that if the ecological interactions are more important to preserve the order then there might be a lot of cross-dating between the metabolically more different bacteria that might be prevalent which is not the case in your data so I just want to add some comments on that question that's a very interesting question and I guess I think from this early analysis we don't really have enough information to determine this what I thought was interesting here is that if you practically look at the evidence we found there was that if you look at the organs that are close to each other in the biofield there seems to be a tendency toward a smaller metabolic distance and I agree that we don't really have enough information to know whether to interpret this as okay there is a just driving force that is the gradient of environmental oxygen or nutrients and so on but there could be also a situation where each organism modifies the environment for the next organism to occur and then the question is and the possibility what I think might be happening is that organisms that are too different from each other they may not have enough cross-feeding opportunities and of course if organisms are too close they will compete but there may be some sweet spot between and we can get back to this I think the answer to this will really come from looking at the more advanced dynamical models which we will analyze soon but I'm happy to go back to this question which I'm interested in so we want to move to these dynamical models and I want to show you how there was a different direction early on and now there are a lot of different experiments of this kind but this is really these early days of trying to think of synthetic cooperation and now the idea of building synthetic microbial communities as a way of testing hypothesis and checking what is really happening when you put the organisms together can microbes crossfeed and so on and so forth and there were these early attempts that were really interesting and a motivation for a lot of things we did later on so this was a paper and collaborators in 2007 and this was an engineered cooperation between two yeast strains one of which could not produce adenine and the other could not produce lysine and the idea was that only when grown together they could really be able to survive and in fact this was indeed the case so this was an engineered cross-feeding interaction that made each of these two strains completely dependent on the other it turns out and Wynin has continued working on this doing beautiful work showing for example that it's not clear that the metabolites that you would expect being exchanged that is the terminal portions of these pathways are the one being exchanged and there is a lot of interesting aspects of these dynamics and I'll mention more later on the other example more focused on just trying to find different types of interactions as opposed to engineering them this was done with E. coli strains library of mutants and the idea was to put these mutants together first I mean if you grow them individually they grow fine in rich medium individually they would grow very poorly in minimal media because they are mutants that lack the capacity to synthesize I think these are only amino acids but occasionally when you put them together you could see synergistic growth so this was a way of trying to detect new interactions and there are a number of new interactions that were detected this was worked by Edwin Tirmuth and Tom Silver so when we started thinking about this we thought it could be interesting to try and mimic some of these ideas using stoichiometric models this was work that a former student in my lab Neils Clipcord pioneered and we tried to do things in a slightly different way so rather than tweaking the internal circuits of the cell as done in these previous examples where you can do mutations and try to induce interactions based on changes in the circuits in particular oxotrophies or removal of genes that are essential for producing essential compounds we thought that perhaps it would be interesting to tweak the environment and so take two organisms that are natural occurring microbial strains and ask whether we can induce an interaction not by changing the circuits inside but changing the environment and the idea was that well first of all this is simpler to test potentially because if you want to test in particular a high throughput interactions of this kind it's much simpler to just provide different nutrients experimentally than having to do mutations to the strains and the other aspects of this which turned out to be really the beginning of a new line of research is that and a lot of people are obviously interested in this the environment clearly has a strong effect on modulating interactions so this is a way of starting to look at how the environment can really you know, can the environment or changes induce interactions and what is the role and how much variability there is in these interactions as a function of environmental composition you can change the carbon, the nitrogen, the sulfur phosphorus source and so on so there is endless combinations of different nutrients that can be used to try and induce these interactions of course one could use a rational approach and we'll see more of this but for now what we did was just simply use flux balance modeling to try and find in a large space of possible compounds some that would induce interactions and I need to tell you a little bit more about how this is done in practice because it's non-trivial right when you look at this we saw how to model an individual organism but how do you know how do you go from a stoichiometric model of an individual organism to a stoichiometric model of a community where you have two organisms together and the answer in the end is really something that existed already in the flux balance world but was used for different purposes and the idea is to use compartments so you can build a compartmentalized model and I'll illustrate this with this very simple example where you have two organisms one and two and they have a very minimal network organism one can produce B from A, organism two can produce C from A but each of them has a biomass that depends on A, B and C so each of them needs all of these three compounds to survive and if A is the only compound provided in the environment the only possibility for these two organisms to survive is to exchange B and C so this is a minimal example of cross feeding if you wish but it also illustrates how you can build a model of a community using stoichiometry and flux balance modeling and the idea is that you can define you'll have multiple versions of each metabolite so you'll have an environmental metabolite A and you'll have an environmental sorry a metabolite A that is in organism one you can label it as A1 and you'll have a metabolite A that is in organism two you can label it as A2 and so on and so forth and you can write the system of reactions just labeling the metabolites based on which organism they occur in and you'll have essentially a block diagonal matrix representing this system of two species interacting so this is in a very superficial way the way this stoichiometric model for communities can be built based on this multi-compartment model this was first proposed by Stolner and David Stahl in a very nice molecular systems biology paper in 2007 so we took this approach and used it to scan systematically systematically the space of possible environmental metabolites and just to illustrate briefly the way this algorithm was designed you can first take two organisms and ask under what conditions can these two organisms grow both a growth rate that is above a minimal threshold and you can search all the possible carbon sources you have all the possible nitrogen sources and you can do the same for other elemental sources but you'll have those that provide growth to the pair of organisms together you'll have a set of putative media for the growth of the whole ecosystem and now what is interesting once you take this media you can ask for each of them whether it also supports growth of each organism on its own and all of this again to remind you you can do easily using flux balance analysis so you have by definition we've had many many different environments all these different combination of carbon and nitrogen that all support growth of species one and two together in this joint stoichiometric model but then you can take a given environment and ask will that environment also support growth of organism one alone and organism two alone and if the answer is yes then you found an environment that supports the pair together but supports also each individual organism and this is a case where the organisms are not really interacting they can grow on their own they can grow together nothing interesting about it where for example the two can grow together that's again how this were originally found organism one can grow but organism two cannot grow and then what this means is that the two organisms grow together but two cannot grow by itself this means that one must be providing an essential component to organism two same situation here so if you find environments that satisfy these conditions these would be environments that would support a commensal interaction where one organism is dependent on the other and if none of the two organisms can grow on its own then again because the pair by definition was growing then what you find is a set of conditions that imposes induces a mutualistic two-way cross-feeding interaction between the two species so what is nice is that you can easily make this list of many many environments and for each of them you can test whether which of these is the case and ask how many times will you find environments that induce for example these mutualistic interactions and I should say when we started doing this we really didn't know what to expect how often would this happen and the idea was to start getting an idea of how frequent how prevalent how large is the space of this possible cross-feeding interactions in metabolism and what Niels found was actually sorry just a quick question maybe I missed this but in the joint FBA what's the objective function is it the sum of both great questions thank you so there are different flavors of this joint FBA in this early so we'll get back to this because that's actually this very question motivated a lot of other things I'm going to talk about but in some part of the models you have to choose an objective function and the very first case was based on maximizing a linear combination of the two biomasses so you can create a new reaction that builds a linear combination of these two biomasses with a fixed proportion but then you determine in advance what is the proportion of the two species you can do this by scanning many different proportions and seeing which one seems to be most faster but it's a little bit tricky so you can see already that this question of what is the objective function of a community turns out to be a really interesting question but also a tricky one and if you choose an objective function for these communities this is a little bit like testing a hypothesis what we did in this case because all we wanted to know is whether we could find an environment that supports growth of each organism so what all we did was in this case ask that the growth rate of each organism has to be above a certain threshold so we asked that each of them grows at least a certain amount doesn't matter they don't have to grow optimally they have to grow above a certain threshold and then what we did we used mixed integer linear programming to minimize the number of exchange reactions so we asked what is the minimal way for these two organisms to potentially exchange something so that they can go grow above a certain threshold which is why if the minimal number is zero that's totally fine maybe the two organisms grow together without having to exchange anything and in this case there will be a non-zero number of exchange reactions but thanks for asking these questions because I forgot to mention this so does it clarify yes, yes thanks okay great so back to the results here which again were quite interesting and this exemplifies some of what we found so these are seven species for which we run all these pairwise interactions and there is organism one and organism two here but these are the same organisms you can look for example at the interaction between E. coli and Salmonella and what you see in this pie chart the overall size of the pie chart represents the number of media of different combination of nutrients that we found that could support the two organisms together so for example if you look at E. coli and Salmonella there are millions of different nutrient combinations that can support growth of the pair whereas for example E. coli and H. pylori have a very small number of sets of nutrients that can support both of them together and then out of all this possible nutrients you can look how many of these are of this neutral kind that is environments that support also growth of each organism on its own and Salmonella and E. coli are very similar in their metabolic capabilities so as expected you find a large proportion this green portion of the pie chart of nutrients, nutrient combination media that are essentially good minimal media both for E. coli and Salmonella and of course they support growth of them together but there is nothing interesting about this but there was also a lot of interactions that are commensal of one organism providing something for the other organism and what was most stunning we really didn't expect that there are a lot of opportunities for this cross feeding all the yellow portions in this pie chart are cases where really this would be environment such that if you feed those nutrients to those two species they really need each other in order to survive I would say that this is only based on stoichiometry if we were to do exactly the experiments in the lab I wouldn't expect all of these interactions to occur because the fact that the stoichiometry have this property doesn't mean necessarily that the organism will have the right regulatory program to express the right genes to induce that interaction and so on so this is a purely theoretical flux balance stoichiometry based diagram but what it illustrates is that there is out there in the microbial world there are millions of opportunities for cross feeding and that they're strongly dependent on the environments in which the organisms grow and also illustrates that in principle if you learn how to manage these possibilities there could be a lot of opportunities for engineering communities where you by designing the environment you could decide whether or not two organisms will depend on each other so this was promising but it was hinting to and the question was hinting to there are some underlying assumptions in this type of model that are somehow tricky and will so in particular the require assumption on this ecosystem level objective of how do you manage these two biomasses there is some interesting hypothesis on the possibility that maybe you could use objective functions so I think this is a fascinating question but there are other limitations of this approach for example in the same way as flux balance will not allow you to predict intracellular metabolite concentrations because those are factor out when you assume the steady state for similar reasons with this type of compartmentalized based approaches you cannot predict the amount of each species in the community which is often one of the main things you would like to be able to know so you cannot predict how much there is of each species at steady state and this is a major limitation of this approach another limitation is that it's very difficult to do spatial temporal dynamics you cannot really do dynamical models based on this because it's all steady state approximations and you can just compute one steady state and as we'll see later the solution to this is going to be what is called dynamic FBA or DFBA in an extension of flux balance analysis which will simultaneously solve a lot of these issues and I think open up a lot of new opportunities now before we go there I want to pause for a second and think about this question of why would microbes exchange metabolites right and there are many different angles for this and we'll see this from different perspectives and this came up and we'll come up again I'm sure in other talks but you know metabolites are part of this strategy that microbes develop to you know grow and produce their own biomass why should they give out metabolites to someone else and as we saw last time right there are some pathways such as fermentative pathways that inherently give rise to secretions and these secretions might be helpful to other organisms but it's not clear how prevalent these secretions are and whether indeed these secretions are typically you know something that would be very costly for microbes to produce and then give rise to questions about stability cheaters and so on and we'll kind of get more into this so one way we started thinking about this was first by quantifying really the cost of metabolic secretions and this is worked by a study in the lab Alan Quacheco in the first observation we made that motivated this initiative by Niels Klickler before it was asking the following question if you take a flux balance model say for E. coli and you can grow it on different combination of nutrients and ask the following question if you impose a secretion flux will you induce reduction in growth rate so how much how much we have to pay in terms of the growth rate if you impose that organ is secret a certain metabolite and as you might expect there are secretions in this case succinate such that if you ask the cell before maximizing growth you say there has to be this amount of flux of succinate going out of the cell and then you maximize growth and the growth rate you obtain in this case for example on glucose and glycerol as carbon sources the growth rate you'll obtain is smaller than the maximal growth rate when you don't impose a secretion flux so this would be a case of a metabolite that is costly kind of corroborating what we're saying earlier whether or not metabolite secretion is costly depends strongly on what are the nutrients on a different set of nutrients succinate production is not very costly until you produce a lot of it but then what is interesting and let's look at this first actually because that's an example we already illustrated before there are metabolites such as acetate if you're growing under oxygen limited conditions this is something that will spontaneously happen and in fact if you impose a secretion flux of acetate that is small or zero the cell will not be able to grow optimally and in fact it will grow better when you impose that there is a high secretion of acetate possible so in this case the secretion is actually beneficial and there are some cases in between such as format where apparently for the two environments explored here secretion doesn't change the objective function doesn't change the growth rate so these are kind of neutral secretions so we we will call both you know these two kinds of secretions for the purpose of this we'll call them costless that is metabolic secretions that do not impose a cost or at least a reduction in the growth rate under this assumption and I want to remind you this is particularly relevant since we just heard about the cost of protein production and the capacity of embedding into flux balance model the or consumer resource model that is also possible in flux balance models the cost of protein production so this doesn't include any of that regular flux balance models but one could extend this kind of approach to models that also include protein production and the cost of protein production so let me show you what we found so what Alan did he wanted to I find how frequent are these costless secretions in the microbial world and again the idea was to scan many different flux balance models looking for how often would we find costless secretions so I designed the following experiment the idea was to have enough variability of environments to explore systematically a large space so again this was done only in silico and I'll mention later there is a follow up work that is being done experimentally now on this but the idea here was to give two different carbon sources chosen out of a pool of different carbons and also choose whether or not to provide oxygen so we could do this in silico experiments aerobically or anaerobically and as hinted to before of course this can have a big consequence on whether or not there are secretions or what secretions being produced and then we computed the possible the maximal growth rate of two organisms of a pair of chosen organisms under these conditions and what we did was estimate if any of the organisms could grow what metabolite could be secreted in a costless way so we asked is there any metabolite that upon maximizing growth each of these organisms could produce and now we did iterations where these costless metabolites was added to the medium and we repeated the experiment growing the same organism but now on the original medium plus the costlessly produced metabolites so the idea of this analysis was that we could get inside both into what costless metabolites could be produced but also whether these costless metabolites were really useful for facilitating growth of a second organism so we did this for these two different conditions of oxygen availability 108 carbon sources 14 different species for a total of over a million unique simulations and I'm not going to show all the details there is a lot of data that came out of this that is available in this Nature Communications paper but I just going to illustrate what summarized what we found which is that there are many different type of secretions much more than what we originally thought and not just the organic acid the organic acids are this light brown portion so there is a large portion of organic acids but there are a lot of other molecules carbohydrates and organic compounds the metals are not necessarily so interesting because they're just coming in and out of the models but there are some peptides some phosphate that are being exchanged and for an overall total of about 60 metabolites and a little bit more when you have no oxygen and as expected somehow there is a little bit a larger number of secretions when oxygen is unavailable which is interesting in itself and again it's interesting to think of this in terms of ecological niches and whether really that's something that would be testable whether indeed there is prevalence or additional metabolic interactions in anaerobic conditions and the other thing that was interesting is that these secretions could really induce interactions across different species so for example out of all the different species if you focus first for example the oxygen dependent one you can count how many how many simulations both organisms could initially grow and there is a certain number and there is this is what is interesting this is the number of combinations of microbes and environments where growth could occur after cross feeding so after at least one round of costlessly produced metabolites being fed back into the medium so there is a large increase in the possible growth capabilities induced by this costless production and these are the proportions of which either zero or one of the two organisms grows so the interesting part is that you can almost double the number of combination of organisms in which there is growth of a pairs of organisms because of this cross feeding interaction so again this was based only on flux balance modeling based on stoichiometry but it's a different illustration and points out the fact that in this case the cross feeding can be induced by metabolites that are really not inducing not causing a decrease in the growth rate of each individual organism and the idea that where there's a lot of interesting work and I'm sure there is many cases in which interactions are due to metabolites that are costly and this could be evolved traits and we're going to talk about this soon but it would be evolved traits where an organism produces a metabolite that is costly because it leads to an advantageous interaction but there is the idea that is merging from this analysis that there are a lot of opportunities out there for costless interactions things that are induced just by organizing what is best for themselves and at the same time in doing so throwing out there something that someone else can use I view this as a little bit like as recycling right in social community social human societies you know there are things you know when you are finished with your milk you know we throw away the bottle and if you can actually recycle it instead it doesn't cost anything to you but it actually can be valuable for someone else and the idea is that this kind of interaction may be very abundant in the microbial world and I this is a map of the specific metabolites that can be exchanged I'm not going to go into the details to this but if anybody is interested you can actually look at what specific metabolites are being secreted under what conditions and there is a lot of interesting follow-up analysis one can do on this but I want to summarize this just by showing that the emerging network that we looked at by analyzing this costless interaction so this is a network of what organism could feed which other organism in this set of organisms we analyze based on costless secretions and the picture that emerged here is that there is a dense network of possible interactions that emerge spontaneously between different species that may not require organisms to give up anything valuable but are just emerging property of the system and somehow this was similar to the result that in parallel Alvaro Sanchez and we'll hear about this found in this organ in this community grown from simple carbon sources from plants and soil and this is one of the illustration from that work which you'll hear I'm sure more but what is interesting again the same picture emerged that each organism could grow on the spent medium of each other organism somehow suggesting again that there is really there are a lot of dense networks of exchange out there and this is somehow very different from what I was expecting initially that is that it's not necessarily individual targeted interaction but there is probably a dense network of possibilities now and as we start thinking about this with these models yeah I think yeah 50 moments as we started thinking of whether in addition to looking at the natural interactions in communities we could use flux balance models to also purposefully design cross feeding interaction between species and the idea was that you know when we look at this natural interactions through stoichiometry we really look at many different organisms many environments and we look at what are the possible outcomes but we wanted to do this in a more targeted way and also potentially get insight into what I'd like to think of as deep symbiosis where the exchange metabolites are not necessarily by-products end of results of by-products of specific pathways such as amino acids but more convoluted interactions that you may not be able to look at or find intuitively such as exchange of two amino acids again so let me show you we'll see in a second what I mean so the idea and this is sorry I worked by Megan Thomas another former student in the lab with the Anis Pascalidis and others and the idea here was the following we took E. coli we can ask the following question you have a certain number of reactions in E. coli about a thousand but you can force E. coli to use a smaller number of either internal reactions or exchange reactions so imagine the way I think about this is you have a knob you can say okay instead of using a thousand internal reactions now you're allowed to only only use 900 and you can ask how well can you do and you can tweak also the number of exchange reactions how many metabolites you can transport to the external environment and at some point you can imagine let's say focus on the internal reaction if you turn this knob too much to the left right you you decrease the number of possible reactions too much at some point the organism will not be able to grow you might see at some point a decreasing growth if you limit the number of reactions and at some point there is no way for the organism to grow but then one can ask the same question for a pair of organisms so you can start with two E. coli that are initially exactly the same and you can impose these constraints on the internal reactions on both of these but now each of them could choose a different set of reactions to use and now you say okay I limit to let's say 7% of the original number of reactions they can use but now this top organism could choose one set of reactions these other organisms could choose another set of reactions and what we were wondering was whether we could find a constraint that would not allow an individual organism to grow but would make it possible for the pair of organisms to grow together again in an obligate synergistic cross-feeding interaction that would be now induced by our arbitrarily tuning the number of possible reactions and again this was done using classical FBA in this multi-compartment model and I'll show you a couple of outcomes of this analysis this was done using mix integer programming linear programming where in addition to the variables representing each flux we had Boolean variables representing whether or not each reaction is present in each of the two organisms and I'm not going to go into the detail of the optimization it doesn't completely trivial and it gets pretty time consuming as you go to full genome scale models so it's there is I think interesting computational work to be done in terms of trying to improve this kind of algorithms but let me show you first the outcome that we got from looking at the core metabolism of E. coli so this is a simplified model of E. coli metabolism and what was fascinating is that one of the solutions that the algorithm found was these two E. coli I would say subspecies right so these are species that are limited in their capabilities and you may recall here this is glycolysis this is the PCA cycle but here each of the two organisms uses a different half of the PCA cycle this species uses one half this other species using this other portion and the exchange with each other multiple metabolites including pyruvate here but also some of the byproducts of this you know this intermediate byproducts of the PCA cycle and what is interesting here is there are a few things one is that it would have been very difficult to come up with such a scheme for possible cross feeding without the algorithm this is again not the exchange of two amino acids this is what again they're called deep symbiosis where two organisms exchange interesting metabolites that are known to be transportable but are part really of central carbon metabolism and there are multiple exchanges that are required for this to happen the other part that is interesting is that in nature there are indeed organisms that do have of the PCA cycle the incomplete PCA cycle as we hinted to some of these are related to the capabilities of producing amino acid through these reactions but it's quite interesting that one of the solutions of this algorithm really resembles some of this half PCA cycle strategies that are found in some marine bacteria now when you go to genome scale models this is much more complicated than it's really impossible to visualize the whole network so this is another way of visualizing what happens so what you see here again is the number of exchange reactions the limit on the exchange reactions and the limit on intracellular reactions so you can imagine right you start from this is where you have this corner top right all the reactions are possible and you gradually can decrease the number of allowed exchange reactions or internal reactions and these areas, shaded areas in green and blue represent the feasibility and the growth rate that is possible as you do this and as you can see if you start with one organism right one organism is feasible in this region so what this means is that if you decrease the number of reactions to below about 250 one all I cannot grow anymore on its own same if you decrease through this seems like a Pareto frontier this exchange reaction if you decrease them too much the single organism cannot grow but this is where you can see again what we're hinting to before that if you have two organisms and each of them has these limitations then there is a much larger space where the two organisms can grow if they grow together in the range of metabolites even under constraints below this 250 thresholds up to 210 or so where individual organisms would not be able to grow but the two organisms can grow together and then there is a lot of data again imagine for each of these cases you have this genome scale models of the E. coli networks and you can look at what is the structure of these networks what metabolites are exchanged and so on just to exemplify the kind of insight you can get you can see that there are regions in this space as shown here where acetate is one of the key exchange metabolites which is not surprising again acetate comes back again but there are regions when you go to the extreme you know you push this pair of organisms to the limit then turns out succinate again one of the TCA cycle intermediate is one of the metabolites you would expect to be exchanged in order for these two organisms to coexist and there are areas where some amino acids need to be exchanged so probably the two organisms will do will perform complementary metabolic functions in exchange amino acids and again there is much more one thing that I you can't really see here but I just want to highlight is that there is an interesting very thin layer here between the one organism the two organism regions where one organism is still possible and two organisms of course are possible but there is an area here where the two organisms growth rate is faster than the one organism single organism growth rate under those conditions so what this would imply is that if you were to put a chemostat experiment and force this organism to grow at a certain rate there would be a situation where the two organisms would outcompete the single organisms hinting to the possibility that this could be a transition where even if a single organism could grow on its own but cross feeding could be evolutionary advantages and give an advantage to a pair of synergistic organisms this is not saying anything about the details of how this could happen in real life but it's showing that in principle there is this overlap that would give an advantage to the two organism solution rather than the single organism solution. We have five more minutes and I will tell you a little more about another aspect of this genome scale model we dealt so far only with organisms that are very well characterized well built models such as E. coli other organisms for which we had very good models but as we hinted to we want to start understanding complex microbiomes or more complex microbial communities and one of the limitations that we have to be aware of is that in many of these cases the knowledge about the metabolic capabilities of these organisms is much more limited than what we have for E. coli or yeast and so on so the question is can we get around some of these limitations and David Bernstein another pharmaceutical lab did some really nice analysis using metabolic percolation in order to address this question and this I think is a really interesting area because if you think about this when you have a metabolic network that is not working if you do flux balance analysis and you cannot produce a certain amino acid the network will just not grow and will give you zero information of why it's not growing so it's a little bit like I think of it as a broken computer and if you don't know what the computer is missing but the computer is not working doing diagnostic could be very challenging and so on and this is all with the situation we face when you build a flux balance model and it's not working it's very challenged to find out why and of course there are methods for doing this you can look at pathway by pathway but the idea that David started thinking about is that one could think of the problem of biomass production of a population problem where metabolites that are present in the environment could be present and chosen with a certain probability and then you can ask about the probability of producing a given metabolite and you can ask this for any metabolite that is part of the biomass of an organism and systematically choose many many different random environments based on these probabilities and ask what is the chance that you will be able to produce one of these biomass components and the advantage of this is that even if the network has some holes you'll occasionally add some of these metabolites by chance and you'll get an overall picture of how producible a metabolite is given all these different chances of different metabolites from the environment being available and this illustrates for example how much more robust this algorithm is relative to regular FBA so for example if you remove randomly reactions from a network with the fraction being let's say 1 in 100 or 1 in 10 FBA will soon not be able to give you really accurate prediction of the growth rate but the producibility will tell you whether or not an organism can grow or whether you remove a pretty large number of reactions from the network so it doesn't give you accurate prediction of the growth rate but it will tell you what is the producibility of each metabolite in the network in a way that is very robust and I'll illustrate one of the applications of this this was done again circling back to the human oral microbiome and I'm showing here just a big heat map 456 strains from the human oral microbiome and 88 metabolites that are part of their biomass and the darkness of the red shade here indicates how producible each metabolite was for each of these strains so this is basically a map of the metabolic capabilities of all these different organisms based on this percolation algorithm and I'll there is a lot of information here that could be compared then to for example co-currents of different organisms in microbiomes and we started doing this but I want to illustrate one specific example that was quite interesting one of these oral microbes is what is called TM7 you can really see it from here but there is a set of organisms that are uncultivated bacteria so these are organisms that are cannot be grown in the lab on a medium and many other bacteria they depend on something else that is unknown and they can only be cultivated in cooperating together with another organism in this case, octinobacteria and there is a lot of laborious work at the foresight and other places to find these partnerships and there is also very recent efforts that allow to sequence these TM7 organisms through metagenomic sequencing or single cell sequencing something that was unthinkable a few years ago but now we have the four genomes of this organism we could compute this and what we found analyzing the capabilities of this TM7 uncultivated bacteria and the host, the octinobacteria we found putative metabolites that are complementary between these two so for example the host, the octinobacteria could provide vitamins and amino acids to the TM7 and what is interesting is that there are cell wall components that could be exchanged potentially also from the TM7 to the host producing two way interactions that gives rise to a lot of testable predictions and that gave rise to some follow up studies but this is just to illustrate how one can expand these ideas of flux balance modeling beyond just computing the detail growth rate and using it to start analyzing real complex microbiomes and their metabolic capabilities and I think it's time to stop and I'll just hint to the fact that what we'll talk about Monday is about how you extend, you know, go beyond this compartmentalized model and look at dynamic flux balance modeling where you can start thinking of really not just a more realistic way of how to model exchange between organisms this will allow us to look at the dynamics and also the spatial structure of communities and a lot more. So I'll stop here and see if there is any questions. Thank you, thank you Daniel for the questions. Yeah, please go ahead. Thank you. So for the example last example, I mean the crossfading between TM7 and the host, have you tried to validate the finding here in virtual? So I haven't this is, I mean we are doing experiments but this is well beyond the kind of capabilities we have. I think there are only a couple of labs that can do this kind of experiments because and this is just really fascinating work that is being done for example the Forstner Institute but just being able to isolate the organisms which are very small to do with the host is a whole lot in itself. So there is, I think listed here, there is some evidence from preliminary studies for example that there is gene expression studies where it's really, it was found that some of these for example, Enacetylbicocosamine is really implicated in the expression of the host in co-pultivation with TM7 so there are now a lot of efforts to try and characterize some interactions and I think it will be very interesting to see if now this is valid. So we are not doing this but it is being done and that there is a lot of interest. This TM7 are a fascinating very big clade in the tree of life that has just starts being characterized. Yeah, thank you. Yeah, I think it's definitely very interesting to delineate which is the metabolites among all of this potential carcinometallites is playing the key role of the interaction that would be really fascinating. Yeah, thank you. That was a raise hand by Miguel Rodriguez. Please. Yes, thank you. I have a question about well, I don't know if it might be more relevant for the DFBA but I will ask anyway so you explicitly model you have been showing how to explicitly model synthrophy and sharing some of these metabolites but is there a way to also explicitly model the fact that competition has a very strong attractor for one dominating species? Yes, absolutely and as you hinted to the answer is going to be dynamic FBA in dynamic FBA this comes very naturally as we'll see because I can just hint to this right in dynamic FBA do this stepwise approximation of the growth curve where you solve flux balance time by time at each time point you predict the growth rate and you know how much nutrient is being depleted so if you have multiple organisms in the same environment this is a little bit like a consumer resource model where you keep track of the nutrient extracellularly and each organism will try to use it in its own best capabilities but they will all compete for the same nutrient and therefore competition will come as a very natural outcome of this simulation which is why now basically most of what we do and I think dynamic FBA is really the way to go for modeling community I don't see any raise hands so if I can ask another question is there a way to explicitly model also toxic bi-products the accumulation bi-products become toxic yeah that's another very good question the short answer is no I mean you can think of reactions if you block internal reactions and there is no way for a metabolite to go this will give an infeasible solution in FBA so that is a little bit similar to toxicity but not really I mean toxicity if you think about this is really about a metabolite having a very high concentration and starting to do stuff that it shouldn't be doing inside the cell but by definition then FBA regular FBA does not have any notion of concentration inside the cell so it really cannot do that you know there are kind of you know just into the fact that if you look at things like shadow prices that give you sensitivity of the biomass production rate to changes in the constraints and including the mass conservation constraint you start having a notion of concentration inside the cell you can do this also with thermodynamic flux balance models where you can put back the concentrations so in principle I think it will be possible to do this but I think it's we're not there yet and I think it's a super important question but the other thing I'll say is that even if you have the concentrations you need to know somehow what compounds do to proteins, why they're toxic so if you have knowledge in advance you can model the kind of rising of a toxicity in a certain situation but whether we can predict the novel the toxicity I think that's even more challenging and I think that's a beautiful question but I don't think I think that's really beyond FBA it would require structural modeling or other approaches I don't seem to see any further question are you sorry I have a question but I cannot find the thing to raise my hand thank you very much for the very nice talk my question is to be related to what was asked before and I wanted to ask like if I understood well in this in the balance analysis like you use genomic data like so already sequenced organisms I was wondering just if this could be applyable to non-cultivated organisms but let's say if it would be too much of a stretch to apply this to max or to differently sequenced organisms I'm thinking of the unculturable extreme environments my community yes yes beautiful question I think again the answer until a few years ago was no because all of these FBA models really are based on a knowledge of the genome and without that you know there may be other ways through phenotypic characterization but I don't think I think you're really in the genome but what is amazing now is that with the new technology of single cell sequencing right if you can sequence a single cell that starts being possible with bacteria but the other thing that I think will really change this dramatically and there are some beautiful example already out there is that from deep metagenomic sequencing now you can get enough resolution to close individual genomes so even in a metagenome if you have an organism that is uncultivated you may have enough information to determine its genome so I think that will open up huge possibilities in terms of looking at this unculturable true flux bonus I was thinking perhaps help guiding like the cultureability of these organisms right I think that would be absolutely fascinating again this is what we're trying to do here the problem I just want to put a caveat there is that we know nothing about this unculturability and there are many different hypotheses if metabolites is what determines those then yes FBA can help but there could be signaling molecules protein factors you know all sorts of other things that are beyond FBA and if those you know for those cases we will not be able to say much thank you very much yeah I have a really good question then you sorry there is a question from Martina okay we'll raise that right no it's okay I mean it's okay if it's related I can wait no problem okay please go ahead yeah so me can I ask or yeah sure Martina so my question is about reconstruction model I mean which is related to the previous question about reconstruction model from metagenomics I mean even if we can't identify the genome the specific genome from the metagenomics is that possible or is there any work to reconstruct the model from the metagenomics by using the conception of a superbus not compartmentalized we probably don't have enough information about the genome of each bacteria in the metagenome but it's possible to construct a superbus like model metagenomics is that possible yeah great question it is possible and we'll talk a little bit about this next time there are some approaches that essentially do a little bit of that but it's not clear to me I think it's not clear to me that you really miss something and destroy something so I'll tell you very briefly one analysis a few years ago was looking at yeast that has compartments and we compared FBA of yeast with compartments to an FBA of yeast where you destroy all the compartments so it's a little bit like a simulation of exactly what you said where you decompartmentalize the model and what we found is that the decompartmentalized yeast you make a lot of mistakes in the predictions basically because anything that depends on energy production across the membrane oxidative phosphorylation doesn't work anymore so I think it might be possible still to do ecosystem flux balanced models I don't know that anybody has actually done this but it could be possible if you keep track maybe also of meta compartments but there are other approaches network expansion based which we'll talk about that actually do a little bit of that so the answer is that it's not clear it's a very interesting area and more to come on this yes that's very much Miss Martina yeah hi so my question is so for example I don't know pH and temperature can change the availability of the byproducts these kind of things is it possible to integrate these things in the flux balance analysis the dynamic one I don't know in general yeah it's a very good question the the it's hard I mean it's not impossible I think there is there's been a little bit of work out there on temperature I mean the tricky part of temperature is that it changes so many things right you could in principle try to put some constraints on the rate of reaction based on the Arrhenius equation and try to figure out some way of putting the temperature in there but you know if you think about this there is denaturation of proteins suboptimal there are so many things that could be happening and I think it would be very difficult it's a very painful thing for me because and many others I think because there is a lot of important applications of how microorganisms would change with temperature for example climate change and so on but unfortunately I don't think there is a very effective way of incorporating temperature and similar for pH we actually we had did some work which is unpublished but on trying to simulate exactly what are the molecules that are being secreted and how this will induce changes in pH in the medium so this is in principle possible but it's very complicated and one of the things is that let's say proton exchange and so on it's very hard to keep track of so I think for pH there is hope for temperature is much harder I would love anybody had ideas of how to do this I think that would be hugely important but so far I think it's not been possible okay thank you great I think we had a very lively and interactive lecture with Daniel more to come over the next days so thanks again Daniel thank you everyone and we'll basically move directly to the next lecture by Mercedes Pasquale Mercedes I think you're here already