 I would like to share today the first session that we briefly introduce myself. My name is Udo Reichel. I'm a director for Max Planck Institute. And I'm focusing on bioprocess engineering. So I've studied biology, did my PhD in chemical engineering. So I'm working at the Interface Biology Engineering. And motivated by yesterday's discussion, I thought I can, in this introduction, a little bit focus on metaproteomics. We had this wonderful microbiome discussion yesterday with one of our speakers sitting over here. And we discussed about options to attack bacteria in the gut with bacteriophages. And I would like to present simply some options to do some more thorough investigations of the communities in such a gut. So we are mainly motivated by something different, which is biogas production, wastewater treatment, contaminated areas. And also, we look for medical applications. But all focusing not on the metagenome to identify the species involved. But we want to look at the functional level at the metaproteome. And for that end, we have established a software tool, which is called the Metaproteome Analyzer, open source, which you can link to genome databases and also to proteome databases to identify bacteria, assign function to proteins, identify pathways, and so on. You know what we do is bottom up proteomics. So you go from the protein, you digest them, you do mass spectrometry. And this really is the basis of our work. So we use the mass spectra to identify the peptides and assign the proteins. And the typical workflow for such a community analysis, you have some input data, which are the spectra. You do some filtering here, quality control. Then you match protein databases. But you can also match genomic information if you want. Then you retrieve meta information, metaprotein grouping. And then you do post-processing. And we are mainly interested here in taxonomy. So you cannot go down to the species level. You end up usually at the family or the order level of these organisms. We're interested in ontologies and enzymes, and in particular also here in the pathways involved. So for instance, if you have a biogas plant, we want to understand the key players and how the key players interact, how their pathways interact. From this, you get a lot of data. And we do this in several years. And I'm getting tired to see these endless lists of proteins in tables. And I thought, this cannot be what we are interested in. I'm not interested in these lists. So I tell my students, don't show me any lists anymore. I want to know what's going on in this plant, but not the list of enzymes whatsoever. So that's my motivation for that intro here. What I asked them to do, and we are in the middle of doing this, is that we take these enzymes. We identify. We identify hundreds of them, thousands of them. And somehow fit them to the pathways we know, so that we have the enzymes. And then you go to the huge pathway maps in Keck and directly fit them to the pathways. And then from that, what I want to see in a few years, hopefully, that we can, from the community, fit the information we have to the pathways, and then identify the pathway for the whole community. So if they grow on ethanol, for instance, what are the pathways used in that community and assign the organisms to them? And for instance, then identify organisms which are key players here. And you can see, in part, this works, this published data. So we can do this already, but we need a little bit more information to cover the whole pathway structure in the biogas plant. And then the next step, if you do this, it's also crucial, I think, you have to understand. Let's say you have identify organisms. There are three of them here in this slide. They feed on ethanol. They use lactate and pyruvate. They have a pool of metabolites. They exchange. So the question now is, can we understand how they interact? What are the substrates they take up? How do they depend on each other? And one of the tools, of course, you can use is metabolic flux analysis. You would not start here, I think, with an ODE, a differential equation-based approach. It would be too complicated. But what I want to motivate here is that we try to set up here the pathways for these individual organisms we have identified in that case, that we consider the main substrates released, products released, substrates taken up, and products released by the organisms, and then try to interfere what is going on in a microbial community. And this is shown here. First attempts from our side is, so we want to design synthetic or minimum communities to perform the task we interested in. And you can, for instance, now ask these models, what is the maximum specific growth rate of the community, not the individual organism? How fast can they grow in such a plant? We have to make some assumptions, because this results in a lot of solutions. It's a large space of solutions here. But we can, for instance, add some more scenario assumptions like, we assume that every single organism is still trying to optimally use its substrate. And this brings down here, in this case, these are three scenarios. This is organism one, organism two, organism three. And you can here have now an optimality degree and say, OK, there is a range for certain percentages of organisms growing together. And according to what we define as optimal, they will perform best in such a biogas plant. And the same, I think, can be done here for wastewater plants, or you could also consider that you do this, for instance, in a gut, that you try to identify the communities, try to understand how they interface, how they depend on each other, and then try from these lists to extract information that you can eventually use, at least to get some quantitative defense and how all these organisms collaborate. OK, this was my motivation here for species models and for the mathematicians, this is getting extremely difficult if you increase the number of organisms. So if you want to do 10 or more, it's a challenge, so computational challenge. OK, that was from my side to motivate micro proteomics, micro microbes. We will have two presentations this morning. The first one will be by Maria Yuck Senar. She will talk because Luis Delano cannot make it, unfortunately, is sick. And she will talk on whole cell models from systems to synthetic biology, and she will focus on microplasms. And then we will switch to Nitin-Valiga, a network strategy to decipher and manipulate complex phenotypes across diverse organisms. And I think we will leave maybe after every presentation five minutes for short discussion, technical questions. And then we have a final discussion before we go for the poster session. OK?