 Okay, good morning, everybody. So, thank you very much for the opportunity of presenting part of the work that we are doing in the lab of Luisa Rano, Luisa Polisaisis, because he couldn't come, he's sick. So I will try to explain the talk that we have named Holes and Model from systems to synthetic biology. And so, the aim of our lab is to try to quantitatively understand a living system. So, it is starting my assistance biology approach that consists in obtaining data to characterize the different components of a cell or of a system from the proteome, transcriptome, so knowing exactly the amount of all the proteins, RNAs and different molecules that you can have in a system, then with all this data try to integrate this data in a computational Holes and Model that could help us finally to develop applications in the synthetic biology field. So, the first question that we had is which is the proper chassis to do this approach? Because depending on the application that you want to develop, you can use, you can think about a simple chassis that it's easy to analyze because you have less number of components and then understanding the network is easy. But also you can think about a very powerful chassis that despite the complexity is very high, it could maybe be easier to develop some kind of applications. So, here is the example. You can do a washing machine that the synthetic circuit is easy to integrate by using a bicycle. Or also you could think about using a rocket, but probably you will have unwanted interactions of engineering circuits and will be very difficult to obtain this washing machine. So, with this rationale, we decide to use mycoplasma as our model organist to do these systems biology approaches. Why mycoplasma? Mycoplasma are the smallest and simplest free-living microorganism. They are members of the molecules class which is characterized by the absence of cell wall and for having genomes with a low GC content. Despite this apparent simplicity, they have a remarkable intricacy. They are parasites and pathogens of a wide range of hosts. For instance, mycoplasma pneumonia that is the object of our study causes a typical pneumonia in humans. Despite... Well, this disease is not very severe, so it can be easily treated with antibiotics. And most of the population, we have mycoplasma pneumonia in the lungs, but only when we are immunodepress or in kids, it can cause pneumonia and, as I said, it can be treated with antibiotics. So, the good characteristic of this bacteria is that it has a minimized genome of only 800 kilobases and comprising around 700 open-reading frames. So, it has become a model for systems biology approaches and during a long time in the lab, several people have been involved in characterizing these bacteria at these different levels, at the level of transcriptome. We have identified all the transcription at the star sites of the different mRNAs in the genome. We have found all the non-coding RNAs that there are in the genome. Also, we know the operon structure of the different genes. At the level of the protein, we have quantified all the proteins in different conditions by using mass spectrometry and how they are interacting by TAPTAC. Also, we have done the first flux balance analysis model of the metabolism, and so we could estimate which are the amounts of ATP that are used for the different biological processes in the cell. I said it's collecting data. We have been collecting a lot of data. And as Hugo said also, we have a list of a lot of data, but finally, we want to try to integrate and understand this data. And for that, we want one of our goals, or the main word that we are developing now, is a computational wholesale model that allows us to integrate all this data and do simulations and predict how different modifications in the genome of the bacteria could affect the growth and then facilitate all the experimental, sensitive biology approaches. So, in this sense, these approaches or these applications that we want to develop at the end is using mycoplasma pneumonia as a living pill. So, using mycoplasma as a GSCs for treating human gland diseases and also to obtain vaccines. So, in regarding the vaccines, we just recently got a project, which is in the Mark of Horizon 2020, and it's called MycocinVac, that consists in using mycoplasma and synthetic biology approaches to develop a universal vaccine using mycoplasma pneumonia as a GSCs. So, for that, we have to determine which are the viral-lens factors of mycoplasma pneumonia and the proteins involved in pathogenicity, remove them and to obtain these GSCs, this first bacteria, that is non-pathogenic and non-viralent, and also we want to optimize this bacteria for the growth and the production at a high scale. So, we want to optimize the growth of this bacteria. Because, sorry, I forgot to mention before, this bacteria grows very slow, so we duplicate every eight hours. And also working with this bacteria in the lab is very tedious because it needs rich media and this media contains CERA, so it's very expensive and very low reproducible. So, we want to also optimize the... or engineer the bacteria to optimize the growth and the production in the high scale. For that, as I said, we are using this whole-cell modelling approach and also we are developing genetic tools. Because another inconvenient is that we don't have the typical conventional genetic tools, like double recombination to delete the genes o to manipulate the genome of this bacteria. So, at the moment, the only tool that we have is transposon mutagenesis. So, by transposon mutagenesis, you can insert genes in the genome, but it's randomly, so you cannot do target insertions or delitions. So, first I will explain about this whole-cell model, what we want to do and which is the state that we are. So, the first computational whole-cell model was done for mycoplasma genitalium. Mycoplasma genitalium is the pet of cryventer, probably you have already listened about this, is closely related with mycoplasma pneumonia. And so, this project was a big challenge in terms of mathematical breakthrough, because they could integrate all the biological processes that are happening in the cell and decompone them in different modules. So, you have the module of DNA replication, the module of transcription, the metabolism, and all these modules are mathematically properly represented and interconnected between them. But the problem is that when they... and they come and that they had, is that they didn't have the quantitative data representing the different molecules of the cell. And so, when they were trying to predict the growth of the bacteria, it was working, but when they were trying to do simulations of how delitions of genes were affecting, on which was the amount of ATP that the bacteria used to grow, they were failing. In fact, they failed to predict the ATP use and the effect of knockouts and also miscellances of the regulation. So, we established a collaboration with them and we started by moving the model of mycoplasma pneumonia to mycoplasma genitalium to mycoplasma pneumonia by changing the functionality of the genes that we knew, and also by including all the data that we had done in the lab. So, when we started to do the first simulations, also we realized about this ATP fail and then we came with the possible explanation by combining the flux balance analysis model of the metabolism that we had already developed in the lab and also the experimental data that we got by measuring the ATP that is consuming the cell. So, when you consider what is the amount of ATP that the cells should spend in producing protein, mRNA and biomass in general, so this is the one that you will expect and this one is the one that we experimentally determined. So, also we got this big gap here. So, how it could be explained? It's because the biggest energy consumption in the cell is to keep this potential membrane. So, the membrane has a volume that has to maintain and also there is a balance of protons. And so, when you add the information of the amount of ATPase that is in the membrane and the ATP that is consumed to maintain this potential, we arise to this experimental value. So, we include all this information in the model. But this one, just one reaction, you have to imagine that this one just reaction in the metabolism of all the matter of the model. And so, we realised that probably there were several things that we have to change and several reactions that we have to change and go one by one of the different modules of the cell and try to characterise it better. So, today, that's one of the goals of our lab. So, we have different people trying to characterise and model properly the transcription, the translation, the metabolism. And so, we are generating the metabolic modules based on this. The transcription, and I will explain today about which are the improvements that we have done in the transcription module. So, in the transcription, as you know, the RNA needs to be produced and also at the same time, it's degraded. So, for the production of RNA, you have the promoters that are the sequence that the RNA polymerase is recognising. And then, do you have also transcription factors that are recognising specific sequences and modulating the levels of expression? And you need to know which is the gene regulatory network, how the different transcription factors are binding to the DNA and regulating the expression of the different genes. So, this is the information that we were missing because we didn't know when we started all this project. But also, at the same time, you need to know how the RNA is degraded. So, which is the RNA decay, which is the RNA half-life. Which are the points where the different transcripts are ending. And also, there are two factors that are not properly known, no characterised, which are the non-coding RNAs, that in some bacteria and in some systems, it has been described that they are regulating the stability of the messenger RNAs by degrading the ones that are complementary or also affecting the translation. And also, which is the link to the metabolis. So, there are some molecules also that can be involved also in signalling that are derived from metabolism. They can play a role in this regulation of transcription. So, I will explain now, which are the different experiments that we have been doing to characterise these different parameters that we need to implement into the model. So, for the promoters. So, what is important to know is the promoter's tanks. We need to know, when the RNA polymerase find a specific promoter in the genome, if it's transcribing or giving rise to a messenger RNA, and which is the strength of that. So, what we found, interestingly, when we were studying the transcriptome of mycoplasma, we found three properties or three characteristics based on the experimental data. So, you have typical promoters, minus 10 boxes that are recognised by the RNA polymerase that give rise to a messenger RNA that comprises all the genes. That's the normal unexpected result. But in some cases, you have also these promoters and you find abortive transcripts that are called TSS RNA. So, the RNA polymerase is binding but not continuing the sequence despite the transcript and the sequence is there. And also, there are other cases where you have the sequence and then you don't have any transcripts produced. So, we wanted to distinguish them to know, which were the reasons behind this behaviour of the cell in the transcription. So, we characterised the promoter sequence by both studying the sequence and the structure proteins of the sequence. So, combining the properties in a random forest classifier, we were able to distinguish between the abortive and productive promoters. And so, we applied this classifier to all the genome and we could distinguish between all the promoters that are giving rise to transcripts, the ones that are the abortive and the ones that are not giving to transcripts. And so, we implement already this in the model and so now we know which of all the sequences which ones can give for not to a transcript. Also, we studied the RNA decay. For the RNA decay, we use a molecule which is the nobobiocin. The nobobiocin, what it does is it binds to the RNA gyrase and so, it inhibits the activity of the RNA gyrase. But unexpectedly, when we were doing chip-sick of the RNA polymerase, what consists in you put attack to the RNA polymerase and include the protein in microplasma. So, you purify by affinity the protein and you identify the DNA that is bound to the protein by ultra sequencing. So, when we were doing this with the RNA polymerase, we found out that when we were adding this drug, the nobobiocin, the RNA polymerase was released from the genome, so it was not bound. So, then, by using the strategy, we could see which was the degradation rate of the RNAs in the presence of this drug. So, we include also these parameters in the model and the most extensive work was determining the regulatory network of microplasma and ammonia. So, this work was mainly done by Cira Martínez and I've been using the lab and it has been a long time project. So, the idea was try, first, identify which are all the proteins that are binding to DNA. So, for that, we did affinity columns where we were put ocefaros columns, where we were binding DNA. We were passing extracts of microplasma and identifying by mass-spec which proteins were bound in these columns. So, we end up with 150 proteins or candidates that could bind DNA. But then, to corroborate them, we express them by using attack, by using these genes or these proteins to attack and we did chip-seq by ultrasequencing, we identified all the regions of the genome that were recognized by these proteins, by these 150 proteins. And also, in parallel, we did transcriptomics and proteomics of the different clones that were over-expressing these proteins. The idea was trying to define what are the physical interactions in the genome and then see what was the effect of these interactions. If they were increasing or decreasing the expression of the different genes and also, it was the impact of proteomic level. So, then, there was an extensive work also done by Veronica Llorenz, which was the data integration. So, how we could integrate all this data to finally define which are the key players in the regulation and how they were regulating also inhibiting or over-expressing the expression of the different genes in all the transcriptome. So, we could define which this gene regulatory network in mycoplasma pneumonia. But... Oh, sorry. And so, now we have already these components, the promoters, the RNA-DK, also the operonorganisation we got from the transcriptional start sites, the regulatory network, and now we're going to explain about non-coding RNAs. So, which is the role of non-coding RNAs. So, with a lot of bacteria, it has been described, and also in different systems, it has been described that some non-coding RNAs are functional and that they are regulating the translation of the complementary messenger RNA or also the RNA half-life. However, when we were doing a comparative study of the number of non-coding RNAs in bacteria and the 80 content, we were curious because there is like a correlation between the percentage of 80 or the 80 content and the number of non-coding RNAs, suggesting that the production of RNAs, or this anti-sense RNAs, coloris, from asporius promoters, do it to transcriptional noise. So, in fact, most of the anti-sense non-coding RNAs in mycoplasma, they have very low levels of expression. And also it has been described that essential genes have tendency to be higher expressed than non-essential. So, just suggesting that if you have lower levels of expression of these non-coding RNAs, it's because maybe they are non-functional or they are not important. So, what we did was just to perform based on these hypotheses that maybe these non-coding RNAs could be products of transcriptional noise. We developed a model of that with different simulations. So, we tried to simulate that when the RNA and the non-coding RNA are hybridized, this duplex is degradated. Then when a non-coding RNA is recognized in the messenger RNA, then only the messenger RNA is degradated or that the binding of both was stabilizing the strands. So, finally, when we were doing simulations and evaluating and comparing with the experimental data that we have, we found out that the... So, here we are simulating the changes in messenger RNA concentration where the antisense RNA induces the duplex degradation. So, what you can see is that there is a dependency between the concentrations of the non-coding RNA and the messenger RNA. And here, what I'm representing is the values where you would expect to have a functionality or a desestabilization of the messenger RNA and here where there is not an impact. So, you need to have the proper amounts of RNA and non-coding RNA to release a functionality. In fact, we over-express some non-coding RNAs in mycoplasma pneumonia that were in this range and we didn't see any effect in the transcription or in the transcriptome. But when you over-express and also when you represent the levels of RNA in other bacteria that they have found that there is a functionality, so they are in this range of here. So, what this data confirms is that non-coding RNAs most of them can be product of transcriptional noise, but only when they say you have the proper amount of the messenger RNA and the non-coding RNA you can see this degradation and dysfunction of the complementary RNA. So, the dosis of both elements is very important for the functionality. So, for not losing the perspective, I'm just explaining some experiments that we have been done, like the gene regulatory network and the others, that are inside the transcription model, but this transcription model is inside the whole cell model. And as I mentioned before, we have different biological processes that we are properly characterizing and I'm not going to enter today in detail, but it's just to give you an overview of all the amount of data and all the amount of experiments that are really required to properly define these different modules and to properly don a whole cell model. So, now, so I will try to explain more in the part of synthetic biology, which is our main problem or our main goal nowadays. What we need is the tools. So, now we have a model that we would like to corroborate also. At the end, we will do simulations and we want to predict which is the impact of these delitions of genes, including genes, in the cell behavior, but then we need to test and we need to remove them. So, we need tools to be able to do that. So, as I mentioned before, the only tool that we have available is the transposon, the mini transposon mutagenesis, which is not the ideal case because you cannot control exactly the genes where the transposon is inserted and also we cannot do delitions in a directed manner. So, we established a collaboration with the group of Alain Blanchard, Carolatri, Erlartic i Nindra and here the idea is to try to implement the genome transplantation technique. So, it consists in that we extract the genome of mycoplasma, pneumonia, and we include this in yeast and then we use the recombination machinery in yeast to modify the genome of mycoplasma and then we put the genome back. It seems very easy, but it's not. Because also the problem is that, as you know, in bacteria, we have the restriction on modification systems. So, it's a really problem because when you try to do the transplantation back, if the DNA is not properly marked in all the methylation sites, the restrictases are recognizing this as exogenous and then it's degradated and you don't get any colony. So, the first step that we had to do was by smart cell sequencing, we identified all the methylation sites in the genome of mycoplasma, pneumonia, and also we found out that there was a new motif of methylation and we also identified which is the methyltransferase that is doing this methylation. So, then we include this methyltransferase in a yeast strain and we transplant the genome from mycoplasma, pneumonia and yeast and we got the proper pattern of methylation of mycoplasma, pneumonia. And nowadays we are working in the transplantation back from the yeast to mycoplasma again. And a nice thing that we have already implemented is the CRISCAS9 system in yeast to modify the genome of mycoplasma, pneumonia, to have a faster way to engineer the genome in yeast. So, that's one of the tools. The other tool that we are working is in the CRISCAS9, not only in yeast but also in mycoplasma, pneumonia. And so, what we have done is we have expressed the CRISCAS9 in mycoplasma, pneumonia and we know that the protein can be expressed. Also, we have expressed the guided RNAs by using different terminators and we, because we have to define the proper sequence to have the right side of the guided RNA. And so, what it was tricky is that really we could not find the genome modification despite the CRISCAS9 is well expressed and the guided RNA is working. So, the question is why it's not working in the system if it's working in all the bacteria and all cells. So, because in mycoplasma, the genome reduction sometimes is un invoking convenient because it cannot, it has not been joining and neither the DNA reparation system is not working properly. So, all the cells that have the cut by the CRISCAS9, so, since the genome is not circular, they are dying. So, it's delatorious, it's toxic, this system in mycoplasma. So, now we are implementing the enjoining system to try to recover and to have this tool working in mycoplasma. But still, despite, once we have the tools and we have the system to delete the genes, we also want to do a proof of concept i, really, we can use this bacteria for developing applications. So, one of the applications that we want to do, as I mentioned, is vaccination. So, try to expose antigens on the surface of the chassis. And the other also is try to secrete proteins to develop applications instead of supplying proteins that can be depleted or defective in different diseases. And I will focus now in one of the applications that we have already developed the first proof of concept that is trying to dissolve biofilms by using mycoplasma pneumonia as a chassis and as a delivery system. So, the first thing is that we needed to identify which are the signals in the different proteins of mycoplasma that direct the secretion of these proteins. So, this work was done by Bernie, one of our students, former students in the lab. So, the idea was try to identify all the secreted proteins of mycoplasma pneumonia and then to find which are the sequences of these proteins that can direct the secretion and fuse them to different heterologous proteins that can have a functionality and application and test if really they are expressed, secreted and active. So, we use dimethylabeling and proteomics approach to identify which are all the enriched proteins in the medium, that mycoplasma secretes. And we also found out around 12 different proteins that are enriched in the medium. So, by using a software that allows us to predict which are the secretion signals, we identified the spectra that should be fused to the proteins and we did the proof of concept with three different proteins, the P53 and Alginelyes and also the A180 because they are proteins that have different properties. The P53 is a tetramer, the Alginelyes is a protein that is easy to test also by activity and that it also could be involved in the dissolving of biofilm and this one that has a strained fold. So, in the three cases we found that the same signal was the best one to fuse and secrete the proteins and the proteins could be expressed and secrete and active. So, by mycoplasma pneumonia. So, then we know that we have a system by the one that we can express the protein and secrete them and so now we want to see if really it can be used for dissolving biofilms in vivo and in vitro. So, first we did an in vitro test, we developed an essay to reproduce bio... It is a collaboration with the Pamplona University. So, we developed biofilms of estafilococcus aureus of different properties, young and old ones and we put together with estafilococcus aureus of wild type strain in green and also on mycoplasma expressing a protein that is dispersing the biofilms. So, as you can see here, we observe a considerable effect of the dissolving of the biofilms. So, this mycoplasma strain is able to disperse the biofilms in about four hours. However, we want to test this also in vivo. We want to see if really we can have mycoplasma in vivo in first in animals and we need it in a mice model. So, in the mice model, so we can... There is a... For estafilococcus aureus valofilm, there is a model that they use catheters where they grow the estafilococcus and estafilococcus is able to do this biofilm. And then they pre-colonize in the mice and then you can have all the evolution of the biofilm in the mice in presence of the immune system. And then you extract the catheters and then you can do an XP-B-B-A, which is that you put the mycoplasma there and then you see if this biofilm can be dissolved or not. These are preliminary studies that we have done and we haven't tested with several animals because first we wanted to do the first stage to test this. It's very preliminary. But what we see is that our mycoplasma strain is able to degrade also these biofilms conforming catheters. First, when we form the biofilm, just using the catheter and putting the estafilococcus aureus, but also when the catheter was extract from the animals. So, now we know that we can express secret proteins that can be active and that could have an application because dissolving biofilms is very important. In cystic fibrosis, in patients, you know that they have problems with that they form these mucus on the lungs and really it's very difficult to treat the bacteria because these biofilms cannot be dissolved easily. So, with this, a big impact, if really we can obtain a mycoplasma that could help to dissolve these biofilms and then facilitate the treatment with antibiotics. However, this mycoplasma has to be non-pathogenic. We need to delete these genes. First, we need to know also which are the genes that we have to delete, apart of knowing how that I already explained before and also if we can delete them or not because if they are essential for the bacteria growth so we cannot delete them. So, for that, we did two different approaches. First, we defined, by using this transposon mutagenesis tool, we define all the regions of the genome that can be essential or not. So, the transposon, as I mentioned, can insert randomly in the genome. So, by combining this transposon insertion mutagenesis with ultrasequencing, we can identify all the regions of the genome that can tolerate these insertions and the regions that they don't have insertions is because they are essential. So, we have a map of the essentiality of the bacteria and now we know which are the regions that, at least, individually, can be deleted. And this information is now being implemented also in the whole cell model because we don't want to remove only one gene. We want to remove several genes maybe at the same time and maybe one gene that is essential when you... It's not essential when you are removing these signals. Gene can become essential if you do simultaneous delitions. And also by using a... So, we know that the regions that we can delete and to identify which ones, we did a comparative... a comparative omics, I would say, a level of genome, transcriptome, proteome, across 22 different clinical isolates of mycoplasma pneumonia that have been isolated from patients that are characterized in different levels of disease, to try to find out which are the genes that are selected in terms of mutation rates. The genes that are non-essential in this experiment of here, but so less tendency to mutate in the infection process, probably is because they become essential in the infection and they are interesting candidates to delete because we can do in the lab, because they are non-essential, but in the infection process, they become essential. So, we identified that mainly there are three or four candidates that are the key roles in the infection process and we are working now in depleting them. So, the other aspect, a part of degenerating the non-pathogenic strain, as I mentioned before, we want to obtain these chases that can be produced at high scale in the fermenter, in the absence... and the bacteria can grow in the absence of serum, in a defined media. But also, the optimal thing would be if it will divide faster, because nowadays it's duplicating every eight hours. So, it's very difficult to grow and also very expensive. So, the goal is to engineer the bacteria to grow efficiently in a serum-free media and the optimal, or what we would like to reach is this duplication of at least two hours, because there are some mycoplasma species that they can do that. So, despite... we have found that there are several mycoplasma species that, despite having similar genome size, they can duplicate faster, they can divide in 30 minutes. So, then, based on this, we did a comparative analysis of all of these different mycoplasma strains to see which were the differences at the level of genome that could make some bacteria grow faster or slower, independently of the genome size. So, what was interesting is that when we look at different bacteria, not only in mycoplasma, the number of ribosomal operons and the doublet type per hours, it seems to be a poor but a correlation. So, based on this, first, we did a trial of putting an extra copy of the ribosomal operon in mycoplasma pneumonia, and we found out that this bacteria can grow faster. This work is done by Carolina Ayu, in her lab. So, mycoplasma now can divide two hours faster. So, we have as a stray that by implementing just one copy of the ribosomal operon, it can grow better. So, now we are doing... We are including more copies to see if we can optimize this growth. For the growth in the absence of the CERA, we are trying to implement the lipid biosynthesis pathway of acoleplasma dadawi, and this is a preliminary work and we are still working on that. Because we have seen that several genes of this pathway are toxic in mycoplasma and we cannot get the strain. That is one of our goals. So, this is a global overview of all the projects that are running in the mycoplasma team. But, as you can see, despite this minimal system, it has a very high complexity. We were expecting to have a bike to develop an application, but finally we realized that it's a bike that's very similar to a rocket. So, because it's very complex to manipulate and to engineer. I would like to announce that we are doing a wholesale course. This is a practical summer school that maybe you can be interested in or some of your students to come. So, we have extended the period of application until the end of the year. So, the idea is to mix students with a background more in basic research, also in experimental side, with students with a computational background, to show them or to teach them about wholesale modelling approaches. It will be done in collaboration also with Jonathan Carr, that is the front stand for... and now it's in the Bonsi Nai, and also at the CRG. So, I would like to thank all the people involved in the project, so that it's a lot of people, it's a big consortium involving different institutions and the Mycoplasma Group, the Mycoplasma team, and also the University of Pamplona, that they are working very highly on the testing of Mycoplasma with different animal models. And thank you very much for your attention. Thank you very much for the very nice presentation. I think there is some time for technical questions. And... Yeah. So, on the one hand, you are trying to make it... you are less pathogenic, i, on the other hand, you are trying to make it grow faster. Very often the endurance of the pathogen is linked to its growth rate, so it's out of being a bad surprise there. I agree with you. So, the idea is that we want to have the bacteria growing faster in a fermenter. And so we are designing... I didn't explain this well, so we are designing safety circuits also to regulate this growth. So, we can inactivate the expression of ribosomal operons at the moment that we want to use the bacteria for the application. So, the idea is having the bacteria growing fast in the fermenter or in the bioreactor. But later on, when we want to use it for applications that maybe we don't want it to grow fast, we can inactivate this. Are you afraid that if you start using it, that it will mutate and start? That's the other thing. So, we are trying to also remove all the recombination machinery to avoid the antigenic variation of the bacteria. But also, you have to play, depending on the moment, you will need to develop or to implement some mechanism or deplete them, no? Because for manipulating the bacteria, maybe you need the recombination machinery, but later on, for the application, you need to remove it. A lot of engineering, yeah. Isn't that a question? How much could you or did you use modeling simulation to help to answer questions, for instance, like what are the critical parameters, the critical notes in your network which may influence the speed of the growth? Did you do that? So, we were doing some simulations in terms of expressing some genes to see which ones were the ones that could promote growth. And we found out that genes involving translation are the ones that probably have a higher impact. However, as I said, these holes and models are still not working properly, so, because when we are trying to also characterize better different parameters, because some of parameters are not properly analyzed. I mean, usually, in such huge systems, the data problem is enormous and you have to ask the question, you know... Yes. What question makes you understand? I think that was one and then the second and then the second one going for the next presentation. So, the order that you're studying has a very minimal genome. So, about 700 or, as you mentioned, it's actually curious, even in the coal line, we don't know all of the functions of the genes, but the microplasma of 700 genes, what percentage can you actually attribute a known gene function to? So, that's another project that we are running, which is trying to characterize the functionality of non-proteins. And so, the idea is, if we can implement properly all these tools to delete and over-express genes, so try to delete them and characterize in terms of phenotype and level of transcription, proteome and guess, which could be the function. Well, I guess my question was, without this new knowledge that you're adding, what percentage from comparative genomics could you actually assign? I was curious. So, we assigned, we have a steel, like around 40 proteins that we don't know exactly which is the function. 40 proteins of 700, it's not a lot. So, we were doing comparative analysis across different bacteria to try to identify the function, but also, I didn't mention, but by using the transcriptome and proteome data, we re-annotate the genome, and we found out that there is a new layer of small proteins or small open-rediframes that they are smaller than 100 amino acids, and some of them are interacting with DNA and they can have regulatory functions. So, it's not only the ones that are annotated, but the ones that are hidden, and also, we have to characterize them. I think that it's an interesting topic also to address. Si entenc correctament, és un pacient obligat d'interessant. No, no. We can grow in anoxic culture. So, we are growing in the lab by using rich media. But in vivo, does it always sit in the cells? Or how does it spread from cell to cell? The experiments shown, but the experiments that have been done is in vitroinfectionase. So, that the bacteria is attached to the surface, other to the surface. In some cases, it can internalize, but it seems not to be essential in the process of survival of the cell in the infection process. I mean, the cells are adhered to the surface and they can internalize, but they can... So, why does they need serum? Is that known? No, but I suppose that it's... I suppose that it's because of the lipids. I mean, the lipids that the bacteria needs to synthesize the membrane and to have the membrane. It's composed mainly by cholesterol and lipids. So, the serum is the main source of these compounds. And so... We have been trying to do a defined media and implement the different lipids that there are in the sera. And so, the bacteria can grow. But it's not reproducing exactly the same growth that in the serum. And probably it's because we are not supplying the lipids in the way that they are in the sera. Because you have the carriers, you have the BSA, there's an alumina that is helping to supply the lipids in the right manner. And we are... So, we are not only trying to put the right lipids, but also trying to implement them in the right way, you know? So, it's... Yeah, yeah, I agree that it's difficult to... Thank you again for the presentation.