 Hello everyone and welcome to this edition of the Permit-CIA webinar series, where Joaquín Dopazo will present Digital Twins for Drug Repurposing by Integrating Mathematical Modeling and Machine Learning, Bridging the Gap between In silico and Real-World Data. My name is Marta Llorell Linares and I'm involved in the Permit-CIA project on behalf of EMBL-EBI and I will be hosting this webinar today. Before we go any further in the webinar, I'd like to let you know that the webinar is being recorded and will be disseminated afterwards on the Permit-CIA website and our YouTube channel. our YouTube channel. After the presentation today, we will have some time to address questions from the attendees, so you can write your questions on the Q&A function in Zoom. And all the material that we share are licensed under CC BY 4.0 license, except where further licensing details are provided in the material. PERMIT-COE is the HPC Exascale Center of Excellence for Personalized Medicine in Europe. It focuses on simulations of cellular mechanistic models which are essential to translate omics data into medical action. The performance of the current simulation software is still not enough to tackle certain medical problems, such as tumor evolution or patient-specific treatments. So, PERMED-COE will scale up cell simulation software to HPC exascale system to enable the creation of models of cellular functions of medical relevance. And it will achieve this goal for a series of objectives. First of all, it will optimize selected cell-level simulation software to run in pre-exascale platforms. Second, PermetCIA is working on a series of use cases to show the use of PermetCIE solutions in areas of clinical relevance, such as tumor evolution or drug synergies for cancer treatment. PERMED-CIE will also train biomedical professionals in the use of PERMED-CIE tools. It will integrate personalized medicine communities into the European HPC exascale ecosystem, and it will build the basis for the sustainability of the Center of Excellence. Today's presenter is Dr. Joaquín Dopazo, the director of the Computational Medicine Platform of Fundación Progresa y Salud. He holds a degree in chemistry and a PhD in biological sciences. He has previously worked in public centers like the research center Principe Felipe in Valencia and the Spanish National Cancer Research Center in Madrid and companies like GlaxoWellcomeSA. His field of work is personalized medicine with an approach that includes systems biology, mathematical modeling, and artificial intelligence. So Joaquín, the floor is yours. Joaquín Pérez- Totally. Let me thank the organizers for inviting me to share some of the ideas of this conference, which has a very long title. Actually, the title was going to be shorter. But after several discussions with some friends, they convinced me that what we were doing with the models was actually a digital twin. I have always been a bit skeptical with the concept of digital twin, especially in medicine. I would comment why. But to some extent, something which is important and probably is the core of the presentation is the use of models or the use of approaches that at some point convey causality that can be used for prediction. So, that would be the definition of a digital twin. There are replicas of objects or physical systems that typically are used for simulating events or interventions in the real world. Point, as I mentioned briefly before, is that in the context of medicine, digital twins have been proposed as a methodology like in other industry or in other fields, but it's a quite controversial concept in medicine, given that build up such models or such digital twins requires a lot of knowledge of the system. And we are far away of having this detail of knowledge of the human body that is at the end what we want to simulate. So, I mean, that would be the idea. If we have a system, we can decompose the system in pieces. Actually, we have different levels of organization within the system. For example, these pieces that you have on the right, I don't know if you can see the mouse. This small collection of pieces would belong to an organ of the car, that would be in this particular case the engine. And we can simulate the way in which these things, or these levels of organization work. So one thing are the pieces and you have below the pieces at work. So if we can simulate the operations of the pieces, how pieces interact among them and simulate this operation, you can simulate what is the functionality of the system. what this I mean the functionality of the system in that case most of the way with the system words can be simulated because we we know absolutely everything if we are in the context of medicine we have a system which is immensely more complex than a car and a car is complex immensely more complex than a car. A car is complex. This is much, much more complex and is poorly known. So we still lack a lot of knowledge. We have also these different levels of organization. Typically, we think in the full body. We think of the organs and we think of parts of organs, and we have at the end, behind everything, the molecular level detail. That would be this representation. And here, the problem starts. So we don't have a clear idea of how these pieces work, how these pieces, in some cases, we have some description of how pieces interact among them, but we don't know exactly how do they work. respond to a digital twin. So we cannot simulate, I mean, we are really probably decades or less, let's talk about decades being optimistic, but many decades away from having a proper simulation of the human body, but still we can simulate some parts of the system. But as I mentioned, we neither know all the pieces, nor we know how to relate to each other. So then how realistic can our digital twin be? So what can we do? Obviously, it depends very much on the speciality of the people working with this concept of digital twins can simulate one or another level. We typically want to focus the problem from the lower level, from the molecular level. So the idea would be we can have a molecular map that describes how molecules interact among them, proteins typically. We can model this interaction, typically, we can model this interaction and we can go from this molecular behavior to the behavior of the organ. That would be the general operation of the system. We can put some context when this general operation happens in a specific organ or happens in a specific condition in a disease or whatever and then we go to the body. Obviously, we are not going to simulate how the body works. We are going to simulate specific phenotypes. We would be very happy if we could go from the molecular level to define a specific phenotype, specific phenotypes. If we would be very happy if we could go from the molecular level to define a specific phenotype in the body. Then we use these maps, these molecular maps that are pathways, probably you are very familiar with pathways, quite common. So you have many different repositories, probably the most known is K, you have ReactOM and the EBI. There is this Wики pathways, there are more, there are other more specific, etc. There are also initiatives like disease maps, where they take specifically the parts of these maps that are relevant for diseases. So there is information, there is a reasonable amount of information on the table map of selectivity that describes the way in which molecules interact with each other. activity that describes the way in which molecules interact with each other. But what's important here is not that the molecules interact with each other, it's why they interact. They interact because they ultimately trigger cell functions which are responsible for the behavior of the cell and at the end the fate of the cell, but at the end the fate of the organ and at the end the fate of the cell but at the end the fate of the organ, at the end the fate of the patient. And this is what we want to simulate. So firstly we have to focus on the concept which is important here, which is the function. So we have these maps but not all the proteins do things by themselves. So some of these proteins simply are transmitting a signal or doing and the proteins do typically do several things. So something which is important is to identify important is to identify the level at which this map is describing an activity, a cell activity, or a cell functionality, which at the end is a holistic concept. So it's not, we have to forget forget the protein-centric or the gene-centric interpretation and go more for a holistic interpretation. There are like three levels. One level would be the pathway level. I mean, let's start by the gene level. So the gene level is what we are used to. So we have this gene which is over-expressed, then we start to think why this gene is over-expressed and what would happen if I inhibit this gene or whatever. But the effect of a gene within this context is quite complex many times. For example, in this case, which is, this is the summary. It's not because it's more complex, but it's a summary of the apoptosis pathway. And typically the pathways recapitulate the biological knowledge on many things. And typically there are many functions that are carried out by the pathways and typically these functions are contradictory, are opposite, not contradictory but opposites. For example this pathway, the apoptosis pathway is responsible both for cell survival and for cell death. cell death. So, if you just think of a specific gene, probably you will have not a clear idea of what is going on. For example, this gene BAD marked here, depending on the partners, it could be involved in apoptosis or it could be involved in apoptosis or it could be involved in survival. So having this inactive says nothing on what is going on in the cell. If you go at the level of the pathways, say, okay, there are many yeast in this pathway which are active. So well, but what is the pathways doing? Is it killing the cell or is it making the cell to survive? So the point here is to go to the specific circuits, to try to look for the activity of specific circuits that at the end do something. So the first thing that we have to do is to identify this part of the pathways which are responsible for a specific function. And from a mathematical point of view, you have to identify those proteins. I mean, this is a directed tree, right? So you have arrows, and these arrows mean some type of interaction. It can be an activation, many different types of activation, or can be an inhibition. So there are arrows that go from one gene to another. So when you have one gene that receives an arrow but there is not an arrow from this gene to another and has an annotation, this is the gene which is at the end of the cascade and do something, right? So we need to detect all the cascade and try to see if this cascade is active. So as this last gene can do whatever the gene is annotated to do, right? So the first thing that we have to do is to define this circuit, which has to do with this functionality. So what we do then is to take a pathway and to identify this final, this effector nodes and the compose the pathway in all the, I mean all the pieces of the subcircuit, that all the part of the subpathways that define thisor. So this, this, subs, these circuits are our units of functionality, right? In which we are going to focus. So we use a specific type of mathematical modeling, which is called mechanistic, because typically conveys causality, right? So the idea is that by using this model, we can provide a quantitative representation of what the cell is doing, right? We use pathways to define these circuits, also how proteins interact with each other. And ultimately one of these proteins triggers a cell functional activity. We define these functional endpoints. And what is interesting here is that we use gene expression as data. And this is the main reason for which we are very focused on this molecular level. Because gene expression nowadays, we have lots of gene expression data. I mean, really lots of this data. There are data which are cheap to obtain. They are quite accurate with the current technologies. And you have measurement for, I mean, absolutely anything that you can imagine. So, there is data we need a lot. So how does this model work? Let's imagine that we have this circuit here on the top right, which would be a very simple circuit composed by two receptors, which would be proteins A and B. Then you have the rest of proteins C, D, E, F that has this topology, this specific topology, we have a different topology. And finally if the signal arrives to G, then G triggers some function, typically by Many, many, many, many ways can be that, right? Then what we can do is to infer what would be the activity, because these are proteins. So we use gene expression as proxies of protein presence, which are the genes of proxy of protein activity. Many people discard that. I mean, you have a gene expressing, it doesn't mean that you have a protein. Yeah, good. But we are not focusing on a specific, unique protein out of context. We are talking about the expression of a chain of protein, which are really a biological subsystem. So if all these proteins are, all these genes are up, it's probably because there is a biological program behind that has accounted for this, right? So it's quite realistic, this assumption. So let's imagine that you have this condition A, in which we have active the genes A, C, D, E, and G. So if we put these genes over this circuit, you see that if A receives a signal, the signal can be transmitted until G that does the function, whatever it is. But you can have a different condition like B, in which you have Gs B, C, E and G active. So you have, under this condition, if B receives a signal, the signal cannot pass because V is making a short circuit. So in that simple way, I mean we use a quantitative formula, it's a little bit more complex, but we use this formula. So using the levels, sorry, of gene activity, we can guess what is the level of activity for this specific function. So this is quite interesting because we can convert something that we can measure, which is the expression, and it's easy to measure, and it's cheap, and it's accurate, in something which is more subtle, which is cell functionality. How can we measure cell division, for example, the activity of cell division? We should prepare a very complex experiment to measure, well, maybe not that complex, but other functions can be really complex to measure. but other functions can be really complex to measure. And that way you can measure the signal that the cell that decays to trigger this function, which in theory should be related to the function, right? So sounds good, but does it work? Let's see. So we took the data of the TCGA, the Cancer Atlas, the Cancer Genomic Project, etc. a lot of data on gene expression of the tumors, biopsies, and we know what happened with the patient, if the patient dies or goes well or whatever. Then we can define functions that has to do with cancer. Probably you are familiar with this whole mass of cancer, which were defined in 2011, and now they go from the third or the fourth definition. There are specific cell functionalities which has to do with the conversion of a cell into a malignant cell, right? So you can even define easily the circuits that trigger DNA replication. So you take patients, some of these patients survive and some of these patients die. You calculate what is the level of DNA replication in the tumor of the patient. And what you can see here is that clearly patient with a high level of DNA replication die more than the others who has not so high the level of replication. Okay, maybe that's by chance. But you identify other holmas like anti-apoptosis. So patients here on top right, patients with high anti-apoptotic activity die more. They have a worse pronostic. Cells, you go top right down. Patients with a low cell, low in that case, cell aggression activity, which probably means that the cells which probably means that the cells go and float in the blood and can colonize and make metastasis, distant metastasis. So these patients die more. So they have a worse pharmacy. For example, angiogenesis patients, which have high angiogenic activity, they have a worse form of disease. So what we did here was to convert what it means of the model gene expression into cell activities. And since we know what cell activities are relevant for the cancer, we can identify them and you see that actually patients with these cancer activities have a bad prognosis. Obviously other unrelated activities have nothing to do with survival of patients and they don't separate patients. And something that you have to keep in mind is that the activity, we are not measuring this activity. The activities are construct. We have inferred the activity from the model. We are not measuring the activity. So we have a tool that to some extent allows us to convert gene expression, which is something that we can measure, into cell activities, right? So that's nice. That's nice. But there is a nice property of this model. This model allows to make causal predictions, meaning that I can take a condition, for example, this condition here, that is represented here, where you have A, C, D, E, F, and G active. So the signal goes through with no problem and the function is active. And then I can simulate, for example, knockout. I can simulate interventions. I can simulate knockout, but I can simulate over-expression. It could simulate the over-expression of B. I can just change the values of the condition and recalculate and go back and see what would happen if I do something. This is a qualitative change because I don't need to measure things. I can have a condition and say, what would happen if I would do that? For example, if I do a knockout on the E gene, probably nothing happens because the signal still pass through F. Maybe it's not that efficient, but it still pass through F. But that would happen if I in D, the signal does not pass through this point. So I have switched off this sequence. In that case it's quite obvious, but you imagine that quite the very complex pathway that we have, you can do that and you can search for a specific intervention that have a specific result that you are looking for. So let me show you this. Again, it sounds good, but it's realistic. Yes, it is. Obviously, it's a prediction. We cannot pretend that all the predictions work, but it works quite well. And actually, this is probably the best paper of my career. This is the paper from which I'm really, really proud of this paper. So what we did here was to predict a knockout that was never done. Well, I mean, we predict several looking for a knockout that kills or reduce the viability of one cell. And we went to a experimental group and we did the, with them the the validation and actually the predictions were correct. So you see this type of approach is completely different to the approach that we are used to. Typically, this type of things are driven by experimental people. And sometimes people who just analyze data, they say, okay, can you analyze the data? And you do the analysis, but the ideas come from the experimental part. So now we are in a moment in which we can start to think in a different way and say, okay, we have model, we have data, and we can start to formulate hypothesis from the in silico directly, right? So this is an introduction to this type of models to show you that they work. So why repurposing? Why we are interested in repurposing? I'm going to talk to you about repurposing in emerging diseases. I mean, we have time. I will tell you another field in which repurposing is also interesting, which is rare diseases. So in the case of emerging diseases, and this is the case of the COVID pandemic that we have a few years ago, and we still have. And actually, we have now two strains which are very contagious. So be careful because now the number of people that go to the hospital is increasing. The number of hospitalizations is increasing. So what do we need in one of these situations? We need something quick. We need a quick response. Quick response, you cannot start from the scratch with a drug. So something which is interesting is if we can repurpose any type of drugs. We need a quick implementation. A drug, because this drug is already in use, can be quickly implemented because it has a known security profile. It has a known mechanism of action, it's already registered, so legally it's quite easy in comparison with a drug from the very beginning. What is the problem with emerging disease? Most of the details of the real mechanism of the disease are unknown. So we have to try to see how can we relate this drugs and the mechanism. So what would be the solution? The solution would be to use this method that I told you, that I have been commenting. And how can we solve relationships that are beyond the models using causal machinery? So I'm going to talk about that. So in this moment of alert, so there were different projects, there was a lot of money. There never has been a lot of money in this country for research. But there was money and they funded several projects. So we got this project and we took advantage that they were involved in this large-scale project, in which they were defining the COVID-19 disease map. So there was a large collaboration that involved more than 230 different biocurators, domain experts, modelers, data analysis, etc. from 120 different institutions in 30 countries. So it was a quite interesting initiative. So finally, they come up with a quite detailed map that we can use as basis for the modeling, right? So we modeled it and we published it in a couple of papers, etc. But here is the importance. So we have the map, right? So now we can model the map with the algorithm that I mentioned. The problem is that, okay, if the drug, if other drugs have a target in the disease map, so the effect of this target, of the effect of the drug on this target, can be easily modulated, as I showed you before. So we can just put the expression data and say, what would happen if I inhibit that? Oh, that's good. It would inhibit this part that has to do with respiratory tract or has to do with quadrupedies or whatever. But what happens if the drugs are outside of the map, which in many cases is what happened. So this is a problem. This is a problem because we cannot model that. I mean, we cannot directly model that using the mechanistic model, right? cannot directly model that using the mechanistic model, right? Then we have to imagine how to relate that with the map. So at the end, the map, as I mentioned in the very beginning, is a collection of proteins and arrows that link these proteins. Any of these arrows is biological knowledge. It's a lot of experiments and validation, etc. Demonstrate that this protein actually is activating or inhibiting this other protein and no other, only this protein. So then you draw an arrow. And this is a lot of experiments, etc. etc. Right? And the is that currently, one third of the genome can be modelled. The other two thirds are not linked by, are not part of this relationship. In other words, we don't have a knowledge, a real knowledge of what is going on between these proteins, how these proteins interact to do something. This is why, this is one of the reasons for which this digital twins has a lot of limitation in the field of when we want to make a real data to it, right? And the problem is that drawing one of these arrows, it takes a lot of time. It's generating, it requires the generation of this biological knowledge and it takes time. So maybe we don't need to do experiments because we have lots of experiments. We have lots of gene expression data and maybe we can use this to in the context of machine learning to try to learn biology from the data. So, I mean, the idea is fantastic. Why not? I mean in other cases you can learn, for example, there are lots of applications, machine learning and deep learning, etc. which they can learn, for example, in image case of image, it works very well. So you can take radiographies or you can take some different types of medical image and you can decide if there is a lesion or there is a problem or whatever. And in some cases, the level of accuracy is even higher than the human experts, right? So at the end the software, the network, the algorithm, the machine learning algorithm is learning from the data and you do more data, you say okay you can learn what happened with these pixels or what pixels are important, and then you learn how to identify something. So this is something that we could do, in theory. We could take all the gene expression data and say, okay, just learn. Learn how proteins interact among them, and what type of interaction it is. Well, I mean, it is realistic, no, it is not realistic because no matter that we have lots of data, we have the course of the initiality. So we have still more variables and the interaction of the variables are much more complex than the interaction that we have pixels in an image. The pixels at the end, they are important pixels around, not the other pixels in the other corner of the image. So, in principle, we are not going to solve all the biology in one shot. This is for sure. But something that we can do is to reduce the initiality of the problem. Not being so ambitious, not trying to learn all the biology in one shot, but something that we could do is, okay, we have a part of the biology that we know, that are these circuits and these circuits involved in the disease, and then we can try to see, okay, what are the relationships between all the proteins and all proteins among all the proteins? But I'm interested in a specific set of proteins, which are these proteins that are targets of drugs already in use. And I can try to learn what are the relationships between these proteins, which are targets of other drugs and the proteins which are in my map of the disease, right? And that case is something which is affordable, the problem. So what we did was to try to learn, well, I mean, the idea is we try to predict from the activities of the targets, of different targets, we try to predict the activities of the circuits, right? the circuits, right? Obviously, most of the drag targets cannot predict anything because they are completely unrelated, but some of the targets can predict some parts of the activity on this map, right? We use, I mean, we use here, I say the multi-help with random forest regression that was used in the original paper but we have tried other approaches I mean they work more or less I mean there are two or three approaches that can work and something which is more important that use this sharp which is more important than use this sharp score,put, multi-output, because the input is R, all the targets and the output are all the circuits. I say, okay, can we predict one for the other and what is important in predicting and what, right? So what we have are diagrams like this in which we can see that some specific targets, but the rest are not important as you can see here, but some specific targets were important for some specific... These are the drugs on the target for some specific or general parts of the pathway, right? Then we are not interested in those drugs which are acting on absolutely everything because probably they're killing the cell and that's it. And actually, it's curious because you have the chloroquine that was very famous at the very beginning because they say, oh, chloroquine works. Yeah, chloroquine that was very famous at the very beginning because they say oh chloroquine works yeah it works and kill the virus kill the cell kill you and kill everything so this is not it's not what we are looking for but there are other other more specific drugs which are more specific of some hormones of the disease. They go directly against, for example, the immune response, exacerbating immune response, against the energetic, inflammatory response, etc. So when we started with this, we made a prediction and said that we didn't have any means of checking if they are real or not. So something which was nice was to realize that in the moment in which we were publishing it, they published this paper, it was a review on the trials that were testing treatments for COVID-19. And there were trials for all the, I mean, we have predicted targets for all the drugs that were trying here that have a target, obviously, other drugs didn't have, I mean, other treatments didn't have a target. Some of them were something like inhalation of gas or something. They don't have a specific target. But those who have a target were predicted by the method. So you say, that's okay. It's not bad. It's not an issue, but we are at least oriented. So we thought, okay, let's go for real data. So we have this big database in Andalusia, which is the population database, and all the health system, which is the lies here, as in other places, all the system every month, on a monthly basis, they dump all the information on this database, which is consolidated. So it has information of absolutely everything that the system has recorded on a patient for over 15 million patients of the health system. So what we did was, I mean, given that there were lots of problems for using the data, what we did was to build up a trusted research environment, which is essentially a computational place in which you can do all the calculation that you need, but in a secure and controlled place. So what we did was to set up this infrastructure, which is available for research, which is a computational infrastructure, which is, which resists within the network of the hospital network. So it's not outside. So in that case, it's quite easy because you can request. Now that would be the typical, on the right is the typical pathway that follow with the typical analysis of clinical data. Typically you request the data to this committee and there is an approval, then they allow you to take the data, the data must be anonymized, they are extracted and they are used. So, in many cases there are problems and the committee rejects the project because there are doubts of security outside. So what we did was just internalize that. So the analysis can be done within the hospital in a trusted environment. And the only thing that is the result. But in that case, it's quite nice. So, okay. Then we start to check some of the drug that we predicted. We started with this because there was a lot of interest in the community because there were several groups working on the vitamin. And actually, there is the calciferol or vitamin D has a clear protective effect. And I'm still having this calciferiol because it protects. It's a fact, right? As you can see here, it has a significant protective effect. And this is not just that you see what happens with the patient that has a consume of vitamin D or not, is that you take into account all of these variables that could be affecting, which are sex, age, other vaccination, other comorbidities like diabetes, disease, cancer, et cetera, et cetera. And you have to take into account that. Meaning that when we say that something is protected at any age, at any sex, at any comorbidity, at any et cetera, right? So it's not that there is a correlation. It's a causal analysis, right? So we did the analysis in more detail and we came up with 21, this is a second revision so probably will be published soon, we came up with 21 quite effective treatments. Some of them are really, really, really effective. And one, I mean, actually there are more, but there are not significant because there were few people. But some of them, some of the drugs were counterproductive. They increased the mortality. What is interesting is that we can not only see if it has an effect, a significant effect, in the mortality of the patient. We can see also if there is a biochemical background behind. So we have access to the analytics. You can see how the lymphocyte count in these cases, the cases of the drug that protect, actually goes very well. While, for example, in the case of the drug which is causing more mortality, you have a lower lymphocyte count. So there is a biochemistry behind which is validating this. So what is interesting is that there is an enrichment, so many of these drugs were predicted by us. So this is something that at the end makes us very happy because we were really predicting correctly. So we did the prediction and then we could do the validation using this real-world data. I was going to mention something on the red diseases, but I'm going to jump on it because maybe we have a short time. But, I mean, it's a similar case. So we essentially did other molecular mass for, we make in an industrial way, many, many molecular mass for many rare diseases. And then we are validating now the results. So we have some cases which they have validated some of the cases with this previous publication that we are publishing that for retinitis, for anemia, for conianemia, for others. So just formulate the hypothesis, to learn how these proteins relate to the maps using public data, and to do a validation using real-world data. All the process from the formulation of the hypothesis to the validation has been done without doing any single experiment. Of course, these experiments were already done. I mean that we particularly didn't do any single experiment. But the important message is that there are lots of data. So for many things that you can do, there are already data that can be used. This is, to me, this is a new paradigm for knowledge generation, because at the end you can use models, modeling you can do modeling which involves biological knowledge and is causal and you can try to to to make prediction and you can validate the prediction using real world data this i mean is far away from what we would expect for a digital twin, but it's a little, it's a little twin, little digital twin that actually works. So, I'm finishing here. I'm really happy. This is the group, these are our sponsors, et cetera, et cetera. This is the hospital where we work, it's a, behind the Rocio. And I'm really happy of taking questions that you have. Thank you. Thank you very much, Joaquín. Yeah, as Joaquín said, if you have any questions, you can write them in the Q&A function in Zoom. And while you do that, I can ask with a general one. OK, so you're telling us now you did all this without experiments that you did, because experiments on the data were already there. How do you see that going forward? So the relationship between the models and the experiments? Will you have like a circuit where, you know, models inform experiments and then inform models, the experiments inform models and all that? How do you see it? Well, I mean, the way we see that is you could enrich the map that you have by validating that your prediction. For example, you predict that that drug is what happened here. You predict that that drug has an influence on this circuit and it will prevent coagulopathies. And actually the patient has no coagulopathies. This is what I observed in the patient. Then I can draw this new line. So for my next simulation, I have a new arrow. So I have a more complete map, et cetera, et cetera. So at the end, this is a real way of learning biology from the data, by science and error. Yes. Thank you. So there are a couple of questions now there. So one is how to distinguish a correlate relationship from a causal relationship? Yeah, good question and this is actually the core of the presentation. So, fairly, the model is causal. So the model Fairly, the model is causal. So the model is causal by principle, because we already know that, because there is knowledge that we already have, that this protein activate the other protein, and activate the other protein, and the final protein is a transcription factor that describes something that does something, right? In that case, if I do something here, there is a consequence here. So the causality is built up in the model. A different thing is how do you distinguish in the case of this influence that we use with the machine learning model? How do you distinguish between correlation and causality? This is not mathematical, formal mathematical causality. But this is, these are, we're using models, we are using the sharp information, which are something which looks very much causality. So they're not formally mathematical causality, but they are machine learning models that involve causality. So it's not just a correlation. It's that when this is up, this is up. OK. But this might be up. So it's sort of check, since it's multi, I don't know how to explain this, since it's multi-input, multi-output, it removes the possibility of there are other different ways and can be just a correlation and the cause could be a different one. So the short answer is that we use machine learning models that to some extent consider causality. It's not just a correlation. Thank you, Joaquín. So next question is, what number of minimum studies are needed as prior information to make a decent digital twin and prediction? I don't know how to answer this because, I mean, by definition, what we are doing is not exactly a digital twin. it depends very much. When we do a prediction of what would happen in this condition that I mentioned at the beginning, so we have the model and you can simulate, you have a condition, you have these genes up, these genes down and you can say, okay, what would happen if I remove the GIF, or if I put the GIF inactive. This is a comparison one-to-one, and there is no test behind. So simply to detect where, because I mean, I saw you very simple, very simple sequence to illustrate that, but there are much more complex. So what we do is to say, okay, what would happen if I make an intervention here? And then you have some circuits which are with chains, right? So it's not when we are within the model, we are not when we are within the model we are not we are not making any type of test we are just pointing out the places with the system chains and then we go for a different type of confirmation that could be experimental or could be other type of confirmation if That could be experimental, or it could be all a type of confirmation. If you were more thinking of, for example, the cases of the survival, and say, okay, is this function involved in survival or not? Then it depends very much on the case. Because, depends very much on the case because in some cases, you know, what's happening in biologists and mainly in medicine, you don't have the number of patients that you like and then when you draw the confidence interval you say, okay, this patient died more but it's not significant because the value say okay this patient died more but it's not significant because the the the confidence intervals overlap and then it's very very i mean again a short answer is it depends very much on what you want to test exactly like in many other scenarios okay thank you for Joaquín. I'm going to share my screen with some announcements. We'll stay, if Joaquín has the time, to answer all the questions so you can see them on the video afterwards if you need to run to another meeting or so. Our next webinar will be next month on the 5th of October and we'll have three of the participants of the summer school who won the poster prizes and we'll have three of the participants of the summer school who won the poster prizes and will be presenting the research on cancer, tailored cancer therapy, high-grade series of ovarian cancer and communication in the tumor microenvironment. This will be one month. If you want more information and to register, go to the PERMED-CIE website. And I also have an announcement on an workshop on computational models of life from molecular biology to digital twins. So more on digital twins. This is going to be at the end of November, close to Barcelona, northeast of Barcelona. And you still have time to send an abstract before the 18th of September. And then the registration is on the 16th. So now, Joaquin, we continue with the questions. So there's a comment there, this is very interesting. Thank you for all this work, congratulations to the team. How do you see this being adapted to multi-omics dataset of the same set of participants? of the same set of participants? Yeah, I mean, actually we have, I have not mentioned that, but we have other approach for this. We have several papers published on that. So we look for activity at the end, right? So we think that you have gene expression plus mutation, you recapitulate a lot of other omics, right? For example, you may have a copy number and you have the gene over a lot of copies and then you will have higher expression. Then what we do is to combine the expression plus the mutations, which is the activity of the gene and the functionality of the gene. So a gene can be active, but can have, for example, a frenzied mutation, so the protein is not active. Then we remove from the model the protein. How can you extrapolate that if you don't have this information? For example, you have, instead of having gene expression, you have methylation. You may, I mean, something that we do sometimes is to say, okay, that the part in which this methylation will affect, we take the normal situation and we go down, go the gene down or up, depending on the level of methylation. I mean, you can do some tricks in which you can reflect this multi-omic data in terms of gene expression and gene integrity, right? So at the end, we play with these two concepts. Obviously, there are, I mean, this is just to see where does the change affect. We cannot be sure that this change, the degree of affectation is real, but it's something that we do. Thank you. So we'll go to the next one. Can you please give details on what is considered a circuit? How is the line drawn between which nodes are part of a circuit and which nodes are not, considering that nodes may have a large amount of edges? You visualize, for example, a signaling circuit. You have proteins, and the proteins are connected by arrows, right? The arrows are directional. It's not like, I mean, this is for signaling circuits. It's not for metabolism. Metabolism, the arrows are not directional. You can go in one direction or the other direction. Now we have the signal traveling in one direction only. So you have these arrows. Then you have proteins which has an arrow pointing to them and no arrow. And typically these proteins are annotated with a function. So these are the effector proteins. And then what we do is to find all the effector proteins that do things in the field sequence and then go backwards, no matter how complex it is. And we found a protein which has an arrow to other but has not an arrow to itself, which is the primary receptor. So we travel from the effector to the receptor, or in many cases there are several receptors, there are many, many, many receptors. And it can be as complex as possible, but at the end what you do is like taking the the end of the of the circuit say okay you go backwards and what happens is that many of the circuits share protein with other circuits and this i mean this is completely normal and many times what happens is that you say, okay, when you see the activity of the genes in the pathway, it make no sense. Then you need to write circuits. You say, okay, now it makes sense because you have enlightened only parts of this pathway which correspond to a circuit. And this is what we do. So we go from the effector backwards until the receptors. In some cases, the receptor, all the receptor does it. Okay, it's one big circuit. Like many cases, there are like, I show a slide in the word decomposed. So at the end what we, do you remember one sort of animation? You can show it if you want. Yeah, let me go for it. So the beginning. I think this one, can you see? This is a very small pathway, but what you can see is these proteins and this is square, this is square in the function annotated. So all these proteins which have a square, this square is not part of the sequence. This square is annotated by us and this is the function. So all these proteins which has this square, which is the label, are final proteins, right? Are effectors. And then what we do is to get, okay, this is an effector, and you go from the effector backwards until the, well, I mean, here, exactly. Exactly. Until the receptors uh you see here can you see my the mouse yes we see the mouse okay so you see okay what happened with this protein because it's connected here but you you can see that this protein is connected not from here to here, but from here to here. So what we do is, okay, arrows that point here, arrows that point this, this and this, arrows that point here, here, here and here, arrows that point this one, no arrows. There are no other protein pointing here. Arrows that point this one, no arrows. There are no other protein pointing here. So for this circuit, if the signal is going here, it goes there to this function. Obviously, it can go through this way, but this is a different circuit. So at the end, So at the end, mathematically it's very easy. You only look for, you go to the last protein and you look for things that are pointing to you. Until you find somebody who is not pointing to anybody. And this is the same thing. Thank you very much for that detailed explanation. We have a few more questions. So there's another, thanks for sharing. What would you say to people that believe that there are many variables that we are not taking into account in this type of modeling and research, that the puzzle is not completed until we integrate the extra layers? For example, this extracellular matrix? Well, I mean, what I have been saying all the time that this is not perfect. This is what we can do in a very simple way. And this is the little, the little, uh, uh, digital twin. There is a lot of detail that is not, is not here. This is, this is true. I tell you that in cancer, for example, this type of simulation works very well because many of the things that happen in cancer is a matter of signaling. So in that case, it works very well. For example, in rare diseases, there are many rare diseases that have nothing to do with signaling. And then we have a lot of difficulties to make the simulation. nothing to do with sinalit and then we have a lot of difficulties to make the simulation. So as you say the puzzle is not complete and we can say only a part of the reality. As soon as we have more arrows and more relationships, we can model more relationships, we will have more reliable digital twins. Vivian, who is the one who asked that question, raise the hand. Vivian, do you want to talk? Yes. Can you listen to me? Yes. Yes. Yeah. Great. Yeah. I asked the question because I have faced this challenge with experimental people. And they're not very convinced that the modeling that we are doing is the right, on the right track, or we're asking the right questions. They feel like we are taking too broad considerations. And I just ask you as an expert, what would be the advice, how to deal with this? Because I believe in this, but many do not. Yeah, well, but this is, I mean, we have found this problem many times. So experimentalists, I mean, when you invade the ecosystem of other people, so they are very protective. So many experimental people don't believe that, mainly because they don't understand, firstly, and secondly, because they feel that you can do without simulation what they do with modes of experimentation, which is a bit depressing for them, right? So, I mean, there is evidence, there are papers that demonstrate that that works. We have published a number, not a lot, but maybe more than 10 papers, 15 papers, and some of these papers have validation. So, to me, it's a demonstration that that works with limitations, and typically what I would recommend you is to try to use some type of persuasion and reverse psychology in a way where they feel that they are at the end they are going to give you the the link that lacks here or something like this but it's more a problem of self-confidence of experimentalists than a real problem because this model i mean with the limitation that they have a lot of limitations. For example, in cancer, they work fantastically well because they predict many things but very, very accurately because cancer is mainly a disease of signaling, right? Then, I mean, just collect evidences of something like this. Thank you. Thank you. So we have another, thanks for the talk, very interesting. According to you, what is the proportion of edges in biological networks that relate to indirect and real direct relationships? And how do you think this affects the predictions of your models? So, Sergián, what is this? What is the proportion, Jean-Baptiste Mournot? What is the proportion of edges in biological networks that relate to indirect and real direct relationships? And how do you think this affects the predictions of your models? Direct and real direct. You mean that they are real direct relationships? And how do you think this affects the prediction of your models? Direct and real direct? You mean that they are real or not? Or... You mean how do we trust in these edges? Or... You mean that... I would say that in the... I mean, something which is very easy is when you compare different. Wait a second, because Jean-Baptiste has raised the hand. So maybe he can clarify. Hi, can you hear me? Yes. Yes. Awesome. Yeah, the question is related to when we're doing like a causal inference or we have some causal relationships. We can have like causal relationships, but these relationships are not physical. You know, we just know that one activity is like the appearance of others, of the activity of downstream different entities, but it's not directly related. There's no physical relationships between the two. So the question is, do we know today what is the proportion of real, let's say, physical relationships and the relationships that are causal, but not, let's say, physical. I don't know if it makes sense. Yeah, yes. We take, I mean, we use mainly for this, we use Keg. In the case of Keg database, there is a quite clear relationship, quite clear description of the relationship between the proteins and we separate in two classes um activation and inhibition and the relation is i mean there are lists uh phosphorylation imagination etc etc some of them are physical interaction and some of them maybe they are not physical interactions, but at the end they fall for a date or whatever. So I don't care if they are physical or not. I only care if the type of interaction, if the interaction is an activation, so it's transmitting the signal or it's not transmitting the signal. How reliable are they? When you compare different databases, there is a core which is constant among them. So probably these are the more reliable. And in signaling, there is a very large part of the signaling which is probably is quite solid in terms of real reliability. So you can trust very much this. There are other, I mean, you go to Reactome or you go to other databases or to wiki pathways, so there are some suggested relationships that maybe they're not real. So probably there will be a process of curation in time. But I think you can trust in what is in the core of the signaling, you can trust most of the relationship and you can try to direct causal, no matter if they are physical or not. This is my opinion. The number, I don't know. Okay. So thank you very much. I hope, Jean-Baptiste, that that answers the question. Yes, thank you. Then go for the last two ones, and then they will all be in the recording. So we have one more comment about thanks for the nice talk and this is a bit of a technical question. Why SHAP in particular for the machine learning model? Did you try any other explainability approaches? I will tell you. We use SHAP because it's the favorite of my postdoc who was doing that. We are planning to use other causal approaches, but we have used that actually the most popular as well. So we wanted to go for something over which there is a consensus, many people use that, but there is no vision beyond that. Okay, thank you. And then the last one is, thanks for the interesting seminar. What would be the differences in applying this modeling in studies with bacterial species as variables, not genes? Would that be possible and equally promising? What should have to be changed? That could be, it could be that. I mean Simply, you should have a different map, which would be the signaling within the bacteria, which is far simpler than the human signaling. And it is possible. Actually, not on the signaling side, but on the metabolic side, Not on the signaling side, but on the metabolism side, there are lots of studies with models of metabolism having made in bacteria precisely because they are simpler. So you can have a look at the metabolic modeling in bacteria and you will get a lot of publication but i guess this doesn't i read the question and i know almost nothing about it it's not about one bacteria what happens in a bacteria but if you have several bacterial bacterial species together because it says using the bacterial species as variable instead of the genes so i guess it's you have one bacteria here that interacts with this other bacteria and interacts with that other bacteria. Yeah, I misunderstood. I mean, why not? But I mean, this model is for transmission of information. If to some extent this bacteria, this bacterial species do something that can be assimilated to transmission of information, meaning that this bacteria needs the other bacteria because otherwise it wouldn't survive, whatever. You can make some type of network among all the different types of bacteria. Probably you could do something like this. Maybe it's not the best approach, but it would be interesting to think of that. Okay, thank you very much Joaquín. Thank you very much, Joaquín. Thank you very much to everybody who has stayed with us until now. And yeah, see you next time. The video will be available, as I said, and you'll receive an email about that. So thank you. Have a nice day. Bye. Thank you. Bye.
Digital twins for drug repurposing by integrating mathematical modelling and machine learning
The rapid development of computational approaches has revolutionised the field of drug discovery and repurposing. Here I will show a practical application of digital twins built by the synergistic combination of mathematical modelling of the cell and machine learning techniques to identify potential drug candidates for repurposing and subsequently validate their efficacy using real-world data. After discussing the inherent challenges associated with traditional drug discovery methods and the need for alternative approaches I will introduce the concept of digital twins, virtual replicas of real biological systems, which leverage mathematical models to capture the intricacies of cellular behaviour and response to drugs. By integrating machine learning algorithms, these digital twins can be trained on vast datasets to predict drug effectiveness and identify promising candidates for repurposing. Moreover, I will emphasise the importance of validating these predictions with real-world data, obtained from biomedical databases, to ensure reliability and efficacy. The presentation aims to showcase the power of integrating mathematical modelling, digital twins, and machine learning for drug repurposing, paving the way for accelerated and cost-effective drug discovery processes.

Speaker: Joaquín Dopazo, Andalusian Platform for Computational Medicine, Fundación Progreso y Salud, Hospital Virgen del Rocío (Sevilla, Spain)

All materials are free cultural works licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license, except where further licensing details are provided.