Welcome to the webinar series of the IT Journal on Future and Evolving Technologies. My name is Alessia Magliarditti from ITU, the International Telecommunication Union. ITU is the United Nations Specialized Agency for Information and Communication Technologies. ITU allocates frequencies to the services that make use of the radio communication spectrum, it develops standards, and assists developing countries in setting up their information and communication infrastructure. ITU and academia share a commitment to the public interest, and this commitment is embodied by the ITU Journal, which offers complete coverage of communications and networking paradigms free of charge for readers and authors. Our journal welcomes submissions at any time on any topic within its scope, and we believe that these new webinar series will inspire more contributions from researchers around the world. It is my pleasure to open today the webinar on information and communication theory with biochemical and molecular components for biological senses and control with Professor Massimiliano Pierrobon from the Molecular and Biochemical Telecommunications Lab from University of Nebraska Lincoln. We count on your support to make this webinar an interesting experience. Please submit your questions via the Q&A channel. We will address them to our speaker during the Q&A session. And after the talk and the Q&A, please stay online. We have something very special for you, the wisdom corner, live life lessons. Professor Pierrobon agreed to a very personal chat. He will share with us some lessons learned over the years that might perhaps be useful for some of you. It is now my pleasure to introduce the moderator of today's webinar, Professor Iana Kildiz, editor-in-chief of the ITU Journal and founder and president of TRUBA from the United States. I'm sure most of you know already Professor Kildiz can bias church professors in telecommunications and meritors at the Georgia Institute of Technology. In the last two decades he has established many research centers around the world, including in South Africa, Spain, Saudi Arabia and Finland. His editor-in-chief emeritus of impact-pacted journals and visiting distinguished professor in several universities around the world. Professor Kildiz is highly cited and on the top of the most prestigious international rankings. So Professor Kildiz, the floor is yours for your opening remarks and to introduce our speaker. Thank you. Thank you, Alessandra. Good morning, good afternoon and good evening all around the world. I again welcome you to our ITU International Telecommunication Union Journal for Future and Evolving Technologies webinar series. I have the great pleasure to introduce you one of the gifted researchers of his generation, Dr. Massimiliano Pirabon, as our distinguished speaker today. Before I present his career, I would like to share my personal experience with Massimo. I met him in person, it's like yesterday, for the first time at Politecnico di Milano, Italy. When I was visiting that time, in fact, it was like September, October, 2007. And I was giving a seminar on nanoscale communications in particular molecular communications. After my talk, Massimo asked, I call him Massimo as a shorter form. Massimo asked many interesting questions and I liked him and he was very curious and very interested. We continued our discussions afterwards.  We kept in touch and he came to my lab as a visiting researcher at Georgia Tech in 2008. And then he liked everything, not only me, but also Georgia Tech and all the city, et cetera, and his friends. He decided to stay to do his PhD with me on molecular communication. And I can say that he's one of my top PhD students that I graduated in my career, almost 50. And also it was immense and great pleasure working with Massimo, a very creative person, very enthusiastic. He's a key person for our Monaco molecular and nanoscale communications multi-disciplinary project. We had like almost 20, 30 people with professors from biology, mechanical engineering, truly multi-disciplinary project. It was supported by NSF from 2012 to 2016. He did really pioneering work on molecular communications. His papers are not only pioneering work, but also received hundreds of citations. Due to Google Scholar, his age index is 25 and number of citations is 3961, 3961. And I can also point that out. Massimo is a unique researcher. He is really for quality, not quantity. And that's why you can you can see that he's not writing tons of papers, but he writes specific papers and receives many citations and also conducts impactful research. And he graduated in 2013 and joined the University of Nebraska Lincoln, where he is an associate professor with School of Computing with joint appointment with Department of Biochemistry at that university. He received many awards from IEEE, also from the University of Nebraska and some companies and other agencies. He is on the crossroads of fundamentals of mathematics, algorithms, statistics, information, communication sciences, and with the focus on molecular communications. We thank you on behalf of ITU, Massimo, for accepting our invitation, and we look forward to your presentation. Thank you very much, Professor Achilles for this very flattering presentation. I hope to live up to the expectations. And with that, I'll start sharing my screen. And, okay, perfect. So I guess you can all see my screen now. And we can start. So, first of all, welcome to this webinar. Thank you, Professor Achilles. Thank you, Alessia. Thank you, ITU, for your invitation. Today, I will talk about information theory with slightly different twists that probably the one you are used to, which is the molecular, biochemical, but more importantly, the life twist. And so, because I want to have this twist today, I will slice my research in a very particular way. This is probably the first time I slice my research in this way. So it's very interesting. So let's start from introducing molecular communication. So molecular communication starts, indeed, with molecules and information. So, if you can see the reality around us, the whole reality is built of mixture of substances. Each substance is a form of matter with specific homogenous chemical composition and properties. And once you isolate one certain substance, that substance is form of identical molecules. The molecule is the smallest identifiable unit of a substance. And so the molecule retains the information that the substance has in the reality that is around us, which is indeed the molecular composition and structure, but also the way the molecule reacts with other molecules. And that is the real basis of the information we talk about when we talk about molecular communication. So, this information is embedded, as we said, in one single molecule of a substance, replicated to all the molecule of the substance that form then the mixture of substances that we look at all around us. The molecule is form of atoms and the atoms are together through strong forces and chemical bonds. And so, the information that the molecule carries can only be changed when we change the structure of the molecule, when there is a change in these bonds, and this change is operated by the chemical reactions. The chemical reaction we arrange is when it happens, the molecule composition and structure in very different ways, depending on the reaction. It's indeed the primary engine that is pumping and it's consuming energy, and it's creating processes where information flows in molecular communication. And that's why we retain in nanoscale because the molecules and the bonds that is the primary structure of the matter is indeed nanoscale dimension and that's why we call molecular communication a true nano communication paradigm. What are then the elements of molecular communication theory. So we talked about the molecules. We talked about the chemical reactions. And the third element that I didn't talk about is the mobilization of molecules, also called molecule transport. So molecules indeed, depending on where they are and which substance it is, they can be moved in many different ways and their movement as you can imagine transports their structure therefore transports information. So, if we put all of these together, and we really look at it, look at it under the lens of communication theory, we can identify a true process where I have the transmission, a channel and and a receiver where information flows.  And, and these whole process can be replicated into a molecular communication network. And so this is a molecular communication what we call a molecular communication link. So what are basic molecular communication systems that we can think of. The molecules propagate through free diffusion, naturally, if you think about a gas, you just release a gas and that will propagate to Brownian motion through the space. When molecules are released, they propagate and because they propagate they can reach certain distance. If they reach a distance with the information that they contain the structure of the molecule, they are also propagating information. So this is one of the most basic and fundamental processes that transport information in nature. And so molecular communication indeed starts from observing how natural in natural environments biological organisms communicate with each other. And, and it's a true biological channel that is living inside us that communicate information at a distance in the space, for example, two elephants communicate information on the math math in cycles through ferro months and the ferro months travel from one elephant to the other. So, if we look at it from a communication perspective, this can be modeled as a process actually can be modeled as a channel and that's the inception of the diffusion based molecular communication channel. Let's go to a more complex molecular communication the human body. So, for example, the hormonal system, our hypothalamus in the brain releases hormones they go into the broad stream, and they transport information to different parts of the body for example some hormones control transport information to the heart and they tell the heart to pump faster or slower. And other other means of information that can be thought of that are somewhat less natural and more artificial are what happens when we inject drug in a human body. And in fact, from these thoughts. We, we have this extremely interesting project that was funded by Samsung say it, where we were starting the molecular communication inside the body where the signal is actually the structure of the drug molecule. And the reception is the healing process at the organ that that needed to receive the drug. Again, this is a molecular communication channel fairly more complicated than the diffusion based, but still can be studied under the lenses of a community of communication theory. Some examples of application application biological as our networks. You can think of having a network of communication among biological devices for example programmable I'll talk about that more later. You can think about using the communication within the bloodstream to better provide the drug around the body, for example focus better methods of injection of the work, or we can use this as a method to augment the functionalities of the human body. Think about being able to interact with, for example, our gut and being able to sense the state and also interact with the functionality. This opens up a truly holistic vision of molecular communication, especially for human health, when we can see system organ and tissue. As a molecular communication system but also going down to the molecular to the cellular and molecular processes until the DNA as one fundamental unit that carries information inside the body and also stores information. With biology, I can also think about reengineering the system and creating devices that can embed functionalities in the biochemical environment until embracing a true biosyber system that is comprised of our body and devices that can sense and actuate functionalities in the biological environment itself. But before going more into those kind of application, we have to understand how information is flowing in biological system. So biological organism have evolved to manipulate information in biological environments, we know that information is stored in DNA genes. DNA genes can be parsed, they have a functionality, they have control statement, pretty much like computer programs. Information flows through the, from the DNA gene transcription and translation through a structure that is composed of little bricks which are called amino acid. That then, because of their relationship with each other, they actually interact and form a three-dimensional structure that has a precise function. So information for function here is an extremely important concept. And then information not only is inside a cell, but information is also exchanged between cells. And so one cell can tell another cell, grow more, grow better, I get a cut on my finger, around the finger the cells are told to grow, to divide faster than the other cells of the body to actually heal my cut. And so all of this brings us to think about what happens when I look at the biological systems, either inside the single cell, which is the fundamental unit of life, or also among cells. And if we can think about that as a true information flow, we can see how information can guide processes, biological process so important, like cell differentiation in tissue and coordination in bacterial infection and even the whole homeostasis of our human body. From the very beginning, well, from a certain point of my work, what I was interested in is studying how a cell indeed absorbs information from the environment and whether this is possible to be interpreted as a communication channel or actually a series of many communications and whether then it is possible to re-engineer this channel for very different purposes. And if you think about the biomedical, that's actually the very first aim of this research. And so, imagine that a cell, wherever it is, either on a unicellular or in a tissue, is constantly bombarded with information from the inside environment, signaling molecules that, for example, hormones, metabolites like nutrients, environmental stressors, it's too hot, it's too cold. All of those information are propagated through biomolecular mechanism to the cell DNA, where the cell indeed processes this information and takes action and takes decision. The decision can be adjust the rate of growth. The decision can be die, so apoptosis to maintain the rest of the population healthy or also to produce to have other sub product, for example, proteins that interact with the existential environment. But if this channel get interrupted, these functionalities cannot take place. So these channels have been studied, have been evolved through the millennia to actually enable information for very precise functions. And so what we look at, what we can look at here is the main networks, each one is a sort of homogeneous network in terms of components that propagate these external information inside the cellular environment. So the first is the signal of transduction that takes signal from outside and propagates to the DNA. The second is the gene expression where the DNA processing information and takes action. And the third one is the metabolic network where the cell indeed processes most of these information and transform knowledge into energy and energy into growing and dividing and perpetuating the species. So one of the first works that we did was to study these channels from signal transduction to gene regulation. We produce a couple of publications on that, and then we study the channels between gene regulation and metabolism. Now, I cannot obviously go through all these results, but I just want to give you an overall idea of what it means to study this channel from the communication perspective. Let's take a signal transduction network. These are molecular processes that convey information from extracellular signals, for example, growth hormone to inside the cell. Two processes are called phosphorylation. There is a very intricate network of chemical reactions inside the cell that is connected also to the processing unit, which is the DNA. And at the end, the cell thanks to this information flow can adjust and fine tune many different functionalities. Now, if I can model these in a more schematic way, I know that there is some input information. I know that there are communication channel inside the cell, and then there is some output information that results in the fine tuning of those functionality. If there are disruptions, if I have information loss, and mainly they are due to mutation in the pathway genes that practically disrupt the functionality of some of these hubs. These are actually network hubs are actually chemical reactions, but if these chemical reaction do not take place properly, there is a problem. And many of these problems are at the basis of actually cancer. And so that's why we actually started in one of those for our work. We started kidney cancer. And so we wanted to verify upon kidney cancer, what is the information loss and where it happens within those networking hubs. And we actually found that most of the information loss happens at the receptor. And at this hub that is one of the molecules that that happens to distribute information because it's very reactive and so it interacts with many other molecules that's why it's a hub.  So what we can observe here is that we have a novel approach to estimate the information flow to a single cell. We came up with novel metrics or if you want to import it down from communication to classify biological system. And we can use them for many different reasons for many different application find control of production on biological components, novel ways to classify organism and pathologies and also ways to reduce web experiments to look for the best experiments with the highest probability of success and also novel strategies for for health care. So we were then we had the opportunity because of our work in information theater applied to biology to propose to the National Science Foundation, the organization of a workshop that took place just before the pandemic in January 2020. So we were in person which is amazing to think about right now. And in these specific workshop we invited all the experts in the world that we're working at the intersection between biology, information communication and coding theory. And we built a community, a community that was not there before a community of people who can now exchange information and work together and collaborate that's thanks to thanks to the funding of NSF NSF and thanks to. Also my my co organized and even aerial from University of Maryland. So, out of that a lot of new collaboration started. And some of them are actually with my own group in one collaboration. We started to look at information and life, which is not something that we saw with the single cell before. So, think about having two substances in the environment, for example these two substances characterized by this molecular structure. They carry information right the molecular structure so the chemical composition but also the time when they are in the environment the location where they are in the environment. So if I look at what happens to two cells that are in these environment. These two cells use this substance for life. So they need them for living and they need to know where they are, and when they are. If the cells have too little information and let's imagine one thing that this information is costly. So every time I acquire one bit of information about it, I actually spend part of my energy. So I want to conserve energy, I end up having too little information so one cell. In this case dies and the other can replicate survivor perpetuated species. So some individual did not know enough too much information cost means that one cell can die, one cell will die, because all the energy will be spent to acquire information. And another cell might replicate but again it's not an ideal situation some individuals spend all resources to know too much. The just right information means the trade off between information cost and survival is what actually evolution has given to the living organisms. So minimum resources spent to know enough information to survive. So it's quite different from the devices we are used to where for example in the Internet we want to be as fast as possible with our cell phone. We want to be as fast as possible here now here we want to be fast enough. So that's why there is a lot of discussion in the mathematical biology leadership about reward fitness that is survival versus information cost so sensing and processes is a trade off found by evolution. And there have been proposed some quantities to measure that so one is the fitness value of information on the goal killing formation. Increasing fitness resulting from an amount of information so I don't just classify a cell from how much information does it get, but is from how much of that information is actually good for the cell to survive. The second is a very nice concept for me that is the semantic information. When do we talk about semantic information information theory, never because Shannon just at the beginning of its seminar paper said a telecommunication designer or communication engineer does not care about semantics we just want to transmit something from point A point B. We don't care what is in the message. But here we the cell actually care what is the most important message is the one that ensures survival. And so the semantic information is defined this is a definition that is that is in the literature is a minimum information to ensure can use a maximum viability, which is fitness. So, from there, we started to study. And we started to understand that many of these work. So we're pointing at an underlying rule of life where more information is always better if it does not cost more. So a little bit closer to let's say our cell phone that I have to save battery, but at the same time they have to be fast. Well, does nature always optimize a communication channel given constraint on the cost of information. Well, the price that nature we're doing that every individual in a certain species in the same environmental condition will end up having the same communication channel right, or the same performance the same amount of information flow. And that's not what we observe. And just like us human, we have a diversity of sensing and perception in many situations so and and bacteria as well. And so what's what's the driving force of that. First of all, we started to shift the constraint from information cost to the capability of an organism to manipulate the information channel so we let not only one one type of optimization take play taking place. But we let each organism Ali at the inside a certain population will let them manipulate their channel to be more effective. And we ended up with something that is interesting. So we ended up building a computational model as a counter example to the previous literature. And so this computational model starts imagining a motile species so a cell that is moving in a one dimensional environment. And then we leave off to nutrients a and B let's say bread and butter. Okay. And absorbs a and B proportionally to the concentration, they are where the cell is at a certain moment in this one D line. They can divide and so perpetuate the species only when both store a and B are over a threshold so they they are these quantities also identify how much cell is grown, and when they are over a threshold they divide the cell senses a and B gradients through chemical receptors So here we introduce the binomial noise that is all night present in chemo receptor chemo reception in biology. And in biological channels. And finally, it moves the cell proportionally to the steeper gradient so the cell because of the receptor can estimate the gradient in the concentration of a and B and chooses the steeper gradient to move in one direction or another. And finally, it constantly consume stored a and B. If a and B, for any reason, a or B, get to zero, the cell dies, it cannot sustain itself anymore. We devise two possible strategies. In the first strategy, the cell keeps all the receptors constant at a constant level. In the second strategy, the cell can adapt its receptors by following a rule. The rule is that I, I put out in my on my outside, more receptors. So I reserve more channels for the quantity, a or B, I have less. So the lesser quantity drives more sensing. So I'm more concerned about the quantity that is depleting. So, doing that, we set up a simulation, we, we read the result using two performance metric. One is information efficiency, which is nothing but the average mutual information of this channel, overall, in all the population. And the second is where we, we, we can parse this mission formation as the mission formation of a certain concentration and the corresponding receptor for that kind of concentration either a or B for that kind of substance. And then the second is the growth rate. So we set the exponential growth or doubling rate, which is a normal parameter in biology and evolutionary theory, but we had to discretize it and so we define a simulation step delta T and we define a simulation time. And we accounted for the number of cell population at only some precise time and we look at the rate, how fast are the cells growing. Just a couple of run of this simulation and they are very interesting. So first of all, I want to focus your attention on how we displaced a and B molecules in the environment. This is a 1D environment. And these molecules of a and these are molecules or B. And we displaced them in a way that they are distributed according to a distribution called fun visas. So these environment is actually a loop. So the, the 100 goes back to zero. It's, it's looked inside itself. And the fun visas distribution is the distribution with the maximum entropy on a periodic support. So what we did is simulate, we simulated actually a couple of cells. And we have these two trajectories here for the equal receptor allocation and here for the adaptive I just want you to notice that the equal receptor allocation has a cell density this one in in this kind of yellowish color. It's much more uniform and so not well adaptive to instead the cell density of the adaptive that follows a little bit the, the, the ridges and the spikes of the fun visas distribution for the two. But the main result is the following. We define a certain cell stress, which is the rate of consumption of a and B and we vary the cell stress which is how difficult, how hard for the sell it is to live in deep. And we found out that for all the values of the cell strength that we considered the adaptive receptor strategy was having a lower information rate of each channel so it was actually getting less information from the environment. It's compared to the equal receptor, but the low the adaptive receptor strategy was more fitted so so the cells having adaptive receptor strategy we're showing a higher growth rate with respect to the equal so less information higher growth rate how did this happen. Well, it happens because that the cells differentiated the channels, and that's what we call subjective information. This is nothing but the variance of the average mutually, so it's the variance or it's the variance of the mutual information of the cells at different cell stresses. And these variants are of course is zero for the adaptive for the equal receptor allocation because they don't change their channel, but instead for the adaptive, it changes with respect to the stress and with respect to the noise this is a noise factor connected to the And so this objective information we believe is the first time in the literature that has been kind of demonstrated with a with a with a toy model. And, and we are moving from there with with this to understand better how information flows in biological organism so what did we learn maximizing information efficiency can result in a lower growth rate. And so evolution can enable a diversity of perception, this is the genesis of why we are perceiving different things different, or the same things differently. So this is a new dimension to understand, live in organisms and they, their relationship to information is a novel inspired approach to design communication system and is a push to revise information theory to address the life component. So we talk about understanding. Now, let's talk about engineering. So what I want to do is characterize model and design systems based on living communicating devices to control the propagation of information in biological and And this was nothing but the statement of, of my proposal that I got funded a couple of years ago that is called wet, wet com of fundamentals of what communication theory where indeed we look at how to take those channel we talked about before and engineer that we had a very, very highly tuned engineering process. And so, and as you see here that there is also a lab component to that and so we are actually, we approach by our collaborators in biology to carry out this type of project. So cells naturally exchange information to molecules as I, as I said, and the idea was to apply communication theory. And prior literature where this type of engineer systems in biological system will have been studied in in communication engineering. But what we did was go a little bit beyond that and trying to first estimate what is the maximum information right that to engineer cells can exchange. We are engineer to transmit information using molecules that diffuse between one cell and the other. And the other topic we were interested in is how to engineer or re engineer the structure to include coding. So, search coding, but more importantly, channel coding. And so we started to think about coding, but the problem is we had to remove algorithm, algorithms, electronics, and we have to apply coding processes and coding structure and coding programs to genetic circuits, which is a whole other Pandora Pandora's box that I opened in the in the past years. So I just wanted to give you an idea, a very quick idea of one of the latest results that we had. So, the, the idea of engineering cells to exchange information is really not new to synthetic biology this has been demonstrated to be successful. And so, the, the idea, though, now is that can we forward engineer this so it's not just a proof of concept we actually want to optimize the system, like we optimize an optical fiber to transmit as much as much information as possible. But of course, with all the things that we learned before. And so we, we look at, we look at priority protocol system when I have two cells that are stimulated biochemically, and they exchange information through diffusion, and inside the cell of course I have a bunch of chemical reactions so and the main idea was, if I have a search signal and a destination signal, what's the communication performance the mutual information and what's the maximum of it. So how much information is delivered in bit per message or in bit per channel use if you want. And so we studied that then a very specific system from synthetic biology for which we have actually physical parameters to to to study. And this is composed of of an engineering structure with biological circuits. And so, what we did is we built a model in math lab symbiology that takes into account not only of the chemical reaction but also the noise that is between chemical reaction, which is the right from that binomial binomial distribution that I told you before and then in this case, it actually becomes in a nested Poisson process. And so we, these are all the equation that we input into math lab these are all the parameters that we got into literature, of course they are not in only one paper we had to just, you know, come a lot of literature from synthetic biology. And finally, we found out a model for which we could actually represent the output distribution of probability density function, given a certain input in this case a uniform distribution input. Again, from from here we have our distribution ha and and we have, we have. For example, here we say we look at an average the mean and the variance and we can observe that this channel has only a linear component here, but the rest as saturation. And so this channel is really not very amenable to analytical study. It's not an AWGN channel at all, unfortunately. And so the main idea is, can we build a structure, an algorithmic structure that we do not with an unknown statistical model can actually build a expression or can actually find numerically unachievable information right. And so there are solutions out there that are ad hoc for that. And one it's the blout are immortal, very, very famous. The problem is the analytical expression of the conditional entropy other the channel of the equation have to be known or estimated. So here is our iterative method that we are presenting here. The comparison that we made is with another methodology that has that has been presented not long ago, where instead they were estimating this distribution with the blout are immortal. So what we did is we used we actually devised a structure or an algorithm that is based on an elder me optimization algorithm. And so in this structure practically we iteratively modify the input distribution according to parameter and we do a search on the parameters of the input distribution. In fact, we use the person family of distribution that has four moments. And so we actually varied each one of the moments and the combinations but of course the search for all the combination it's intractable. And so what we use to simplify that is the Nelda mid which is an optimization process where you build simple access so you build a series of points in the distribution where you can find those points corresponds to a certain mutual information that can be numerically estimated from the simulation. And then the structure is actually is very similar to the gradient descent but it's multi dimensional. It moves along the steeper the steepest gradient or steeper gradient until going to the optimum that in this case our optimum was the highest mutual information and so the achievable information right compared to the slimy for an AWGN channel so without knowing that it's at AWGN you apply that and you can also use the McKellips lower bound and upper bound as a comparison. Our methodology works pretty much like the slimy at lower SNR but for higher SNR the slimy starts to diverge from the upper bound so it's higher than the upper bound. And our methodology goes sometimes a little bit lower than the lower bound, but still it's pretty consistent. And so going to our data, our biological data, we found out that if you see here the optimal mutual information this is the one that we would estimate with the slimy. This is indeed what we estimated a different iteration of our algorithm you see that a certain point they get quite stable and so we are in the ballpark of this number. We also tried to see the mutual information with a triangular input distribution and a Gaussian but what is the best input distribution indeed for this kind of system engineer system is actually something like this, this blue one that you see. And so it has a spike at lower input power and another spike at higher input power. And so the this lets us know that the viability of information transfer optimization can be increased. If I design properly the distribution of the input message and I have a iterative algorithm to to estimate it. Can we apply coding theory to enhance this performance. The answer is yes but we need to study synthetic biology to understand how to program, but biochemical process to actually do the coding. And so all this body of work was devoted to that, I will not go too much into detail now just in the eastern interest of time. So what did you learn, what do we learn for this so we presented a computational method to estimate information exchange of information is feasible to apply information theory based concept to synthetic biology. And we presented a proof of concept design methodology and that is, although in the papers that I didn't show now, but they are for you for reference to see to actually implement this performance and move one step forward to the achievable information rate that we found with the in the results that I just presented. So biological surface seem extremely versatile to design communication structures. So now, very quickly just a round down of the concept of internet or biome of things. Imagine the human body and imagine to have devices in the human body that can be inherently biological like the fish and organ artificial got microbes that are designed through synthetic biology, but also electrical devices are now, for example implantable devices, like like a smartwatch, which is not implantable of course but but an insulin pump for example a gas to stimulate or a peacemaker and so on. So, our vision in the in the internal biome of things is to be able to gather information and connect this information to the internet. This is not scary enough, we also would like to control these processes through the internet. The real problem is that we studied a little bit of everything. But now that one of the focuses is how do I realize the interface between the biological and the electrical domain. So the main idea that was actually comes from my collaborator in University of Manila College Park is to build a device that is based on the chemical reactions that in biology exchange electrons between molecules and can also exchange an electron so an electrical interface. And so what we started and then of course it was coordinated by by NSF, a big NSF grant that started a couple of years ago as well. To study the system and to study how to make an integrated device that uses many of the system to build biomedical devices that interact with the human body think about here having for example a tissue. And on the other side, you have just a tree of electrics that you can connect or connectors that you can connect to your electrical devices. So these comprises the study of communication, memory and computation. So what we did is, first of all, study this interface. And so we opened up the interface study that as a communication channel. So, these are the classical, classical boxes of communication from search to destination. And the interesting fact is that this, this is the whole interface, and the law interface is able to read differences in concentration of certain kind of molecules which are redox active so they can exchange elective which are omnipresent in biology by the way. And, and it uses it takes this molecule inside and then circulate these molecules with other molecules that are embedded in the in the device to then read these differences concentration in the in the output. So this is a fairly complicated model. But what we did is we realized this model in and it's really not. We express it analytically but is, as you can imagine not very tractable. And we, we realize this simulation in simulink. And just to give you an idea, this is the experimental device that we used to get results and and validate our simulation. So, this is an electrode. And this is a solution containing redox active molecule. And then we can vary with the pipette their concentration and study how the signal changes. And so we use ferrocene in our experiments. And we use some parameters. These are just parameters taken from biochemistry from electrochemistry literature. And so these are some of our simulation result or this just one simulation result. And then we identified also things that are missing in our simulation to be really, really effective. And then this is the simulation using different signals actually different amplitudes of our signal. But of course, your question might be, what's the mutual information what's the performance of such a such a device so the device that our collaborators have. For which we use the parameters physical and chemical parameters showed in our simulation based scenario, an estimated mutual information of almost one bit so it's pretty slow per channel use when the but when the concentration that I want to read of that biological molecules is one phantom molar to 1.9 to also very little concentration and this is that these are the concentration at which we can actually extract information from a cell from a population cell from a tissue. And so this is just an idea of the different output distribution when I have input that is uniform. So to conclude, I want to take a stand to thank but also invite everybody to have an agent team in your institution if I if you are in higher education or even if you're in high school. So having an agent team is just great, especially for somebody like me who started not knowing anything about synthetic biology, an agent team enables you to work with undergraduate students to actually build during summer months. A artifact of of a of a genetically engineer organism usually it's a bacterium or yeast to actually participate to a competition in Boston that is every year. So we had multiple years running agent, and it was just a blast and some of my students that are working who are working now in my lab and and produce some of the results that you saw today. And I have to thank them too. Of course, they are actually started being in my agent team and being them get the passion for this type of research, and they are computer engineering students. And so we we got multiple awards through the years and a lot of visibility because don't forget Nebraska didn't have an agent team before. So, while there are 300 plus agent team all around the world, especially on the West and East Coast and especially especially in Europe. And so many, many things. So we are running nano calm 2022 I'm one of the co general chairs, it will be in a beautiful city Barcelona. And, please, a look at our website. There are all the deadlines and the submission, the submission links to it through this. Please consider submitting a contribution if you're working on non escape computer communication, not only molecular communication but also at large any non scale nano bio system or nano system. We also are running. And I'm a credit or in chief or nano communication networks, and I have to say that both ACM nano com and nano communication metal was funded by Professor Achilles with here with us so he started this discipline. And he started this movement that now it's kind of exploded. And it's out there in all the major conferences, I just, for example, chair a special truck. Sorry, tracking a symposium in ICC on molecular communication. I just chair, or I'll be chairing another conference. And so, these are all my collaborators and students and whatnot who worked with me to produce these results and without them, my results wouldn't look good. So I have and without them I couldn't even be here. And without some of them I couldn't even start this kind of career. And we know who you're talking about, of course. And so, thank you. Thank you to all of you and thank you to all the funding that I received through the year and I continuously receive just the latest one is, is, is from defense, actually. And, but although defense work is not here. That's probably for another time. Thank you for your attention. It's been a blast to be with you today. And I'm open to any question and I'm so happy to chat with all of you. Thank you very much. Thank you, Massimova. I knew that it will be excellent and it was indeed excellent and clear and I'm really proud of you. And also happy that you have a lot of activities really well so there are many questions here. And before I ask my questions I have to be fair to the others.  Lokendra is asking, could you please discuss about the simulation methods. I mean, this is too long question, but I will, I suggest that, you know, you mentioned about what simulation tool you use or something. Yes, of course. No, no, but it's pretty simple just to introduce them. So the main simulation I started from is indeed Matlab Simbiology. It's a relatively new package in the matter of distribution. So if you're in higher education institution, usually you should have it. And so that contains the major processes in chemistry and biochemistry that you need to actually set up your simulation in a very, very, you know, simplified settings of course. The other simulator that I used and I mentioned is Simulink. So for Simulink we actually designed, well, actually we use Simulink in conjunction with Simbiology and Simulink is another Matlab add on, of course, and Simulink is has very good modules to design electronics. And so when we wanted to do to model the interface between an electrical system and a molecular we actually use Simulink. Other simulators or other data processing tools that I used are bio, are bioinformatic processes. But I want to just take a stand to thank KBase. So if you look at KBase website, KBase is a website where bioinformatic simulator. Are available and they are public domain. And that's how we simulated those biochemical networks that you saw in the first part of my presentation. And they contain already, I don't know how many species. They contain all the genome and all the annotation. And they contain also some of the models of the biochemical processes that undergo in a cell. But that is the three things that I wanted to manage there to introduce. We have many questions. So Costa Janakis says, excellent talk. Thank you. I really enjoyed it. So his question, he has two questions, but the first one is, do the cells discussed in those papers come from particular model organisms or ant tissues. Given the differences among cell types, how universal is the proposed framework? Good question. Yes, yes, very good. So that's a very good point. So we, we started from being simple. And so something that maybe I should have mentioned and remark a little bit better is we, we looked for now at unicellular organisms. So we, we don't look at tissues. But we look at single cells that interact together. For example, bacteria or yeast. One of the primary reason is that we had the ability of experimenting with them because of IGM and the wet lab that my collaborators have. But also those are the main models in mathematical biology to apply information theory. And I, but I agree with you that it's a totally different animal actually in the real sense of the word is totally different animal to think about multi cellular organisms and the type of communication channels they have. I looked at those in other projects that I had, for example, I looked at the nervous system. But, but that's another story for another time. But yes, those are unicellular organisms. Costa is also asking, what is the lifespan of the solutions used in the experiments? Are they stable enough to test long term tests? Wow, okay. This goes really to the point. So I was, I was hoping to skip that. No, I just kidding. I'm just kidding. No. Okay, so currently, the solution that you saw, for example, to build the interface, okay, is, is pretty stable, meaning that we can have, for example, a full day of experiments with the solution. But the device that I showed you, let's say if I, if I can go a little bit. This device, this device here that contains cells. It's a one time use. Because the cells here are immobilized and they cannot move, and they are working just as hubs, they transduce information. I didn't say much about this device. And you can go to our paper or, or this paper actually where they describe the device. This doesn't include me because it's when they build the device. And so these cells die after some hours. So, so that's a problem of these devices. They are one time use, like the COVID test. Okay. So, there is another one toxin alkaline. Excellent talk. I remember this guy, but I don't know from where so is there an equivalent channel limit in biological channels. You should refer him to our transaction information theory. You know, really. You have a healthy capacity interval for living entities. I hope you have a slide on the paper that we had. No, or grab. Okay, so, so we have. Okay, I can grab it. It will be just one second. That was a long process to produce. Now, people go to toilet and then in two weeks they write papers and that took us one and a half years or maybe longer. Remember in Barcelona started it. And click 13 2013. Yeah, yeah, but excellent information theory. I wish I could grab it right away. No, no, I'm just fine. No, no, I'm on my website with channel memory and molecular noise information theory. Here it is. I'm just sharing my screen one second. Here it is. Okay, so this was a pain. Yeah, this was a, this was a way to say a really hard birth. A lot of beatings. We have, we, we might have a lot of anecdotes on this, but so in this paper, let me show you a better picture. So this. So in this paper, what we, what we did is actually look at the the diffusion channel alone. If I squeeze the transmitter and the receiver to the simplest, simplest thing possible that I can think of. So the transmitter is the meeting is a hole that emits molecules. And the receiver is just, it's just a balloon that is permeable to the molecules that is able to count the molecules that get inside so the diffusion process is just to a minimum. So what we did is we, we took, we, we, this is the only case that I could find in molecular communication where I could build an analytical solution to the Shannon capacity. And so what we found is, oh my God. Okay, what we found is, is that we have to take into account different processes, the fixed diffusion and the particle location displacement the fixed diffusion is the model of diffusion macroscopic and the particle location displacement is practically the the, the stochasticity in the location of the particles given, given the distribution that the fixed diffusion imposed to the particle at a specific timing. So it's practically that the molecule diffuse, but we don't know exactly where they are located that's a stochastic process. And so if you take into account these two processes and you go on and you kind of work it out. And, and you also relate, of course, statistical mechanics entropy, which is the thermodynamic entropy to the Shannon entropy, which is the amount of information you can actually get using some a couple of theorem. A big expression, which is this one. This was the, the last capacity and then we had to of course find the, the maximum the mutual information in terms of input distribution, and then we found these inform these expression this information this expression is dependent on many important parameters. So the temperature of the system. Then the bandwidth of the system so they want the boundary that which I vary the concentration that I want to transmit. And, and then the entropy power which has the function of the power constraint in Shannon theory, but the origin is a little bit different so the expression is really not the same. And so this was only confined to diffusion. Once we move the way to diffusion. And that's part in part of the things that I presented today in part of the result. You cannot solve it analytically and trust me I tried. It's just doesn't simplify the nested Poisson processes are actually unsolvable. What you can do is trying to devise a methodology to find bounce right. And so that's what we do when the channel and intractable in communication theory to problem is even to find the bounds. It's not exactly simple, and you've seen that we have that new medical method that is a kind of optimization method that goes to find the bound, but still we are. We are sure that the bound should be below the upper bound, but we haven't yet found the upper bound of the system of a molecular system biochemical system with biochemical reactions. And so that's actually the work that we are continuing with with my student. Thank you for the question. There is one more from the same, I assume it's a lady. I have no idea where she's located. She says excellent thought and thank you for your precise information. You are the God. No, I added that one. So, can the molecular communication can really be interfaced with the very high frequency devices. I mean she says very high frequency and says around gigahertz but normally, when you say very high frequency you talk about terrors but you know, so she said about around gigahertz. Okay, I'm glad you asked that. And it's a it's a very interesting question so all the processes that you've seen so far, including our interface. They are at fractional hurts. Okay, to give you an example. But that doesn't mean that there is no interface at higher frequency. I didn't show it today but this, when I when I teach molecular communication I always show a slide where I took the spectrum, right of communication. And I take the spectrum and I say okay here's the radius spectrum, which is wave. Here is the light spectrum, right higher frequency, which is, which is light. And it's, and it's both wave and particle photon at the same time. And here is the communication is molecular communication spectrum which is only matter so particles. And with that I ignore something that I found out and that's one of my research direction right now I haven't presented here because it's very new is that new new like of this year, I started studying radiation. I started studying ionizing radiation. Why, because ionizing radiation which is extremely high frequency and extremely wide band. Okay, and we are talking about radiation that is over the gigahertz. Okay. That radiation that is not studied in communication for obvious perspective because it's harmful biology in low quantity is actually used at the lower side of the spectrum is using radiotherapy. It interacts with our tissues right and so that that communication so high frequency high boundary has the potential to interact with biology, and it does. We always perceive it as something negative because it breaks, usually molecules it creates free radicals it breaks DNA create mutation. And if we could channel that power into something that is safe, and indeed it is already done in radiotherapy. We could channel that power, we have a huge bundle that we can write directly in the biological substrate. And that that is one of the possible interfaces I'm moving to right now for devices. So ionizing radiation.  Fortunately, there's a lot to study cut throat generation. I have a couple questions one is, you had one slide about you know the slight pages, you show this fun mysist distributions. So two cells remember, usually. Yes, here. Yeah. So usually, there are a lot of dependencies between the cells right. So when you have larger number of cells. How much are they affecting each other and are the results still valid. Okay. Very, very, very good. So, yes, it's true that that cells can affect each other they can even communicate, but they can also just the, the minimum, the, the simplest way they can affect each other is that they, they take nutrients from each other right. And so that's an effect that we consider here secondary, which means that to make the simulation through to reality, we should indeed consider them. But for now, in this time model, we just wanted to limit ourselves to study what happens to cells when they don't interact with each other. And so we eliminated that interaction, but I agree with you. For example, one of the assumption that I didn't really get out is that we assume that although the concentrations are different and different points of the of the distribution. Here I have unlimited source of food, which is not the case in nature. And so interactions of that type can also work when we look at evolution. In a way to shape the channels. And so I agree that the information channels are also shaped by those interaction. And they have to be taken into account. But we didn't so far. Yeah. I would say a couple things and then I have a remark or a question again and I think we should conclude the session. This is mostly for the younger people. And most million is a very good example that or an eye witness that what we went through when we started this research, like almost 14 years ago, or 15 years ago and there are a lot of people. They were attacking us like there's a science fiction. And he was also in some of the conferences, how some people were attacking for some nasty comments about the area. And we believed in ourselves right so we continued and you know now, as much as I mentioned, there are so many people, thanks God right. They're working on this. And this is fantastic to see how this area developed, especially from the telecommunications perspective. And now what I observe is, I exclude must me on because it's very impressive as you can see from his presentation that's why I also invited him that many people. What I observe is they're doing epsilons meaning add on work.  I do this idea that here's a transactions paper. But you know, it's really time now to show the value of these theories so in the paper that we wrote in 2019 and I triple proceedings from theory to applications. I mentioned that we tried to somehow put the, you know, path, the path to that direction. However, it's not that easy. That's why people take it easy out and keep writing these theoretical papers. Now, my question here is, must you know you mentioned here and there but for the newcomers or you know for external people. It's yeah exactly this paper. So, it will be good to like tell us, like as an example a lot of people think about these COVID now right there's all this viruses, like we did bacteria communication. And in fact, you know they use this type of models you know there's RNA, you know the cells and you know that's, that will be interesting to mention that just to repeat you mentioned that I know but maybe it will be good for the audience to just appreciate that. I don't like the semantic right semantics, not the semantic you mentioned but know why do we need this right, all of these theory and then we'll close the session. Okay. So, yeah, that's, that's a great point. So, why, why do we want to apply molecular communication or, let's say, information and communication theory to these kind of systems is, is because we have the tools and the capability to understand information flow in this system. And as you can see information flow is guiding many many processes and he's actually guiding life itself. So, just to site. I, I'm always quite close to the NSF program directors in in MCB monoclonal biology they are always setting. We're always trying to get proposal, talking about rules of life right and they say what is the rule of life, because biology is so scattered. There are biologists that work on systems biology work on. And so, and, and what when they asked me, and we have several different town halls and meetings and the workshop. I always say, well, for me the rules of life, the rule of life is information. Information is everywhere at different scales and drives props processes drives evolution drives existence as I demonstrated maybe we have to rephrase it better. And so, what's the important of studying information in the system is that I can understand better the evolution of the system. I don't have just description like for example in medicine, they don't use models right they, they mainly they use description of this works like that, or they are close models. This was like that in the medical, the medical, the medical doctor makes the diagnosis, right. But can we have instead working models where I can input, for example, the parameters of a patient in terms of information about the patient, and I can have a model that propagates it.  And so, in other words, information is a lens to read to see these processes and communication theory is a lens to see this process. But it is also a homogenous lens information as always the same metric of measures. And so that's why it's universal. And also, when we learn how to engineer information processes and synthetic biology is a perfect tool to do that. We actually learn how to adjust this channel. Do we want to do we have a channel that is not working cancer. Do we want to fix it. Maybe I just don't need radiotherapy to kill all the cells that are cancers. Maybe I can have a virus that goes inside and reprograms the cancer cells and reverse them back to not proliferating, for example. And so the origin of those problems are, I believe our communication related and that's how we will make a great impact impact in humanity and in our society in the years to come. We started something big. And I believe firmly that you become bigger and bigger as our devices will move from being in electronics to being more and more embedded with biologists. Just look at the 6g standardization how it is going. Biology is all there. Thank you. Thank you. I really thank you for everything. And now I'm closing this session. And I give the microphone or the podium or whatever you call it now to Alessia. Please stay with us and stay online. Actually, your last comment introduced us to the wisdom corner. Yes. Already some experience. I have shared some lessons so thank you and stay with us. So thank you very much for your excellent talk. And now we are ready to start the wisdom corner, live life lessons, which is based upon the idea to give a unique and special angle to this series of webinar. We want to add the personal touch. And so successful researchers like Professor Pierre Bond today will guide students and young scholars in the field of current ICT research, and we'll also share some impactful life lessons. We know that life is always a journey of discovery and learning. And we also know that success is not because we never give up, but it's because we never fail. So I would like to address my first question to Professor Peter Bond. So which is your heart earned life lessons or failure that he would like to share with us today that might help somebody attending the webinar. Okay. Okay, I go very personal now. So, when Professor Achilles actually mentioned when we met in Milan. And, and a lot of memories came to my mind, and just didn't date with me. And then one feeling came to my mind that was at that time when I was in Milan, and I was a bit lost on my, I was actually starting. My PhD there. First, and I was a little bit lost with with I was doing a different research. But the real problem was not the research topic was just I was right. I was not believing in myself. So, I, I could do great things. But somehow I was saying no I'm not worthy, or whatever I do, it has already been done, maybe. So, I was ready to give up. I was really ready to give up. I met somebody with a lot of energy and enthusiasm and new topics. And I, I, and I managed to see like many of my colleagues in Georgia Tech who were working with him I managed to see beyond that. And, and that was the realization that I had a turning point there. So, should I stay in Milan and, you know, just have my normal life and have a mediocre research output. Or, I can, or can I put my capabilities. Can I can I overcharge my capabilities or express myself. And so that's the best if the life can give you after health is, and kids in a good family. The other good gift the life can give you is to be able to express yourself at the full potential. So, we had many, many discussion with Professor Achilles actually to about what it means to be successful and so for example, take a musician so the musician is successful because it expresses herself of himself to the instrument right and the musician is whoever can transmit that success and that enthusiasm to the audience. I've been very recently in a, this is a funny story but I am in a PhD committee of a PhD of a PhD student in the music department. And I go there. So it's, it's really, it's really, it's a long story but in short, I was at his press it all. It's a part of the PhD exams that I have in the departmental department of music he's a violinist. And when I, when I look at him there in the stage expressing himself. He is achieving his full potential. And he was, it was beautiful. And it gives a lot of power. The same power that I perceive when I saw Achilles giving a presentation of polymy and Milan, the same power is like, Oh, this is a person who's achieving the full potential. I want to do that too. I want to, I want to feel like that. And so that's how everything started. That's how, how I want, how I pushed to go to Georgia Tech and, and that's what world and everything, everything is something that happened because of that.  And yeah, it's like an incremental result and whatever I go to the conference I enjoyed the city. And I don't know, like, things that know I, I wanted to do more. And so I jumped up. And I said, No, I take, I take ownership of my life.  So it was very difficult to leave my family. Professor kill this knows very well. And, but at the same time I just, I just took ownership of my life and I say, I do it. I just jump to the other side. And, and I'm glad I did. So that's a hard, hard learn life lesson for me. Great. Thank you so much for the being personal for sharing this story. Very inspiring. My second question would be which strengths and capabilities students, young scholars researchers should be more focused on developing. And, and how should they plan to accomplish in this. First, first being open. So, many times we study a lot of very nice subject and we study education networks and we study medical analysis, whatever is your, your major. But then, and then we are kind of anchored and think that discipline are actually closed and say and so if you think okay what do I want to work on well I like programming okay then I do programming. But that's not the way it should be more and more we have to think big. And we have to think that when we have a degree, we just know one tiny part of the full picture. So, how do we know the full picture. Well, we can have multiple degrees. That's not feasible. Right. Because, well, for example, just an anecdote to be meta, the very very famous orchestra director, right to be meta. He was very critically acclaimed worldwide famous orchestra director but he wanted to do more so he started to be a he studied he wanted to study to be a medical doctor, something completely different. He was interested and curious which is great prize that he's he's put his body to so much stress. So, he died of a stroke. I mean, he was pretty young, still, with a lot of success in front of him. So, I'm not saying I don't want to go dark now but this is becoming very personal, but what I want to say is, it is very important to understand that, yes you have a degree, and that's your specialty, but learning doesn't stop there. So, when I started studying biology, I was, I mean, I have zero biology, my high school biology, and I was here assistant prof, before in Georgia Tech a little bit and then mainly here. And I just asked around for books, and then I started looking at the book, and my students were feeling very stressed, because they were not working on a subject that they knew very well, but we were starting together. And that's a beautiful path. So, so my main life lesson here is enjoy the journey. Enjoy studying reading and writing. Professor Kiddies mentioned I'm not a prolific paper writer. And that's a drawback of that approach, right, because I like a lot to study. And when I write a paper is when I'm really sure that I've done my duty to study a subject and I can make an impact, even in that discipline that I studied, right. And it was hard. It was hard to study electrochemistry, for example, and that's happening in the couple to couple, last couple of years. But I went back to the book I went back to the drawing board, and I restarted everything. And I, and then I put together all the knowledge that I require. And then I started doing creative. So creativity doesn't come by itself, but it has to be inspired. And the best way to inspire creativity is study. And the other thing that I want to say, talking to people. So we are social entities. And as Italian, I might be particularly social, but I found very useful throughout the old years, talking to people. For example, my proposal got rejected. I just took the plane that was before the pandemic just took the plane went to DC, went to the desk of the program director who rejected my proposal and say why. And so we had two hours conversation. And then she said, I appreciate your research. It's just, maybe the call was not good. It was not the best for your research. Try this. It was a matter of fact, after a couple of months, I resubmitted it to another program, and it got in to the program that she was suggesting. That's because I reached out. I had a good idea, some three, four years ago, that became my project actually. And I wanted to test it out. And I call friends in Boston, and I say, can you schedule for me something in MIT? Can I can I give a lecture to the MIT PhD and faculty. It became reality. So a week later I was in Boston, and I gave a lecture and I was testing my idea with experts in synthetic biology, wrong bias group and such. And that was the beginning of all the work in synthetic biology that I did later. So it's good to keep the ideas for yourself, but it's also good to try them out. And so it's a balance. Of course, but I was trying them out with people who were not from my field, right, they were from a different field, synthetic biology.  Alessia, I think you're muted. Sorry, can you hear me now? Sorry. Yes, thanks a lot. Thanks for sharing this advice, which I, well, I share too. My next question would be, which fields, let's go to the technical details and which fields and which topics would you recommend students to study today. Okay. You, I think you are expecting me to say AI machine learning, right. No, no, no, just kidding. So, so I want to be a little bit less trendy. And I tell you this, I did what I did, not because I studied fancy topics, but because I knew the basis. And so, start with the basis. Understand physics, chemistry, and if you can also study biology, just go for it. Which doesn't mean multiple degrees but again, for example, if you're, if you're, if you're an undergrad in US or a grad student in US it's really simple to build an interdisciplinary program of studies. Some of my students are doing that. I send them around, right. And, and so try to try to build to be interdisciplinary but study the basis. For example, you're a PhD student that's standing in this field, and you feel that you don't have any chemistry background. Go take a chemistry course. Don't be afraid to do that. You're going to have something more than your peers. Even if you go out and look for a job, you will be different than others. And so, go to the basis, and then think about the details later on. It's not worthy to study. Super trendy subject just to know how things can be set up to have a machine learning algorithm going on instructing data for you. It's actually better to study the math behind. And then understand why the machine learning works. That's you can do that later. Actually, this is the advice that the other speakers that we have that the webiness gave us so start with fundamentals, fundamentals, basics are really extremely important. Professor Achilles told me we can do what we do together, because we have the basis. And, and part of the reason is in Milan. I actually attended the whole system, the old system of degree, where we had full two, three years, all chemistry, all math, physics from thermodynamics to electromagnetics. And so there were just two years where I was not thinking about telecommunication or computers. We were doing exam pen and paper, and I had books. So I was just without devices without destruction. Well, that time, the devices were a little bit different. But, but that was actually so important. And I realized that later I was kind of a big complainer at that time. Oh, we have to study all these and then I go and work on something that I don't need all this knowledge to do that. Instead, I did. I didn't I used everything I started later on. Great. Okay, I have another question. Please tell us one of the most tangible contributions that you have made in your career that hadn't direct or indirect impact on your professional even for even on your personal life that you're most proud of. Okay, I will start from what I killed this was telling us that is the, the fundamental goal of being successful in research and, you know, transforming society through research and research results. At the end, at the end of the day is the people you have around and the people you form the people you work with. And so I would say that one of my best contribution so far is to work with students. is working with students and and and helping them grow. And so students as for a kill this. When it was my turn. They were kids for me and the and I consider them my kids and I and I just spend as much time as I can. And we started together with just. I have, I, I really get my hands very dirty with them.  And you see them that they are starting to be realized even just here just at the beginning. And so I think that if you if you don't like the pay of the university professor and you don't like the benefits and you think a company is much better. And elsewhere, you do not have this feeling which is so that is, you are continuously building a family. It's like a, it's like a family factory. It's continuous building a family. And, and that's one of the and I couldn't really understand that when when Professor kid this was telling me when I was his student. And when when it came my turn, I really got this and that's what keeps me here. That's what keeps me coming to my office every day teach and do research is because of the people is because of the young people I have around. So, so that's, that's really the, the best gift of this profession, apart from the time flexibility which is great when you have kids. But the other, the bigger gift is really being able to work with young people and let them grow. Thanks a lot. And I have my last question. Is there a motto and now for is a book, a movie, a piece of art or music that describes you best or your professional path that you simply want to share with us before we close this webinar. Okay, so two things one motto and one movie. Okay, so the motto is information is a difference that makes a difference, which is from Gregory Bateson, who is one of the fathers of cybernetics, as we know it today. And it's a motto that I have in my first slide of my information theory course. And so what what it means is that every time you have a, you have something that has a variation in the reality around us. It automatically becomes information. If I can perceive it. It's information per se, and if I can perceive it is information that flows through me or in me. My motto is that when, when you make a difference, you are actually generating information in the community. And so even if you look at the bigger picture when when you make a difference and you make something that is different is when you generate impact is when you generate information, and you're just not another epsilon different from another algorithm, but you're generating something different you're generating information, which means that you are helping the scientific community. Movie, totally different, a movie that most of people ignored from years ago. And that for me it was, it last, it had a lifelong long impact. It is patch Adams with with Robin Williams. And so many says that is the worst movie or Robin Williams, whatever patch Adams himself didn't like it seems like but in any case. Why is because it's the story of a person who thought everything was lost in his life, and he didn't have a purpose. And he was, he was practically crazy, getting crazy, not having a purpose, while being while having these underneath capabilities, and he was closing this loop. Well, as a matter of fact, he came out, and he built an amazing way of doing medicine, disrupting the school where he was in and disrupting society and the way hospitals were working. And so his impact now is everywhere, even if we don't know it, actually, his impact in the healthcare is everywhere. And so that movie and that story, life story of somebody who said, Oh, I don't have any purpose in life, I lost everything. And then suddenly, suddenly realizes no I want to be a medical doctor I want to help people but I want to get people smile, I want to have less suffering. And so, for me that that is a movie that impacted my life and impacted my interpersonal relationship a lot. Wow. Yeah, I agree. I watched it and enjoyed it a lot. Thank you so much. Thank you so much for being for for going deep and sharing so much personal, but yourself. Thank you. I've learned a lot, and I really, really appreciate it. I'm sure that all attendees enjoyed it. And so Ian, I let you close this webinar before we invite you to join our next one on the 11th of May with Dr. Michelle dollar, who will speak about 60 and the metalers at the same time so Ian the floor is yours. Thank you so much, Professor Pierre Bonham. Thank you. Thank you again. Thanks a lot. Massimo. I would like to point out something and I hope still people around. After listening to my son, I want to share another view of my life of my career. It is about advisors and the students. People who know me I always try to find connections to other parts of life for example in music industry or Hollywood. Actually, I want to be an entertainment business but I became a researcher. So our football for example same thing I was playing football in the 16th that's why I have this connection to easily. I love each other in soccer in those years. So the point I try to make is the following. So if you take a look at the soccer right football. So if a trainer is super. And the team players, the football players are top, then you have top results. And when you look at all the successful teams. That's the reality and same thing in the conductor must be perfect top, the players must be top, then you have a top result right. So the point is, you can be a top advisor but if the students around you are not on your level, you will produce results but they will not be top, right or vice versa. You can be a top student really very intelligent must know I was keep saying it, and then you know your advisor is not interested or he's not into the subject or whatever. And then the students struggle and they cannot produce top results, you know, we have a lot of those cases. So the point is that if there is a match, right, the top advisor and top student, then you produce excellent results and then I'm really happy. I mentioned before, one of the best gifts of my life is having all this incredible top notch people around me, and they could somehow realize my vision and my objectives, and the results are out there right. So thank you again my Similiana. And yes, before I leave. I would like to encourage also the audience and whoever will watch us on YouTube. Submit your papers to our journal. We just started in August 2020, and it takes some time to build up for the ISI status, but I went through that, you know, the last 20 years with four journals with another. And that was a private entity, not like it is a United Nations entity and like nonprofit plus, you know, it's an umbrella organization so it's a very prestigious organization, and I hope through that attraction. I would like to have more papers and hopefully reach the ISI status that will be one of my hopefully last achievements hopefully before I leave this world. So thank you. I'm healthy. I'm still healthy so don't say oh he's leaving. There's still a lot of time we can do more than that. Okay. Thank you again. Thank you so much. Thank you to the speaker. Bye bye. Thank you. Thank you. Thank you. Have a great day.