 Okay, so it's it's two o'clock. Let me introduce the session. So welcome everyone. We are in the very last day of our meeting. We have three talks now, three 15-minute talks. I will try to be strict with the timing. So if you agree, we can start this one past two. So let me introduce you, Neda, I won't try to produce this. I'm sorry, from the Institute of Foreign Retirement Science. So the stage is yours, please. Go ahead. Yeah, yeah. So hello everybody. My name is Neda, Rafael Hosseini, and I would like to talk for you about the thermal conductivity of the cell membrane. So all of us are aware of the importance of temperature in chemical and biochemical reactions and in biological processes as well. But we might have thought less about the thermal gradients or heat transport at the cellular level. So I will start with a very nice application of this field. If you assume that we want to have a selective therapeutic method for cancer therapy, you can refer to this very nice paper, which is a recent experimental paper in this field. And the picture here at the bottom show you some nanoparticles. Actually, they are called up converting nanoparticles that produce heat after being radiated by an external source. So these nanoparticles are dispersed. Actually, they are covered by a lipid bilayer, and they are dispersed in water. And there is a thermocouple in the water that measure the temperature of the water and solvent environment. So by giving radiation with nanoparticles, they produce heat, and we expect that the heat transports through the bilayer and be transported to the water, to the environment. And this way they can measure the thermal conductivity of the lipid bilayer. Actually, it's interesting that they found that thermal conductivity of the bilayer is a function of power density of the source. And at a specific threshold, they see the lowest possible thermal conductivity of the lipid bilayer, and it means that lipid bilayer acts as a thermal barrier at this threshold. But after that, it shows a higher thermal conductivity and can transport heat very well. So if you want to look from an application point of view, we can say that without having any understanding of the behavior of membrane in thermal transport, we cannot get a high efficiency in this method. So with this introduction, I will jump into the computational studies that are done before, and actually one of them is done by Muller-Paleth. And actually it's the base of the method that we use in our study. Muller have devised an algorithm, actually a reverse non-equilibrium molecular dynamics algorithm to compute thermal conductivity through a biological membrane. And as you can see in the right-hand side, this is the simulation box. There are two lipid bilayers parallel to each other, and there are water molecules between the bilayers and at the top and bottom of the box. So I will explain the Muller algorithm in the next few slides in more details, but I just want to mention some of the findings of this work that they calculated the thermal conductivity profile very locally, and this is what someone cannot do in experiment on only in computation. It's possible and they found very low drops in thermal conductivity profile at the interface between two lipids and also between water and lipid head groups. And I will come back to this in the next few slides. Okay, I used a method that I explained for you, a reverse non-equilibrium molecular dynamics, and why it is called reverse, because actually in experiment we have a cause and a effect, an effect. So the cause is usually a temperature gradient in thermal conductivity field. We have a temperature gradient and as a result of that gradient, we have a heat flux throughout the system, but here we do the reverse. We impose a heat flux to the system and we calculate or actually obtain a temperature gradient that let us calculate thermal conductivity using the Fourier formula. So here you can see our simulation box in the right hand side and I can explain for you the Muller-Palette algorithm in detail. Actually we have two membranes parallel to each other as I explained to you, and we divided the whole box in the z direction into several boxes or several slabs. We did it in 100 slabs and the middle slab or middle layer of our box will be the hot region and the two upper and lower parts will be the cold region. So there is a reason to do like this because of the parity boundary condition in the direction of z axis. So we create an energy exchange this way. First we explore in the middle layer the coldest particles and we try to exchange their velocity with the velocity of the highest particles in the layers in the upper or lower parts of the box. So this way we create a non-physical heat flow from the cold to the hot region because we are actually exchanging energy in this way which is not usual in nature. But after a while we reach to a steady state in which a physical heat flow will be created and be formed through the membrane and the rate of physical and non-physical heat flows becomes somehow equal. So you can see in the bottom plot that after a while, after starting our NEMD algorithm, we reach to this steady state. Actually the exchange rate that we use here is 0.1 picosecond and we use lambs molecular dynamics package to do our simulations. Here you can see that the density... Excuse me, may I ask a question? Yes, sure. In the previous slide, in the direction of the temperature gradient do you use periodic boundary condition here? In the direction of temperature gradient, you mean in the z direction? Yes. Yes, yes. Okay. So then the hot end of the box is periodically bound to the cold end of the box. Am I right? Can you ask again? So if the particle exits through the boundary which is hot, re-enter the box from the boundary which is cold, am I right? The boundary of the box? Actually I don't get your question, Val. Well, okay. I assume that it is periodic as you tell. Maybe this looks a quite technical question. You could go ahead and maybe ask later. Yeah, sure. Yeah, okay. Here in this figure you see the density profiles of the water and lipid. I show the density of water with the dashed blue line and the density of lipid with the yellow line and through the z direction. So here is the slab number and here is the density. After imposing the heat flux to the system and creation of these two heat flows, we got a temperature profile like this which shows that if we compare temperature profile with density profile, it provides us with this information that we are always above the phase transition temperature of the lipids. That is 314 Kelvin. Okay. We looked into different model membranes specifically for membranes. I had a question for you. The phase transition temperature of the lipid you said is 314. Exactly. From the APP. From experiments or from force field? Okay, it's a very good question. But actually it is mentioned in the literature in computational works. So I assume that it's from computational. Okay, because the two may not be the same. Yeah. Okay. They don't match. Exactly. Okay. And which force field did you use? We used Charmin. Charmin. And for water also Charmin? SBE. Yeah, SBE, SBCE. SBE. Exactly. So we looked at four different concentrations of cholesterol in membrane because we know that cholesterol has a very significant role in the membrane composition and it was and has not been investigated so far. We also know that thermal conductivity is a structure dependent property. So we guess that we might have found an interesting relation between these inclusion of cholesterol into the membrane and change in the membrane structure and as a result in membrane thermal conductivity. So the model membrane we use here is DPPC lipid molecules with different concentrations of cholesterol and in all the membranes we have the 36 lipid molecules in each leaflet. So our results suggest that there is a decrease in the area per lipid of the of the lipid bilayer as a function of cholesterol concentration and this is somehow in correlation with the increase in the thermal conductivity. So yeah, so a decrease in area per lipid and an increase in thermal conductivity as a function of cholesterol concentration. And this is interesting for us because we also refer to the previous studies and we saw this similar result in this paper previously. Actually they investigated different archaeal lipid membranes and they saw that in the type of archaea that has a lower area per lipid the thermal conductivity is higher at two different temperatures actually. And what actually I understood from this seminar two days ago from Ali Rajapur's presentation, I understood that they looked at the density of water around some nanoparticles and they saw an increase in thermal conductivity there. They have a higher concentration of water, so a higher density of water. And we also know from the literature that inclusion of cholesterol has an effect on the hydration of the lipid head groups and the more the concentration of cholesterol in membrane the more hydrated the lipid head groups. So actually it came to our mind that okay if Mueller suggests that there is a very high drop in thermal conductivity in the interface of water lipid, so we can see this in our simulation and we can test if we get a higher density of water near the lipid head groups. And so this way we can somehow explain an increase in the thermal conductivity profile. If you want to compare... Never, you should finish in one minute. Okay, okay I'm almost finished. So I just refer to this previous study to compare our results which is an agreement and I want to tell you about the important effect of asymmetry of cholesterol in membrane. And actually the way we study this is that we impose the heat flux in two directions, in the forward and backward direction and we obtained almost the same results. So the difference was not significant, it was not statistically significant, but in the case that we include protein, amyloid precursor protein in the membrane and we obtained this forward and backward thermal conductivity coefficients. We saw a statistically significant difference in both systems and this guide asked that maybe this protein or this protein membrane mixture can be a thermal rectifier with the thermal rectification factor calculated this way. So the thermal rectification factor that we obtained here is in agreement with the values obtained before in the similar studies. So at the end I would like to show my gratitude to my PhD supervisor, Professor Isaij Tahadi. I'm also thankful to Ali Hassan Ali for his very good advices through the project and also because he's our collaborator in another project. I'm thankful to the staff of Instinct for Research and Fundamental Sciences in Iran that I did my project. We are not seeing again your screen. Your screen was locked in the results slide and also your time is over. Okay, it's just my last slide. But we are not seeing your slide. Sorry. Okay, can I share it again? Okay, I just try to do it again. Is it okay? Yeah. Okay. So yeah, I was in my last slide which is, yeah, that I want to thank ICTP for hosting me during my last visit. They are the organization in Germany that grants me with a one-year research grant and the University of Duisburg-Essen in which I'm doing my college project. Thank you. Okay, thank you very much. Very nice talk. I'm sorry. There is no time for