15:03:34 So, thanks the organizers for for putting together this interesting program.
15:03:41 I was there last week and had a great time. There is hard to leave. And I want to thank it for making is happening in person so
15:03:52 it's really feels like a little oasis during this hard time. So, even though I don't really work on microbial communities when I look at the title of the workshop.
15:04:04 I hope to tell you about some new co evolutionary processes that happen in the immune system. And it turns out to be essential for understanding how our immune system and the meeting viruses, co evolve.
15:04:21 So, I think.
15:04:30 So I think this past year, we are reminded that we're sharing this planet with all sorts of pathogens and especially viruses.
15:05:12 so here I'm showing the philosophy.
15:05:16 For many different human viruses. So on the left, we're saying, HIV and HCV that are showing this really divergent patterns, so each individual branch is circulating string that is specific.
15:05:38 So HCV is showing similar patterns as HIV. One reason is they share similar tactics in invading house, which I will explain in detail in a minute. So on the left on the right, we're seeing is influenza, philosophy, which shows very distinct patterns from
15:05:58 HIV hc morphology, so are seeing this characteristic single Trump
15:06:07 morphology showing that even if he's continuously evolving over the years. No, but it's maintains a pretty limited diversity at any given time.
15:06:20 And of course for other subtypes so he finds his occasional renting process, which actually indicates immune system is having a certain range of productivity, which I will also explain in detail so this is from Richard and or his work.
15:06:40 And so my point of showing this is to, and in between then guidance Zika is somewhere in between these two extremes. So this is showing the difference or is variation in morality of it is reflecting interaction of wires with our immune system.
15:07:01 So they have different infection mechanism have different time scales of infection, for example, HIV HCV are happening you know, single person. And, but for influence or the transmission is so efficient, so you can roughly consider a collective humanity
15:07:18 of the globe. So the people around the world are transmitting this virus collectively responsible.
15:07:26 And so my point is the immune system is one of the major driving forces of parents and. And so in order to enter ascend co evolution, especially when the evolution is happening on similar times goes as the new response, we have to consider both sides
15:07:47 simultaneously.
15:07:51 So did that to community.
15:07:53 So there are two branches. As you may know, one is T cell the others, ESL so T cells are in charge of discriminating self from themselves so that it's removing the foreign substance without harming ourselves tissues.
15:08:10 And, um, but the B cell branch is evolving rapidly evolving branch, which can keep up with invading viruses, even as it evolves.
15:08:23 So,
15:08:23 one of the amount of first experiment that really tracks, both evolving branches, both the adaptive community here to be cell French, and also the viral evolution in one person who's infected by HIV by tracking this person for several years.
15:08:45 So this is showing.
15:08:48 So the sequence local, which is sharing the relative frequency of different amino acid at the sites that are considered to be targeted by the antibodies.
15:08:57 So it's on the envelope, protein, which is what the virus is using to find to the surface receptor of cell alerting infected. In this case, is invading our immune cells, which is cd 40 cells.
15:09:16 And so here you can show you can see that over time. the virus is diversifying, in particular, see, in this site. This new polymorphous and will emerge over a long period of time so this is weeks and say this is in total several years.
15:09:34 So as the virus diversify the antibodies evolve. what evidence is, it can it can broaden its coverage, meaning. At the beginning, the virus, the antibody can only neutralize the virus that's first infecting it, the founder arrives.
15:09:53 But over time, as the end of it was this unusual ization problems, so it can neutralize at relegates virus, meaning.
15:10:13 So, this indicates that the antibodies are talking to him some shared features across different viral streams, so that it can react to variants that are not trained up.
15:10:17 Some virus string that has an encounter, or hasn't directly evolved against, but it can now recognize those not encountered by broadening its coverage.
15:10:31 So, this is
15:10:34 the truth tree of the, of the antibodies. This is the unmuted armor sister. You can see it's heavily branching and there's evidence that color interference is playing a significant role at least sometimes long time scales.
15:10:49 So here, after it is several years of evolution is particular individual develops, some antibodies called Robin neutralizing, or it's so these antibodies, each one of them can neutralize almost all the major certainly constraints in the globe.
15:11:09 So it's really by seeing some particular example of the virus inside this person is common features, that's shared by things it. Variants with hadn't seen.
15:11:23 So that's in the sense a generalist that's learning is common feature through.
15:11:32 And that's what interests us.
15:11:35 And on the bottom at the bottom is showing the Katie, so does the association coefficient, which indicates binding affinity of the involved into buddy smaller Katie means from the binding.
15:11:49 So the rather color means more potent.
15:11:54 So you can see this is a new first trace of this already. Yes.
15:12:01 for the ancestor on as opposed what's important here is to show so the left is an alternate hosts virus and the right is a hetero.
15:12:13 You can see over time, only this Venus.
15:12:18 Robin. Robin utilize antibodies can gang reactivity to the header all of this, without losing in finance at to optimize
15:12:33 for this is the observation. And over the past decade, and there are more discoveries of different classes, brought antibodies that are targeting different one of those sites of the wires that are relatively concerned because of functional fitness reasons
15:12:52 that virus needs to maintain those sites for infection,
15:12:58 And what was puzzling was surprising is, as I said earlier, HIV and HCV has this very similar morphology of allotting. However, the consequence, or the outcome of this call evolution can be very different.
15:13:16 So here I'm showing this from the same experiment, as the last point, there are tracking this HIV patient.
15:13:24 And here the green line is showing the viral load, which is the number of RNA copies in the plasma, and over this time to see this characteristic also military trajectories, which indicates predator prey type of dynamics that the antibodies are chasing
15:13:46 after the virus, without success in catching it. So the virus is staying a step ahead, all the time.
15:13:54 This is
15:13:58 what will contrast, if see me. It has similar strategy to you, evading your response is employed also has very high genetic diversity, and it can mask is vulnerable sites using variable structures.
15:14:15 And also, it kind of formed a reservoir of wires.
15:14:20 However, in this to humans, we track these shows on Pena's clearance of each CV wires, without any treatments.
15:14:30 So, it can see this person. There's a monotonic decline of viral load, and eventually is cleared. I brought antibodies, so they do functional essays to show the rest of the plasma and also monoclonal antibodies from the past, and in the other person.
15:14:53 Eventually, also clear survives. However, there's a significant job very steep job in a viral load.
15:15:01 And after almost undetectable level.
15:15:06 There is a rebound of the viral load.
15:15:10 Eventually, after some inimical who says the inner eventually managed.
15:15:16 So these shows, and also you notice the time scale is relatively short that year so clearance can spontaneous.
15:15:28 But this.
15:15:29 At least it shows an example that is highly news for viruses can be cool.
15:15:37 So we want to understand what's the difference. What factors can determine divergent evolutionary outcome, more general sense.
15:15:48 And, theoretically we need to provide a framework to describe this revolution, where evolution and ecological processes happen pretty much similar times ago.
15:16:00 So we cannot cover them. And also we cannot separate the time scale between the wires, and in the system so they have to be treated.
15:16:12 This is the motivating.
15:16:14 No, no.
15:16:16 Now I need to tell you a bit about how the sales involved.
15:16:20 So it's actually a pretty remarkable structure, a very hierarchical physical structure that supports this evolutionary process. So, on the largest skilled.
15:16:35 This is showing one who can have hundreds of miles across our body.
15:16:41 Within each link knows, you see the status clusters of cells.
15:16:46 So the red indicates antigen presenting so this case follicular dendritic cell that our residents in the lymph node. So they formed this dense cell networks that presents antigen on their surface.
15:17:03 And this siren which colors indicate the B cells.
15:17:08 So this structure is called terminal center is also know centerpiece else today.
15:17:16 In this micro-environment evil.
15:17:19 And there are some some green asking between those are helper T cells that are shared mountain This is from Los Angeles.
15:17:30 They have their own population.
15:17:32 So, if we zoom in, into one of these demo centers, is a pretty dense environment as you see, and so bonus density gradient and chemical reagent, we'll drive movement of the cells, between two films.
15:18:05 One is called that toxin, the others lights on.
15:18:05 The reason is the darker mature darker, because of sensor new in this stone Where is our replication.
15:18:05 This.
15:18:07 This is essence image of again the B cell and.
15:18:14 So the B cells, once they are activated by the antigen the encounter.
15:18:21 They will form is micro-environment by rapid evasion and during the division they make errors, which is called somatic hypermutation.
15:18:33 So, the reason, these are evolution is so fast, so that we can make useful flu antibody within a week or so, is because the hypermutation is happening at a rate, about a million times higher than the normal tissue cells.
15:18:52 And they make many different variants. Through this error prone process, and then then migrate to the light zone, where they encounter antigen presenting on the surface of the presenting itself.
15:19:06 So this is actually an interesting process in itself, because the cell, cell contact will form interesting patterns that will vary as a cell go through different developmental stages, and then sell this ethos will actually use forces to extract antigen
15:19:26 and their success in extracting antigen will determine their success rate in acquiring additional signal for survival, which is provided by this helper T cells.
15:19:42 So it will polarize to the B cell that presents more antigen on the surface. So these are will internalize the antigen processes and present in small peptides and HTC complexes surface which T cell can read to determine which is always more successful
15:20:02 in recognizing a graphic question. Yes. So what is the mechanism by which the mutation rate gets elevated by six orders of magnitude or during this hyper hyper.
15:20:18 It's, it's, it's due to an enzyme called a ID. So it's
15:20:25 that process, yeah that that's what triggers this very fast process and that enzyme actually had some bias in their binding site. So actually the gene antibody encoding gene will have some variability in their mutation rate at different sites.
15:20:46 So that's a rough, rough number.
15:20:51 But it's due to this.
15:20:56 So it's only working in this micro environment.
15:21:09 So, if the, if the diesel, I successful in acquiring this helper T cell signal, it will have two possibilities.
15:21:20 Either it will go to the next cycle to evolve for this, or it will exit the terminal center as memories or.
15:21:41 So we're not here about how differentiation password.
15:21:41 But what's clear is the outcome of this cyclic action of mutation and selection will generate increasingly higher affinity finders, and they will form a new memory, which allows the vaccines to work.
15:21:58 So when we see similar viral.
15:22:16 So, these, these are receptors in this memory and bound form.
15:22:25 This is the process.
15:22:31 And, yeah, by the way, a important feature of, so all these B cells we have 10 billions of cells and T cells, forming in your repertoire, is the collection of new receptors that are distinct, so they are covering the antigenic space, and an essential
15:22:52 property of the new receptor, or even a repertoire is cross reactivity. Which means a single receptor react to similar versions modified versions of the same antigen, and vice versa.
15:23:06 the same antigen can be recognized by different users.
15:23:11 Some By the way, you can see.
15:23:15 And this is important in the sense that different people will have different you know repertoires can cover the same diversity of common pathogens, different ways.
15:23:33 Yeah, this is, this is showing from the molecular binding to forming the cell, cell interface, and then competition of the cell within our population, and eventually a collection of populations that are potentially connected by migration.
15:23:50 We all together, provide an ensemble response that fights meters. So it's a sense of physical hierarchies of course this propagation of information from finding have two systems.
15:24:18 And this populations have certain mobility bolster makeup and diversity of receptions, because the, the naive ethos, which are inexperienced within teacher will form the initial diversity.
15:24:24 So that's me know, is fun by the vdj recombination process up to 18 segments.
15:24:31 Was this coming authorial diversity will produce this naive repertoire. And once evolution happens through encounter of antigen.
15:24:42 He repertoire, we will organize.
15:24:45 As a result, due to this situation.
15:24:51 It's clear. This is the image. The ballad. Now to understand why the model.
15:25:03 So, so now it's model. We're trying to understand the CO evolution process. So the other the GC demo center reaction has been studied.
15:25:18 See, 15 years. So is this authentic maturation process was first discovered by horizon. So, 1964. And so this is basically this basic process is cyclic action of mutations election was discovered.
15:25:38 And later worked at the beginning by Alan, and Auster has a very important conceptual made a very important conceptual advance in a sense that they are presenting a way to think about the antigenic space and a B cell coverage, which I will tell you more
15:26:01 about is called the shapes days where cross activity is modeled by the coverage or the size of the activation ball. Each receptor is at a center, and the range of the ball is reflecting the antigenic space, volume, and from experimental measurements of
15:26:22 diversity. One can back up estimate the range of cross activity that's needed, you know there to cover that was.
15:26:32 So here, so that's his well study for a given antigen is a thinking maturation is pretty clear major steps.
15:26:45 Here we need to introduce this exclusive interaction between the wires and the result.
15:26:52 So you know, do that.
15:26:54 So in order to do that, we introduce compartment which contains the B cell, the antibodies secreting cells right the plasma cells, because those are the cells that go into our circulation to scan or to monitor invaders.
15:27:10 And they will impose selection pressure on the antigen that are also in the circulation.
15:27:18 And the antigens that are managed that managed to escaped in your recognition, or they just means the matching plasma cells will survive and grow number.
15:27:30 And some of them will be captured by the new cell and predict cells and transport to the general centers were presented as a sub sample of the plasma.
15:27:44 And then that feels your motivation, and this
15:27:49 is the differentiation of the
15:27:56 circle and mathematically, We can think of this as a binding ingredient in a sense that, because the binding happens really on a second.
15:28:04 And we're interested in how the quantity and composition of antigen will affect the cells.
15:28:33 And this is the set of antigen encountered by a visa.
15:28:40 So why is empty.
15:28:43 This is the probability of a visa to successfully engage and internalize antigen.
15:28:50 And therefore, is a chance to read the competition, have to help, and survive.
15:28:58 And personally, the viral, new terms is selected by the antibodies in circulation, so this x is the antibody subset, that a particular virus virus stream.
15:29:12 Why is he counting and see now is the concentration.
15:29:20 So this basically we're assuming this binding equilibrium captures the major money narrative. Translate translates binding up to the probability of survival.
15:29:33 And it's a reciprocal interaction so this is the probability for virus to be recognized. So one minus is survival.
15:29:45 And so.
15:29:49 So through this, you can see the interaction will both affect the absolute and the relative witness of individual Bible streams.
15:30:01 So that's his concentrations where ecological.
15:30:12 The other important and interesting phenomenology that we try to capture is the binding is really a physical process. So this is showing this is a viral spite of HIV virus particle.
15:30:27 So if you look at the finding footprints. So this is like your afternoon graph of the binding interface, and the yellow up offline is marking the honorable region, meaning less mutable region on the wireless.
15:30:57 And the colors region is showing finding meaning, you have to contact contact between the viral coating and the Beast.
15:31:09 So you can see over time. This is showing the breath is increasing over time.
15:31:16 yourself Kings Cross, your time is finding footprint is shift is shifted over time as well.
15:31:23 So its first. It shows the potential binding surface is greater than the actual foot.
15:31:33 So in other words, post or receptor and the virus are evolving, you know, larger states space than where the contact and therefore selection direct.
15:31:44 And also, yeah, this is, this shift is showing that there is a flexibility in the recognition process actually physically molecules can search for the best docking site where it optimizes the binding of.
15:32:01 So this is pretty common in medical recognition in general.
15:32:06 So I wonder how does this molecular level.
15:32:10 Physical dynamics and potentially play a role, you know, affecting the collaboration. Now, it turns out to be really interesting and.
15:32:24 So now we try to connect the physical space, binding process to the recommendation space, because we have to have representation to to describe how well the B cells are recognizing the Lars, and to define our fitness function is tennis space.
15:32:49 But this is actually the intuition proposed by Alan and also, I think, 40 years ago, where it introduced this shape space, which basically the distance in the space represents binding quality.
15:33:19 affinity larger separation or match in complementarity. So here we are inheriting this nice property where this distance in Euclidean space can represent affinity.
15:33:41 map the real space in terms of the mutability of the targets into the movement, or the speed in this phenotypic recognitions things like that I mean.
15:33:52 So here again, it's a virus, where antibody combined regions. And this color is showing all conserved is rescue groups are.
15:34:03 You can see there are conserved spots, meaning is very costly mutate those sites.
15:34:10 In terms of the viral fitness.
15:34:12 So, by the virus protects those and surf sites by first surrounding it with variable structures.
15:34:26 And second, this is now showing that they use the variable structure to log or partially mask, because of sites, so that is very poorly accessible for the antibody.
15:34:40 So, we translate this into the fusion past tense, or mutation step size in this female to the expense. So there will be
15:34:56 fast versus slow dimensions. So the first dimension is where the virus can use it in new types, but also the B cells can easily access those sites as well because they are more exposed and conserve dimension is where the virus can hardly, you take because
15:35:15 it's costing the V cells kind of account also had a hard time chasing because. is less accessible. The kinds of targets are less.
15:35:27 There is this asymmetry or I stood up in this chaotic space where
15:35:36 this is.
15:35:40 So in this case, we say in extreme strain the virus into the variable plane without allowing motion in concert dimensions, but this is, this can be relaxed and still get clearance long without is.
15:35:58 So is that is the total dimensionality of your phenotype space, and a parameter you just fix that in the model, how do you decide on a good question. So there's asked me from password, especially is where the estimate is between two and 10 dimensions
15:36:16 are these kind of shapes this, although there I think that the number of dimension represents the properties that time bio chemical properties relevance for finding.
15:36:26 So here we assume yeah we use it can use smaller or larger number, within that range. But the important thing here is we are having this finding suspects.
15:36:40 So, so by that I mean, this is one of the binary subspace where the actual binding footprint is ok.
15:36:49 But the potential binding surface is much larger, which means the overall dimension of the of this recognition space is great, which was that eight, choose the subs subspace 3d this case for it.
15:37:08 So that's very important because this, there's a physical optimization process that I mentioned that the cell can search the cell receptor can search for the best complementarity optimize across his potential by nIESR, find the optimal subspace where
15:37:28 it has highest overall
15:37:34 is subspace description allows us to incorporate flexibility of finding
15:37:41 this space naturally has this episode phases because where the South locate will affect the fitness effect.
15:37:56 Yeah, this is just sick Matt schematically is showing what I mean by this is a cough message.
15:38:04 So at the beginning, the antibodies are easily targeting the variable parts which are exposed, but through how evolution is footprint and shift to mix region.
15:38:21 And eventually, hopefully, it can locate conserved region.
15:38:26 So are showing that within a certain range is going to happen.
15:38:31 Shift open path.
15:38:47 And, mathematically, we just say the actual affinities determined by this maximization of identity within possible substance.
15:38:47 So, and these the convention.
15:38:53 And the consequence of this footprint shift is directly affecting the distribution of fitness effects of mutations.
15:39:01 So the yellow is the distribution for affinity change with limitation, without
15:39:13 meaning the target of the receptor is what hits recognizes as, as a naive cell will stay the same.
15:39:22 The blue is showing the outcome of the can see there are two effects. One is if the mutation.
15:39:34 The detrimental mutation that the terrorist mutation happens within the context, then the B cell can switch to a different time to buffer that that he cares.
15:39:44 So there are some shorter tail have fewer and smaller size of them.
15:39:52 On the other hand, it can catch beneficial beneficial mutations outside of her in contact by shifting to that substance that will create a longer tails have more abundant and efficient imitations of larger size.
15:40:09 So together This will provide a mechanism to accelerate new adaptation deputation, especially along the snow dimensions. The soul dimension is where there is a promise that will be self involved right directly.
15:40:29 So here I'm showing
15:40:32 this model. And this evolution.
15:40:36 Circle, was this footprint shift and create a divergent evolutionary outcome, depending on key partners we try to attend. But I mean first show us in a sub defining subspace see the blue our class Marcel read or virus
15:40:59 can see at the beginning of the cell we assume it has a coral in geometry, the founder Iris is located at the origin.
15:41:07 So over time you can see the virus can escape, because in this case,
15:41:15 predominantly variable damage fascinations, so the viruses can easily escape in various directions and the B cells are chasing defamation distribution.
15:41:29 And you'll see this as a literary get three for viral load.
15:41:33 This is monotonic because this is humility, ask myself.
15:41:40 This is the so called persistence space where these are always staying behind.
15:41:51 So this is the clearance.
15:41:57 So clearance happens if the B cells managed to converge to the center because the anti viruses are not able to escape.
15:42:11 So the engine is this case, let's see.
15:42:18 The cell concentration will increase. First of all, and eventually it concentrate on it.
15:42:28 So that's how they can catch the virus.
15:42:34 And finally, this rebound phase, what we saw in this HCV trajectory patients.
15:42:42 So this takes a larger initial biodiversity.
15:42:48 See, there is a consumption of on the outside, inside.
15:43:00 But some escape string will later.
15:43:05 Expand after going through the autumn.
15:43:09 until eventually been asked, and drive the wires.
15:43:18 So seems like this model can create is observed outcomes.
15:43:24 So now the question is what are the important parameters that lead to.
15:43:33 So, this is a little busy but you can first look at this is showing the bio load, the antigen population, together with the narrow was as broad, is our response.
15:43:45 So the broad versus narrow is the first person's way to defy different these old images. So we say, based on the target the conservation level of target, we can call it broad versus narrow.
15:44:01 So the broad and his buddies have at least one subspace, which is, sir.
15:44:09 So if I see, so that's why, or very low antigen conservation, there won't be true, Rod antibodies, and you'll see this.
15:44:24 Also,
15:44:24 in between.
15:44:26 Here you can see a transition from clearance to rebound.
15:44:31 As the initial antigen diversity increments. So by initial I mean, the time when these are response starts.
15:44:40 So, after the viral infection there could be a delay. So during the lag, then the viruses can diversify.
15:44:47 So this is the diversity, when the ESL first respond. First, now you yourself.
15:44:56 So you can see here for the clearance. This dashed line is the faster, but specific response because in the past I mentioned is so rapidly chase the virus
15:45:11 and reduce the viral load.
15:45:15 But then it false, and the peak in the narrow response with last virus.
15:45:23 But that's where the broad response stars rise because he now has a selective advantage, because he can respond to one of diverse things, and the specific ones.
15:45:33 So, this will possibly lead to clearings.
15:45:38 But you initial antigen diversity is higher, cause a critical value, you can see is the bottleneck, followed by the rebound. Until eventually.
15:45:49 So what happens, can be understood through the antigen dynamics.
15:45:55 So it really acts like a resource for yourself.
15:45:59 So, here again, the dash line is the specific antibodies to see it again rise quickly, even more quickly because now the diverse entity makes more likely more results, respond.
15:46:15 So, the peak, because escape.
15:46:20 So it's false.
15:46:21 And now because the antigen is already so rare so it's very scars to even activate the most interaction.
15:46:30 See this plateau in the broad response.
15:46:34 Until now the virus is growing unchecked, because the GC reaction basically stop until the wires, again rise and diversifies, then the broad response now, and pick up, and eventually develops dress cleared wires.
15:46:51 So really this rebound face is reflecting is ecological feedback, because the antigen abundance in this case will modulate interaction between the broad, the narrow and the broad response.
15:47:07 And depending on the viral load the resource attendance at the initiation of the proper response will lead to different.
15:47:17 You don't want autonomy, or.
15:47:29 And this is showing was the why this is important.
15:47:35 So without that.
15:47:37 There's no this broad, because there's even if it starts targeting the concert sites can be easily outcompete it is more specific ones because of a slower accumulation of
15:47:51 changes.
15:47:53 But with the footprint shift.
15:47:56 The system can maintain his precursors.
15:48:05 So, be more precise with pathway I will show next slide is, even if it's initially out competed.
15:48:08 You can regenerate through this flexible recognition.
15:48:14 Without a footprint shift is persistence face is the only outcome, no matter where we are on that.
15:48:32 This is showing what are the past.
15:48:35 So the basic message here is, is flexibility in will allow a plastic, you know.
15:48:44 By that I mean, if I look at the cells that are starting was a certain type. And so this is pointing from the germline type to the current time.
15:48:59 So for example the blue bar is what stays stays targeting concert regions and end to be rule is showing some specific strings specific requests or a later switch into abroad.
15:49:19 So you can see this is a different initial antigen diversity. The show similar patterns. So the blue first decrease, and then replaced by green.
15:49:29 So this is showing the pathway that the broad with clones switch, first to a narrow one, so that he can survive the short term competition, which is very severe.
15:49:44 Later, when it's evolved in enough. Breath in the concert dimension. so dimensions, you can switch back into those subspace. And then take over.
15:50:06 So the antigen diversity will modulate reality attendance for half the ones that stay broad will be less abundant than this one. Taking advantage identity.
15:50:25 And this is showing the timing of the switch.
15:50:30 So this diversity advantage also affects how quickly the cell will switch.
15:50:37 Here I'm showing the visa affinity for the holes findings recognitions versus only within the conserve suspects.
15:50:47 So I can see
15:50:50 if the visa managed to accumulate beneficial mutation conserve substance, where it barely it's hardly imposed any selection pressure on the virus.
15:51:05 Then his appearance a neutral mutation, and moments for the current environment will later become substantially beneficial if the cell can switch.
15:51:14 Because right it's a symmetric. It's improves his own quality without imposing selection pressure, pressure on a
15:51:24 sign that says is conditional.
15:51:27 And depending on the initial antigen diversity, you can have different efficiency of accumulating such potential patients.
15:51:38 So higher and the diversity will demand.
15:51:44 Faster relation.
15:51:45 because the competition from this specific strings strong.
15:51:53 But the speed is common comes at a cost of what it is you have to take time to accumulate more rest conferring mutations. The overall and see, there's a trade off if you take longer time to evolve higher quality and higher abundance of broadens funds.
15:52:12 So maybe some, some intermediate diversity will be balanced, so you have the time to clearance without much compromise.
15:52:28 And I finally, because we saw this spatial segregation of yourself operations.
15:52:37 We can ask, how does that affect the efficiency here, lawyers, so if there is a fixed target is.
15:53:06 The reason is, In this case, if you look at retail distribution function.
15:53:07 There's a conserved. Fighting aside. Then we see more connected publishing a lot of single population will do better in terms of speed in population.
15:53:12 So the center is the origin where the founder wires, this, this is showing different subdivision level, how to be self, distribute, so this is for a single large population is strongly faster impulse and trading to listen.
15:53:33 There is a higher efficiency in exploiting the good solution because it doesn't change the target doesn't change.
15:53:44 If the targets are moving, meaning the variable dimensions are dominant.
15:53:50 Then the Sufi vision of the population into smaller ones can slow down the escape of the wires.
15:53:57 So the reason is the constraint is spatial, there's this interesting duality, the spatial constraint will lead to higher mobility in the space space secret space,
15:54:13 because it allows wider exploration of potential solutions and is more likely to find lives.
15:54:29 And this hasn't consider migration, which could be interesting, and there is some indication that the memory cells can transport between GMOs and, and, and also, we see it is heterogeneity population size.
15:54:47 And also, we see it is heterogeneity population size. So potentially there's some way that the system balance to prosper nice one is to converge to the good solution, it is power.
15:54:57 And also maintain the mobility to catch a moving target.
15:55:05 So this work is done by my graduate student union, who just earned his PhD, a couple months ago.
15:55:14 So I think this work basically showing by connecting the physical dynamics, on molecular scale to the equal evolutionary process on population scale, we can gain some intuition about how antigen mediates interaction between different response codes, this
15:55:34 In this case, a fast and specific response was slow, but broad response. So the engine will be the mediator, determine the timing and advocacy responsible.
15:55:48 And also, this footprint ships idea is showing that evolving larger space space, and where selection directly.
15:56:06 Could be a way to accelerate adaptation, or open new pathways that are not possible. If
15:56:08 the whole binding. It was returned, all the dimensions contribute to buy me.
15:56:16 So that's those are the main message. And there's another work.
15:56:23 I did with students on down which we were we use analytical coach, you know, lower dimensional space to show the cross reactivity is a symmetric between the wires and the PCL.
15:56:38 It can also generate very diverse outcomes, and they symmetry is coming from the fact that activation of the new cells and removal of the antigen may not be requiring the same condition, because the activation of immune cell expands on this active probing
15:56:59 process. But, remove all of the antigen can happen in the solution, which is some equity the environment.
15:57:09 So,
15:57:13 so there are other things, we would thumb.
15:57:17 But I think in contact with this program.
15:57:20 There are two things. I have some connections. So one is. So these antibodies are these viruses, they can have many competing.
15:57:32 So here is showing the discovered broad antibodies targeting different points on the same way.
15:57:41 And people have done is a lot of work to do competition sa to see how antibodies targeting different attitudes, interact.
15:57:52 Meaning, if we incubate with one type of antibody, and then introduce another, how does the finding efficiency of the second is affected by the presence.
15:58:13 That noise, red. But interestingly the blue, and because synergy. So we always think antibodies are interfering and competing with each other, but this is showing there's a possibility that there's cooperation between the challenges.
15:58:17 First, so that's what creates this interaction table, interaction matrix, Red indicates competition.
15:58:29 There's also some experiment, it's showing that, especially the CO evolution context.
15:58:35 One disadvantage, and drive the wires into a state that is beneficial to the other image to gain breath.
15:58:45 So this so called immuno dominance hierarchy, meaning what are the target size is not fully understood. So, if we can think of this from the integer as a resource perspective.
15:59:01 And there's multiple episode.
15:59:04 In this sense, means different ways to use that resource, can we predict how the dominance hierarchy is effective, and the weather with antigen dynamics, can we have different regimes like multiple stable solutions, was this like solutions.
15:59:22 So, and those potentially.
15:59:29 The other thing is, we were looking at this evolution of journalists, you know, broader sentence is like an evolutionary learning. When the environment changes on timescale comparable to the intrinsic dynamics of the system.
15:59:45 We can try the system to regions that are not explored naturally. For instance this journalist regions, which are very rare in natural cases that's why most of us don't develop broad antibodies, which is very desired, or example ones are wanting universal
16:00:07 vaccine that's working for everyone that can use this broad response. So we want to understand what condition can drive the system to those naturally rare states.
16:00:21 So we are now thinking, how does spatial structure and migration affect the evolution.
16:00:41 Because most. I think the literature on spatial structure migration was established outcome is a lot of work is done in the area of antibiotic resistance, but they're mostly environment is just a concentration, or some concentration, distribution.
16:00:56 And we try to understand you have related, a different fitness landscape in different spatial locations, and you have migration mechanism. How does the migration rate affects the composition.
16:01:14 And the dynamics of this connected system.
16:01:17 Oh, I think both of those benefit from my time interacting with participants last week, hopefully.
16:01:31 Yeah, So I think I'm done with this part. So, if there are questions I would rather to talk about this. I do have a second part, which is about to this evolution of generalist,
16:01:47 which I can do. If there are not many questions. So, I think I prefer.
16:01:54 Thank you so much and let's see firstly if there are questions, are you guys feeling questions. Yes.
16:02:05 Thanks, that was wild.
16:02:07 So, do targets of adaptive immunity ever overlap with targets of innate immunity, and could that constrain or delay.
16:02:19 So
16:02:20 I'm wondering how you trap a pathogen.
16:02:25 By that I mean, I'm wondering if there's a way to stack the deck. Right.
16:02:32 I work with plants, so I never think about adoptive immunity.
16:02:38 So, so for the adapt, adapt to humanity, the coupling between Nate and adaptive wrench.
16:02:46 I don't think there's much work done, along the line. So we have some simple models to think about how exactly how the interaction between them how the delay between them effect protection over along.
16:03:04 And so here I was talking about, within a host evolution, but was more relevant for a lifetime is, there's a history of infection.
16:03:16 And it keeps using and before remodel the memory rapids.
16:03:24 And there's a coupling between you Nate and active in the way that he needs is fast. So the information is one of the major response may system launch.
16:03:37 And if it doesn't, is not sufficient, control the passage and then he will send a signal for mastery signal signal to the adaptive music.
16:03:48 So that is more expensive costly response with them starts.
16:03:55 And then, as people age, as they encounter history of presidents, their memory repertoire executive branch might be skewed so that there are vulnerability in it.
16:04:13 When old people encounter normal positive, then there's a high risk that it will is imbalance in the in your repertoire will make it nice, efficient, controlling the wires and to make up that innate system, the information will do what it does, just to
16:04:34 use the information to control the wires, but it's not able to evolve, catch.
16:04:41 So that's what's called inflammation, as people each have this chronic low level. Inflammation response to compensate for effective adaptive
16:04:55 partially answered the question so the adaptive branch is costly.
16:04:59 So it's not immediately triggered, unless it's needed.
16:05:06 And they never there never the same targets right for recognition. Yeah, so the mutes. So people call that pattern recognition, it's like there is some particular feature.
16:05:17 They are hard coded to recommends or the adaptive branch is, they have to deal with the normal thing. So they need to update their repertoire.