## SMB conference in Utah

July 21, 2017

Now Subgroups are being set up within the Society to concentrate on particular subjects. One of these, the Immunobiology and Infection Subgroup had its inaugural meeting this week and of course I went. There I and a number of other people learned a basic immunological fact which we found very surprising. It is well known that the thymus decreases in size with age so that presumably our capacity to produce new T cells is constantly decreasing. The obvious assumption, which I had made, is that this is a fairly passive process related to the fact that many systems in our bodies run down with age. We learned from Johnna Barnaby that the situation may be very different. It may be that the decrease in the size of the thymus is due to active repression by sexual hormones. She is involved in work on therapy for prostate cancer and said that it has been found that in men with prostate cancer who are getting drugs to reduce their testosterone levels it is seen that their thymus increases in size.

There were some recurrent themes at the conference. One was oncolytic viruses. These are genetically engineered viruses intended to destroy cancer cells. In modelling these it is common to use extensions of the fundamental model of virus dynamics which is very familiar to me. For instance Dominik Wodarz talked about some ODE models for oncolytic viruses in vitro where the inclusion of interferon production in the model leads to bistability. (In reponse to a question from me he said that it is a theorem that without the interferon bistability is impossible.) I was pleased to see how, more generally, a lot of people were using small ODE models making real contact to applications. Another recurrent theme was that there are two broad classes of macrophages which may be favourable or unfavourable to tumour growth. I should find out more about that. Naveen Vaidya talked about the idea that macrophages in the brain may be a refuge for HIV. Actually, even after talking to him I am not sure if it should not rather be microglia than macrophages. James Moore talked about the question of how T cells are eliminated in the thymus or become Tregs. His talk was more mathematical than biological but it has underlined once again that I want to understand more about positive and negative selection in the thymus and the related production of Tregs.

On a quite different subject there were two plenary talks related to coral reefs. A theme which is common in the media is that of the damage to coral due to climate change. Of course this is dominated by politics and usually not accompanied by any scientific information on what is going on. The talk of Marissa Blaskett was an excellent antidote to this kind of thing and now I have really understood something about the subject. The other talk, by Mimi Koehl, was less about the reefs themselves but about the way in which the larvae of snails which graze on the coral colonize the reef. I found the presentation very impressive because it started with a subject which seemed impossibly complicated and showed how scientific investigation, in particular mathematical modelling, can lead to understanding. The subject was the interaction of microscopic swimming organisms with the highly turbulent flow of sea water around the reefs. Investigating this involved among other things the following. Measuring the turbulent flow around the reef using Doppler velocimetry. Reconstructing this flow in a wave tunnel containing an artificial reef in order to study the small-scale structure of the transport of chemical substances by the flow. Going out and checking the results by following dye put into the actual reef. And many other things. Last but not least there was the mathematical modelling. The speaker is a biologist and she framed her talk by slides showing how many (most?) biologists hate mathematical modelling and how she loves it.

## Conference on reaction networks and population dynamics in Oberwolfach

July 9, 2017

This is a belated report on a conference in Oberwolfach I attended a couple of weeks ago. The title includes two elements. The first held no suprises for me but the second was rather different from what I had expected. My expectation was that it would be about the evolution of populations of organisms. In fact it was rather focussed on models related to genetics, in other words with the question of how certain genetic traits spread through a population.

There was one talk which did have a connection to population biology in way closer to what I had expected. It happens all the time that ecosystems are damaged by exotic species imported, deliberately or by accident, from other parts of the world. There are also well-known stories of the type that to try to control exotic species number one exotic species number two is introduced and is itself very harmful. It is nice to hear an example where this kind of introduction of an exotic species was very successful. It is the case of the cassava plant which was introduced from South America to Africa and became a staple food there. Then an insect from South America (species number one) called the mealy bug was introduced accidentally and caused enormous damage. Finally an ecologist called Hans Herren introduced a parasitic wasp (species number two) from South America, restoring the food supply and saving numerous lives (often the number 20 million is quoted). More details of this story can be found here.

I want to mention one statement made in the talk of Gheorghe Craciun in Oberwolfach which I found intriguing. I might have heard this before but it did not stick in my mind properly. The statement is that the set of dynamical systems which possess a complex balanced steady state is a variety of codimension $\delta$, where $\delta$ is the deficiency. There seemed to be some belief in the audience that this variety is actually a smooth manifold. On one afternoon we had something similar to the breakout sessions in Banff. I suggested the topic for one of these, which was Lyapunov functions. The idea was to compare classes of Lyapunov functions which people working on different classes of dynamical systems knew. This certainly did not lead to any breakthrough but I think it did lead to a useful exchange of information. I documented the discussion for my own use and I think I could profit by following some of the leads there.

To finish I want to mention a claim made by Ankit Gupta in his talk. It did not sound very plausible to me but I expect that it at least contains a grain of truth. He said that these days more papers are published on $NF\kappa B$ than on all of mathematics.

## Conference on mathematical analysis of biological interaction networks at BIRS

June 9, 2017

I have previously written a post concerning a meeting at the Banff International Research Station (BIRS). This week I am at BIRS again. Among the topics of the talks were stochastic chemical reaction networks, using reaction networks in cells as computers and the area of most direct relevance to me, multiple steady states and their stability in deterministic CRN. Among the most popular examples occurring in the talks in the latter area were the multiple futile cycle, the MAPK cascade and the EnvZ/OmpR system. In addition to the talks there was a type of event which I had never experienced before called breakout sessions. There the participants split into groups to discuss different topics. The group I joined was concerned with oscillations in phosphorylation cycles.

In the standard dual futile cycle we have a substrate which can be phosphorylated up to two times by a kinase and dephosphorylated again by a phosphatase. It is assumed that the (de-)phosphorylation is distributive (the number of phosphate groups changes by one each time a substrate binds to an enzyme) and sequential (the phosphate groups are added in one order and removed in the reverse order). A well-known alternative to this is processive (de-)phosphorylation where the number of phosphate groups changes by two in one encounter between a substrate and an enzyme. It is known that the double phosphorylation system with distributive and sequential phosphorylation admits reaction constants for which there are three steady states, two of which are stable. (From now on I only consider sequential phosphorylation here.) By contrast the corresponding system with processive phophorylation always has a unique steady state. Through the talk of Anne Shiu here I became aware of the following facts. In a paper by Suwanmajo and Krishnan (J. R. Soc. Interface 12:20141405) it is stated that in a mixed model with distributive phosphorylation and processive dephosphorylation periodic solutions occur as a result of a Hopf bifurcation. The paper does not present an analytical proof of this assertion.

It is a well-known open question, whether there are periodic solutions in the case that the modificiations are all distributive. It has been claimed in a paper of Errami et. al. (J. Comp. Phys. 291, 279) that a Hopf bifurcation had been discovered in this system but the claim seems to be unjustified. In our breakout sessions we looked at whether oscillations might be exported from the mixed model to the purely distributive model. We did not get any definitive results yet. There were also discussions on effective ways of detecting Hopf bifurcations, for instance by using Hurwitz determinants. It is well-known that oscillations in the purely distributive model, if they exist, do not persist in the Michaelis-Menten limit. I learned from Anne Shiu that it is similarly the case that the oscillations in the mixed model are absent from the Michaelis-Menten system. This result came out of some undergraduate research she supervised. Apart from these specific things I learned a lot just from being in the environment of these CRN people.

Yesterday was a free afternoon and I went out to look for some birds. I saw a few things which were of interest to me, one of which was a singing Tennessee warbler. This species has a special significance for me for the following reason. Many years ago when I still lived in Orkney I got an early-morning phone call from Eric Meek, the RSPB representative. He regularly checked a walled garden at Graemeshall for migrants. On that day he believed he had found a rarity and wanted my help in identifying it and, if possible, catching it. We did catch it and it turned out to be a Tennessee warbler, the third ever recorded in Britain. That was big excitement for us. I had not seen Eric for many years and I was sad to learn now that he died a few months ago at a relatively young age. The name of this bird misled me into thinking that it was at home in the southern US. In fact the name just came from the fact that the first one to be described was found in Tennessee, a migrant. The breeding range is much further north, especially in Canada. Thus it is quite appropriate that I should meet it here.

## Becoming a German citizen

May 24, 2017

I first moved to Germany in 1987 and I have spent most of the time since then here. The total time I have spent elsewhere since my first arrival in Germany does not add up to more than two years. There is every reason to expect I will spend the rest of my life here. I am married to a German, I have a job here I like and a house. I could have applied for German citizenship a long time ago but I never bothered. Being an EU citizen living in Germany I had almost almost all privileges of a native. The only exception is that I could not vote except in local elections but since I am not a very political person that was not a big issue for me. It was also the case that for a long time I might have moved to another country. For instance I applied for a job in Vienna a few years ago and I might well have taken it if it had been offered to me. Now the chances of my moving are very small and so there is no strong argument left against becoming a German citizen.

What is more important is that there are now arguments in favour of doing so. With the EU showing signs of a possible disintegration the chance that I could lose the privileges I have here as an EU citizen is not so small that it should be neglected. The referendum in which the Scots voted on the possibility of leaving the UK was the concrete motivation for my decision to start the application process. Scotland stayed in the UK but then the Brexit confirmed that I had made the right decision. At the moment there is no problem with keeping British citizenship when obtaining German citizenship and I am doing so. This may change sometime, meaning that I will have to give up my British citizenship to keep the German one, but I see this as of minor importance.

As prerequisites for my application I had to do a number of things. Of course it was necessary to submit a number of documents but I have the feeling that the amount of effort was less than when obtaining the documents needed to get married here. I had to take an examination concerning my knowledge of the German language, spoken and written. It was far below my actual level of German and so from that point of view it was a triviality. It was just a case of investing a bit of time and money. I also had to do a kind of general knowledge test on Germany and on the state where I live. This was also easy in the sense that the questions were not only rather simple for anyone who has lived in the country for some time but they are also taken from a list which can be seen in advance. Again it just meant an investment of time and money. At least I did learn a few facts about Germany which I did not know before. In my case these things were just formalities but I think it does make sense that they exist. It is important to ensure that other applicants with a background quite different from mine have at least a minimal knowledge of the language and the country before they are accepted.

After all these things had been completed and I had submitted everything it took about a year before I heard that the application had been successful. This time is typical here in Mainz – I do not know how it is elsewhere in Germany – and it results from the huge backlog of files. People are queueing up to become German citizens, attracted by the prospect of a strong economy and a stable political system. Yesterday I was invited to an event where the citizenship of the latest group of candidates was bestowed in a ceremony presided over by the mayor. There were about 60 new citizens there from a wide variety of countries. The most frequent nationality by a small margin was Turkish, followed by people from other middle eastern countries such as Iraq and Iran. There were also other people from the EU with the most frequent nationality in that case being British. My general feeling was one of being slightly uneasy that I was engaged in a futile game of changing horses. It is sad that the most civilised countries in the world are so much affected by divisive tendencies instead of uniting to meet the threats confronting them from outside.

## The Routh-Hurwitz criterion

May 7, 2017

I have been aware of the Routh-Hurwitz criterion for stability for a long time and I have applied it in three dimensions in my research and tried to apply it in four. Unfortunately I never felt that I really understood it completely. Here I want to finally clear this up. A source which I found more helpful than other things I have seen is https://www.math24.net/routh-hurwitz-criterion/. One problem I have had is that the Hurwitz matrices, which play a central role in this business, are often written in a form with lots of … and I was never sure that I completely understood the definition. I prefer to have a definite algorithm for constructing these matrices. The background is that we would like to understand the stability of steady states of a system of ODE. Suppose we have a system $\dot x=f(x)$ and a steady state $x_0$, i.e. a solution of $f(x_0)=0$. It is well-known that this steady state is asymptotically stable if all eigenvalues $\lambda$ of the linearization $A=Df(x_0)$ have negative real parts. This property of the eigenvalues is of course a property of the roots of the characteristic equation $\det(A-\lambda I)=a_0\lambda^n+\ldots+a_{n-1}\lambda+a_n=0$. It is always the case here that $a_0=1$ but I prefer to deal with a general polynomial with real coefficients $a_i, 0\le i\le n$ and a criterion for the situation where all its roots have negative real parts. It is tempting to number the coefficients in the opposite direction, so that, for instance, $a_n$ becomes $a_0$ but I will stick to this convention. Note that it is permissible to replace $a_k$ by $a_{n-k}$ in any criterion of this type since if we multiply the polynomial by $\lambda^{-n}$ we get a polynomial in $\lambda^{-1}$ where the order of the coefficients has been reversed. Moreover, if the real part of $\lambda$ is non-zero then it has the same sign as the real part of $\lambda^{-1}$. I find it important to point this out since different authors use different conventions for this. It is convenient to formally extend the definition of the $a_i$ to the integers so that these coefficients are zero for $i<0$ and $i>n$.

For a fixed value of $n$ the Hurwitz matrix is an $n$ by $n$ matrix defined as follows. The $j$th diagonal element is $a_j$, with $1\le j\le n$. Starting from a diagonal element and proceeding to the left along a row the index increases by one in each step. Similarly, proceeding to the right along a row the index decreases by one. In the ranges where the index is negative or greater than $n$ the element $a_n$ can be replaced by zero. The leading principal minors of the Hurwitz matrix, in other words the determinants of the submatrices which are the upper left hand corner of the original matrix, are the Hurwitz determinants $\Delta_k$. The Hurwitz criterion says that the real parts of all roots of the polynomial are negative if and only if $a_0>0$ and $\Delta_k>0$ for all $1\le k\le n$. Note that a necessary condition for all roots to have negative real parts is that all $a_i$ are positive. Now $\Delta_n=a_n\Delta_{n-1}$ and so the last condition can be replaced by $a_n>0$. Note that the form of the $\Delta_k$ does not depend on $n$. For $n=2$ we get the conditions $a_0>0$, $a_1>0$ and $a_2>0$. For $n=3$ we get the conditions $a_0>0$, $a_1>0$, $a_1a_2-a_0a_3>0$ and $a_3>0$. Note that the third condition is invariant under the replacement of $a_j$ by $a_{n-j}$. When $a_0a_3-a_1a_2>0$, $a_0>0$ and $a_3>0$ then the conditions $a_1>0$ and $a_2>0$ are equivalent to each other. In this way the invariance under reversal of the order of the coefficients becomes manifest. For $n=4$ we get the conditions $a_0>0$, $a_1>0$, $a_1a_2-a_0a_3>0$, $a_1a_2a_3-a_1^2a_4-a_0a_3^2>0$ and $a_4>0$.

Next we look at the issue of loss of stability. If $H$ is the region in matrix space where the Routh-Hurwitz criteria are satisfied, what happens on the boundary of $H$? One possibility is that at least one eigenvalue becomes zero. This is equivalent to the condition $a_n=0$. Let us look at the situation where the boundary is approached while $a_n$ remains positive, in other words the determinant of the matrix remains non-zero. Now $a_0=1$ and so one of the quantities $\Delta_k$ with $1\le k\le n-1$ must become zero. In terms of eigenvalues what happens is that a number of complex conjugate pairs reach the imaginary axis away from zero. The generic case is where it is just one pair. An interesting question is whether and how this kind of event can be detected using the $\Delta_k$ alone. The condition for exactly one pair of roots to reach the imaginary axis is that $\Delta_{n-1}=0$ while the $\Delta_k$ remain positive for $k. In a paper of Liu (J. Math. Anal. Appl. 182, 250) it is shown that the condition for a Hopf bifurcation that the derivative of the real part of the eigenvalues with respect to a parameter is non-zero is equivalent to the condition that the derivative of $\Delta_{n-1}$ with respect to the parameter is non-zero. In a paper with Juliette Hell (Math. Biosci. 282, 162), not knowing the paper of Liu, we proved a result of this kind in the case $n=3$.

## Mathematical models for T cell activation

May 2, 2017

The proper functioning of our immune system is heavily dependent on the ability of T cells to identify foreign substances and take appropriate action. For this they need to be able to distinguish the foreign substances (non-self) from those coming from substances belonging to the host (self). In the first case the T cell should be activated, in the second not. The process of activation is very complicated and takes days. On the other hand it seems that an important part of the distinction between self and non-self only takes a few seconds. A T cell must scan the surface of huge numbers of dendritic cells for the presence of the antigen it is specific for and it can only spare very little time for each one. Within that time the cell must register that there is something relevant there and be induced to stay longer, instead of continuing with its search.

A mathematical model for the initial stages of T cell activation (the first few minutes) was formulated and studied by Altan-Bonnet and Germain (PloS Biol. 3(11), e356). They were able to use it successfully to make experimental predictions, which they could then confirm. The predictions were made with the help of numerical simulations. From the point of view of the mathematician a disadvantage of this model is its great complexity. It is a system of more than 250 ordinary differential equations with numerous parameters. It is difficult to even write the definition of the model on paper or to describe it completely in words. It is clear that such a system is difficult to study analytically. Later Francois et. el. (PNAS 110, E888) introduced a radically simplified model for the same biological situation which seemed to show a comparable degree of effectiveness to the original model in fitting the experimental data. In fact the simplicity of the model even led to some new successful experimental predictions. (Altan-Bonnet was among the authors of the second paper.) This is the kind of situation I enjoy, where a relatively simple mathematical model suffices for interesting biological applications.

In the paper of Francois et. al. they not only do simulations but also carry out interesting analytical calculations for their model. On the other hand they do not follow the direction of attempting to use these calculations to formulate and prove mathematical theorems about the solutions of the model. Together with Eduardo Sontag we have now written a paper where we obtain some rigorous results about the solutions of this system. In the original paper the only situation considered is that where the system has a unique steady state and any other solution converges to that steady state at late times. We have proved that there are parameters for which there exist three steady states. A numerical study of these indicates that two of them are stable. A parameter in the system is the number $N$ of phosphorylation sites on the T cell receptor complex which are included in the model. The results just mentioned on steady states were obtained for $N=3$.

An object of key importance is the response function. The variable which measures the degree of activation of the T cell in this model is the concentration $C_N$ of the maximally phosphorylated state of the T cell receptor. The response function describes how $C_N$ depends on the important input variables of the system. These are the concentration $L$ of the ligand and the constant $\nu$ describing the rate at which the ligand unbinds from the T cell receptor. A widespread idea (the lifetime dogma) is that the quantity $\nu^{-1}$, the dissociation time, determines how strongly an antigen signals to a T cell. It might naively be thought that the response should be an increasing function of $L$ (the more antigen present the stronger the stimulation) and a decreasing function of $\nu$ (the longer the binding the stronger the stimulation). However both theoretical and experimental results lead to the conclusion that this is not always the case.

We proved analytically that for certain values of the parameters $C_N$ is a decreasing function of $L$ and an increasing function of $\nu$. Since these rigorous results give rather poor information on the concrete values of the parameters leading to this behaviour and on the global form of the function we complemented this analytical work by simulations. These show how $C_N$ can have a maximum as a function of $\nu$ within this model and that as a function of $L$ it can have the following form in a log-log plot. For $L$ small the graph is a straight line of slope one. As $L$ increases it switches to being a straight line of slope $1-N/2$ and for still larger values it once again becomes a line of slope one, shifted with respect to the original one. Finally the curve levels out as it must do, since the function is bounded. The proofs do not make heavy use of general theorems and are in general based on doing certain estimates by hand.

All of these results were of the general form ‘there exist parameter values for the system such that $X$ happens’. Of course this is just a first step. In the future we would like to understand better to what extent biologically motivated restrictions on the parameters lead to restrictions on the dynamical behaviour.

## New hope for primary progressive multiple sclerosis?

April 12, 2017

Multiple sclerosis is generally classified into three forms. The relapsing-remitting form is the most common initial form. It is characterized by periods when the symptoms get much worse separated by periods where they get better. The second form is the primary progressive form where the symptoms slowly and steadily get worse. It is generally thought to have a worse prognosis than the relapsing-remitting form. In many cases the relapsing-remitting form converts to a progressive form at some time. This is then the secondary progressive form. In the meantime there is a big variety of drugs on the market which are approved for the treatment of the RR form of MS. They cannot stop the disease but they can slow its progression. Until very recently there was no drug approved for the treatment of progressive MS. This has now changed with the approval of ocrelizumab, an antibody against the molecule CD20 which is found on the surface of B cells. It has been approved for both the RR form and some cases of the progressive form of MS.

Ocrelizumab acts by causing B cells to be killed. It has been seen to have strong positive effects in combatting MS in some cases. This emphasizes the fact that T cells, usually regarded as the main culprit causing damage during MS, are not alone. B cells also seem to play an important role although what role that is is not so clear. There previously existed an antibody against CD20, rituximab, which was used in the therapy of diseases other than MS. Ocrelizumab has had problemtic side effects, with a high frequency of infections and a slightly increased cancer risk. For this reason it has been abandoned as a therapy for rheumatoid arthritis. On the other hand the trial for MS has less problems with side effects.

One reason not to be too euphoric about this first treatment for progressive MS is the following. It has been shown to be effective against patients in the first few years of illness and those where there are clear signs of inflammatory activity in MRT scans. This suggests to me a certain suspicion. The different types of MS are not clearly demarcated. Strong activity in the MRT is typical of the RR form. So I wonder if the patients where this drug is effective are perhaps individuals with an atypical RR form where the disease activity just does not cross the threshold to becoming manifest on the symptomatic level for a certain time. This says nothing against the usefuleness of the drug in this class of patients but it might be a sign that its applicability will not extend to a wider class of patients with the progressive form in the future. It also suggests caution in hoping that the role of B cells in this therapy might help to understand the mechanism of progressive MS.

## Poincaré, chaos and the limits of predictability

March 5, 2017

In the past I was surprised that there seemed to be no biography of Henri Poincaré. I recently noticed that a biography of him had appeared in 2013. The title is ‘Henri Poincaré. A scientific biography’ and the author is Jeremy Gray. At the moment I have read 390 of the 590 pages. I have learned interesting things from the book but in general I found it rather disappointing. One of the reasons is hinted at by the subtitle ‘A scientific biography’. Compared to what I might have hoped for the book concentrates too much on the science and too little on the man. Perhaps Poincaré kept his private life very much to himself and thus it was not possible to discuss these aspects more but if this is so then I would have found it natural that the book should emphasize this point. I have not noticed anything like that. I also found the discussion of the scientific topics of Poincaré’s work too technical in many places. I would have preferred a presentation of the essential ideas and their significance on a higher level. There are other biographies of great mathematicians which made a better impression on me. I am thinking of the biography of Hilbert by Constance Reid and even of the slim volumes (100 pages each) on Gauss and Klein written in East Germany.

On important discovery of Poincaré was chaos. He discovered it in the context of his work on celestial mechanics and indeed that work was closely connected to his founding the subject of dynamical systems as a new way of approaching ordinary differential equations, emphasizing qualitative and geometric properties in contrast to the combination of complex analysis and algebra which had dominated the subject up to that point. The existence of chaos places limits on predictability and it is remarkable that these do not affect our ability to do science more than they do. For instance it is known that there are chaotic phenomena in the motion of objects belonging to the solar system. This nevertheless does not prevent us from computing the trajectories of the planets and those of space probes sent to the other end of the solar system with high accuracy. These space probes do have control systems which can make small corrections but I nevertheless find it remarkable how much can be computed a priori, although the system as a whole includes chaos.

This issue is part of a bigger question. When we try to obtain a scientific understanding of certain phenomena we are forced to neglect many effects. This is in particular true when setting up a mathematical model. If I model something using ODE then I am, in particular, neglecting spatial effects (which would require partial differential equations) and the fact that often the aim is not to model one particular object but a population of similar objects and I neglect the variation between these objects which I do not have under control and for whose description a stochastic model would be necessary. And of course quantum phenomena are very often neglected. Here I will not try to address these wider issues but I will concentrate on the following more specific question. Suppose I have a system of ODE which is a good description of the real-world situation I want to describe. The evolution of solutions of this system is uniquely determined by initial data. There remains the problem of sensitive dependence on initial data. To be able to make a prediction I would like to know that if I make a small change in the initial data the change in some predicted quantity should be small. What ‘small’ means in practice is fixed by the application. A concrete example is the weather forecast whose essential limits are illustrated mathematically by the Lorenz system, which is one of the icons of chaos. Here the effective limit is a quantitative one: we can get a reasonable weather forecast for a couple of days but not more. More importantly, this time limit is not set by our technology (amount of observational data collected, size of the computer used, sophistication of the numerical programs used) but by the system itself. This time limit will not be relaxed at any time in the future. Thus one way of getting around the effects of chaos is just to restrict the questions we ask by limits on the time scales involved.

Another aspect of this question is that even when we are in a regime where a system of ODE is fully chaotic there will be some aspects of its behaviour which will be predictable. This is why is is possible to talk of ‘chaos theory’- I know too little about this subject to say more about it here. One thing I find intriguing is the question of model reduction. Often it is the case that starting from a system of ODE describing something we can reduce it to an effective model with less variables which still includes essential aspects of the behaviour. If the dimension of the reduced model is one or two then chaos is lost. If there was chaos in the original model how can this be? Has there been some kind of effective averaging? Or have we restricted to a regime (subset of phase space) where chaos is absent? Are the questions we tend to study somehow restricted to chaos-free regions? If the systems being modelled are biological is the prevalence of chaos influenced by the fact that biological systems have evolved? I have seen statements to the effect that biological systems are often ‘on the edge of chaos’, whatever that means.

This post contains many questions and few answers. I just felt the need to bring them up.

## Kestrels and Dirichlet boundary conditions

February 28, 2017

The story I tell in this post is based on what I heard a long time ago in a talk by Jonathan Sherratt. References to the original work by Sherratt and his collaborators are Proc. R. Soc. Lond. B269, 327 (more biological) and SIAM J. Appl. Math. 63, 1520 (more mathematical). There are some things I say in the following which I did not find in these sources and so they are based on my memories of that talk and on things which I wrote down for my own reference at intermediate times. If this has introduced errors they will only concern details and not the basic story. The subject is a topic in population biology and how it relates to certain properties of reaction-diffusion equations.

In the north of England there is an area called the Kielder Forest with a lake in the middle and the region around the lake is inhabited by a population of the field vole $Microtus\ agrestis$. It is well known that populations of voles undergo large fluctuations in time. What is less known is what the spatial dependence is like. There are two alternative scenarios. In the first the population density of voles oscillates in a way which is uniform in space. In the second it is a travelling wave of the form $U(x-ct)$. In that case the population at a fixed point of space oscillates in time but the phase of the oscillations is different at different spatial points. In general there is relatively little observational data on this type of thing. The voles in the Kielder forest are an exception to this since in that case a dedicated observer collected data which provides information on both the temporal and spatial variation of the population density. This data is the basis for the modelling which I will now describe.

The main predators of the voles are weasels $Mustela\ nivalis$. It is possible to set up a model where the unknowns are the populations of voles and weasels. Their interaction is modelled in a simple way common in predator-prey models. Their spatial motion is described by a diffusion term. In this way a system of reaction-diffusion equations is obtained. These are parabolic equations and to the time evolution is non-local in space. The unknowns are defined on a region with boundary which is the complement of a lake. Because of this we need not only initial values to determine a solution but also boundary conditions. How should they be chosen? In the area around the lake there live certain birds of prey, kestrels. They hunt voles from the air. In most of the area being considered there is very thick vegetation and the voles can easily hide from the kestrels. Thus the direct influence of the kestrels on the vole population is negligible and the kestrels to not need to be included in the reaction-diffusion system. They do, however, have a striking indirect effect. On the edge of the lake there is a narrow strip with little vegetation and any vole which ventures into that area is in great danger of being caught by a kestrel. This means that the kestrels essentially enforce the vanishing of the population density of voles at the edge of the lake. In other words they impose a homogeneous Dirichlet boundary condition on one of the unknowns at the boundary. Note that this is incompatible with spatially uniform oscillations. On the boundary oscillations are ruled out by the Dirichlet condition. When the PDE are solved numerically what is seen that the shore of the lake generates a train of travelling waves which propagate away from it. This can also be understood theoretically, as explained in the papers quoted above.

## Conference on cancer immunotherapy at EMBL

February 5, 2017

I just came back from a conference on cancer immunotherapy at EMBL in Heidelberg. It was very interesting for me to get an inside view of what is happening in this field and to learn what some of the hot topics are. One of the speakers was Patrick Baeuerle, who talked about a molecular construct which he introduced, called BiTE (bispecific T cell engager). It is the basis of a drug called blinatumomab. This is an antibody construct which binds both CD3 (characteristic of T cells) and CD19 (characteristic of B cells) so that these cells are brought into proximity. In its therapeutic use in treating acute lymphoblastic leukemia, the B cell is a cancer cell. More generally similar constructs could be made so as to bring T cells into proximity with other cancer cells. The idea is that the T cell should kill the cancer cell and in that context it is natural to think of cytotoxic T cells. It was not clear to me how the T cell is activated since the T cell receptor is not engaged. I took the opportunity to ask Baeuerle about this during a coffee break and he told me that proximity alone is enough to activate T cells. This can work not only for CD8 T cells but also for CD4 cells and even regulatory T cells. He presented a picture of a T cell always being ready to produce toxic substances and just needing a signal to actually do it. Under normal circumstances T cells search the surfaces of other cells for antigens and do not linger long close to any one cell unless they find their antigen. If they do stay longer near another cell for some reason then this can be interpreted as a danger sign and the T cell reacts. Baeuerle, who started his career as a biochemist, was CEO of a company called Micromet whose key product was what became blinatumomab. The company was bought by Amgen for more than a billion dollars and Baeuerle went with it to Amgen. When it came on the market it was the most expensive cancer drug ever up to that time. Later Baeuerle moved to a venture capital firm called MPM Capital, which is where he is now. In his previous life as a biochemical researcher Baeuerle did fundamental work on NF$\kappa$B  with David Baltimore.

In a previous post I mentioned a video by Ira Mellman. At the conference I had the opportunity to hear him live. One thing which became clear to me at the conference is the extent to which, among the checkpoint inhibitor drugs, anti-PD1 is superior to anti-CTLA. It is successful in a much higher proportion of patients. I never thought much about PD1 before. It is a receptor which is present on the surface of T cells after they have been activated and it can be stimulated by the ligand PD1L leading to the T cell being switched off. But how does this switching off process work? The T cell is normally switched on by the engagement of the T cell receptor and a second signal from CD28. In his talk Mellman explained that the switching off due to PD1 is not due to signalling from the T cell receptor being stopped. Instead what happens is that PD1 activates the phosphatase SHP2 which dephosphorylates and thus deactivates CD28. Even a very short deactivation of CD28 is enough to turn off the T cell. In thinking about mathematical models for T cell activation I thought that there might be a link to checkpoint inhibitors. Now it looks like models for T cell activation are not of direct relevance there and that instead it would be necessary to model CD28.

I learned some more things about viruses and cancer. One is that the Epstein-Barr-virus, famous for causing Burkitt’s lymphoma also causes other types of cancers, in particular other types of lymphoma. Another is that viruses are being used in a therapeutic way. I had heard of oncolytic viruses before but I had never really paid attention. In one talk the speaker showed a picture of a young African man who had been cured of Burkitt’s lymphoma by … getting measles. This gave rise to the idea that viruses can sometimes preferentially kill cancer cells and that they can perhaps be engineered to as to do so more often. In particular measles is a candidate. In that case there is an established safe vaccination and the idea is to vaccinate with genetically modified measles virus to fight certain types of cancer.

In going to this conference my main aim was to improve my background in aspects of biology and medicine which could be of indirect use for my mathematical work. In fact, to my surprise, I met one of the authors of a paper on T cell activation which is closely related to mathematical topics I am interested in. This was Philipp Kruger who is in the group of Omer Dushek in Oxford. I talked to him about the question of what is really the mechanism by which signals actually cross the membrane of T cells. One possibility he mentioned was a conformational change in CD3. Another, which I had already come across is that it could have to do with a mechanical effect by which the binding of a certain molecule could bring the cell membranes of two interacting cells together and expel large phosphatases like CD45 from a certain region. In the paper of his I had looked at signalling in T cells is studied with the help of CAR T-cells, which have an artifical analogue of the T cell receptor which may have a much higher affinity than the natural receptor. In his poster he described a new project looking at the effect of using different co-receptors in CAR T-cells (not just CD28). In any case CAR T-cells was a subject which frequently came up at the conference. Something which was in the air was that this therapy may be associated with neurotoxicity in some cases but I did not learn any details.

As far as I can see, the biggest issue with all these techniques is the following. They can be dramatically successful, taking patients from the point of death to long-term survival. On the other hand they only work in a subset of patients (say, 40% at most) and nobody understands what success depends on. I see a great need for a better theoretical understanding. I can understand that when someone has what looks like a good idea in this area they quickly look for a drug company to do a clinical trial with it. These things can save lives.  On the other hand it is important to ask whether investing more time in obtaining a better understanding of underlying mechanisms might not lead to better results in the long run.