Archive for the ‘mathematical biology’ Category

Proofs of dynamical properties of the MAPK cascade

April 3, 2014

The MAP kinase cascade, which I mentioned in a previous post, is a biochemical network which has been subject to a lot of theoretical and experimental study. Although a number of results about mathematical models for this network have been proved, many widely accepted results are based on numerical and/or heuristic approaches. Together with Juliette Hell we set out to extend the coverage of rigorous results in this area. Our first results on this can be found in a paper we just posted on q-bio.

The system of equations which is fundamental for this work is that of Huang and Ferrell discussed in my previous post on the subject. I call it the MM-MA system (for Michaelis-Menten via mass action). When this system can be reduced to a smaller system by means of a quasistationary approximation the result will be called the MM system (for Michaelis-Menten) (cf. this post). With a suitable formulation the MM system is a singular limit of the MM-MA system. The MAPK cascade consists of three coupled layers. The first main result of our paper concerns the dual futile cycle, which can be thought of as the second layer of the cascade in isolation (cf. this post). We proved that the MM system for the dual futile cycle exhibits a generic cusp bifurcation and hence that for suitable values of the parameters there exist two different stable stationary solutions (bistability). Using the fact that this system is a singular limit of the system arising from the MM-MA description of the same biological system we then used geometric singular perturbation theory (cf. this post) to conclude that the MM-MA system also shows bistability.

The second main result concerns the system obtained by truncating that of Huang-Ferrell by keeping only the first two layers. It is subtle to find a useful quasistationary approximation for this system and we were put on the right track by a paper of Ventura et. al. (PLoS Comp. Biol. 4(3):e1000041). This allowed us to obtained an MM system which is a limit of the MM-MA system in a way which allows geometric singular perturbation theory to be applied. This leads to the following relative statement: if the MM system for the truncated MAPK cascade has a hyperbolic periodic solution then the same is true for the MM-MA system. To get an absolute statement it remains to prove the existence of periodic solutions of the MM system, which in this case is of dimension three. That there are solutions of this kind is indicated by numerical work of Ventura et. al.

The Hopf-Hopf bifurcation and chaos in ecological systems

February 11, 2014

This post arises from the fact that there seems to be some constructive interference between various directions I am pursuing at the moment. The first has to do with the course on dynamical systems I just finished giving. This course was intended not only to provide students in Mainz with an extended introduction to the subject but also to broaden my own knowledge. I wrote lecture notes for this in German and having gone to the effort of producing this resource I thought I should translate the notes into English so as to make them more widely available. The English version can be found here. Both versions are on the course web page. The second thing is that I will be organizing a seminar on bifurcation theory next semester and I want it to achieve a wide coverage, even at the risk that its waters, being broad, may be shallow (this is paraphrase of a quote I vaguely remember from Nietzsche). The connections between these two things are that I treated simple bifurcation theory and a little chaos in the course and that going further into the landscape of bifurcations necessarily means that at some point chaos rears its ugly head. The third thing is the fact that it has been suggested that the MAPK cascade, a dynamical system I am very interested in from the point of view of my own research, may exhibit chaotic behaviour, as described in a paper of Zumsande and Gross (J. Theor. Biol. 265, 481). This paper attracted my attention when it appeared on arXiv but it is only now that I understood some of the underlying ideas and, in particular, that the Hopf-Hopf bifurcation plays a central role. This in turn led me to a paper by Stiefs et. al. on chaos in ecological systems (Math. Biosci. Eng. 6, 855). They consider models with predator-prey interaction and a disease of the predators.

A Hopf-Hopf (or double Hopf) bifurcation arises at a stationary point where the linearization has two pairs of non-vanishing purely imaginary eigenvalues. Of course it is necessary to have a system of at least dimension four in order for this to occur. The subset of parameter space where it occurs has codimension two and lies at the intersection of two hypersurfaces on which there are Hopf bifurcations. For this system there is an approximate normal form. In other words the system is topologically equivalent to a system given by simple explicit formulae plus higher order error terms. The dynamics of the model system ignoring error terms can be analysed in detail. For simple bifurcations a system in approximate normal form is topologically equivalent to the model system. For the Hopf-Hopf bifurcation (and for the simpler fold-Hopf bifurcation with one zero and one pair of non-zero purely imaginary eigenvalues) this is no longer the case and the perturbation leads to more complicated dynamics. For instance, a heteroclinic orbit in the model system can break as a result of the perturbation. A lot of information on these things can be found in the book of Kuznetsov. In the paper on ecological systems mentioned above a Hopf-Hopf bifurcation is found using computer calculations and this is described as ‘clear evidence for the existence of chaotic parameter regions’. My understanding of chaos is still too weak to be able to appreciate the precise meaning of this statement.

Using computer calculations Zumsande and Gross find fold-Hopf bifurcations in the MAPK cascade (without explicit feedback) indicating the presence of complex dynamics. If chaos occurs in the ecological system and the MAPK cascade what biological meaning could this have? Ecosystems can often be thought of as spatially localized communities with their own dynamics which are coupled to each other. If the dynamics of the individual communities is of a simple oscillatory type then they may become synchronized and this could lead to global extinctions. If the local dynamics are chaotic this cannot happen so easily and even if a fluctuation which is too big leads to extinctions in one local community, these can be avoided in neighbouring communities, giving the ecosystem a greater global stability. One point of view of chaos in the MAPK cascade is that it is an undesirable effect which might interfere with the signalling function. It might be an undesirable side effect of other desirable features of the system. In reality MAPK cascades are usually embedded in various feedback loops and these might suppress the complex  behaviour in the free cascade. Zumsande and Gross investigated this possibility with the conclusion that the feedback loops tend to make things worse rather than better.

Monotone dynamical systems

December 29, 2013

In previous posts I have written a little about monotone dynamical systems, a class of systems which in some sense have simpler dynamical properties than general dynamical systems. Unfortunately this subject was always accompanied by some confusion in my mind. This results from the necessity of a certain type of bookkeeping which I was never really able to get straight. Now I think my understanding of the topic has improved and I want to fix this knowledge here. There are two things which have led to this improvement. One is that I read an expository article by Eduardo Sontag which discusses monotone systems in the context of biochemical networks (Systems and Synthetic Biology 1, 59). The other is that I had the chance to talk to David Angeli who patiently answered some of my elementary questions as well as providing other insights. In what follows I only discuss continuous dynamical systems. Information on the corresponding theory in the case of discrete dynamical systems can be found in the paper of Sontag.

Consider a dynamical system \dot x=f(x) defined on an open subset of R^n. The system is called monotone if \frac{\partial f_i}{\partial x_j}\ge 0 for all i\ne j. This is a rather restrictive definition – we will see alternative possibilities later – but I want to start in a simple context. There is a theorem of Müller and Kamke which says that if two solutions x and y of a monotone system satisfy x_i(0)\le y_i(0) for all i then they satisfy x_i(t)\le y_i(t) for all i and all t\ge 0. This can be equivalently expressed as the fact that for each t\ge 0 the time t flow of the dynamical system preserves the partial order defined by the condition that x_i\le y_i for all i. This can be further reexpressed as the condition that y-x belongs to the positive convex cone in R^n defined by the conditions that the values of all Cartesian coordinates are non-negative. This shows the way to more general definitions of monotone flows on vector spaces, possibly infinite dimensional. These definitions may be useful for the study of certain PDE such as reaction-diffusion equations. The starting point is the choice of a suitable cone. This direction will not be followed further here except to consider some other simple cones in R^n.

A monotone system in the sense defined above is also sometimes called cooperative. The name comes from population models where the species are beneficial to each other. Changing the sign in the defining conditions leads to the class of competitive systems. These can be transformed into cooperative systems by changing the direction of time. However for a given choice of time direction the competitive systems need not have the pleasant properties of cooperative systems. Another simple type of coordinate transformation is to reverse the signs of some of the coordinates x_i. When can this be used to transform a given system into a monotone one?. Two necessary conditions are that each partial derivative of a component of f must have a (non-strict) sign which is independent of x and that the derivatives are symmetric under interchange of their indices. What remains is a condition which can be expressed in terms of the so-called species graph. This has one node for each variable x_i and an arrow from node i to node j if \frac{\partial f_j}{\partial x_i} is not identically zero. If the derivative is positive the arrow bears a positive sign and if it is negative a negative sign. Alternatively, the arrows with positive sign have a normal arrowhead while those with negative sign have a blunt end. In this way the system gives rise to a labelled oriented graph. To each (not necessarily oriented) path in the graph we associate a sign which is the product of the signs of the individual edges composing the path. The graph is said to be consistent if signs can be associated to the vertices in such a way that the sign of an edge is always the product of the signs of its endpoints. This is equivalent to the condition that every closed loop in the graph has a positive sign. In other words, every feedback in the system is positive. Given that the other two necessary conditions are satisfied the condition of consistency characterizes those networks which can be transformed by changes of sign of the x_i to a monotone system. A transformation of this type can also be thought of as replacing the positive orthant by another orthant as the cone defining the partial order.

Next I consider some examples. Every one-dimensional system is monotone. In a two-dimensional system we can have the sign patterns (+,+), (-,-) and (+,-). In the first case the system is monotone. In the second case it is not but can be made so by reversing the sign of one of the coordinates. This is the case of a two-dimensional competitive system. In the third case the system cannot be made monotone. A three-dimensional competitive system cannot be made monotone. The species graph contains a negative loop. Higher dimensional competitive systems are no better since their graphs all contain copies of that negative loop.

A general message in Sontag’s paper is that consistent systems tend to be particularly robust to various types of disturbances. Large biochemical networks are in general not consistent in this sense but they are close to being consistent in the sense that removing a few edges from the network make them consistent. This also means that they can be thought of as a few consistent subsystems joined together. Since biological systems need robustness this suggests a topological property which biochemical networks should have compared to random networks. Sontag presents an example where this has been observed in the transcription network of yeast.

A more sophisticated method which can often be used to obtain monotone systems from systems of chemical reactions by a change of variables has been discussed in a previous post. The advantage of this is that together with other conditions it can be use to show that generic solutions, or sometimes even all solutions, of the original system converge to stationary solutions.

Geometric singular perturbation theory

November 29, 2013

I have already written two posts about the Michaelis-Menten limit, one not very long ago. I found some old results on this subject and I was on the look-out for some more modern accounts. Now it seems to me that what I need is something called geometric singular perturbation theory which goes back to a paper of Fenichel (J. Diff. Eq. 31, 53). An interesting aspect of this is that it involves using purely geometric statements to help solve analytical problems. If we take the system of two equations given in my last post on this subject, we can reformulate them by introducing a new time coordinate \tau=t/\epsilon, called the fast time, and adding the parameter as a new variable with zero time derivative. This gives the equations x'=\epsilon f(x,y), y'=g(x,y) and \epsilon'=0, where the prime denotes the derivative with respect to \tau. We are interested in the situation where the equation g(x,y)=0, which follows from the equations written in terms of the original time coordinate t, is equivalent to y=h_0(x) for a smooth function h_0. The linearization of the system in \tau along the zero set of g automatically has at least two zero eigenvalues. For Fenichel’s theorem it should be assumed that it does not have any more zero (or purely imaginary) eigenvalues. Then each point on that manifold has a two-dimensional centre manifold. Fenichel proves that there exists one manifold which is a centre manifold for all of these points. This is sometimes called a slow manifold. (Sometimes the part of it for a fixed value of \epsilon is given that name.) Its intersection with the plane \epsilon=0  coincides with the zero set of g. The original equations have a singular limit as \epsilon tends to zero, because \epsilon multiplies the time derivative in one of the equations. The remarkable thing is that the restriction of the system to the slow manifold is regular. This allows statements to be made that qualitative properties of the dynamics of solutions of the system with \epsilon=0 are inherited by the system with \epsilon small but non-zero.

Due to my growing interest in this subject I invited Peter Szmolyan from Vienna,who is a leading expert in this field, to come and give a colloquium here in Mainz, which he did yesterday. One of his main themes was that in many models arising in applications the splitting into the variables x and y cannot be done globally. Instead it may be necessary to use several splittings to describe different parts of the dynamics of one solution. He discussed two examples in which these ideas are helpful for understanding the dynamics better and establishing the existence of relaxation oscillations. The first is a model of Goldbeter and Lefever (Biophys J. 12, 1302) for glycolysis. It is different from the model I mentioned in a previous post but is also an important part of the chapter of Goldbeter’s book which I discussed there. The model of Goldbeter and Lefever was further studied theoretically by Segel and Goldbeter (J. Math. Biol. 32, 147). On this basis a rigorous analysis of the dynamics including a proof of the existence of relaxation oscillations was given in a recent paper by Szmolyan and Ilona Kosiuk (SIAM J. Appl. Dyn. Sys. 10, 1307). The other main example in the talk was a system of equations due to Goldbeter which is a kind of minimal model for the cell cycle. It is discussed in chapter 9 of Goldbeter’s book.

I have the feeling that GSPT is a body of theory which could be very useful for my future work and so I will do my best to continue to educate myself on the subject.

The Michaelis-Menten limit

July 2, 2013

In a previous post I wrote about the Michaelis-Menten reduction of reactions catalysed by enzymes in which a single equation (effective equation) is the limit of a system of two equations (extended equations) as a parameter \epsilon tends to zero. What I did not talk about is the sense in which solutions of the effective equation approximate those of the extended ones. I was sure that this must be well-known but I did not know a source for it. Now I discovered that what I had been seeking is to be found in a very nice form in a book which had been standing on a shelf in my office for many years. This is the book ‘Asymptotic Expansions for Ordinary Differential Equations’ by Wolfgang Wasow and the part of relevance to Michaelis-Menten reduction starts on p. 249. Michaelis-Menten is not mentioned there but the key mathematical result is exactly what is needed for that application. The theorem is due to Tikhonov but the original paper is in Russian and so not accessible to me. For convenience I repeat the equations from the previous post on this subject. \dot u=f(u,v),\epsilon\dot v= g(u,v). This is the type of system treated in Tikhonov’s theorem, including the possibility that u and v are vector-valued.

The statement of the theorem is as follows. On any finite time interval [0,T] the function u in the extended system converges uniformly to the solution of the reduced system as \epsilon\to 0. Given a solution of the reduced system it is possible to compute a corresponding function v. On the time interval (0,T] the function v in the extended system converges to the function v coming from the reduced system uniformly on compact subsets. Of course this conclusion  requires some hypotheses on the functions f and g. The key thing is that for a fixed value of u we have an asymptotically stable stationary solution of the equation for v (with \epsilon\ne 0).

With this result in hand it is possible to compute higher order corrections in \epsilon. This was first done by Vasileva and is also explained in the book of Wasow. The result was extended to a statement global in t by Hoppensteadt, Trans. Amer. Math. Soc. 123, 521. I expect that there are more modern treatments of these things in the literature but I find the sources quoted here very helpful for the beginner like myself. There remains the question of the relation to the usual Michaelis-Menten procedure. This is nicely discussed in a paper by Heineken et. al., Math. Biosci. 1, 95.

Population dynamics and chemical reactions

June 21, 2013

The seminar which I mentioned in a recent post has caused me to go back and look carefully at a number of different models in biology and chemistry. It has happened repeatedly that I felt I could glimpse some mathematical relations between the models. Now I have spent some time pursuing these ideas. One aspect is that many of the systems of ODE coming from biological models can be thought of as arising from chemical reaction networks with mass action kinetics, even when the unknowns are not chemical concentrations. In this context it should be mentioned that if an ODE system arises in this way the chemical network which leads to it need not be unique.

The first example I want to mention is the Lotka-Volterra system. Today it is usually presented as a model of population dynamics. Often the example of lynx and hares is used and this is natural due to the intrinsic attractiveness of furry animals. The story of Volterra and his son in law also has a certain human interest. The fact that Lotka found the equations earlier is usually just a side comment. In any case, the population model is equivalent to an ODE system coming from a reaction network which was described by Lotka in a paper in 1920 (J. Amer. Chem. Soc. 42, 1595). The network is defined by the reactions A_1\to 2A_1, A_1+A_2\to 2A_2, A_2\to 0 and A_1+A_2\to 0. The last entry in the list can be thought of as an alternative reaction producing another substance which is not included explicitly in the model. A simpler version, also considered in Lotka’s paper, omits this last reaction. In his book ‘Mathematical aspects of reacting and diffusing systems’ Paul Fife looks at the second system from the point of view of chemical reaction network theory. He computes its deficiency \delta in the sense of CRNT to be one. It has three linkage classes. The second model also has deficiency one. All the linkage classes have deficiency zero and so the deficiency one theorem does not apply. The chemical system introduced by Lotka was not supposed to correspond to a system of real reactions. He was just looking for a hypothetical reaction which would exhibit sustained oscillations.

Next I consider the fundamental model of virus dynamics as given in the book of Nowak and May which has previously been mentioned in this blog. Something which I only noticed now is that in a sense there is a term missing from the model. This represents the fact that when a virion enters a cell to infect it that virion is removed from the virus population. This fact is apparently not mentioned in the book. In an alternative model discussed in a paper of Perelson and Nelson (SIAM Rev. 41, 3) they also omit this term and discuss possible justifications for doing so. The fundamental model as found in the book of Nowak and May can be interpreted as the equations coming from a network of chemical reactions. This is also true of the modified version where the missing term is replaced. Both systems (at least the ones I found) have deficiency two.

Several well-known models in epidemiology can also be obtained from chemical networks. For instance the SIR model can be obtained from the reactions S+I\to 2I and I\to 0. This network has deficiency zero and is not weakly reversible. The deficiency zero theorem applies and tells us that there is no equilibrium. Of course this fact is nothing new for this example. The SIS model is similar but in that case the system has deficiency one and a positive stationary solution exists for certain parameter values. You might complain that the games I am playing do not lead to useful insights and you may be right. Nevertheless, seeing analogies between apparently unrelated things is a notorious strength of mathematics. There is also one success story related to the things I have been talking about here, namely the work of Korobeinikov on the standard model of virus dynamics mentioned in a previous post. He imported a Lyapunov function of a type known for epidemiological models in order to prove the global asymptotic stability of stationary solutions of the fundamental model of virus dynamics.

Modelling the Calvin cycle

March 18, 2013

Some years ago the Max Planck Institute for Molecular Plant Physiology organized a conference on metabolic networks. I decided to see what was going on in the institute next to the one where I work and I went to some of the talks. The one which I found most interesting was by Zoran Nikoloski. His subject was certain models for the Calvin cycle, which is part of photosynthesis. A motivating question was whether photosynthesis can work in two different stable steady states. If that were the case it might be possible to influence the plant to move from one state to another and, in the best case, to increase its production of biomass. This is of interest for biotechnology. Mathematically the question is that of multistationarity, i.e. whether a system of evolution equations admits more than one stationary solution. Beyond this it is of interest whether there can be more than one stable stationary solution. In fact in this context the issue is not that of absolute uniqueness of stationary solutions but of uniqueness within a given stoichiometric compatibility class. This means that the solution is unique when certain conserved quantities are fixed. One thing I found attractive about the presentation was that the speaker was talking about rigorous mathematical results on the dynamics and not just about numerically calculating a few solutions.

If the system is modelled deterministically and diffusion is neglected there results a system of ordinary differential equations for the concentrations of the substances involved as functions of time. It is necessary to choose which substances should be included in the description. In a basic model of the Calvin cycle there are five substances. In the work discussed in the talk of Nikoloski and in a paper he wrote with Sergio Grimbs and others (Biosystems 303, 212) various ODE systems based on this starting point are considered. They differ by the type of kinetics used. They consider mass action kinetics (MA), extended Michaelis-Menten kinetics where the enzymes catalysing the reactions are included explicitly (MM-MA) and effective Michaelis-Menten (MM) obtained from the system MM-MA by a singular limit. The systems MA and MM consist of five equations while the system MM-MA consists of nineteen equations. In the paper of Grimbs et. al. they show among other things that the system MM never admits a stable stationary solution, whatever the reaction constants, while the system MM-MA can exhibit two different stationary solutions.

After the talk I started reading about this subject and I also talked to Nikoloski about it. Later I began doing some research on these systems myself. Some technical difficulties which arose (which I wrote about in a previous post) led me to consult Juan Velázquez and he joined me in this project. Now we have written a paper on models for the Calvin cycle. In a case where there is only one stationary solution and it is unstable it is of interest to consider the final fate of general solutions of the system. For some initial conditions the concentrations of all substances tend to zero at late times. For other data (a whole open set) we were able to show that all concentrations tend to infinity as t\to\infty. We called the latter class runaway solutions. These do not seem to be of direct biological relevance but they might be helpful in choosing between alternative models which are more or less appropriate. The proof of the existence of runaway solutions for the MA system is somewhat complicated since this turns out to be a system with two different timescales. The system MM-MA also admits runaway solutions. Although the system is larger than MA the existence proof is simpler and in fact can be carried out in the context of a larger class of systems. Runaway solutions are also found for the system MM.

In the paper of Grimbs et. al. one system is considered which includes the effect of diffusion. Restricting to homogeneous solutions of this system gives a system of ODE called MAdh which is different from the system MA. The difference is that while the concentration of ATP is a dynamical variable in MAdh it is taken to be constant in MA. We showed that the system MAdh has zero, one or two solutions depending on the values of the parameters and that all solutions are bounded. Thus runaway solutions are ruled out. Intuitively this is due to the fact that the supply of energy is bounded but this heuristic argument is far from providing a proof. There are many other models of the Calvin cycle in the literature. In general they consider the reactions between a larger class of substances. It is an interesting task for the future to extend the results obtained up to now to these more general models. This post has been very much concerned with the mathematics of the problem and has not said much about the biology. The reactions making up the Calvin cycle were determined experimentally by Melvin Calvin and I can highly recommend his Nobel lecture as a description of how this was achieved

Absolute concentration robustness

February 20, 2013

In the past years I have been on the committees for many PhD examinations. A few days ago, for the first time, I was was on the committee for a thesis on a subject belonging to the area of mathematical biology. This was the thesis of Jost Neigenfind and it was concerned with a concept called absolute concentration robustness (ACR).

The concentration of a given substance in cells of a given type varies widely between the individual cells. (Cf. also this previous post). It is of interest to identify mechanisms which can ensure that the steady state concentration of a particular substance is independent of initial data. (This is a way in which the output of a system can be independent of background variation.) In saying this I am assuming implicitly that more general solutions converge to steady states. A more satisfactory formulation can be obtained as follows. In a chemical reaction network there are usually a number of conserved quanitities, say C_\alpha. These define affine subspaces of the state space, the stoichiometric compatibility classes. For many systems there is exactly one stationary solution in each stoichiometric compatibility class. The condition of interest here is that the value of one of the concentrations, call it x_1, in the steady state solution is independent of the parameters C_\alpha. (The other concentrations x_i,i>1 will in general depend on the C_\alpha.) This property is ACR. I first heard of this in a talk by Uri Alon at the SMB conference in Krakow in the summer of 2011. The basic idea is explained clearly in a paper of Shinar and Feinberg (Science 327, 1389). They present a general theoretical approach but also describe some biological systems where ACR (in a suitable approximate sense) has been observed experimentally. In the terminology of Chemical Reaction Network Theory (CRNT) the examples they discuss have deficiency one. They mention that ACR is impossible in systems of deficiency zero. There is no reason why it should not occur in systems of deficiency greater than one but in those more complicated dynamics make it more difficult to decide whether the property holds or not.

The result of Shinar and Feinberg only covers a class of reaction networks which is probably very restricted. What Neigenfind does in his thesis is to develop more general criteria for ACR and computer algorithms which can check these criteria for given systems. The phenomenon of ACR is interesting since it is a feature which may be more common in reaction systems coming from biology than in generic systems. At least there is a good potential reason why this might be the case.

Talk on mathematical modelling in Karlstad

November 20, 2012

Yesterday I was in Karlstad in Sweden to give a talk on the uses of mathematical modelling in the natural sciences. I was invited to do this by Claes Uggla and I was very happy to have the opportunity to present some of my ideas on this subject. The talk was structured as a series of examples involving applications of different mathematical techniques. Many of these examples have been discussed in some form in this blog during the past few years and indeed a lot of my ideas on the subject were developed in conjunction with the blog posts. The subjects were William Harvey and the circulation of the blood, multidrug therapy for HIV-AIDS, the lizard Uta stansburiana, oscillations near the big bang, Liesegang rings, modelling oscillations in vole populations using a reaction-diffusion system, signal transduction in T cells.

As well as presenting a variety of applications of different types of mathematics I also wanted to explain some mathematical connections between these subjects. One central idea is that structural stability is an issue of key importance in modelling natural phenomena. Most phenomenological models involve parameters or other elements which are not known exactly. Thus to be of interest for applications features of the dynamics of the model should be invariant under arbitrary small perturbations of the system. More precisely, if a model does not possess an invariance of this type but is nevertheless useful this requires some explanation. One possible source of an explanation is the presence of what I call ‘absolute elements’ in the model. For instance, in population dynamics if a population is zero at some time then it will definitely remain zero. This fact is independent of the details of how the population grows when it is non-zero. Similarly a spacetime singularity can define an absolute element in cosmology. When the spacetime metric breaks down this ends the dynamics in a way which is independent of the details of the dynamics of the matter away from the singularity. Thus structural stability can be weakened to the condition of invariance under small perturbations which leave certain submanifolds fixed. This can lead to the appearance of relevant heteroclinic cycles although these are not structurally stable in the absolute sense. It explains the appearance of heteroclinic cycles in the models for lizards and for the big bang in a unified way. In a similar way, restricting the perturbations of a system of chemical reactions to those which leave a particular reaction irreversible can furnish the homoclinic orbit needed to model Liesegang rings.

I have now put a slightly extended version of this talk with references on my web page. On the same day there was a talk by Bernt Wennberg on models for the collective motion of birds and fish, concentrating mainly on kinetic models related to the Boltzmann equation. At the start of his talk he showed some of the well-known pictures of flocks of Starlings over Rome. In the evening I had my own pleasant experience with a flock of birds. A large number of Jackdaws (a couple of hundred) were flying around the central square in Karlstad and calling. For some reason I have become increasingly attached to the Jackdaw over the years. At this point, and without a good excuse, I want to tell a story about Jackdaws from the book ‘King Solomon’s Ring’ by Konrad Lorenz. It is a long time since I read the book and so I hope I do not distort the story too much. At one time Lorenz was living in a small village in Austria where he was regarded by the locals as a bit crazy. One of his interests was the social life of Jackdaws. There were Jackdaws living on the roofs of the houses and he climbed up to get close to them. In order to fit in better with his black subjects he decided to dress in black. The only ‘suitable’ black clothing he could find was a devil’s costume left over from a fancy dress party. No doubt the spectacle of him climbing over the roofs dressed as the devil perfected his reputation with the local inhabitants.

Conference on systems biology of T cells in Baeza, part 2

October 25, 2012

In the remaining one and a half days of the conference there were another fourteen talks and I will mention some aspects of their contents which attracted my attention. One recurring theme was that the encounter of a T cell receptor (TCR) with the peptide it recognizes bound to an MHC molelcule (pMHC) is often not just the encounter of one TCR with one pMHC but of multiple players. It can be shown by electron microscopy that the TCR tend to cluster on the surface of a T cell even before it has encountered antigen. This is done by attaching gold particles to the TCR so that they show up as black dots on the electron micrograph. It was shown in the talk of Hisse van Santen that a similar thing happens with the pMHC on the surface of antigen presenting cells. Judging from the discussion after the talk it seems that the explanation for this is that the pMHC, which are well known to be produced in the interior of the cell, are exported to the surface in groups. There also seems to be a widely held opinion that signalling through the T cell receptor is absolutely dependent on clustering of TCR. This makes life more complicated than it otherwise might have been. I learned at this conference that experiments on T cell signalling in vitro are often done by using tetramers, i.e. groups of four pMHC which are bound together covalently. In the talk of Wolfgang Schamel described experiments using tetramer binding. He said that this work was linked with some mathematical modelling, done by Thomas Höfer and others, but he did not want to take questions on that. My impression was that the model was an extension of the kinetic proofreading model. It has not yet been published and so I did not yet have an opportunity to look at it. Carmen Molina-París and Balbino Alarcón discussed cooperative effects in T cell receptor binding.

Michal Polonsky showed pictures of individual T cells trapped in small wells in a microfluidic device. When activated they wriggle very vigorously. These are the kind of pictures which could easily make you take a very anthropomorphic view of T cells. The aim of this work is to observe the differentiation, division and death of the cells over long periods (several days). If they were not trapped it would be extremely difficult to follow them under the microscope since they would be liable to run away. A break from the purely scientific talks was provided by a presentation of Dinah Singer about the systems biology programme at the National Cancer Institute in the US, a programme which she runs. Apart from concrete information about funding another aspect of this was the question of what might be learned about the potential for applying systems biology to immunology from existing applications of these ideas to cancer research. Dipankar Nandi talked about a phenomenon I had never heard of before and would never have expected – atrophy of the thymus as a consequence of certain diseases. Finally, I was on more familiar ground with the talk of Isabel Mérida about certain signalling pathways in T cell activation. The substance at the centre of her talk, diacylglycerol kinase, was not familiar to me but the context was. Right at the end of the conference there was a general discussion session planned. This session, which was led by Ed Palmer, ended up being very short. This was due to the (in itself positive) fact that the discussions after (and during) the individual talks had taken up more time than planned. The final discussion was interesting despite its brevity. The basic theme was: if mathematicians are collaborating with immunologists what can each side do to help the other in this process? Interesting points were brought up and we were all sent home with some things to think about.


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