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Speakers and provisional programme
 9:3010:30
Andrew George (Imperial College London)
Extraordinary specificity and sensitivity  the story of the T cell
In most sensing systems there is a trade off between specificity and
sensitivity. However, the T cell is remarkable in being able to
respond to antigen with both exquisite sensitivity (responding to as
few as 110 peptide MHC complexes) and also extreme sensitivity (with
single amino acid substitutions altering the response). For this to
occur it is necessary for there to be mechanisms that operate at the
level of the T cell receptor, that integrate the signals at the level
of the cell and finally to interact at the level of the population of
responding cells. We have shown that enzymatic feedback pathways can
contribute to specificity at the level of the receptor. However, these
are not enough, and stochastic dissociation means that cross talk
between receptors and some integration of signals at the level of the
cell is important. In addition, the induction of anergic signals by
weak ligands prevents a ``Russian roulette'' activation of T cells by
suboptimal ligands. Thus consideration of the system at three levels
is necessary if we are to understand how T cell responses manage to
combine sensitivity and specificity.
 10:3011:30
Ed Evans (Oxford)
Quantitative analysis of the ensemble behaviour of Tcell surface receptors
Most proteins on the lymphocyte cell surface have now been identified. The
new challenge is to understand the nature of their interactions and hence
their function. The complex array of responses each of these cells can
make on encountering other cells and the huge number of different molecules
involved means that studies of single receptors in isolation are unlikely
to give a clear understanding of their roles. We have begun to model the
behavior of small sets of Tcell surface molecules using rigorously
determined biophysical parameters and expression levels to understand the
sources of complexity in Tcell responses. Initial work simulating the
synaptic accumulation of costimulatory or inhibitory complexes formed
between CD28 or CTLA4 and B71 or B72 showed the importance of these
parameters and of competition effects on functional outcomes. Further
studies of the interactions of the coreceptor CD4 shows how proper modeling
of its biophysical properties limits the possibilities for its function.
Finally, preliminary work on the random interactions possible between
molecules in the T cell membrane or between these molecules and those in
the cytoplasm suggest refinements to the kinetic proofreading model
showing that the capacity for differential signaling is intrinsic to the
architecture of the Tcell receptor triggering apparatus itself. Overall,
our initial forays into the modeling of complex systems based on firm
biophysical data have highlighted the importance of crossreactivity,
competition and hierarchies of affinities between leukocyte surface
molecules.
 11:3012:00 coffee
 12:001:00
Shev MacNamara (Oxford)
Stochastic modeling of T cell homeostasis for two
competing clonotypes via the master equation
Stochastic models for competing clonotypes of T cells by
multivariate, continuous
time, discrete state, Markov processes have been proposed in the
literature by Stirk, MolinaParis
and van den Berg (2008). A stochastic modeling framework is important
because of rare events
associated with small populations of some critical cell types.
Usually, computational methods for
these problems employ a trajectorybased approach, based on Monte
Carlo simulation. This is partly
because the complementary, probabilitydensity function (PDF)
approaches can be expensive but
here we describe some efficient PDF approaches by directly solving the
governing equations, known
as the Master Equation. These computations are made very efficient
through an approximation of
the state space by the Finite State Projection and through the use of
Krylov subspace methods when
evolving the matrix exponential. These computational methods allow us
to explore the evolution of
the PDFs associated with these stochastic models, and bimodal
distributions arise in some parameter
regimes. Timedependent propensities naturally arise in immunological
processes due to, for example,
agedependent effects. Incorporating timedependent propensities into
the framework of the Master
Equation significantly complicates the corresponding computational
methods but here we describe
an efficient approach via Magnus formulas. Although this contribution
focuses on the example
of competing clonotypes, the general principles are relevant to
multivariate Markov processes and
provide fundamental techniques for computational immunology.
 1:002:00 lunch
 2:003:00
Thomas Höfer (Heidelberg) Mathematical models for
proliferation and differentiation decisions of T cells The last
decade has seen an enormous advance of our knowledge on the molecules
of the immune system and their interactions. By comparison, we still
know rather little about the dynamics of the regulatory networks that
are formed by the molecular players. We have developed an iterative
approach of experimentation and mathematical modelling to dissect the
regulatory networks that govern the proliferation and differentiation
decisions of T cells. The models address different levels of molecular
organization, including the activation of a gene in its chromatin
context, the kinetics of generegulatory networks and spatiotemporal
patterns in intercellular signalling. Both qualitative analyses of
dynamic behavior and quantitative techniques for parameter estimation
and model selection are being employed. In this talk, I will show how
such mechanisticallybased kinetic analyses have helped to uncover the
origin of stochastic regulation of cytokine genes, find novel
regulatory interactions in gene networks, and identify emergent
phenomena that underlie the decision of a cell to proliferate upon
antigenic stimulation.
 3:004:00
Andrew Sewell (Cardiff)
Better Tcell immunity: you can improve any antigen and you can improve any Tcell receptor
 4:004:30
Andrew Phillips
A dynamical systems model of relative MHC class I antigen
presentation MHC class I molecules direct cytotoxic T lymphocytes
to destroy virusinfected or cancerous cells, thereby preventing
disease progression. MHC class I functions as a molecular detector by
binding to peptides arising from intracellular protein turnover and by
presenting these peptides at the cell surface. Peptides are selected
for presentation on the basis of their ability to form a stable
complex with MHC class I, by a process known as peptide editing. A
better understanding of the molecular mechanisms underpinning the
editing and relative presentation of peptides is important for our
understanding of immunodominance, the predominance of some T
lymphocyte specificities over others, which can determine the efficacy
of an immune response, the danger of immune evasion, and the success
of vaccination strategies. To aid our understanding of peptide editing
we have created a dynamical systems model that can be used to predict
the relative presentation of peptides for different MHC class I
alleles. The model proposes a principle of peptide filtering to
quantify relative presentation as a function of peptide supply and
stability of the MHC class Ipeptide complex. It also takes into
account the effects of the chaperone molecule tapasin, which has been
shown to influence the relative presentation of peptides to different
extents for different MHC class I alleles. The model is in agreement
with recent experimental observations of peptide editing both over
time and at steady state, and forms the basis of a powerful predictive
tool. The model is used to predict the relative presentation of Human
Immunodeficiency Virus peptides by MHC class I.
 4:305:00
Round table discussion: Joinedup Immunology

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List of participants
Becca Asquith 
Alistair Bailey 
Robert Busch 
Robin Callard 
Anne Corcoran 
James Currie 
Neil Dalchau 
Mark Day 
Omer Dushek 
Marjet Elemans 
Edward Evans 
Stephanie Foan 
Andrew George 
Thomas Hofer 
Thea Hogan 
Joanna Lewis 
Thomas Mazzocco 
Shev McNamara 
Carmen MolinaParís 
William Mwangi 
Andrew Phillips 
Carmen Pin 
Pavel Riha 
Benedict Seddon 
Andrew Sewell 
Mahima Swamy 
Najl Valeyev 
Hao Zhang 
 