System Dynamics Modelling of the Processes Involving the Maintenance of the Naive T Cell Repertoire

The study of immune system aging, i.e. immunosenescence, is a relatively new research topic. It deals with understanding the processes of immunodegradation that indicate signs of functionality loss possibly leading to death. Even though it is not possible to prevent immunosenescence, there is great benefit in comprehending its causes, which may help to reverse some of the damage done and thus improve life expectancy. One of the main factors influencing the process of immunosenescence is the number and phenotypical variety of naive T cells in an individual. This work presents a review of immunosenescence, proposes system dynamics modelling of the processes involving the maintenance of the naive T cell repertoire and presents some preliminary results.Comment: 6 pages, 2 figures, 1 table, 9th Annual Workshop on Computational Intelligence (UKCI 2009), Nottingham, U

Immunodepression and Immunosuppression During Aging

International audienc

Juxtaposition of system dynamics and agent-based simulation for a case study in immunosenescence

Advances in healthcare and in the quality of life significantly increase human life expectancy. With the aging of populations, new un-faced challenges are brought to science. The human body is naturally selected to be well-functioning until the age of reproduction to keep the species alive. However, as the lifespan extends, unseen problems due to the body deterioration emerge. There are several age-related diseases with no appropriate treatment; therefore, the complex aging phenomena needs further understanding. It is known that immunosenescence is highly correlated to the negative effects of aging. In this work we advocate the use of simulation as a tool to assist the understanding of immune aging phenomena. In particular, we are comparing system dynamics modelling and simulation (SDMS) and agent-based modelling and simulation (ABMS) for the case of age-related depletion of naive T cells in the organism. We address the following research questions: Which simulation approach is more suitable for this problem? Can these approaches be employed interchangeably? Is there any benefit of using one approach compared to the other? Results show that both simulation outcomes closely fit the observed data and existing mathematical model; and the likely contribution of each of the naive T cell repertoire maintenance method can therefore be estimated. The differences observed in the outcomes of both approaches are due to the probabilistic character of ABMS contrasted to SDMS. However, they do not interfere in the overall expected dynamics of the populations. In this case, therefore, they can be employed interchangeably, with SDMS being simpler to implement and taking less computational resources

Antigenic variation in the African trypanosome: molecular mechanisms and phenotypic complexity

Antigenic variation is an immune evasion strategy that has evolved in viral, bacterial and protistan pathogens. In the African trypanosome this involves stochastic switches in the composition of a variant surface glycoprotein (VSG) coat, using a massive archive of silent VSG genes to change the identity of the single VSG expressed at a time. VSG switching is driven primarily by recombination reactions that move silent VSGs into specialized expression sites, though transcription-based switching can also occur. Here we discuss what is being revealed about the machinery that underlies these switching mechanisms, including what parallels can be drawn with other pathogens. In addition, we discuss how such switching reactions act in a hierarchy and contribute to pathogen survival in the face of immune attack, including the establishment and maintenance of chronic infections, leading to host-host transmission

Molecular mechanisms underlying the control of antigenic variation in African trypanosomes

African trypanosomes escape the host adaptive immune response by switching their dense protective coat of Variant Surface Glycoprotein (VSG). Each cell expresses only one VSG gene at a time from a telomeric expression site (ES). The [`]pre-genomic' era saw the identification of the range of pathways involving VSG recombination in the context of mono-telomeric VSG transcription. A prominent feature of the early post-genomic era is the description of the molecular machineries involved in these processes. We describe the factors and sequences recently linked to mutually exclusive transcription and VSG recombination, and how these act in the control of the key virulence mechanism of antigenic variatio

Multi-scale immune selection and the maintenance of structured antigenic diversity in the malaria parasite Plasmodium falciparum

The most virulent malaria parasite, Plasmodium falciparum, makes use of extensive antigenic diversity to maximise its transmission potential. Parasite genomes contain several highly polymorphic gene families, whose products are the target of protective immune responses. The best studied of these are the PfEMP1 surface proteins, which are encoded by the var multi-gene family and are important virulence factors. During infection, the parasite switches expression between PfEMP1 variants in order to evade adaptive immune responses and prolong infection. On the population level, parasites appear to be structured with respect to their var genes into non-overlapping repertoires, which can lead to high reinfection rates. This non-random structuring of antigenic diversity can also be found at the level of individual var gene repertoires and var genes themselves. However, not much is known about the evolutionary determinants which select for and maintain this structure at different ecological scales. In this thesis I investigate the mechanisms by which multi-scale immune selection and other ecological factors influence the evolution of structured diversity. Using a suite of theoretical frameworks I show that treating diversity as a dynamic property, which emerges from the underlying infection and transmission processes, has a major effect on the relationship between the parasite’s transmis- sion potential and disease prevalence, with important implications for monitoring control efforts. Furthermore, I show that an evolutionary trade-off between within-host and between-host fitness together with functional constraints on diversification can explain the structured diversity found at both the repertoire and parasite population level and might also account for empirically observed exposure-dependent acquisition of immunity. Together, this work highlights the need to consider evolutionary factors acting at different ecological scales to gain a more comprehensive understanding of the complex immune-epidemiology of P. falciparum malaria

The Rules of Human T Cell Fate in vivo.

The processes governing lymphocyte fate (division, differentiation, and death), are typically assumed to be independent of cell age. This assumption has been challenged by a series of elegant studies which clearly show that, for murine cells in vitro, lymphocyte fate is age-dependent and that younger cells (i.e., cells which have recently divided) are less likely to divide or die. Here we investigate whether the same rules determine human T cell fate in vivo. We combined data from in vivo stable isotope labeling in healthy humans with stochastic, agent-based mathematical modeling. We show firstly that the choice of model paradigm has a large impact on parameter estimates obtained using stable isotope labeling i.e., different models fitted to the same data can yield very different estimates of T cell lifespan. Secondly, we found no evidence in humans in vivo to support the model in which younger T cells are less likely to divide or die. This age-dependent model never provided the best description of isotope labeling; this was true for naïve and memory, CD4+ and CD8+ T cells. Furthermore, this age-dependent model also failed to predict an independent data set in which the link between division and death was explored using Annexin V and deuterated glucose. In contrast, the age-independent model provided the best description of both naïve and memory T cell dynamics and was also able to predict the independent dataset