BBSRC MATSYB network  I2M: Immunology, Imaging and Modelling

third meeting of the network: 15 Sep 2008

School of Mathematics, University of Leeds.

[c]

Artificial Immune Systems

Programme

  • 9:30-10:15

    Jon Timmis

    York

    Immune Systems and Computation: An interdisciplinary Adventure

    Artificial Immune Systems (AIS) is a diverse area of research that attempts to bridge the divide between immunology and engineering and are developed through the application of techniques such as mathematical and computational modeling of immunology, abstraction from those models into algorithm (and system) design and implementation in the context of engineering. Whilst AIS has become known as an area of computer science and engineering that uses immune system metaphors for the creation of novel solutions to problems, we argue that the area of AIS is much wider and is not confined to the simple development of new algorithms. In this talk we would like to broaden the understanding of what AIS are all about, thus driving the area into a true interdis- ciplinary one of genuine interaction between immunology, mathematics and engineering.
  • 10:15 -11:00

    Andy Hone

    Kent

    Theoretical results on artificial immune systems

    Artificial immune systems are a relatively new area of bio-inspired computation. The inspiration for artificial immune algorithms has come from biological models of the natural immune system, especially the theories of clonal selection, immune networks and negative selection. Moreover, these algorithms have been successfully employed in a variety of different application areas. However, until quite recently their has been a dearth of theoretical studies to justify their use. In this talk, the existing theoretical work on artificial immune systems is reviewed, and some of the future challenges in this area are highlighted.
  • 11:00-11:30 coffee(level 9)
  • 11:30 -12:15

    Uwe Aickelin

    Nottingham

    AIS applications

    This talk will cover various applications of AIS from their IS inspiration to their computational implementation. I will point out what in my opinion has worked well and what hasn't. I will probably cover 5-6 different applications, one of them in greater detail to give an idea how we do it (using artificial Dendritic Cells for Anomaly detection - based on Matzinger's Danger Theory).
  • 12:15 -12:45

    Paul Andrews

    York

    Simulating biology: Towards understanding what the simulation shows

    When building simulations of complex systems the task of validation is often overlooked. Validation helps provide confidence in the simulation by exploring the link between the models that we build and the real complex system. In this talk I will highlight how software engineering validation techniques can be used on complex systems. I provide an example of a simulation of lymphocytes migrating from high-endothelial venules into lymph nodes through the walls of the blood vessel. This example suggests that explicitly stating the modelling and simulation assumptions is key to the process of validating the simulation.
  • 12:45 -1:15

    Nick Owens

    York

    Modelling the Tunability of Early T Cell Signalling Events

    The Tunable Activation Threshold hypothesis of T Cells is investigated through computational modelling of T cell signalling pathways which may contribute towards tuning. Modelling techniques involving the pi-calculus and use of the PRISM model checker are presented, then applied to produce a stochastic model of T cell signalling. Finally we present initial results from analysis of the model.
 


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