Academic Lead – Jonathan Timmis  (York)

Immune responses involve interactions between a plethora of different cell types and involve complex
intracellular signalling pathways and extra-cellular signals. Although experimental approaches have provided
a great deal of information on the molecular and cellular mechanisms governing outcomes of immune
responses, understanding and predicting outcomes of immune responses is extremely complex. Modelling
immune responses and associated signalling pathways utilizing computer simulations and mathematical tools
will help determine both rules governing complexity and provide a framework to predict outcomes of immune
responses. These outcomes will be utilized as the basis for further experimentation, creating an iterative
experimental and simulation process. Work undertaken within the network will allow for the evaluation of the
various modelling approaches and their applicability (or otherwise) to the modelling of the variety of
immunological processes.

Modelling immune responses requires a cross-disciplinary, cohesive approach involving active interactions
between immunologist, computer scientists and mathematicians. By applying a multi-disciplinary approach to
this key scientific problem provides a framework for key advances in immunology, mathematics, modelling
and simulation. Specific emphasis is made to maintain a bidirectional flow of information. While initially,
the biological data will constitute the basis for the modelling, predictions made will be tested in in vitro
experiments, and if confirmed, be used to direct experimental approaches and designs, as previously
described (Pogson et al., 2008).

Complementarity: Each of the PhD projects involves a pairing between a physical scientists (primary
supervisor) and experimental immunologist (secondary supervisor), and utilise a variety of modelling
approaches (x machines, agent, mathematical) to tackle different immunological problems. This creates a
highly synergistic network driven by complementation of the different scientific approaches.

Added Value: The network provides a unique opportunity to bring together experimental and physical scientists
and PhD-students in a synergistic network whose teams have not previously worked together. The network
provides a unique forum to discuss the application of different modelling approaches to complex biological
problems. The network provides the blue print for further collaborative between experimental medicine
and modelling. The network utilizes key White Rose resources including the White Rose Grid e-Science
Centre.

Strategic Relevance: The project integrates scientists from across the White Rose Universities providing
true integration between basic and applied experimental immunology with advanced modelling techniques a
key research priority area for the BBSRC, MRC and EPSRC. Individually this set of researchers are
international experts in their respective fields, the White Rose Immune Modelling Network provides the
forum for international leadership in this emerging field.

Network Projects

Agent Based Modelling of the NF-κB Pathway

Principal Supervisor – Jonathan Timmis (York)

Modelling Tumour – NK cell dynamics

Principal Supervisor – Mike Holcombe (Sheffield)

Stochastic Modelling of the Aging Immune System

Principal Supervisor – Carmen Molina-Paris (Leeds)