The Mathematical and Computational Modelling of Cytokine Networks

The immune system presents a complex multi-scale network, with cellular, inter- and intra-cellular components and serves as an exemplar of multiscale connectivity and signalling; from cell to cell, cytokine to cell and chemical to chemical within cells. In particular the cytokines are diffusing signaling molecules of fundamental importance in the immune system that orchestrate the communication among diverse immunological cells. Cytokine interactions may be summarised in terms of complex networks, overlaid with a dynamical system and thus often represented as coupled systems of nonlinear ordinary differential equations. One aim of this doctoral project will be to examine and understand the problem of network uncertainty for model selection and parameter estimation in inferring cytokine networks from experimental data, as well as the use of dynamical system theory to simplify and investigate the resulting models. For instance, a further aim of this work will be to investigate the stability of dynamical systems on such networks in the presence of temporal and spatial perturbations. A final aim will be to apply such understanding to interrogating modelling studies investigating the temporal dynamics of patient responses, and the behaviour of potential biomarkers, in the context of cytokine-targeting treatments for autoimmune and inflammatory diseases.
The initial studies will examine the dynamical system associated with the inferred network from a perturbation study of the cytokine profiles relevant to inflammatory bowel disease that are observed within in-vitro monocytes sourced from healthy human donors, who had been recruited via the Oxford gastrointestinal biobank (11/YH/0020 and 16/YH/0247) [1]. Further network inference studies based on this data from either this platform may be considered or similar datasets from the Arthritis Therapy Acceleration Programme, that is being run within the Kennedy Institute of Rheumatology at the University of Oxford.
The study impact will be in developing methodologies for the rational development of in silico models for cytokine interactions using multidimensional perturbation studies, in turn offering the scope for systematically informing potential targets for intervention in cytokine treatments for auto-immune disorders. The novelty of this study concerns the systematic use of the theory underlying networks, dynamical systems and Bayesian inference for the study of large systems of cytokines and aligns with the EPSRC areas of mathematical biology, nonlinear systems together with statistics and applied probability. Finally the project will involve extensive interaction with researchers from GlaxoSmithKline, who are part funding the studentship via an EPSRC iCASE award.