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Modelling autocrine cytokine networks

Jit, Kresna Mark Surinder; (2003) Modelling autocrine cytokine networks. Doctoral thesis (Ph.D), UCL (University College London). Green open access

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Abstract

Cytokines are pleiotropic mediators of intercellular signaling. They interact with one other, their specific receptors and other mediators to form complex networks. This thesis employs results from nonlinear dynamics, cell engineering, pharmacology and immunology to develop a framework within which these networks can be understood. We begin by modelling kinetic and trafficking processes between a cytokine and its receptor using ordinary differential equations. Various forms of these equations under different assumptions are considered, and put in the context of existing cell signalling models. This theory is used to investigate two situations with experimental and clinical relevance. The first is regulation by interleukin-1 (IL-1) of its receptor in fibroblasts. A model fitted to experimental data shows that up-regulation by IL-1 of its receptor is mediated by an intracellular eicosanoid which may not be prostaglandin E2 as previously assumed. The second situation is the use of anti-tumour necrosis factor-α (TNF-α) drugs to combat rheumatoid arthritis. Kinetic parameters for two such drugs are used to simulate a model of their effect on TNF-α dynamics. The single cytokine model is extended to relationships between several cytokines and their receptors. The ways in which cytokines combine to affect response are formalised using drug response theory. This is used to construct and compare models based on hypotheses in the literature about the cytokine network involved in inflammation. It is further generalised to examine the outcomes possible when a configuration of cytokines interact in general ways. We explore through analysis and simulation what a network's dynamical behaviour says about its intrinsic structure. Finally, we consider a stochastic model where cells in the same population produce cytokines at different rates due to time-dependent variation. Results are compared to experimental findings using flow cytometry to investigate IL-1 production in monocytes exposed to a lipopolysaccharide stimulus.

Type: Thesis (Doctoral)
Qualification: Ph.D
Title: Modelling autocrine cytokine networks
Open access status: An open access version is available from UCL Discovery
Language: English
Additional information: Thesis digitised by ProQuest.
Keywords: Biological sciences; Differential equations
URI: https://discovery.ucl.ac.uk/id/eprint/10098522
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