The Mathematical and Computational Modelling of Cytokine Networks

免疫系统呈现复杂的多尺度网络，具有细胞、细胞间和细胞内组分，并且充当多尺度连接和信号传导的范例;从细胞到细胞、细胞因子到细胞以及细胞内的化学物质到化学物质。特别地，细胞因子是免疫系统中至关重要的扩散信号分子，其协调不同免疫细胞之间的通信。细胞因子的相互作用可以概括为复杂的网络，覆盖一个动态系统，因此通常表示为耦合系统的非线性常微分方程。这个博士项目的一个目的是研究和理解网络不确定性的问题，用于从实验数据推断细胞因子网络的模型选择和参数估计，以及使用动力系统理论来简化和研究所产生的模型。例如，这项工作的另一个目的是研究在时间和空间扰动的存在下，这种网络上的动力系统的稳定性。最终的目标将是将这种理解应用于调查患者反应的时间动态的询问建模研究，以及潜在生物标志物的行为，在细胞因子的背景下，针对自身免疫性和炎症性疾病的靶向治疗。最初的研究将检查与从观察到的炎症性肠病相关的细胞因子谱的扰动研究中推断的网络相关的动力学系统。在来源于健康人供体的体外单核细胞中，这些供体通过牛津胃肠道生物库招募（11/YH/0020和16/YH/0247）[1]。基于来自该平台的数据的进一步网络推理研究可以被考虑，或者来自关节炎治疗加速计划的类似数据集，该计划正在牛津大学肯尼迪流变学研究所内运行。该研究的影响将是开发方法，用于使用多维扰动研究合理开发细胞因子相互作用的计算机模型，从而为系统地告知自身免疫疾病细胞因子治疗干预的潜在目标提供范围。这项研究的新奇涉及系统地使用网络，动力系统和贝叶斯推理理论来研究大型细胞因子系统，并与数学生物学，非线性系统以及统计学和应用概率的EPSRC领域保持一致。最后，该项目将涉及与葛兰素史克研究人员的广泛互动，他们通过EPSRC iCASE奖为学生提供部分资金。

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.