The Mathematical and Computational Modelling of Cytokine Networks

免疫系统呈现一个复杂的多尺度网络，由细胞、细胞间和细胞内组成，是多尺度连接和信号传递的典范；从细胞到细胞，从细胞因子到细胞，从细胞内的化学物质到化学物质。特别是，细胞因子正在扩散免疫系统中至关重要的信号分子，这些信号分子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，覆盖着一个动力系统，因此通常被表示为非线性常微分方程组的耦合系统。这个博士项目的目的之一将是检查和理解从实验数据推断细胞因子网络中模型选择和参数估计的网络不确定性问题，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将是研究在存在时间和空间扰动的情况下这类网络上的动力系统的稳定性。最终目的是将这种理解应用于在自身免疫和炎症性疾病的细胞因子靶向治疗的背景下调查患者反应的时间动力学和潜在生物标志物行为的模拟研究。初步研究将检验从与炎症性肠病相关的细胞因子图谱的扰动研究中推断的网络相关的动力系统，这些细胞因子图谱来自健康的人类捐赠者的体外单核细胞，这些单核细胞是通过牛津胃肠生物库招募的(11/YH/0020和16/YH/0247)[1]。基于来自该平台的数据的进一步的网络推断研究可以被考虑，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风湿病研究所内运行。这项研究的影响将是开发利用多维扰动研究合理开发细胞因子相互作用的电子模型的方法，进而为系统地告知干预自身免疫疾病的细胞因子治疗的潜在靶点提供范围。这项研究的新颖性涉及到系统地使用网络、动力系统和贝叶斯推理背后的理论来研究细胞因子的大系统，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，该项目将与葛兰素史克的研究人员进行广泛的互动，他们通过EPSRC iCASE奖为该奖学金提供部分资金。

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.