Mathematical models of the epidermal growth factor receptor to quantify

该项目在数学生物学和生物化学领域，是基于这样一个假设：受体酪氨酸激酶信号通路的新型随机数学模型的发展将使我们能够为一些生物学挑战提供答案：信号通路中给定蛋白质的拷贝数如何影响细胞反应的类型和时间尺度，以及受体结合位点的蛋白质竞争如何通过打开/关闭不同的细胞内回路（如内吞作用、降解、再循环或蛋白质合成）来驱动不同的细胞命运。该项目的目标是：-开发新的数学模型，以了解控制受体EGFR（或表皮生长因子受体）动态的机制，例如二聚化，磷酸化，内化，降解，-开发新的数学模型来理解细胞系之间的差异，以便能够预测作为配体浓度函数的信号反应，以及不同配体，-开发包括EGFR突变的数学模型，以便评估突变如何改变EGFR- egf系统的动力学，从而了解突变如何赋予目前市场上可用的一些EGFR药物抑制剂耐药性，并探索不同数学模型的行为，以确定破药甚至耐药EGFR药物的理想分子目标。研究项目的新颖性该项目在数学上的新颖性和挑战性在于将分子、细胞和种群尺度结合在一起。学生将利用生灭马尔可夫过程，随机描述子理论[?]]和基于agent的建模，以便与AZ的实验数据一起，她可以预测患者对不同EGFR信号通路抑制剂的选择。学生将使用新颖的矩阵分析方法来研究和分析一些随机描述子。该学生还将利用新颖的贝叶斯统计方法，将在AZ生成的实验数据与项目中开发的受体信号的数学模型结合起来，以进行参数推断。她还将开发新的基于主体的模型，以表征由受体信号传导介导的细胞反应。该项目的潜在应用从工业角度来看，该项目的新颖性在于将AZ产生的实验数据和新颖的随机数学和计算方法结合起来，研究健康和疾病中的EGFR信号通路。数学模型将考虑上皮细胞可能具有不同的RTKs突变亚型，以及不同数量的结合复合物（或配体）与受体之间的相互作用。这种联合（实验和数学）的方法将使我们能够研究细胞的命运，从而了解细胞的异质性和上面讨论的耐药机制。特别是，该项目的一个潜在应用是开发新的受体信号传导和小分子抑制剂在肿瘤学中的作用的数学模型，以提供对耐药性出现机制的更综合的理解，以便开发抗药甚至新的耐药药物。在过去的二十年里，阿斯利康一直在开发EGFR抑制剂，并在这一领域保持活跃。事实上，他们最新的非小细胞肺癌治疗药物Tagrisso在2016年上半年筹集了超过1.43亿美元。

The project, in the fields of Mathematical Biology and Biological Chemistry, is based on the hypothesis that the development of novel stochastic mathematical models of receptor tyrosine kinase signalling pathways will allow us to provide answers to some biological challenges: how does the copy number of a given protein in the signalling pathway affect the type and timescale of cellular response and how does protein competition for binding sites on receptors drive different cellular fates by turning on/off different intra-cellular circuits, such as endocytosis, degradation, recycling or protein synthesis.The objectives of the project are:- to develop new mathematical models to understand the mechanisms that control the dynamics of thereceptor EGFR (or epidermal growth factor receptor), such as the relative importance of dimerisation,phosphorylation, internalisation, degradation, recycling and synthesis of EGFR in relation to signalling,- to develop new mathematical model to understand the differences across cell lines in order to be able topredict signalling responses as a function of ligand concentration, and for different ligands,- to develop a mathematical model that includes EGFR mutations in order to evaluate how mutations change the dynamics of the EGFR-EGF system, and thus understand how mutations confer resistance to some of the EGFR drug inhibitors currently available in the market, and- to explore the behaviour of different mathematical models to identify ideal molecular targets for resistance breaking or even resistance-proof EGFR drugs.Novelty of the research projectThe mathematical novelty and challenge of the project is to bring together the molecular, cellular and population scales. The student will make use of birth and death Markov processes, the theory of stochastic descriptors [?] and agent-based modelling, so that together with the experimental data from AZ, she can predict patient selection for different inhibitors of the EGFR signalling pathway. The student will make use of novel matrix analytic methods to study and analyse a number of stochastic descriptors. The student will also make use of novel Bayesian statistical methods to bring together experimental data generated at AZ with the mathematical models of receptor signalling developed in the project in order to carry out parameter inference. She will also develop novel agent-based models to characterise cellular responses mediated by receptor signalling.Potential applications of the projectFrom an industrial perspective the novelty of the project resides in bringing together experimental data generated at AZ and novel stochastic mathematical and computational methods to study the EGFR signalling pathway in health and disease. The mathematical models will consider that epithelial cells may have different mutant isoforms of RTKs, as well as the interactions between different number of binding complexes (or ligands) and receptors. This joint (experimental and mathematical) approach will allow us to study cell fate and thus, to understand cellular heterogeneity and the resistance mechanism discussed above.In particular, a potential application of the project is to develop novel mathematical models of receptor signalling and the role of small molecule inhibitors in Oncology, to provide a more integrated understanding of the mechanisms of resistance emergence in order to develop both resistance-busting and even new resistance-proof drugs. AZ has been developing EGFR inhibitors for the past twenty years and remains very active in this area. In fact, their latest non-small cell lung cancer treatment, Tagrisso has raised over $143 million in the first half of 2016.