Stochastic models of T cell receptor and cytokine receptor signalling: a mathematical and computational approach

T 细胞受体和细胞因子受体信号传导的随机模型：数学和计算方法

• 批准号: 1665655
• 金额: --
• 依托单位: University of Leeds
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2015
• 资助国家: 英国
• 起止时间: 2015 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1665655
• 关键词: Stochastic; models; cell; receptor; cytokine

This project supports the Healthcare Technologies Theme by providing cost-effective computational tools to aid development and optimisation of treatments for intracellular bacterial infections using Francisella tularensis as a case study.Mathematical models and statistical models will be developed to enable simulation of the within-host mechanisms of infection and immune response allowing potential interventions to be explored in silico. A wide range of biological and immunological data will be integrated to increase understanding of the mechanisms at molecular, cellular and population levels.This application intersects the EPSRC research areas od biological informatics, complexity science and non-linear systems, employing mathematical, statistical and numerical techniques. The work will provide novel computational tools for operational research which will be utilised by the Project Partner.

该项目支持医疗保健技术主题，提供具有成本效益的计算工具，以帮助开发和优化细胞内细菌感染的治疗方法，并以土拉弗朗西斯菌为例进行研究。将开发数学模型和统计模型，以模拟宿主内感染和免疫反应机制，从而在计算机上探索潜在的干预措施。将广泛的生物学和免疫学数据整合，以增加在分子，细胞和群体水平上的机制的理解。这个应用程序交叉EPSRC的研究领域的生物信息学，复杂性科学和非线性系统，采用数学，统计和数值技术。这项工作将提供新的计算工具，用于项目合作伙伴将使用的运筹学。

项目成果

A Novel Stochastic Multi-Scale Model of Francisella tularensis Infection to Predict Risk of Infection in a Laboratory.

• DOI: 10.3389/fmicb.2018.01165
• 发表时间: 2018
• 期刊: Frontiers in microbiology
• 影响因子: 5.2
• 作者: Carruthers J;López-García M;Gillard JJ;Laws TR;Lythe G;Molina-París C
• 通讯作者: Molina-París C