Development of a computational mechanistic modelling framework for prediction of the development of antimicrobial resistance in homogenous populations

开发计算机械模型框架，用于预测同质人群中抗菌素耐药性的发展

• 批准号: 1654720
• 金额: --
• 依托单位: University of Aberdeen
• 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-1654720
• 关键词: Development; computational; mechanistic; modelling; framework

Resistance to antimicrobial agents is recognized as the major issue facing both industry and medicine. However, there is less consensus on the role, in generation of resistance, of other antimicrobials that are used in consumer products. Triclosan is a widely used antimicrobial in consumer products and a public opinion on it and antimicrobial resistance was published by the Scientific Committee on Consumer Safety1. Despite intensive effort, there is no quantitative framework available to describe and predict the development of resistance to 'actives'. In this project we will develop a modelling framework using microbiological and molecular techniques for the data generation and state-of-the art modelling approaches and computational capabilities for the framework development. This will provide the PhD student with an excellent training in microbial physiology and in systems biology. In parallel, the student will gain experience of industrial research through investigating a problem that is important both scientifically and for society.The proposed project will use systems biology approaches and laboratory generated data to inform and parameterise the models that will be developed. Models will be created using molecular dynamics, kinetic modelling and other methodologies. Unilever has access to high performance computing capabilities (both company-based and externally-located), that will be used for development and running of such models. Triclosan will be used as a case study. This compound is a well-studied 'active' with a very specific target and mechanism of action.The key outputs fall under the following categories:A modelling framework, integrating molecular dynamics, that can predict the effect of mutations on the affinity of Triclosan for FabIA modelling framework that will be able to predict the kinetics of FabI integrated into the metabolic pathway in the presence of Triclosan that takes into account transient cell to cell variations in FabI and the other members of the pathway that arise via gene duplications and the stochastic effects in gene expressionAn integrated multi-scale model that will predict the reaction of a homogenous population of microorganisms to the exposure to an antimicrobial agent, incorporating the new models.

对抗菌剂的耐药性被认为是工业和医学面临的主要问题。然而，对于消费品中使用的其他抗菌剂在产生耐药性方面的作用，还没有达成一致意见。三氯生是一种在消费品中广泛使用的抗菌剂，消费者安全科学委员会发表了关于三氯生的公众舆论和抗菌素耐药性1。尽管进行了密集的努力，但没有可用的量化框架来描述和预测对“活性物质”的抗药性的发展。在这个项目中，我们将使用微生物学和分子技术开发一个模型框架，以生成数据，并使用最先进的建模方法和计算能力来开发框架。这将为博士生提供微生物生理学和系统生物学方面的出色培训。同时，学生将通过研究一个对科学和社会都很重要的问题来获得工业研究的经验。拟议的项目将使用系统生物学的方法和实验室产生的数据来告知和参数化将要开发的模型。将使用分子动力学、动力学建模和其他方法创建模型。联合利华可以获得高性能计算能力(包括公司内部和外部)，这些能力将用于开发和运行此类模型。三氯生将被用作案例研究。这种化合物是一种经过充分研究的具有非常特定的作用靶点和作用机制的活性物质。主要成果分为以下几类：一个集成分子动力学的建模框架，它可以预测突变对三氯生亲和力的影响。该模型框架将能够预测在三氯生存在的情况下，整合到代谢途径中的Fabi的动力学，它考虑到Fabi和途径的其他成员通过基因复制和基因表达中的随机效应而产生的瞬时细胞到细胞的变化。一个集成的多尺度模型，将结合新的模型，将预测同质微生物种群对抗菌剂暴露的反应。