权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Simulation Package for Efficient Experimental Design and Inference in Microbiology

用于微生物学中高效实验设计和推理的仿真包

基本信息

批准号：
BB/M020193/1
负责人：
Olivier Restif
金额：
$ 17.71万
依托单位：
University of Cambridge
依托单位国家：
英国
项目类别：
Research Grant
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=BB%2FM020193%2F1
关键词：
Simulation Package Efficient Experimental Design

项目摘要

There is growing recognition within biological sciences that mathematical modelling provides powerful methods to improve our quantitative understanding of the dynamics of biological systems. Harnessing the full potential of these methods requires a complete integration of experimental data and dynamic models within the proper statistical framework. However, progress in this area has been patchy: while some fields of biology (systems biology) lead the way, others are lagging behind. Our proposal aims to develop and deliver a free computational package that will facilitate the complete integration of dynamic models and laboratory experiments, with an initial focus on research into host-pathogen interactions. This open-source software will have two related and essential functions: - statistical inference (SI): given a mechanistic model combining current knowledge and hypotheses about a biological system, how much information can be extracted from new experimental data about mechanisms that cannot be directly observed? - Optimal experimental design (OED): given a mechanistic model and preliminary data, what is the best way to design an experiment within set (budgetary or technical) constraints in order to maximise the expected gain of information?Recent progress in scientific computing has allowed the rapid development of algorithms for SI and OED, but they have been applied independently to other areas of research. Our project will deliver the first "one-stop shop" for inter-disciplinary research projects in microbiology. We will use state-of-the-art methods from applied statistics and tailor them to the specific needs of experimental biologists. An important novelty will be our focus on stochastic simulations, which allow random variations in the dynamics of a system: as in experiments with living organisms, repeats of the same procedure never yield exactly the same results. Because they capture this essential feature of real systems, stochastic models allow more reliable and accurate inference, albeit at the cost of greater computational complexity. Our many years of expertise at the interface of statistical modelling and experimental biology put us in a very strong position to tackle these challenges.This 18-month project will enable us to develop and test the functionality of the package with two experimental systems using existing and new data, before releasing it for free and public use in inter-disciplinary biological research. The software will be delivered as a package for use within the R software, which is a free statistical platform.

在生物科学领域，越来越多的人认识到，数学建模为提高我们对生物系统动力学的定量理解提供了强有力的方法。要充分利用这些方法的潜力，就需要在适当的统计框架内将实验数据和动态模型完全结合起来。然而，这一领域的进展参差不齐：虽然生物学的一些领域（系统生物学）走在了前面，但其他领域却落在了后面。我们的提案旨在开发和提供一个免费的计算包，以促进动态模型和实验室实验的完全整合，最初的重点是研究宿主-病原体的相互作用。这个开源软件将有两个相关的基本功能：-统计推断（SI）：给定一个结合当前知识和关于生物系统的假设的机制模型，从新的实验数据中可以提取多少不能直接观察到的机制信息？-最佳实验设计（OED）：给定一个机制模型和初步数据，在设定（预算或技术）约束条件下设计实验以最大化预期信息增益的最佳方法是什么？科学计算的最新进展使SI和OED的算法得以快速发展，但它们已独立应用于其他研究领域。我们的项目将为微生物学的跨学科研究项目提供第一个“一站式服务”。我们将使用应用统计学中最先进的方法，并根据实验生物学家的具体需求进行调整。一个重要的新奇之处将是我们对随机模拟的关注，它允许系统动力学中的随机变化：就像在生物实验中一样，重复相同的过程永远不会产生完全相同的结果。因为它们捕捉到了真实系统的这个基本特征，随机模型允许更可靠和准确的推断，尽管代价是更大的计算复杂性。我们在统计建模和实验生物学方面多年的专业知识使我们在应对这些挑战方面处于非常有利的地位。这个为期18个月的项目将使我们能够利用现有和新数据开发和测试两个实验系统的功能，然后将其免费发布给跨学科生物学研究的公众使用。该软件将作为软件包交付给R软件，R软件是一个免费的统计平台。