Simulation Package for Efficient Experimental Design and Inference in Microbiology

在生物科学中，人们越来越认识到数学建模提供了强有力的方法来提高我们对生物系统动力学的定量理解。利用这些方法的全部潜力需要在适当的统计框架内完全整合实验数据和动态模型。然而，这一领域的进展是不协调的：虽然生物学（系统生物学）的一些领域领先，但其他领域则落后。我们的建议旨在开发和提供一个免费的计算包，这将有助于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-病原体相互作用。这个开源软件将有两个相关的和基本的功能：-统计推断（SI）：给定一个结合了当前知识和关于生物系统的假设的机械模型，可以从新的实验数据中提取多少关于无法直接观察到的机制的信息？- 最优实验设计（OED）：给定一个机械模型和初步数据，在设定的（预算或技术）约束条件下设计实验的最佳方法是什么，以最大限度地提高预期的信息增益？科学计算的最新进展使得SI和OED算法的快速发展成为可能，但它们已被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”。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。一个重要的新奇将是我们对随机模拟的关注，它允许系统动态的随机变化：就像在生物体实验中一样，重复相同的过程永远不会产生完全相同的结果。由于随机模型捕捉了真实的系统的这一基本特征，因此可以进行更可靠和更准确的推断，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领域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使用现有和新的数据开发和测试两个实验系统的功能包，然后将其免费发布并公开用于跨学科生物研究。该软件将作为一个软件包提供，供R软件使用，R软件是一个免费的统计平台。

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.