Simulation Package for Efficient Experimental Design and Inference in Microbiology

在生物科学界越来越多的人认识到，数学建模提供了强大的方法来提高我们对生物系统动力学的定量理解。充分利用这些方法的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，这一领域的进展参差不齐：虽然生物学(系统生物学)的一些领域走在了前面，但另一些领域却落后了。我们的建议旨在开发和提供一个免费的计算程序包，以促进动态模型和实验室实验的完全集成，最初的重点是宿主-病原体相互作用的研究。这个开源软件将有两个相关和基本的功能：-统计推理(SI)：给定一个结合了当前关于生物系统的知识和假设的机械模型，可以从关于不能直接观察的机制的新实验数据中提取多少信息？-最佳实验设计(OED)：给定一个机械模型和初步数据，在设定的(预算或技术)约束下设计实验的最佳方式是什么，以使信息的预期收益最大化？科学计算的最新进步使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领域的跨学科研究项目提供第一个“一站式商店”。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特定需求进行调整。一个重要的新颖性将是我们对随机模拟的关注，它允许系统动态的随机变化：就像在用活的有机体进行实验一样，重复相同的过程永远不会产生完全相同的结果。因为它们抓住了真实系统的这一基本特征，所以随机模型允许更可靠和准确的推理，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处于非常有利的地位来应对这些挑战。这个为期18个月的项目将使我们能够利用现有和新的数据，通过两个实验系统开发和测试整套系统的功能，然后将其发布给公众免费使用，用于跨学科的生物学研究。该软件将以包的形式交付，供R软件使用，该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.