Simulation Package for Efficient Experimental Design and Inference in Microbiology

点击翻译按钮获取中文摘要

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.