Leveraging genomics and artificial intelligence to develop predictive pesticide risk assessment frameworks for wild bees (Ref 4559)

项目大纲：保护野生蜜蜂免受杀虫剂的伤害：你是否有兴趣对决定蜜蜂传粉者对杀虫剂等毒素的敏感性的分子系统进行基础性发现，同时产生可用于保护蜜蜂及其提供的重要生态系统服务的知识和工具？这个令人兴奋的项目将使用一系列尖端的计算和实验方法来解决在控制破坏性作物病虫害的同时保护蜜蜂等“无辜的旁观者”物种的挑战。蜜蜂是世界上最重要的环境和经济昆虫之一，为各种开花植物授粉，并在各种粮食和商品作物的生产中发挥关键作用。然而，在进行这种生态系统服务时，蜜蜂可能会接触到各种潜在的有害毒素，如农业中使用的杀虫剂。目前的蜜蜂传粉者杀虫剂风险评估依赖于为西方蜜蜂等少数“模型”管理蜜蜂物种收集的实验数据。然而，蜜蜂是一个高度多样化的昆虫群体，包括20,000多个已知物种。因此，迫切需要找到一种方法，在农药风险评估中不再使用来自少数管理模式蜜蜂物种的数据作为野生蜜蜂的代理。该项目将利用蜜蜂可用基因组信息的急剧增加，结合酶结构和酶表达的三维建模的最新进展，开发用于蜜蜂农药风险评估的新工具和预测管道。监督团队在蜜蜂-农药相互作用的主题上有着良好的工作和出版记录，其中包括将为该项目提供额外资金的工业合作伙伴，以及确保基础知识在保护蜜蜂传粉者方面产生应用影响的极佳途径。您将接受各种最先进的方法的培训，这些方法在学术界和工业界非常受雇主的追捧。这些将包括生物信息学(基因组序列分析)、酶结构的三维建模、昆虫(蜜蜂和果蝇)生物分析、分子生物学(基因克隆和表达)和生物化学(酶分析)。

Project Outline:Protecting wild bees from pesticides:Are you interested in making fundamental discoveries into the molecular systems that determine the sensitivity of bee pollinators to toxins such as pesticides, while also generating knowledge and tools that can be used to protect bees and the vital ecosystem services they provide? This exciting project will use a range of cutting-edge computational and experimental approaches to address the challenge of controlling damaging crop pests while protecting 'innocent bystander' species such as bees.Bees are among the world's most environmentally and economically important group of insects, pollinating a remarkable diversity of flowering plants and playing a key role in the production of a wide range of food and commodity crops. However, while carrying out this ecosystem service bees can be exposed to a variety of potentially harmful toxins such as pesticides used in agriculture. Current bee pollinator pesticide risk assessment relies on experimental data collected for a handful of 'model' managed bee species such as the western honeybee. However, bees are a highly diverse group of insects comprising more than 20,000 known species. Thus, there is an urgent need to find ways to move beyond the use of data derived from a few managed model bee species as a proxy for wild bees in pesticide risk assessment.This project will leverage the dramatic increase in genomic information available for bees, in combination with recent advances in three-dimensional modelling of enzyme structures and enzyme expression, to develop new tools and predictive pipelines for bee pesticide risk assessment. The supervisory team have a strong track record of working and publishing on the topic of bee-pesticide interactions and include an industrial partner who will provide additional funding for the project, and an excellent route to ensure the fundamental knowledge generated results in applied impact in terms of protecting bee pollinators.You will be trained in a variety of state-of-the-art approaches that are highly sought-after by employers in academia and industry. These will include bioinformatics (analysis of genome sequences) three-dimensional modelling of enzyme structures, insect (bee and Drosophila) bioassays, molecular biology (gene cloning and expression) and biochemistry (enzyme assays).