Using comparative genomics to develop 'digital twins' to support SMART ecotoxicological predictions

我们快速变化的世界正在使重要的生态系统处于前所未有的精神压力之下，这种压力包括暴露于广泛的化学毒物。该博士学位的首要目标是利用基因组和其他traitresources提供有毒物种敏感性的机械知情估计。生态系统的有效保护需要化学毒性的知识。然而，这些信息必须在3R目标的范围内获得，即努力减少、改进和取代对动物的使用。在没有化学暴露实验的情况下准确预测物种对有毒物质的敏感性将是实现这一目标的重要一步。然而，实现这一目标需要对相关生物学途径、其在物种间的保护以及非洲的深入机械理解，以便于进行比较和评估。幸运的是，这些目标现在已经可以实现，因为快速增加的基因组资源包含了控制污染物敏感性的分子组分的比较数据宝库。这项博士研究将为理解化学品对生态系统的影响做出宝贵的贡献，而无需进行动物暴露。博士生将获得必要的技能和理解，以生成生物体的计算机表示（称为“数字孪生”），这将为比较生态毒理学评估提供信息。为了产生这样的“数字双胞胎”，博士生将研究和设计一个系统，将分子信息，物种特征数据和建模工具集成在一个模块化框架内。这项工作的目的是建立一个自动化的数据基础设施，其中包含各种数据类型（例如基因组数据、能量和表型性状），可用于快速检索与毒物敏感性预测相关的跨物种信息。一旦基础设施建立，博士生将奋进开发方法（例如使用人工智能），以帮助更好地预测各种物种特征对敏感性的复杂和多变量贡献。这种人工智能方法的输出将与已建立的生态毒理学数据相结合，以验证物种特征和观察到的敏感性之间的联系。自动化的数据基础设施和它所产生的机械洞察力将提高生态毒理学家和环境管理者预测未测试物种敏感性的能力

Our rapidly changing world is placing critical ecosystems under unprecedentedenvironmental pressures, pressure that includes exposure to a wide-range of chemicaltoxicants. The overarching aim of this PhD is to harness genomic and other traitresources to deliver mechanistically informed estimates of toxicant species sensitivity.The efficient protection of ecosystems requires knowledge of chemical toxicity. However,such information has to be obtained within the context of the 3Rs goal, i.e. the effort toReduce, Refine and Replace the use of animals. Accurate prediction of species sensitivityto toxicants without chemical exposure experiments would represent a major step towardfulfilling this ambition. However, realising this aim requires a deep mechanisticunderstanding of relevant biological pathways, their conservation across species, and aframework to facilitate easy comparison and assessment. Fortunately, such objectivesare now achievable, as rapidly increasing genomic resources contain a treasure trove ofcomparative data on the molecular components governing pollutant sensitivity. This PhDresearch will make an invaluable contribution to understanding chemical effects onecosystems without performing animal exposures.The PhD student will obtain the skills and understanding necessary to generate in silicorepresentations of organisms (termed 'digital twins') that will inform comparativeecotoxicological assessment. To generate such 'digital twins', the PhD student willinvestigate and design a system to integrate molecular information, species trait data andmodelling tools within a modular framework. This will be done with the aim of producingan automated data infrastructure containing varied data types (e.g. genomic data,energetic and phenotypic traits), that can be used to rapidly retrieve cross-speciesinformation relevant to toxicant sensitivity predictions. Once the infrastructure isestablished, the PhDstudent will endeavor to develop approaches (e.g. using artificialintelligence) to help better predict the complex and multi-variate contributions made byvarious species characteristics to sensitivity. The output of such artificial intelligenceapproaches will be combined with established ecotoxicological data to validate linksbetween species characteristics and observed sensitivity. Both the automated datainfrastructure and the mechanistic insight it produces will increase the capacity ofecotoxicologists and environmental regulators to predict sensitivity in untested species