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

使用比较基因组学开发“数字双胞胎”以支持 SMART 生态毒理学预测

• 批准号: 2874184
• 金额: --
• 依托单位: CARDIFF UNIVERSITY
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2874184
• 关键词: Using; comparative; genomics; develop; digital

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

我们快速变化的世界正在将关键的生态系统置于前所未有的环境压力之下，其中包括接触各种化学毒物。该博士学位的首要目标是利用基因组和其他性状资源来提供有毒物质敏感性的机械知情估计。有效保护生态系统需要了解化学毒性。然而，此类信息必须在 3R 目标的背景下获得，即努力减少、改善和替代动物的使用。在不进行化学暴露实验的情况下准确预测物种对毒物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途径、跨物种保护以及促进轻松比较和评估的框架有深入的机械理解。幸运的是，这些目标现在是可以实现的，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成分的比较数据宝库。这项博士研究将为了解生态系统的化学效应做出宝贵贡献，而无需进行动物暴露。博士生将获得在硅片中生成生物体（称为“数字双胞胎”）所需的技能和理解，这将为比较生态毒理学评估提供信息。为了生成这样的“数字双胞胎”，博士生将研究和设计一个系统，将分子信息、物种性状数据和建模工具集成到模块化框架内。这样做的目的是生成一个包含各种数据类型（例如基因组数据、能量和表型特征）的自动化数据基础设施，可用于快速检索与毒物敏感性预测相关的跨物种信息。一旦基础设施建立起来，博士生将努力开发方法（例如使用人工智能）来帮助更好地预测各种物种特征对敏感性造成的复杂和多变量的贡献。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。自动化数据基础设施及其产生的机制洞察力都将提高生态毒理学家和环境监管机构预测未经测试物种敏感性的能力