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

点击翻译按钮获取中文摘要

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