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

Our rapidly changing world is placing critical ecosystems under unprecedented environmental pressures, pressure that includes exposure to a wide-range of chemical toxicants. The overarching aim of this PhD is to harness genomic and other trait resources 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 to Reduce, Refine and Replace the use of animals. Accurate prediction of species sensitivity to toxicants without chemical exposure experiments would represent a major step toward

fulfilling this ambition. However, realising this aim requires a deep mechanistic understanding of relevant biological pathways, their conservation across species, and a framework to facilitate easy comparison and assessment. Fortunately, such objectives are now achievable, as rapidly increasing genomic resources contain a treasure trove of

comparative data on the molecular components governing pollutant sensitivity. This PhD research will make an invaluable contribution to understanding chemical effects on ecosystems without performing animal exposures. The PhD student will obtain the skills and understanding necessary to generate in silico

representations of organisms (termed 'digital twins') that will inform comparative ecotoxicological assessment. To generate such 'digital twins', the PhD student will investigate and design a system to integrate molecular information, species trait data and modelling tools within a modular framework. This will be done with the aim of producing

an automated data infrastructure containing varied data types (e.g. genomic data, energetic and phenotypic traits), that can be used to rapidly retrieve cross-species information relevant to toxicant sensitivity predictions. Once the infrastructure is established, the PhDstudent will endeavor to develop approaches (e.g. using artificial

intelligence) to help better predict the complex and multi-variate contributions made by various species characteristics to sensitivity. The output of such artificial intelligence approaches will be combined with established ecotoxicological data to validate links between species characteristics and observed sensitivity. Both the automated data

infrastructure and the mechanistic insight it produces will increase the capacity of ecotoxicologists and environmental regulators to predict sensitivity in untested species