Digital Daphnia: Unravelling Multi-Omics Mechanisms of Environmental Chemical Stress Using Graph Representation Learning

Chemical pollution poses a significant threat to ecosystems and human health. Traditionally, assessments of toxic effects have relied on animal testing, which is ethically contentious, time-consuming, and expensive. High-throughput omics technologies, such as transcriptomics and metabolomics, enable comprehensive analysis of biological samples, providing multi-omics data that reflect biomolecular responses to environmental chemical stress. This data is information-rich and well-suited for integrative in-silico modelling, offering the potential to accurately simulate an organism’s biological processes while reducing reliance on animal testing. However, the complexity of multi-omics data, characterised by small sample sizes, high dimensionality, and heterogeneity in data acquisition and processing, presents significant challenges for computational modelling and biological interpretation. Cutting-edge artificial intelligence approaches, such as graph representation learning and graph neural networks, can tackle these challenges by effectively integrating multi-omics data into comprehensive, coherent models.

Daphnia is a non-sentient model organism widely used in ecotoxicology research. It serves as an early warning indicator for environmental health due to its sensitivity to pollutants. This project aims to holistically model environmental multi-omics data using graph representation learning to create a digitalised Daphnia, unveiling the biomolecular mechanisms responding to environmental chemical pollution. By integrating experimental data with advanced computational technologies, this digital twin model will simulate biological processes across multiple levels of organisation.

This PhD project synergies with the EU H2020 project PrecisionTox, which is co-led by both supervisors at the University of Birmingham. The student will focus on multi-omics data from Daphnia magna, with the scope potentially broadening to include the other four model organisms utilised in the PrecisionTox project.

The PhD student will work at the interface between computational modelling (primary supervisor) and biological interpretation (secondary supervisor), acquiring a unique multidisciplinary profile with expertise in AI, systems biology, and environmental science. The student will also be embedded in two large pan-European initiatives, the Horizon 2020 consortium PrecisionTox and the Horizon Europe PARC. These initiatives provide an extensive multidisciplinary network of experts in diverse fields.

Funding notes:

This project is funded by the Midlands Integrative Biosciences Training Partnership (MIBTP), a BBSRC-funded doctoral training partnership between the universities of Warwick, Birmingham, Leicester, Aston, Harper Adams and Coventry.

The studentship includes payment of fees, a tax-free stipend in line with the UKRI rate (£19,237 in the 2024/25 academic year), a travel/conference budget, a generous consumables budget, and use of a laptop computer for the duration of the programme. International student fees will be waived (not including visa and health insurance surcharge).

For more information about MIBTP: https://warwick.ac.uk/fac/cross_fac/mibtp/

University of Birmingham MIBTP projects: https://www.birmingham.ac.uk/research/activity/mibtp

Applying the PhD: https://sits.bham.ac.uk/urd/sits.urd/run/siw_ipp_lgn.login?process=siw_ipp_app&code1=FR167D&code2=0004

References:

[1]. P. Barbiero, R. Viñas Torné, and P. Lió, ‘Graph Representation Forecasting of Patient’s Medical Conditions: Toward a Digital Twin’, Front. Genet., vol. 12, p. 652907, Sep. 2021, doi: 10.3389/fgene.2021.652907.

[2]. W. Ju et al., ‘A Comprehensive Survey on Deep Graph Representation Learning’, Neural Networks, vol. 173, p. 106207, May 2024, doi: 10.1016/j.neunet.2024.106207.