2025-B32 Integrating Transcriptomics and Phenomics for accelerating chemical safety assessment

Project highlights

Multidisciplinary science – This project combines toxicology, bioinformatics, and computational science to develop computational modelling for integrating transcriptomics and phenomics approaches.

Big data science – This project uses advanced computational tools to analyse big data sets of both bio-molecular sequences and behavioural images for network modelling. This action enables a deeper understanding of chemical toxicity at both the molecular and organismal levels.

Regulatory Innovation: This project aims to advance regulatory science by developing computational models that align with the Adverse Outcome Pathway (AOP) framework. By providing a mechanistic link between molecular changes and phenotypic outcomes, this approach supports the regulatory acceptance of integrated omics data, facilitating more accurate, non-animal-based chemical safety assessments.

Overview

Chemical pollution poses a severe threat to human and environmental health, with over 350,000 chemicals present in commercial products and the environment. However, comprehensive toxicity data for these chemicals are largely lacking, hindering their safety assessment. Traditional animal testing methods face limitations due to their low predictive value for humans, high costs, ethical concerns, and societal pressure. There is a growing global focus on developing innovative safety assessment methods that are more relevant to human health, provide mechanistic insights, and allow for the efficient screening of large numbers of substances and their mixtures without relying on animals.

‘Omics’ approaches—such as genomics, proteomics, transcriptomics, and metabolomics—have revolutionized non-animal safety assessments by measuring global biomolecular changes in cells exposed to chemicals. However, traditional omics approaches face significant challenges in connecting biomolecular alterations with toxicological outcomes of regulatory concern. Conversely, phenotype-based whole organism screening methods (referred to as phenomics can assess individual-level effects by direct observations of behavioural toxicity. However, phenomics approaches fall short in providing specific insights into the mechanisms through which chemicals exert their toxic effects, which can hinder the development of precise assessment of chemical safety.

This project aims to develop a novel multi-omics methodology that integrates transcriptomics and phenomics approaches to overcome the current limitations of omics methods in regulatory toxicology. This will be achieved by deploying high-throughput transcriptomics and phenomics approaches to identifying global gene expressions and behavioural changes induced by chemicals in Daphnia magna. The transcriptomics and phenomics data will be integrated into an adverse outcome pathway (AOP) network framework to develop a computational modelling for chemical toxicity prediction. The team combines the knowledge base expertise in developing novel tools for multi omics-based assessment of environmental chemicals and computational modelling for accurate prediction of chemical toxicity with the integration of both high-throughput molecular and phenotype-level data, accelerating current regulatory acceptance of omics approaches in chemical safety assessment.

Figure 1: Key steps to integrate transcriptomics and phenomics approaches for predicting toxic effects of chemicals.

Host

University of Birmingham

Theme

Organisms and Ecosystems

Supervisors

Project investigator

Pu Xia, University of Birmingham ([email protected])

Co-investigators

Prof. John Colbourne, University of Birmingham ([email protected]), Chair of Environmental Genomics, Director of Centre for Environmental Research and Justice (CERJ)

How to apply

Each host has a slightly different application process.
Find out how to apply for this studentship.
All applications must include the CENTA application form.
Choose your application route

Methodology

Wetlab Skills: The DR will gain expertise in high-throughput RNA-Seq and behavioral screening technologies to evaluate the bioactivity of environmental chemicals in Daphnia magna. This will involve sample preparation, RNA extraction, sequencing, and the use of automated platforms for behavioural toxicity assessment.

Computational toxicology: Data on gene expression and behavioral responses will be generated from over 200 chemicals tested on Daphnia magna. The DR will learn to analyze these complex datasets using advanced bioinformatics tools, and data integration techniques to identify concentration-dependent effects.

Machine Learning Modelling: The DR will develop machine learning models to predict chemical toxicity by integrating transcriptomics and phenomics data within an Adverse Outcome Pathway (AOP) framework. This will involve training algorithms to identify patterns, assess predictive accuracy, and refine models for robust, data-driven toxicity prediction, enhancing regulatory decision-making in chemical safety assessment.

Training and skills

DRs will be awarded CENTA Training Credits (CTCs) for participation in CENTA-provided and ‘free choice’ external training. One CTC can be earned per 3 hours training, and DRs must accrue 100 CTCs across the three and a half years of their PhD.

The DR will receive multidisciplinary training spanning from computational toxicology and ecotoxicology (Xia), and high-throughput biology and functional genomics (Colbourne). The DR will benefit from access to the most cutting-edge high-throughput transcriptomics and phenomics platforms in the country.

Further details

For inquiries please contact Dr Pu Xia [email protected].

 To apply to this project: 

You must include a CENTA studentship application form, downloadable from: CENTA Studentship Application Form 2025.

You must include a CV with the names of at least two referees (preferably three) who can comment on your academic abilities.

Please submit your application and complete the host institution application process via: https://sits.bham.ac.uk/lpages/LES068.htm.   Please select the PhD Bioscience (CENTA) 2025/26 Apply Now button. The CENTA Studentship Application Form 2025 and CV can be uploaded to the Application Information section of the online form.  Please quote CENTA 2025-B32 when completing the application form.

 Applications must be submitted by 23:59 GMT on Wednesday 8th January 2025.  

Possible timeline

Year 1

Literature review and project planning. Training in high-throughput RNA-Seq, behavioral screening, and bioinformatics analysis. Initial experimental setup and optimization using Daphnia magna. Begin collecting transcriptomics and phenomics data from selected chemicals.

Year 2

Complete high-throughput data collection for all chemicals. Analyze gene expression and behavioral data using bioinformatics and multivariate statistics. Develop and refine data integration strategies within the AOP framework. Begin training and validation of machine learning models for toxicity prediction.

Year 3

Finalize computational models and validate predictive performance. Integrate findings into a comprehensive framework for regulatory toxicology applications. Prepare manuscripts for publication and present findings at conferences. Write up the thesis and incorporate feedback from supervisors and peers.