CERJ-PARC PhD Cohort

Title: Eco Digital Twins: Leveraging Environmental DNA and Artificial Intelligence for Monitoring Biodiversity Loss

• Primary Supervisor: Dr Jiarui Zhou
• Supervisor: Professor Luisa Orsini
• Supervisor: Dr Louis Dawson

Project description

Societal and economic prosperity relies on healthy, resilient ecosystems sustained by biodiversity, which includes taxonomic, functional, and genetic diversity ¹. Biodiversity supports essential services like food, medicine, clean air and water, and protection from natural disasters ^2-4. Despite global efforts like the Kunming-Montreal Global Biodiversity Framework and the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES), biodiversity is declining at an unprecedented rate, pushing the Earth beyond six of the nine planetary boundaries–limits for humanity to operate safely without destabilizing Earth systems ⁵. In 2023, the supervisory team has started a PARC case study (BioAI), which aims to transform our understanding of the long-term effects of chemical mixtures on biodiversity by leveraging lake sediment archives and AI-based forecasting. This project responds to a wider interest in PARC in developing new metrics and tools to define Ecological Risk Assessment for biodiversity.

Research question

Currently, there are no effective tools to predict the impact of chemicals on natural biodiversity, limiting our ability to protect critical ecosystems and the services they provide. A major challenge for conservationists is assessing the severity of biodiversity loss and identifying its key drivers. This project will leverage advanced AI techniques applied to environmental and biodiversity data, including environmental DNA (eDNA), to develop a predictive framework for biodiversity loss under various climate and pollution scenarios. By utilizing data from the BioAI case study, the project aims to pinpoint the causes of biodiversity decline—both now and in the future—to support regulatory interventions that mitigate the effects of chemical pollution and inform conservation strategies.

Methodology and techniques used throughout the PhD:

The PhD applicant will utilize Temporal Graph Networks (TGNs) to analyse biodiversity and environmental data collected from freshwater ecosystems in the BioAI case study. TGNs, a state-of-the-art AI model, will be employed to develop a digital twin for freshwater biodiversity. This digital twin will integrate historical eDNA records and environmental data, dynamically modelling their interactions over time. By providing multi-scale, holistic modelling, it will track changes across taxonomic groups in both spatial and temporal dimensions, linking them to environmental shifts and identifying key drivers of biodiversity loss.

Using these temporal trends, the digital twin will forecast biodiversity loss under both business-as-usual and restoration scenarios, offering valuable insights for conservation planning. With the potential to become a leading tool for biodiversity management, this approach could revolutionize conservation strategies.

To ensure accessibility for end-users, the PhD researcher will develop an intuitive analytical dashboard, enabling direct assessment of the impact of land use, production processes, and human activities on biodiversity. By integrating cutting-edge computational and bioinformatics techniques with real-world conservation needs, this project aims to bridge the gap between traditional conservation methods and science-driven, technology-enhanced solutions for biodiversity preservation.

The PhD student will develop the following technical skills:

1. Temporal Graph Networks (TGNs) will be employed to model dynamic changes in biodiversity over time and space in response to environmental factors. Designed to model graph-structured data that evolve over time, TGNs can effectively capture temporal dependencies and node interactions, facilitating monitoring and prediction of biodiversity loss.

Digital Twins. Digital twins will be constructed as a data-driven representation of real-world ecosystems. The biodiversity digital twin will integrate environmental factors and biodiversity quantified with eDNA, utilising TGNs to dynamically model their interactions, offering multi-scale and multi-taxa predictions.

Translation. To create long-lasting impacts beyond the project, the PhD student will design a user-friendly interface that will be tested and applied to real-world cases by regulators and other stakeholders in PARC to ensure the transfer of knowledge and drive the translation of research findings into end-user applications.

Anticipated outcomes:

This PhD project will have a significant impact on scientists, regulators, and PARC community by advancing biodiversity forecasting. By integrating AI-driven forecasting with environmental and biodiversity data, it will provide new insights into the long-term effects of chemical mixtures on biodiversity. The development of a digital twin for freshwater ecosystems using Temporal Graph Networks will enable scientists to dynamically model biodiversity changes over time and space, identifying key drivers of loss. Additionally, the project’s user-friendly analytical dashboard will make advanced AI tools accessible, fostering broader adoption of data-driven biodiversity assessments.

For regulators and PARC in particular, this project directly supports Ecological Risk Assessment by offering predictive tools to assess biodiversity loss under various climate and pollution scenarios. The digital twin will enable scenario testing, helping policymakers evaluate the impact of different regulatory actions before implementation. By developing a standardized, scalable framework for biodiversity risk assessment, this project aligns with PARC’s strategic goals, ensuring that research findings translate into evidence-based conservation policies and regulatory decisions at national and European levels.

Timeline:

Year 1: To develop an explainable and generalisable biodiversity forecast model for lake ecosystems using historical biodiversity data from sediment archives and environmental variables. Student conference in Birmingham. Write the literature review.

Year 2: Application of Temporal Graph Networks (TGNs) and other AI models to identify drivers of biodiversity loss under current and future scenarios of pollution and climate change. Draft empirical paper.

Year 3: Working with stakeholders in PARC and CASE partner. Develop and test the user-friendly dashboard for the end-user application. Write thesis. Attend international conference.

References:

1 Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 486, 59-67, doi:10.1038/nature11148 (2012).

2 Baert, J. M., Janssen, C. R., Sabbe, K. & De Laender, F. Per capita interactions and stress tolerance drive stress-induced changes in biodiversity effects on ecosystem functions. Nat Commun 7, 12486, doi:10.1038/ncomms12486 (2016).

3 Bonebrake, T. C. et al. Integrating Proximal and Horizon Threats to Biodiversity for Conservation. Trends Ecol Evol 34, 781-788, doi:10.1016/j.tree.2019.04.001 (2019).

4 Ruckelshaus, M. H. et al. The IPBES Global Assessment: Pathways to Action. Trends Ecol Evol 35, 407-414, doi:10.1016/j.tree.2020.01.009 (2020).

5 Richardson, K. et al. Earth beyond six of nine planetary boundaries. Sci Adv 9, eadh2458, doi:10.1126/sciadv.adh2458 (2023).

Contact person: Dr Jiarui Zhou

Title: Resilience through Multi-hazard Early warning and Systemic Chemical-risk Understanding for Sustainable Lake Ecosystem (RESCUE)

• Primary supervisor: Professor David Hannah
• Supervisor: Professor Iseult Lynch
• Supervisor: Dr Tahmina Yasmin

Project description

Work on an exciting interdisciplinary project exploring chemical risks and multi-hazard interactions in Lough Neagh, combining environmental science and human geography approaches.

Gain hands-on experience in water sampling and quality analysis, stakeholder dialogue, resilience building strategies by developing both technical and social research skills.

Contribute to addressing urgent environmental challenges, enabling early detection, enhance risk communication, and promote sustainable management of freshwater ecosystem facing ecological and human-induced threats.

Scientific motivation and research gaps

Lough Neagh, the largest freshwater lake in the UK and a vital ecological and economic resource for Northern Ireland, is under increasing threat from complex environmental challenges. Nutrient enrichment from excessive fertilisation of farmland, climate change, and the proliferation of invasive species are among the key drivers of water quality decline and are driving the increasing prevalence, frequency, and intensity of Harmful Algal Blooms (HABs) since the mid-20th century (Ho, Michalak, and Pahlevan, 2019). These toxic events, also referred to as eutrophication, disrupt aquatic ecosystems services, harm public health, and impact economic activities (Amorim and Moura, 2020). Factors such as land-use changes, urbanization, deforestation, irrigation, and water abstraction exacerbate HABs impacts, while climate change contributes by increasing surface water temperatures and altering precipitation patterns with frequent flooding events (Griffith and Gobler, 2020). Invasive species further disrupt ecosystems by altering grazing, nutrient cycling, and water filtration (Hallegraeff, 1992; Pal et al., 2020).

In addition to contaminated drinking water, toxins from HABs can become airborne and transported inland, posing additional health risks through inhalation secreted by (Lim et al., 2023). However, the relationship between HAB aerosols and health outcomes remains unclear despite the potential for population-level exposures. Together, these interconnected risks demand the need for systemic approaches to managing ecosystem and human health.

The 2023 HAB event in Lough Neagh exposed critical ecological and public health risks, including nutrient pollution, presence of thirteen human pathogens and Ireland’s first-documented harmful cyanotoxins (anabaenopeptins) (Reid et al., 2024). marked by its severity and duration, revealed significant ecological and public health risks. Key findings included elevated nutrient inputs from livestock, wildfowl, and untreated wastewater, the presence of 13 pathogens capable of causing human illnesses, and disrupted biodiversity, water quality, tourism, and livelihoods, highlighting the urgent need for sustainable, evidence-based management.

Research Aim

The RESCUE project aims to develop a comprehensive understanding of systemic chemical risks and multi-hazard interactions in Lough Neagh to enable early detection, effective communication, and sustainable management of ecological and anthropogenic threats.

Research Objectives

To achieve this aim, the following four objectives have been formulated:

Objective 1: Examine how chemical and biological risks and multi-hazard interactions, such as flooding and HABs, interconnect and propagate within Lough Neagh, impacting its ecological integrity and essential ecosystem services, and the datasets that need to be integrated and aligned (in terms of spatial scale, resolution etc.) to facilitate this.

Objective 2: Identify and validate ecological and anthropogenic indicators from the integrated datasets suitable for the early detection of threats to water quality, biodiversity, and critical ecosystem services, including drinking water and aquatic habitats.

Objective 3: Design and implement a stakeholder-specific early warning framework that integrates water quality and health surveillance data to ensure timely identification and mitigation of risks.

Objective 4: Develop tailored communication strategies to engage diverse stakeholders, such as lake owners, local authorities, and communities, fostering awareness, collaboration, and coordinated action.

Methodological approach and skills development

The research will employ a mixed-methods approach, integrating spatial-analysis, indicator development, stakeholder communication design, and field-based studies (including water quality monitoring and assessment of aerosol generation) to achieve its objectives. A GIS-based tool will be utilized to map the cascading impacts of multi-hazard interactions, such as flooding and algal blooms, using spatial and temporal datasets. This analysis will include a focus on the water-health nexus by examining data on hospital visits before and after HAB incidents—providing insights into the broader societal impacts of these events. To develop tailored indicators for early warning systems, the research will identify and validate datasets capturing both ecological and anthropogenic factors. Key ecological indicators, such as algal biomass and dissolved oxygen levels, changes in aerosol compositions, will be combined with anthropogenic indicators like land-use patterns and nutrient inflows to provide a comprehensive understanding of the lake’s vulnerabilities. The study will also design and test stakeholder-specific communication strategies. These strategies will ensure the inclusion of diverse groups, such as local authorities, lake owners, and community organizations, promoting awareness and collaboration for risk mitigation.

Timeline and significance

Field studies will be conducted in two phases, integrating data collection, modelling, and participatory feedback loops. Gerry Darby from the Lough Neagh Partnership (LNP) will be supporting the field work and contributing to the development of stakeholder engagement strategies. These iterative phases will help refine the early warning framework, ensuring its effectiveness and adaptability to local contexts. Tentative plan for the work includes

Year 1: Baseline assessment, stakeholder engagement, and data acquisition and curation.

Year 2: Development and validation of chemical risk indicators; preliminary risk modelling.

Year 3: Pilot implementation of early warning systems in selected sites; stakeholder workshops.

Year 3.5: Publication of results, and dissemination of findings.

References

• Amorim, C. A., & Moura, A. N. (2020). Effects of the manipulation of submerged macrophytes, large zooplankton, and nutrients on a cyanobacterial bloom: A mesocosm study in a tropical shallow reservoir. Environmental Pollution, 265, 114997.
• Griffith, A. W., & Gobler, C. J. (2020). Harmful algal blooms: A climate change co-stressor in marine and freshwater ecosystems. Harmful Algae, 91, 101590.
• Hallegraeff, G. M. (1992). Harmful algal blooms in the Australian region. Marine pollution bulletin, 25(5-8), 186-190.
• Ho, J. C., Michalak, A. M., & Pahlevan, N. (2019). Widespread global increase in intense lake phytoplankton blooms since the 1980s. Nature, 574(7780), 667-670.
• Pal, M., Yesankar, P. J., Dwivedi, A., & Qureshi, A. (2020). Biotic control of harmful algal blooms (HABs): A brief review. Journal of environmental management, 268, 110687.
• Reid, N., Reyne, M. I., O’Neill, W., Greer, B., He, Q., Burdekin, O., ... & Elliott, C. T. (2024). Unprecedented Harmful algal bloom in the UK and Ireland’s largest lake associated with gastrointestinal bacteria, microcystins and anabaenopeptins presenting an environmental and public health risk. Environment international, 190,

Contact person: Professor David Hannah

Title: Integration of Transcriptomics and Phenomics within AI-Enhanced AOP Framework for Next-Generation Risk Assessment

• Primary supervisor: Dr Pu Xia
• Supervisor: Dr Iestyn Stead
• Supervisor: Dr Zhiling Guo
• Supervisor: Biyao Han

Project description

The over 350,000 chemicals present in commercial products and the environment poses a severe threat to human and environmental health. However, due to the low efficient and high cost of traditional animal-based toxicological testing, comprehensive toxicity data for these chemicals are largely lacking. 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 while minimizing reliance on animals.

Toxicogenomics approaches measure global molecular changes to better understand the adverse health effects of toxicants, revolutionizing next-generation chemical risk assessment. Transcriptomics is central to toxicogenomics, capturing early snapshots of transcriptional responses to stressors and providing quantitative, cost-effective, high-throughput data. However, transcriptomics data at the biomolecular level alone is challenging to interpret in the context of toxicological outcomes. Phenomics can enhance the systematic understanding of chemical toxicity patterns by analyzing traits—features that define toxic phenotype—through computer-assisted image analysis. Yet, a critical challenge remains on fully integrating transcriptomics and phenomics data to reveal meaningful relationships and predict chemical toxicity.

Artificial intelligence (AI) has been instrumental in analyzing large toxicogenomic datasets, but the “black-box” nature of AI models limits their interpretability and regulatory acceptance of toxicogenomics. Interpretable AI addresses this limitation by providing transparency into model decision-making, helping to uncover toxicity mechanisms and support regulatory decision-making. However, current interpretable AI models are not specifically designed to integrate transcriptomics and phenomics signatures for toxicity prediction.

The Adverse Outcome Pathway (AOP) framework offers a promising solution by linking molecular changes to toxicological outcomes in a structured and mechanistic manner. However, its application in omics data interpretation has been largely restricted to manual case studies, lacking automated methods to translate omics data into chemical toxicity predictions. Developing AOP-based AI model presents a promising yet underexplored opportunity to address this gap.

This project aims to develop a novel methodology that integrates transcriptomics and phenomics approaches within the AOP framework to accelerate the use of toxicogenomics in risk assessment. Using Daphnia magna as a model organism, high-throughput RNA-Seq and behavioral screening will identify molecular and phenotypic changes induced by 250 chemical substances in Precision Tox project. These datasets will be leveraged to develop AOP-based AI models for toxicity prediction, aligning with PARC’s goals of advancing regulatory science and reducing reliance on animal testing.

The PhD student will receive multidisciplinary training in computational toxicology (Xia), behavioural screening (Iestyn), environmental toxicology (Guo) and ecotoxicology (Han) supported by both academic and industry mentorship. The project, co-developed with industrial partner, ExxonMobil Biomedical Sciences (EMBSI), focuses on advancing New Approach Methodologies (NAMs) using transcriptomics, phenomics, and AI to improve safety assessments while ensuring scientific rigor and regulatory relevance.

Research Questions
This PhD research aims to address key challenges in next-generation chemical risk assessment by integrating transcriptomics and phenomics data using interpretable AI within the Adverse Outcome Pathway (AOP) framework. The central research questions include:

1. How can transcriptomics and phenomics data be effectively integrated to improve the understanding and prediction of chemical toxicity?
2. Can interpretable AI models enhance the predictive power and regulatory acceptance of toxicogenomics data?
3. How can AOPs be computationally modelled to provide a mechanistic link between molecular-level omics changes and toxicological outcomes?
4. To what extent can a high-throughput, AI-driven approach reduce reliance on animal testing while maintaining or improving the accuracy of toxicity assessments?

Methodology and Techniques
This research employs a multidisciplinary approach combining toxicogenomics, AI-driven data analysis, and computational modelling within the AOP framework. The study will be conducted in three key phases:

1. Data Collection and Experimental Design

• Model Organism: Daphnia magna, a widely used aquatic invertebrate model for ecotoxicology.
• Chemical Exposure Studies: Selected environmental chemicals with known and unknown toxicological profiles will be used to assess dose-dependent responses.
• High-Throughput Transcriptomics: RNA-Seq will be performed to capture early molecular responses to chemical exposure (This dataset will be directly retrieved from Precision Tox project).
• Automated Behavioral Screening: Computer-assisted image analysis will quantify phenotypic traits, including locomotion, feeding behavior, and morphological changes.

2. Data Integration and Feature Selection

• Multimodal Data Fusion: Integration of transcriptomics and phenomics data to establish connections between molecular perturbations and observable toxic effects.
• Mechanistic Interpretation: Identification of key biological pathways and phenotypic markers linked to toxicity.

3. Interpretable AI and Machine Learning Models

• Model Development: Use of interpretable machine learning algorithms (e.g., SHAP, LIME, decision trees) to enhance the transparency of toxicity predictions.
• AOP-Based Computational Modelling: Development of predictive models aligning with AOPs to map molecular and phenotypic changes to adverse outcomes.
• Validation: Comparison with existing toxicological databases and regulatory benchmarks to ensure model reliability and applicability.

Anticipated Outcomes and Impact

1. Academic Contributions

• Development of novel methodologies for integrating transcriptomics and phenomics.
• Advancements in interpretable AI for multi-omics data analysis.
• Contribution to the expansion of AOP-based predictive modelling for chemical risk assessment.
• High-impact publications in toxicology and environmental science journals.

2. Broader Intellectual and Public Impact

• Improved understanding of chemical toxicity mechanisms.
• Development of an open-access database linking transcriptomics and phenomics signatures to toxicity outcomes.
• Enhancement of regulatory science by providing AI-driven tools for rapid toxicity assessment.

3. Commercial and Industrial Potential

• Potential applications in pharmaceutical, chemical, and environmental industries for rapid toxicity screening.
• Collaboration with industry partners to integrate omics and AI-driven approaches into safety assessment pipelines.
• Contribution to the development of non-animal testing alternatives, aligning with global sustainability and ethical initiatives.

Timeline

Year 1: training and experimental setup

• One year of structured training in toxicogenomics and AI modelling.
• Establishment of experimental protocols and initial pilot studies using Daphnia magna.
• Development of computational pipelines for transcriptomics and phenomics data processing.

Years 2-3.5: active research

• Execution of phenotypic screening experiments.
• Retrieve high-throughput RNA-Seq data from Precision Tox.
• Data analysis to identify key molecular and phenotypic markers.
• Optimization of multimodal data integration approaches.
• Development and training of interpretable AI models for toxicity prediction.
• Integration of AOP-based computational modelling.
• Validation using independent datasets and regulatory benchmarks.
• Engagement with industrial stakeholders to explore real-world applications.
• Presentation of findings at conferences and publication in high-impact journals.
• Knowledge transfer through workshops, industry collaborations, and policy engagement.
• Thesis writing and defense.

Contact person: Dr Pu Xia

Title: Restoring environmental justice with Next Generation Risk Assessment (NGRA) for diverse ethnic populations

• Primary supervisor: Dr Archana Sharma-Oates
• Supervisor: Dr Rosie Day
• Supervisor: Dr Synda Obaji

Project description

Environmental pollution is a significant global health issue, causing approximately 9 million deaths annually and disproportionately affecting socioeconomically deprived communities. Factors such as residential location, occupational hazards, and lifestyle choices contribute to this burden. However, less is known about how behavioural patterns influence exposure to environmental pollutants and toxicities. Minority ethnic populations in the UK, including South Asian and African Caribbean communities, often live in urban areas with high air pollution and are more likely to work in occupations with increased exposure to harmful substances. Combined with dietary, lifestyle factors and a genetic predisposition, these environmental exposures contribute to higher incidences of Type 2 Diabetes, immune-mediated diseases, and cardiovascular diseases, as well as earlier disease onset and longer durations. This highlights the complex interplay of social, environmental, and genetic factors influencing health.

Understanding the behavioural patterns and specific risks associated with long-term, low-level exposure to pollutants is crucial for developing targeted public health interventions.

The precise focus of the research is to be developed by the researcher, but may address such questions as:

• How do occupation, transport, and / or behaviours in the home influence exposure to pollutants in minority ethnic populations in the UK?
• What are the health impacts of long-term, low-level exposure to pollutants in these communities, and how do these impacts compare between deprived and affluent areas?
• How do exposures to emerging pollutants, such as plastics, correlate with health outcomes?
• What targeted public health interventions and policies can modify behaviours to mitigate the health impacts of environmental pollution and reduce health disparities?

Research methods are expected to involve some or all of:

• Qualitative data collection and analysis through interviews and focus groups to understand behaviours influencing pollutant exposure.
• Linking health records with pollution data at the population level to identify large-scale associations between exposure and health status or disease prevalence.
• Developing models to estimate exposure levels based on behavioural patterns and socioeconomic factors.
• Using tools to determine exposure levels of emerging pollutants (e.g., plastics, nanoparticles).
• Assessing health impacts through biomarkers (e.g. blood pressure, glucose levels) medication usage and other health indicators.

It is expected that research findings will be translated into regulatory policy recommendations with support from legal experts.

Person specification

Applicants should have a bachelor’s degree at upper second class (or equivalent), or master’s with merit (over 60%) in a relevant area i.e. in health Data Science, Public Health, Environmental Health, Sociology or a related discipline.

Applicants should be familiar with programming languages such as Python and R and have strong written and verbal communication skills.

Experience with qualitative and quantitative research would be desirable although some training will be provided. Prior experience of engaging with diverse communities would be an advantage. An interest in translating research into policy impact would be beneficial.

Application enquiries: Dr Archana Sharma-Oates

Title: 3D Printing and Magneto-Acoustic Techniques to Establish Lung-On-A-Chip Platforms for Chemical Safety Assessment

• Primary supervisor: Dr Zhiling Guo
• Supervisor: Dr Pu Xia
• Supervisor: Professor Iseult Lynch

Project Description

This Phd proposal aims to develop a lung-on-a-chip (LoC) platform integrating 3D printing and magneto-acoustic techniques to improve chemical safety assessment by providing a physiologically relevant, high-throughput alternative to traditional toxicity testing. The key research questions include:

(1) How can 3D printing and magneto-acoustic stimulation enhance lung-on-a-chip functionality for toxicity studies?

(2) Can this platform accurately model inhalation exposure to airborne pollutants and predict toxicity outcomes?

(3) How can this system be optimized for regulatory applications and real-world chemical risk assessment?

To achieve these goals, the project will use advanced 3D printing to fabricate microfluidic lung-on-a-chip devices with tunable mechanical properties that closely mimic human alveolar microenvironments. The incorporation of magneto-acoustic stimulation will allow dynamic regulation of cell cultures, simulating breathing patterns for more realistic toxicant exposure assessments. The platform will feature co-culture models of human alveolar epithelial cells, endothelial cells, and lung fibroblasts, maintained at an air-liquid interface (ALI) to replicate real-world inhalation conditions. A range of environmental airborne chemicals, will be tested to evaluate dose-response relationships and toxicity mechanisms. High-throughput omic technologies (transcriptomics, proteomics, metabolomics) will be used to assess cellular and molecular changes following exposure.

The anticipated outcomes of this research include the development of a scalable and reproducible lung-on-a-chip model that can serve as a viable alternative to animal testing, supporting the 3R (Replacement, Reduction, Refinement) principles in regulatory toxicology. Academically, the work will contribute to advancing organ-on-a-chip technology, microfluidics, and magneto-acoustic stimulation in toxicity testing, leading to high-impact publications and conference presentations. Beyond academia, this platform has significant regulatory and industrial applications, with potential use in pharmaceutical safety testing, environmental toxicology, and consumer product assessment. Additionally, this system could be adopted by biotech companies, environmental agencies, and research institutions for cost-effective and high-throughput toxicity screening.

The project will be structured  for 3.5 years, with the first year focused on literature review, microfluidic chip design, and 3D printing optimization. The second year will involve cell culture development, magneto-acoustic components, and validation of ALI models. The final year will emphasize validation with air pollutants, regulatory discussions, and dissertation writing. By the end of the PhD, the project aims to deliver a validated lung-on-a-chip platform that can be used to assess inhalation toxicity in a more ethical, efficient, and mechanistically informative manner compared to conventional methods.

Contact person: Dr Zhiling Guo

Title: Unravelling Species Sensitivity to Chemicals Through Comparative Cellular Toxicology

• Primary supervisor: Dr Scott Glaberman
• Supervisor: Professor Iseult Lynch

Project Description

This a novel project that will use diverse cell lines from multiple animal species to uncover the cellular basis of species variation in chemical sensitivity. While extensive research has been conducted on human-related cell lines and model organisms, comparative cell toxicology remains largely unexplored. This project will address key methodological and discovery-driven questions to establish an exciting foundation for cross-species cellular toxicology and its relevance to whole-organism effects.

Methodological Questions

1. Establishing Comparable Cultures – How can we develop concurrent cultures of similar cell types across diverse species (e.g., multiple vertebrate groups such as birds, reptiles, mammals, and fish, as well as invertebrate groups) to enable like-for-like experiments at the cellular level? How can we develop this at scale to conduct robust and meaningful experiments that don’t use whole organisms.
2. Selecting Candidate Chemicals – What are the ideal first candidates for meaningful pilot testing of chemicals? For example, can broad-acting cytotoxic chemicals be used to assess cell viability? Can these endpoints be measured efficiently using high-throughput approaches such as live-cell imaging or commercial assays?

Discovery Questions

1. Interspecies and Intraspecies Variation – How much variation in toxicity response exists among cell lines within and between species?
2. Correlation with Whole-Organism Effects – Do major cytotoxicity effects observed in cell lines correlate with whole-organism responses, such as mortality in Daphnia or fish?
3. Mechanistic Basis of Sensitivity – Can we leverage variation in cellular responses to different contaminants, particularly in species with reference genomes, to identify genetic and network-level drivers of species sensitivity? Specifically, how do these variations align with known adverse outcome pathways?

Potential Impact

This project has potential to pioneer a new field of comparative cellular toxicology, providing a framework for understanding interspecies differences in chemical sensitivity. While near-term commercialization potential is uncertain due to the novelty, it offers significant long-term implications for:

• Refining Regulatory Toxicology – Assessing how well cellular responses predict whole-organism effects to advance 3Rs principles (Replacement, Reduction, Refinement) in toxicological testing.
• Applied Toxicology & Risk Assessment – Demonstrating the utility of diverse animal cell lines in developing endpoint-driven values for risk assessment, with clear biological relevance to adverse outcomes that inform chemical hazard evaluation.
• Mechanistic Insights into Sensitivity – Identifying cellular biomarkers for adverse outcomes and utilizing available reference genomes to explore how underlying molecular differences drive variation in toxicity responses and adverse outcomes.

Contact person: Dr Scott Glaberman

Title: 🚀Revolutionizing Chemical Safety: A One Health Approach Using Nematodes

• Primary supervisor: Dr Scott Glaberman
• Supervisor: Professor Ferenc Mueller

Project Description

Can one tiny organism transform how we understand pollution?

This exciting interdisciplinary PhD project will develop a high-throughput, multi-strain, multi-species nematode testing system to assess how chemicals impact both soil ecosystems and human health.

Using advanced imaging, high-throughput screening, and molecular biology tools, you’ll explore how pollutants affect development, behaviour, and biodiversity — bridging environmental health, molecular science, and regulatory innovation.

The project brings together expertise from academia, government science, and industry partners to train the next generation of environmental health scientists.

Project Highlights:

• Develop a novel One Health toxicity testing platform using Caenorhabditis elegans and related nematode species
• Explore how genetic and species-level differences influence sensitivity to agrochemicals and other compounds
• Use molecular tools (CRISPR, transcriptomics, comparative genomics) to study developmental and neurotoxic effects
• Contribute to regulatory innovation for sustainable chemical design and risk assessment

Training & Collaboration:

• Collaborate with the University of Birmingham, Bayer Crop Science (Germany), the UK Centre for Ecology & Hydrology (UKCEH), and Johns Hopkins University (USA)
• Receive training in high-throughput phenotyping, computational imaging, FAIR data practices, and mechanistic toxicology
• Participate in EU Partnership for the Assessment of Risks from Chemicals (PARC) activities, including science-to-policy training
• Opportunities for research visits and collaboration across UK, Europe, and the US

Required Qualifications:

• Background in biology, toxicology, environmental science, or a related field
• Interest in molecular biology, risk assessment, and applied toxicology
• Strong motivation to work at the interface of science, health, and environmental policy
• General lab and/or field experience in biology

Preferred Qualifications:

• MSc in biology, toxicology, environmental science, or a related field
• Peer-reviewed publication(s) in a related field
• Specific experience working with nematodes or other model organisms

Location:

University of Birmingham, UK (with research visits to Germany and the UK)

Start Date:

Fall 2025

How to Apply / Learn More:

📩 For informal inquiries and application details, please contact:

Dr. Scott Glaberman

Email: Dr Scott Glaberman

Title: Transitioning to New Approach Methodologies (NAMs) in Toxic Substances Testing: Exploring Behavioural and Economic Drivers

• Primary supervisor: Professor John Colbourne
• Supervisor: Dr Rebecca McDonald 
• Supervisor: Professor Iseult Lynch

Project Description

Testing the toxicity of chemical substances is a critical step in ensuring public health and environmental protection. Historically, regulatory frameworks and industrial practice have relied heavily on animal-based toxicological assays to evaluate the safety profiles of chemicals, consumer products, and pharmaceuticals. These traditional methods are increasingly recognised as being time-consuming, expensive, and often ethically challenging. Animal models can also yield uncertain extrapolation to human health, raising concerns about the reliability and predictive accuracy of results. In the United Kingdom, regulatory agencies, such as the UK Health and Safety Executive (HSE), are under pressure to balance effective chemical safety assessments with ongoing commitments to ethical guidelines and the 3Rs principle—replacement, reduction, and refinement of animal testing.

New Approach Methodologies (NAMs) encompass a broad array of in vitro and in silico tools, high-throughput screening technologies, computational models, and integrated testing strategies that aim to predict toxicological outcomes without the need for extensive animal testing. These methodologies leverage advances in cell biology, genomics, proteomics, and bioinformatics to provide mechanistically relevant, human-relevant data in a more cost-effective, rapid, and ethically responsible manner. By integrating human-based cell models and computational predictive frameworks, NAMs can offer more accurate extrapolation to human health conditions, thereby increasing confidence in hazard assessments.

In 2020, over 2.9 million procedures involving living animals were reported in Great Britain, reflecting both the scale of ongoing reliance on animal testing and the difficulty of transitioning towards alternative approaches. This project seeks to understand the behavioural dimensions that influence firms’ and broader societal support for the adoption of NAMs. By examining the underlying economic and social drivers we aim to identify what motivates, hinders, or accelerates the shift from established practices to NAMs. Through this deeper understanding of behavioural factors, the project aims to build the socio-economics case for early adoption of NAMs in the UK.

The PhD Thesis will be divided in three chapters:

1. The first chapter will establish a comprehensive baseline understanding of the current testing landscape, both the entrenched reliance on animal-based methods and the evolving presence of NAMs. This analysis will involve mapping the key industrial sectors, chemical classes, and animal use patterns, as well as identifying where and how NAMs are beginning to be adopted. Drawing on an extensive review of regulatory frameworks, industry reports, and academic literature, supplemented by interviews with professionals in industry, regulatory bodies, academia, and civil society, this chapter aims to pinpoint the economic factors that either motivate or hinder the transition. By clarifying how costs, investment considerations, market signals, and financial risk perceptions influence decision-making, this initial inquiry provides a critical economic lens through which we can better understand the pace and scope of NAM adoption.
2. The second chapter will employ a detailed cost-benefit analysis to compare traditional animal-based testing practices with the newer NAMs approach. This analysis will account for direct costs such as laboratory expenses, time requirements, and regulatory compliance, as well as indirect costs related to ethical considerations, reputational risks, and long-term economic viability. In parallel, the analysis will quantify the potential advantages offered by NAMs, such as shorter testing timelines, more scientifically reliable data directly relevant to human health, and reduced ethical and reputational challenges associated with animal-based testing. To achieve this, the PhD student will work closely with external partners of the project to gather and assess data from existing industry databases and through interviews with key stakeholders. By translating these cost and benefit factors into a clearer economic framework, this chapter aims to understand how financial considerations influence decision-making, shaping the willingness of firms and regulators to embrace NAMs. Ultimately, understanding these behavioural drivers will guide the development of targeted incentives, policies, and communication strategies that can accelerate the transition towards more economically sound, humane, and scientifically robust testing methods.
3. The third chapter will employ stated preference methods, specifically choice experiments, to quantify the broader economic value that society places on transitioning from animal-based testing to NAMs. An online questionnaire will be administered to a representative sample of the UK public, using respondent pools such as Amazon Mechanical Turk or Prolific, ensuring a diverse array of perspectives and backgrounds. By presenting respondents with a range of hypothetical testing scenarios, this approach will help disentangle and measure the relative importance of various attributes associated with the shift, such as reduced animal harm, faster assessment times, improved reliability of results, and overall ethical considerations. Understanding this valuation process is crucial as it not only highlights the economic incentives for more widespread adoption of NAMs but also provides guidance to policymakers, industry leaders, and advocacy groups on how to align regulatory frameworks, market signals, and communication strategies with public priorities.

The PhD project is expected to produce three academic manuscripts suitable for submission to leading journals at the intersection of environmental policy, economics, and chemical regulation

Ultimately, by combining a comprehensive baseline assessment of the testing landscape, a rigorous cost-benefit analysis of NAMs versus traditional methods, and a nuanced examination of societal values through stated preference methods, this thesis will provide a deeply informed perspective on the behavioural, economic, and social dimensions driving the transition to NAMs. In doing so, it will strengthen the broader economic case for adopting these methods and offer targeted insights that help policymakers, industry leaders, and other stakeholders align incentives, regulations, and communication strategies with both market realities and public priorities.

Specific beneficiaries:

1. Twenty-five UK industry associations and downstream chemical user groups.
2. Defra, supporting the UK Chemicals Strategy and drafting the regulatory reform under UK-REACH.
3. The FSA, aiding in the implementation of its UK Roadmap for the Regulatory Acceptance of NAMs.
4. The European Commission (DG-JRC), informing recommendations for alternative methods under EU-REACH.

Potential impact:

1. Stimulating a profitable environmental biomarker industry, allowing the UK to benefit early from NAMs under its post-Brexit regulatory regime.
2. Supporting the Government’s 25-year plan to enhance environmental protection.
3. Strengthening the UK’s leadership in promoting non-animal testing alternatives, exemplified by NC3Rs.
4. Providing evidence to guide market readiness, technology leadership, and policy decisions, enabling a regulatory shift that can influence both Europe and North America.

Timeline:

The PhD student will be based in both CERJ and the Department of Economics, benefiting from their cross-disciplinary expertise. The student will develop a detailed research plan that includes a comprehensive literature review, as well as quarterly checkpoints to refine research design, data collection, analysis, and policy implications. These efforts will be supported by academic supervisors at UoB and senior advisors from the project’s external partners, along with a dedicated six-month period for impact and engagement activities. Supervision will occur through regular in-person and online meetings, and placement opportunities with the external academic partners will offer valuable practical experience and industry engagement