DC10 Correlation studies between spectrometric, spectroscopic, biochemical, and microbiome sequencing data after intake of bilberry and olive bioactives
Supervisor: Dr Isabel Garcia-Perez, Department of Metabolism, Digestion and Reproduction, Imperial College London (ICL), United Kingdom
Co-Supervisors: Prof. Maija Dambrova, Department of Pharmaceutical Chemistry, Riga Stradiņš University (RSU), Latvia (Academic); Dr Rene Roth-Ehrang, Finzelberg GmbH & Co. KG, Germany (Industrial)
Secondments: This project is carried out in strong collaboration with the following groups, and visits to their institutions are expected during the project:
- Division of Immunology, Medical University of Graz (MedUniGraz), Austria
- Department of Pharmaceutical Chemistry, Riga Stradiņš University (RSU), Latvia
- Finzelberg GmbH & Co. KG, Germany
Project description
My project focuses on urine metabolomics and multi-modal data integration within the BioTransform network. Urine samples collected during human dietary intervention studies will be analysed by ¹H-NMR spectroscopy to generate metabolic profiles. These profiles will be used to objectively assess participants’ dietary intake and adherence to the supplement. In addition, discriminant biomarkers between the study groups will be identified by multivariate data analysis methods.
The second part of my project is integrating different types of the data generated by the other DCs. These include metabolite profiles by UPLC-HRMS/MS from urine, plasma and faeces (DC4 and DC6) and from in vitro GIDM-colon (DC1, DC2), 16S rDNA and whole-metagenome sequencing of the gut microbiota (DC7), microsomal metabolism, metabolite synthesis and bioactivity data (DC8, DC11), in silico ADMET and physiologically based PK prediction (DC9), biochemical markers of inflammation and oxidative stress (DC3, DC5), and the dietary records from the trials. These data will be analysed using state-of-the-art machine learning and deep learning approaches to explore the interrelationships between the different data modalities and how they are linked to health outcomes.
Professional background
I am Taeyeub Lee, a doctoral candidate in the BioTransform Doctoral Network at Imperial College London. I completed my bachelor’s degree in Biochemistry at the University of Edinburgh and my master’s degrees in Drug Sciences (Medicinal Chemistry) at University College London and Biomedical Research (Data Science) at Imperial College London. My previous research focused on developing machine learning and deep learning models that predict molecular properties and drug toxicity.
The metabolome is often described as the most downstream omics layer where host biology, diet and gut microbial activity converge. BioTransform introduces me to metabolomic data, which offers a route towards a more mechanistic view of how dietary and microbiome-derived compounds affect health. I am excited to bring my expertise in data analysis, machine learning and cheminformatics to the network.