External Seminar: Nicola Richmond (GSK)

[Centre for Mathematical Medicine and Biology Seminar]

Nicola Richmond (GSK)

Understanding the holes in the metabolome

The metabolome refers to the complete set of both endogenous and exogenous small molecule metabolites that are either produced naturally as a bi-product of a biological process or as a result of the external environment. Quantifying changes in the metabolome can help diagnose disease, understand disease mechanisms, identify novel drug targets and understand drug safety and efficacy. As such, metabolomics is now widely used in the pharmaceutical industry throughout the drug discovery and development process. The annotated human metabolome now stands at over 350K metabolites and 25K pathways. It is therefore unsurprising that analysing metabolomics data presents a major challenge. The current gold standard approach is highly subjective and does not account for pathway-level, structural information. Hypotheses tend to be established a priori and validated through manual navigation of data rather than letting the data speak. At GSK, we have established a fully automated, data-driven approach to analysing metabolomics data using concepts from topological data analyses. Our analysis pipeline provides bench scientists with an automated approach for validating their hypotheses, allows data scientist, with no understanding of biology, to generate meaningful hypotheses and potentially fills gaps in our understanding of the metabolome.