Health research uses large scale (omics) methods to study the state of an individual, organs, and increasingly tissues and even single cells. These methods can measure gene expression, epigenetics, and protein abundances. While great steps forward are made in these fields, a further omics approach is upcoming. Metabolomics methods complement the aforementioned methods, by studying the abundances of small molecular compounds in bodily fluids, tissue samples, breath, etc. Combining data for low molecular compounds with data for gene products will provide an insight in how personal genetics leads to disease related metabolic phenotypes. Changes in metabolism are relevant for many diseases, such as metabolic diseases, hereditary diseases, and cancers, and is also relevant in the symbiotic interaction of the gut microbiome and the (human) body.
Pathway and network approaches are extensively used to integrate various data types and other information sources, in order to understand measurements and results in their biological context. However, these methods need to be further developed to deal with the data generated by new types of omics that measure hundreds of molecular abundances in single experiments.
This PhD project will focus on improved data analysis in ongoing research projects taking advantage of as much experimental data as possible, including unidentified metabolite peaks. Particularly, this project will address a number of underlying issues. For example, ontologies will be used to overcome the intrinsic mismatch between experiments and knowledge bases. This will, for example, allow lipid classes to be accurately mapped to fat metabolism pathways. Moreover, new methods will be developed to accommodate for incomplete experimental characterization, partly known chemical identity, and uncertainty in those. This will require a multidisciplenary combination of skills and the project demands knowledge in chemistry, biology, and informatics.
With this novel approach we will extract much more data from metabolomics experiments to integrate with other available omics data. To further accommodate integrative analyses, the project will develop novel methods to calculate pathway enrichment based on multiple types of omics data. Furthermore, network approaches may further help analyze the network of metabolites, pathways, genes, regulatory and epigenetic aspects involved in the diseases being studied.