| dc.description.abstract | In recent decades, the use of stable isotopes in metabolomics has fueled groundbreaking discoveries in metabolism research, enabling the tracking of metabolically active compounds in living organisms. Mass spectrometry has become pivotal in this field, facilitating the quantification of the enrichment caused by the metabolism of tracers containing stable isotopes. Despite the rapid development of bioinformatic tools, their full integration with the technological advancements in high-resolution mass spectrometry (HRMS) remains incomplete.
This dissertation addresses this gap by focusing on the development of bioinformatic tools for the automated analysis of stable isotope-resolved metabolomics experiments using HRMS data. Two tools, SUNDILE and PIRAMID, were developed with the objective of optimizing the data analysis workflow of untargeted and targeted metabolomics, respectively, leveraging the HRMS capabilities to infer biological information from the data. Together, these tools form the foundation of an efficient workflow in stable isotope-based metabolomics. These tools were tested in ex-vivo and in-vivo experiments, providing insights into the metabolism of soybeans and mice under obesogenic conditions. | |
