Statistical Methods for the Reconstruction of Metabolite Networks in Humans and Plants

Summary

This PhD focuses on developing statistical methods for estimating metabolite networks. Metabolites have a central role in biology, acting as intermediates between genetic variation and phenotypic traits. To fully understand their function, they must be studied as a network rather than separately. However, analyzing metabolite networks is challenging due to noise and variation arising from other sources of biological and technical conditions. To address these challenges, this thesis develops a statistical framework that reduces unwanted variation by incorporating study design information through mathematical modeling. Accounting for known sources of noise and integrating data from other biological levels, enables constructing clearer and more meaningful metabolite networks. By refining network estimation, this approach helps identify key metabolite groups, offering clearer biological insights.