The study of the gut microbiome and its importance in human health constitutes one of the major areas of research in the current biomedical landscape. In particular, the interaction between diet and the gut microbiome has garnered significant attention, being addressed from various perspectives. Among these, the use of metabolic models of the human gut microbiome, which aim to integrate the metabolic networks of the organisms present in the human gut—including their reactions, metabolites, and involved proteins—has experienced considerable growth in recent years. The purpose of these models is to predict how the gut microbiome degrades dietary compounds and releases metabolites (either beneficial or harmful) to the rest of the body.

Previously, the Computational Biology group at Tecnun developed a metabolic model of the gut microbiome, called AGREDA, which innovatively incorporated the metabolism of important dietary compounds. This model was used to rank foods for each individual according to their gut microbiome, measured using 16S rRNA sequencing data. AGREDA is based on a metabolic model previously published in the literature, called AGORA, which has been recently updated, increasing the number of gut microbiome organisms.

The main objective of the doctoral thesis is to improve the personalized nutrition and gut microbiome algorithms previously developed by the Computational Biology group at Tecnun to apply them to various nutritional interventions in which the group is participating. Specifically, AGREDA2 will be developed, a new version of the human gut microbiome metabolism that integrates AGREDA and AGORA2.

One of the major challenges in achieving this integration is automating the functional annotation of the reactions involved in the metabolism of dietary compounds, which were previously characterized manually using literature-based data. To this end, various predictive methods for functional annotation based on the underlying chemical transformations of orphan reactions will be studied and applied. Furthermore, a comparative study between AGORA, AGREDA, AGORA2, and AGREDA2 will be carried out, validating the introduced improvements with experimental data. The food ranking algorithm will be adapted to use metagenomics and metatranscriptomics data. Finally, AGREDA2 and the food ranking algorithm will be applied in at least two nutritional interventions: patients with diabetes and breast cancer.