Congratulations Dr. Maria Barranco Altirriba
A brilliant defence
Today, April 24, 2026, Maria Barranco Altirriba successfully defended her doctoral thesis in computational metabolomics at the Universitat Politècnica de Catalunya. On behalf of the entire B2SLab team, I want to extend our warmest congratulations to Dr. Maria Barranco Altirriba — this is a well-deserved achievement after years of rigorous, creative, and impactful work.
Maria joined B2SLab with a double background in Biomedical Engineering from the Universitat de Barcelona, and from her very first days in the group it was clear that she brought both the analytical precision and the biological curiosity that metabolomics research demands.
Research in computational metabolomics
Maria’s doctoral work spans the full arc of modern metabolomics: from the algorithmic challenge of annotating unknown molecules, to the clinical application of metabolite panels for disease prediction, to the use of deep learning on molecular structure itself.
Metabolite annotation with mWISE
One of the core contributions of her thesis is mWISE, a novel algorithm for metabolite annotation. Metabolite annotation — assigning biological identities to the thousands of signals detected in an untargeted metabolomics experiment — remains one of the most difficult problems in the field. The vast majority of detected features are never confidently identified, which limits the interpretability of metabolomics studies. mWISE addresses this challenge with a principled computational approach, substantially expanding the proportion of annotatable compounds and improving the reliability of downstream biological interpretation.
Lipids, diabetes, and subclinical cardiovascular disease
A major thread throughout Maria’s work is the application of metabolomics to understanding Type 2 Diabetes Mellitus (T2DM) and its complications, with a particular focus on lipidomics. In one key study, Maria and collaborators identified specific lipids significantly associated with subclinical carotid atherosclerosis in individuals with T2DM — a finding with direct clinical relevance, since cardiovascular disease is the leading cause of death in diabetic patients and early vascular changes are often silent and detectable only through imaging or biomarkers.
Predictive metabolomics: early warning years before diagnosis
In a complementary line of work, Maria led a prospective metabolomics study asking whether blood metabolites could flag future T2D risk years before clinical diagnosis. Using an untargeted approach in a discovery cohort and validating findings in an independent set of over 2,000 individuals, the study identified guanine and pregnenolone sulfate as robust predictors of incident T2D detectable more than 7 years before diagnosis — pointing to early disruptions in purine metabolism and steroid hormone pathways that precede overt disease.
Barranco M, Granado M, Yanes Ó, et al. Guanine and pregnenolone sulfate are associated with incident type 2 diabetes in two independent populations. Frontiers in Endocrinology, 2025. https://doi.org/10.3389/fendo.2025.1706886
Molecular language models for SMILES
During a nine-month stay at LipiTUM (Technical University of Munich), Maria extended her computational toolkit into deep learning for molecular structure. Together with Enrico Manzini and colleagues, she developed Smile-to-BERT, a BERT-based architecture trained on molecular SMILES strings to generate dense molecular embeddings and predict physicochemical properties. These pre-trained representations can be transferred across datasets — a valuable tool in drug discovery and metabolite property prediction.
Barranco-Altirriba M, Manzini E, Würf V, Pauling JK, Perera-Lluna A. Smile-to-BERT: A BERT architecture trained for physicochemical properties prediction and SMILES embeddings generation. bioRxiv, 2024.
What lies ahead: DTU and the green transition
Maria joins the Biotechnology Research Institute for the Green Transition (BRIGHT) at the Technical University of Denmark (DTU), where she will work within the Data Science Platform and the Multi-omics Network Analytics group, in collaboration with the DTU Microbes Initiative.
It is a fitting destination. The challenges of the green transition — understanding microbial communities, engineering biological systems, interpreting complex multi-omic data — call for exactly the computational metabolomics expertise that Maria has built. We have no doubt she will make a strong contribution there.
Thank you, Maria
Maria has been a cornerstone of B2SLab’s metabolomics research for years. Her rigour, her willingness to tackle hard problems, and her generosity in sharing knowledge with the rest of the team have made her an irreplaceable colleague. Watching her grow from a talented master’s student into the accomplished scientist who defended today has been a privilege.
The field of computational metabolomics is better for the work she has done. And wherever this next chapter takes her, we will be watching with great pride.
Congratulations, Dr. Barranco Altirriba. The best is yet to come.
/Àlex