August 2025 was no exception with regard to the ISB Group’s international engagements. After the engaging YoungBMC Symposium at Vrije Universiteit Amsterdam on 26th August where Ralph Monte presented his work “Using Machine Learning Models and Postmortem Metabolomics for Prediction of Ketoacidosis-related Deaths” for fellow young scientists, Ralph, together with Maria-Anna Sotiropoulou headed for the Benelux Metabolomics Days at Leiden University.
The Benelux Metabolomics Days on 27th and 28th August brought together researchers from the Netherlands, Belgium and Luxembourg to exchange cutting-edge advances in metabolomics and its biomedical applications. This year’s program emphasized the integration of experimental metabolomics with computational approaches and clinical research, covering diverse areas such as cancer metabolism, microbiome dynamics, exposomics, organ-on-chip technologies, and technological advances in mass spectrometry.
Maria-Anna Sotiropoulou and Ralph Monte were there presenting their work in the poster sessions with titles “Identifying Alzheimer’s Disease (AD)-Associated Metabolic Changes Using 13C Metabolic Flux Analysis (MFA)” and “Using postmortem metabolomics to predict ketoacidosis deaths: comparing three machine learning methods” respectively. Maria-Anna presented her work that applied 13C MFA to neuronal models of AD to quantitatively map intracellular metabolism. Using 13C-labelled Glucose and Glutamine tracers combined with advanced EMU-modeling, preliminary results and flux estimations were presented. In his presentation, Ralph explained how supervised machine learning models can be used to accurately predict ketoacidosis-related deaths. This indicates that machine learning models could be used in forensic settings, e.g., for cause of death determination using the postmortem metabolome.
One of the most impactful talks came from Sarah-Maria Fendt, who presented her group’s research on how liver steatosis worsens colorectal cancer liver metastasis outcomes. Analyzing patients’ data, she showed that liver fat accumulation correlates with the transition from encapsulated metastases (associated with better prognosis) to invasive replacement metastases (linked to poor prognosis). Using mouse models and 3D tumor spheroid cultures, her team demonstrated that excess fatty acids promote tumor aggressiveness through upregulation of proline metabolism, which fuels collagen biosynthesis. This allows cancer cells to build their own extracellular matrix and invade healthy liver tissue.
Alongside this, Marcel Kwiatkowski presented advances in proteo-metabolomics using Metabolic Flux Analysis, highlighting the regulatory interplay between metabolic fluxes and protein modifications. Justin van der Hooft demonstrated how machine learning methods can dramatically expand the reach of mass spectrometry-based metabolomics. He presented computational metabolomics tools that use machine learning to enhance metabolite annotation and analogue searches across complex data sets.
Finally, Alexandra (Sasha) Zhernakova shared insights into microbiome-metabolome dynamics during pregnancy and early life. Her work connects maternal and infant microbiomes with health outcomes, highlighting how early environmental and metabolic exposures shape immune and developmental outcomes.
