Oropeza-Valdez, J. J. | bioRxiv Science

bioRxiv2025

MICOMWeb: a website for microbial community metabolic modeling of the human gut

SummaryMICOMWeb is a user-friendly website for easy microbial community metabolic modeling of the human gut. This website tackles three constraints when generating in silico metagenome-scale metabolic models: (i) the prior Python user knowledge for metabolic modeling using flux balance analysis with MICOM Python package, (ii) predefined and user-defined diets to generate ad hoc metabolic models, and (iii) the high- throughput computational infrastructure required to obtain the simulated growth and metabolic exchange fluxes, using real abundance from metagenomic shotgun or 16S amplicon sequencing; we present MICOMWebs features to easily run in silico experiments as a functional hypothesis generator for experimental validation on previously published data. Availability and ImplementationMICOMWeb is freely accessible for academic purposes at https://micomweb.c3.unam.mx.

systems biology↗

bioRxiv2024

Exploring Metabolic Anomalies in COVID-19 and Post-COVID-19: A Machine Learning Approach with Explainable Artificial Intelligence

The COVID-19 pandemic, caused by SARS-CoV-2, has led to significant challenges worldwide, including diverse clinical outcomes and prolonged post-recovery symptoms known as Long COVID or Post-COVID-19 syndrome. Emerging evidence suggests a crucial role of metabolic reprogramming in the infections long-term consequences. This study employs a novel approach utilizing machine learning (ML) and explainable artificial intelligence (XAI) to analyze metabolic alterations in COVID-19 and Post-COVID-19 patients. By integrating ML with SHAP (SHapley Additive exPlanations) values, we aimed to uncover metabolomic signatures and identify potential biomarkers for these conditions. Our analysis included a cohort of 142 COVID-19, 48 Post-COVID-19 samples and 38 CONTROL patients, with 111 identified metabolites. Traditional analysis methods like PCA and PLS-DA were compared with advanced ML techniques to discern metabolic changes. Notably, XGBoost models, enhanced by SHAP for explainability, outperformed traditional methods, demonstrating superior predictive performance and providing different insights into the metabolic basis of the diseases progression and its aftermath, the analysis revealed several metabolomic subgroups within the COVID-19 and Post-COVID-19 conditions, suggesting heterogeneous metabolic responses to the infection and its long-term impacts. This study highlights the potential of integrating ML and XAI in metabolomics research.

molecular biology↗