NARILIS lunch seminar | Prof. Janneth González Santos, Colombia

We are pleased to invite you to a seminar given by

Prof. Janneth González Santos

Janneth González is a computational biologist at Pontificia Universidad Javeriana. She specializes in systems biology and multi-omics approaches to study neurodegenerative diseases. Her research focuses on the response of human astrocytes to lipotoxicity and metabolic stress. With over 100 publications , she has extensively investigated astrocyte genome-scale metabolic models (GEMs), integrated metabolomics, and lipidomics. Her expertise encompasses the use of machine learning and network pharmacology for drug discovery and repositioning. Additionally, her work extends to the study of mild cognitive impairment and the development of simulation platforms, such as the Applied Neuro-Simulation Platform (ANSeP), to explore therapeutic targets and disease mechanisms.

Her seminar is entitled: 

From Molecules to Multi-Omics Models: Data-Driven Approaches to Neurodegeneration Research

Neurodegenerative research is increasingly shifting toward systems biology to address the complex systemic alterations that traditional, single-target approaches often fail to capture. This presentation details a multi-omics framework that integrates metabolomic, transcriptomic, and proteomic data to investigate how human astrocytes respond to lipotoxic stress. While astrocytes are essential for maintaining brain metabolism and neuroprotection, their functional dynamics under chronic lipid challenge remain largely uncharacterized. The research utilized high-throughput technologies, including RNA-seq and mass spectrometry, to identify significant immune-metabolic dysregulation in human astrocytes. Findings include the activation of antiviral responses and cholesterol biosynthesis pathways, alongside the identification of key regulatory protein hubs. Furthermore, the study explores non-coding RNA-mediated mechanisms by constructing regulatory networks that connect metabolic stress to early neurodegeneration.To unify these diverse data layers, Genome-Scale Metabolic Models (GEMs) were employed as a predictive computational framework. This led to the development of the first global, manually curated metabolic reconstruction of a human astrocyte. Recent advancements demonstrate that integrating multi-omic data through specialized computational approaches significantly enhances the predictive power and accuracy of these models. These tools provide scalable methods to identify metabolic bottlenecks and novel therapeutic targets in neurodegenerative diseases.

Invited by Prof. Gipsi Lima Mendez, UNamur, URBM