The Network Biology Lab focuses on the development and application of computational methods to study complex biological systems through networks. Our work integrates concepts from graph theory, machine learning, and systems biology to model how genes, proteins, microbes, and metabolites interact within dynamic environments.
We develop algorithms and computational tools for the inference and comparison of gene regulatory networks, with applications in cancer, inflammatory and neurodegenerative diseases, and host–pathogen interactions. Another research line explores microbiome organization, designing predictive tools to identify cross-feeding interactions and guide the rational construction of microbial consortia with biomedical and biotechnological potential.
By combining computational approaches with collaborations across experimental groups, the lab provides predictive insights into molecular mechanisms and disease heterogeneity, aiming to translate network biology into impactful advances in health, biotechnology, and personalized medicine.