Who we are
Welcome to the webpage of the Human Systems Biology group in the National Institute for Genomic Medicine at Mexico City, INMEGEN. Our group is interdisciplinary and have the objective to develop a systems biology framework to analyze mainly human diseases and metabolic phenotype in microorganisms through the use of computational models and high-throughput technologies. Currently, our laboratory focuses on the analysis of metabolic alterations in cancer cells by the implementation of genome scale metabolic reconstructions and assess the predictions in terms of experimental data at different scales.
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Biological Physics Mexico City 2019
from 06-09-2019 to 04-09-2019
Frontier Science at the Intersection of Physics, Math and Biology The BioPhys Mexico City 2019 conference, the third in a biennial series, is intended as an international, multidisciplinary scientific forum to discuss the latest developments in biological physics, including experimental, theoretical and computational methods, from a single molecule perspective to complex multi-component environments. The conference is expected to boost the new paradigm of interdisciplinary approaches converging into specific problems in biological physics.
Latest Publication
mb-PHENIX: Diffusion and Supervised Uniform Manifold Approximation for denoising microbiota data
Bioinformatics 2023
Cristian Padron-Manrique, Aaron Vazquez-Jimenez, Diego A Esquivel-Hernandez, Yoscelina Estrella Martinez-Lopez, Daniel Neri-Rosario, Jean Paul Sanchez, David Giron-Villalobos and Osbaldo Resendis-Antonio
Motivation Microbiota data encounters challenges arising from technical noise and the curse of dimensionality, which affect the reliability of scientific findings. Furthermore, abundance matrices exhibit a zero-inflated distribution due to biological and technical influences. Consequently, there is a growing demand for advanced algorithms that can effectively recover missing taxa while also considering the preservation of data structure. Results We present mb-PHENIX, an open-source algorithm developed in Python that recovers taxa abundances from the noisy and sparse microbiota data.