Christian Diener and Osbaldo Resendis Antonio

micom is a Python package for metabolic modeling of microbial communities developed in the Human Systems Biology Group of Prof. Osbaldo Resendis Antonio at the National Institute of Genomic Medicine Mexico.

micom allows you to construct a community model from a list on input COBRA models and manages exchange fluxes between individuals and individuals with the environment. It explicitly accounts for different abundances of individuals in the community and can thus incorporate data from 16S rRNA sequencing experiments. It allows optimization with a variety of algorithms modeling the trade-off between egoistic growth rate maximization and cooperative objectives.

scPhenix

Crístian padrón Manrique and Osbaldo Resendis Antonio

sc-PHENIX was developed to improve imputation of scRNA-seq data avoiding over-smoothing; it falls into the category of smooth-based imputation based on benchmarking. However, the methods used in sc-PHENIX to obtain the low dimensional manifold is UMAP(Uniform Manifold approximation and Projection), and the Mt (exponentiated Markov matrix) is from diffusion maps, both techniques based on manifold learning being part of the nonlinear dimensionality reduction methods category, a subfield of machine learning. In this work, our approach is an improvement to the popular method MAGIC by integrating UMAP in the imputation process. Consequently, there is an improvement in the computation of Mt reflecting the denoised cell-neighborhood that captures local, continuum and global data structures. The advantage of preserving data structures with sc-PHENIX compared to MAGIC is that we can share gene expression among more accurate nearest neighbors cells on the manifold of Mt sc-PHENIX. Following these procedures, we obtain more biological insights and at the same time mitigate the risk of over-smoothing data among spurious distinct cell phenotypes.

Our microbiome pipeline

Christian Diener and Osbaldo Resendis Antonio

This repository contains the standardized analysis pipeline for 16S and metagenome data. It serves as a testing ground for what will be required to analyze around 500 samples.

CORDA for Python

Christian Diener and Osbaldo Resendis Antonio

This is a Python implementation based on the papers of Schultz et. al. with some added optimizations. It is based on the publications of Schultz et. al. [1, 2].

CORDA, short for Cost Optimization Reaction Dependency Assessment is a method for the reconstruction of metabolic networks from a given reference model (a database of all known reactions) and a confidence mapping for reactions. It allows you to reconstruct metabolic models for tissues, patients or specific experimental conditions from a set of transcription or proteome measurements.

Christian Diener and Osbaldo Resendis Antonio

Dycone (“dynamic cone”) allows you infer enzymatic regulation from metabolome measurements. It employs formalisms based on flux and k-cone analysis to connect metabolome data to distinct regulations of enzyme activity. Most of the analysis methods can be applied to genome-scale data.