Pre- and post-sampling features to leverage flux sampling at both the strain and the community level

The first genome-scale models of metabolism appeared in 1999 and 2000. In the following years fundamental Microbial Systems concepts were developed such as in silico methods and algorithms to analyze their properties. Genome-scale Metabolic models (GEMs) built on genome-scale metabolic network reconstructions (GENREs) are capable of deciphering the complex interactions occurring in bacteria, through predictions of the possible mechanism underlying these interactions. dingo is a Python package that supports a variety of methods to sample from the flux space of metabolic models, based on state-of-the-art random walks and rounding methods. Aim of this project is to enhance dingo by incorporating a preprocessing class to reduce and simplify metabolic networks, by removing certain reactions and metabolites. To identify correlated reactions, i.e. the flux of one depends on the others, I will develop a post-processing function to calculate correlation metrics and compare pairwise reaction fluxes. Additionally, another function will create graphs from the correlation matrix aiming to predict metabolic pathways. In the second half of the project, I will implement approaches for building microbial communities models. Two main approaches are to be implemented: Bag of Genomes and Bag of Genes. I will develop : a new class (“dingo.Community()”) that will read and store a community model of two microbes based on the user’s approach of choice. Its output will be then used as input to the dingo.PolytopeSampler to proceed with sampling steps. Sampling in the community flux space with the different sampling methods already supported by dingo and benchmarking of the community modeling approaches against real data will be delivered. Refactoring current dingo functions to adjust the new functionality will be done too.