FLYCOP (FLexible sYnthetic Consortium OPtimization) is a framework that improves the understanding of the metabolic behaviour of microbial consortia and to automatize the modeling of those communities, by designing and optimizing enginered microbial consortia given a particular goal.

FLYCOP contributes with multiple and assorted applications, such as simulating different scenarios before in-vivo experiments; defining medium composition and detecting limiting nutrients; discovering the biological metric optimized in an evolutionary process; optimizing cross-feeding relationships; optimizing strain ratios in the consortium; etc.

Citation: This repository contains the code and configuration files reproducing the study cases described in (please, cite us if you use FLYCOP in your work):

Beatriz García-Jiménez, José Luis García, Juan Nogales; FLYCOP: metabolic modeling-based analysis and engineering microbial communities, Bioinformatics, Volume 34, Issue 17, 1 September 2018, Pages i954–i963, doi: 10.1093/bioinformatics/bty561

FLYCOP software run in LINUX OS. FLYCOP can be run installing the pre-requisites individual software by yourself. Define the location of your personal gurobi solver license (required by COMETS) in the container (for example, <path_to_gurobi_license>=/home/user):

GRB_LICENSE_FILE=<path_to_gurobi_license>/gurobi.lic

FLYCOP pipeline uses some software (and all their dependencies), which must be installed before:

• COMETS (v2.10) (faster with gurobi solver)

Additionally, R software is required.

#Create a conda environment
conda create -n FLYCOP python=3.8 pip
# Activate the environment
conda activate FLYCOP
# Install SWIG requirement
conda install gxx_linux-64 gcc_linux-64 swig
# INSTALL FLYCOP package
pip install FLYCOP

TODO

GEMs used by FLYCOP cases of study can be obtained from BiGG models database or from their respective publications (in SBML format):

• iJO1366 [Orth et al.,2011]

FLYCOP provides different resources for robustness, sensitivity and data analysis support, being the most relevant the following ones:

• Best configuration given the strains, media, fitness function and parameter configuration
• Scatterplot showing explored values by each parameter
• Correlation values and ellipse plots between different parameter and fitness values
• Tab file with all configurations including parameter and fitness values, and some other interesting metrics (such as medium concentration of some relevant metabolites). This output would be important for further data analysis.
• Growth curves of all explored consortium configurations