Sparse Quantitative Flux Coupling Analysis

Tests	Coverage

sparseQFCA architecture

The sparseQFCA repository offers a is a Julia package for applying convex optimization techniques to constraint-based modeling of metabolic networks. It includes functions for consistency checking, reversibility correction, and flux coupling analysis. The repository also introduces QuantomeRedNet, a module designed to provide a loss-less method for the quantitative reduction of metabolic networks, which is particularly beneficial for metabolic engineering and can be effectively used in strain design algorithms. All optimization algorithms are modeled using the JuMP.jl package and are implemented with parallel processing, utilizing Distributed.jl for efficient execution.

How to get started

Prerequisites and requirements

• Operating system: Use Linux (Debian, Ubuntu or centOS), MacOS, or Windows 10 as your operating system. sparseQFCA has been tested on these systems.
• Julia language: In order to use sparseQFCA, you need to install Julia 1.0 or higher. Download and follow the installation instructions for Julia here.
• Hardware requirements: sparseQFCA runs on any hardware that can run Julia, and can easily use resources from multiple computers interconnected on a network. For processing large datasets, you are required to ensure that the total amount of available RAM on all involved computers is larger than the data size.
• Optimization solvers: sparseQFCA uses JuMP.jl to formulate optimization problems and is compatible with all JuMP supported solvers. However, to perform analysis at least one of these solvers needs to be installed on your machine. For a pure Julia implementation, you may use e.g. HiGHS.jl, but other solvers (GLPK.jl, CPLEX.jl, MosekTools.jl,...) work just as well.

💡 If you are new to Julia, it is advisable to familiarize yourself with the environment first. Use the Julia documentation to solve various language-related issues, and the Julia package manager docs to solve installation-related difficulties. Of course, the Julia channel is another fast and easy way to find answers to Julia specific questions.

Quick Start

To get started, first run import Pkg; Pkg.add("sparseQFCA") to install the sparseQFCA package.

License

sparseQFCA is distributed under the GNU General Public License v3.0.