This package is used to decompose steady state metabolic fluxes onto elementary flux mode (EFM) weights and atomic elementary flux mode (AEFM) weights. EFM flux decomposition works only for closed-loop networks of unimolecular reactions. AEFM flux decomposition works on metabolic flux networks with known metabolite structures.
Click the "docs" badge above to access package the documentation.
To install this package, open a julia session and enter:
julia> ]
(@v1.10) pkg> add https://github.com/jchitpin/MarkovWeightedEFMs.jl.gitAlternatively, you can load the Pkg package and install by:
julia> using Pkg
julia> Pkg.add("https://github.com/jchitpin/MarkovWeightedEFMs.jl.git")AEFM-specific analyses depend on the atom mapping program RXNMapper. This package must be installed and built with PyCall.jl after installing MarkovWeightedEFMs.jl. Tested with Python version 3.10
$ pip install virtualenv
$ virtualenv --python="/usr/bin/python3.10" "virtualenv" # name of virtual environment
$ source virtualenv/bin/activate
(virtualenv) $ pip install rxnmapper
(virtualenv) $ pip install rdkit
(virtualenv) $ pip install requests
(virtualenv) $ pip install tdqm
(virtualenv) $ pip install bs4
(virtualenv) $ pip install CTSgetPy
(virtualenv) $ juliajulia> using Pkg, PyCall
julia> ENV["PYTHON"] = joinpath(ENV["VIRTUAL_ENV"], "bin", "python")
julia> Pkg.build("PyCall")Once installed, the package is loaded in a julia session by typing:
julia> using MarkovWeightedEFMsPlease read the docs here for guided tutorials and function descriptions.
Justin G. Chitpin and Theodore J. Perkins, Atomic elementary flux modes explain the steady state flow of metabolites in flux networks. biorXiv preprint: https://doi.org/XX.XXXX/XXXX.XX.XX.XXXXXX
Justin G. Chitpin and Theodore J. Perkins, A Markov constraint to uniquely identify elementary flux mode weights in unimolecular metabolic networks. J Theor Biol. 2023 Nov 7;575:111632. doi: 10.1016/j.jtbi.2023.111632.
This software is released under the MIT license.