Installation#

GECKO is available as a MATLAB toolbox and as the Python package geckopy. Both implement the same procedure and can reconstruct, simulate and analyze the same ecModels: pick whichever fits your existing workflow.

Equipment#

A computer running macOS, Windows or Linux, with an internet connection to reach KEGG, UniProt, PubChem and GitHub. Data from these sources can also be downloaded or reconstructed manually elsewhere, but downloading it directly through GECKO functions is the most convenient route.

The starting point is a conventional constraint-based metabolic model, at genome scale or smaller, in SBML format L3V1 FBCv2. A well-curated model produces a well-performing ecModel, but the procedure works on any valid model, regardless of whether it belongs to a model organism.

Software#

Ⓜ️ MATLAB

MATLAB version 2019b or above (MathWorks). No additional MathWorks toolboxes are required.
RAVEN toolbox version 2.9.2 or later (repository), which provides the reconstruction functions GECKO builds on, and some of the functions used to simulate and analyze ecModels.

🐍 Python

Python 3.11 or above.
geckopy (repository), built on cobrapy for the constraint-based modeling layer and raven-toolbox (the Python port of RAVEN) for model-manipulation primitives and ecModel YAML I/O. Installing geckopy pulls in both dependencies automatically.

Both toolboxes also need a linear-programming solver (see Choosing a solver below) and, for DLKcat, Docker.

RAVEN toolbox / raven-toolbox#

Ⓜ️ MATLAB

Installation instructions are in the RAVEN documentation: the repository is downloaded via git clone, as a ZIP archive from GitHub, or installed as a MATLAB Add-On. Instructions for installing the recommended solver Gurobi are in the same page, under Choosing and configuring a solver.

After finishing installation, run the checks in MATLAB:

checkRaven;

🐍 Python

raven-toolbox is a dependency of geckopy and installs automatically with it (see GECKO Toolbox / geckopy below); there is no separate installation step.

GECKO Toolbox / geckopy#

Ⓜ️ MATLAB

The latest GECKO release installs as a MATLAB Add-On: Home → Add-Ons → Get Add-Ons → search "GECKO Toolbox".

Cloning the repository with git is recommended instead, since it makes upgrading a git pull rather than a full reinstall:

git clone --depth=1 https://github.com/CompMetEng/GECKO

or download a ZIP archive from the GitHub releases page and extract it to a location with read and write access.

After cloning or extracting, navigate to the GECKO folder in MATLAB and install it, which adds the GECKO subfolders to the MATLAB path:

cd('path/to/GECKO')
GECKOInstaller.install

To remove:

GECKOInstaller.uninstall

Upgrading, with any install method. Run GECKOInstaller.uninstall first, regardless of how GECKO was installed: it clears the old GECKO folder from the MATLAB path so a later session does not warn about paths that no longer exist. Then update (Add-On manager's Check for Updates, a fresh ZIP extract, or git pull on the main branch) and run GECKOInstaller.install again.

🐍 Python

geckopy and raven-toolbox are both pre-release on PyPI, so --pre is required:

pip install --pre geckopy

geckopy's dependency on raven-toolbox already names a pre-release specifier, which opts pip into matching pre-releases for that package too; --pre alone is enough, raven-toolbox does not need naming separately. To pin the exact versions of both instead of "whatever's newest":

pip install raven-toolbox==3.0.0b1 geckopy==4.0.0b1

For the development branch, ahead of the latest PyPI release, install from GitHub instead:

pip install \
    git+https://github.com/CompMetEng/raven-toolbox.git@develop \
    git+https://github.com/CompMetEng/geckopy.git@develop

Verify the install:

python -c "import geckopy; print(geckopy.__version__)"

To remove:

pip uninstall geckopy raven-toolbox

geckopy also installs a geckopy command-line tool, used to scaffold new ecModel projects and to download UniProt/KEGG data.

Choosing a solver#

Solver	License	Good for
Gurobi	Free academic	Genome-scale models, sensitivity tuning, large ecModels (recommended)
SoPlex (SCIP Optimization Suite)	Open source	An open-source alternative to Gurobi

Both MATLAB and Python use the same underlying solvers: MATLAB through RAVEN and the COBRA Toolbox, Python through cobrapy.

Tip

Avoid GLPK GLPK, the default solver of RAVEN, is not recommended for GECKO: ecModels are larger and more numerically demanding than the conventional GEMs GLPK is adequate for. Configure Gurobi or SoPlex before working with an ecModel of genome scale.

Docker#

DLKcat runs in a Docker container. Installation instructions are at docs.docker.com/get-started/get-docker.

Tip

Running DLKcat without Docker If installing Docker Desktop is difficult, the src/dlkcat-gecko/ folder of the GECKO repository contains the Python scripts and data needed to run DLKcat directly from the DLKcat.tsv output of writeDLKcatInput (Python: write_dlkcat_input).

Tutorial code#

Ⓜ️ MATLAB

In GECKO/tutorials:

full_ecModel/protocol.m reconstructs and analyzes a tutorial ecModel for Saccharomyces cerevisiae.
light_ecModel/protocol.m builds a light ecModel of the generic human-GEM.

🐍 Python

In geckopy/tutorials:

full_ecModel/protocol.py mirrors the MATLAB full-ecModel tutorial step for step, covering the full pipeline for S. cerevisiae.
light_ecModel/protocol.py builds a light ecModel from a small 5-reaction demo model (geckopy/examples/ecTestGEM), so it runs in seconds without external data.

Both tutorials, in both languages, generate ecModels suitable only for use within the respective tutorials; the reconstructed models may need additional curation before other use.