Legacy tutorials¶
These five hands-on tutorials were part of the original RAVEN 1 paper
(Agren et al., 2013). The code has been updated to run with current RAVEN, but
the exercises themselves are otherwise unchanged. They use the MATLAB
toolbox and build up from running a simulation on an existing model to
reconstructing a genome-scale model from sequence data. The scripts and all the
data files they use live in the
tutorial/
folder of the RAVEN repository.
| # | Tutorial | What you learn |
|---|---|---|
| 1 | Import a GEM and run FBA | Load a model, set constraints and an objective, run FBA, visualise fluxes |
| 2 | Construct a functional small model | Build a model from scratch in Excel; exchange reactions and the steady-state assumption |
| 3 | Knockouts, MOMA and omics data | Gene deletions, MOMA, and using a GEM as a scaffold for microarray data |
| 4 | Fix an erroneous model | Systematic quality control: find and fix mass-balance and naming errors |
| 5 | Reconstruct a GEM from KEGG | De novo reconstruction from protein sequences using KEGG |
Before you start¶
- These tutorials use the MATLAB toolbox. Make sure RAVEN is installed and
checkInstallationpasses — see Installation. - Tutorials 2–4 involve editing models in RAVEN-compatible Excel format, so
you need a working
importExcelModel(i.e. the Excel parser must passcheckInstallation). - To run a section of a script in MATLAB, highlight it, right-click, and choose "Evaluate selection".
- Tutorials 2, 3 and 4 ship with a
*_solutions.mcompanion script containing the completed exercise.
Python users
The reconstruction concepts carry over directly to raven-toolbox. Look up the
snake_case equivalent of each function in the API reference
(for example importExcelModel → import_excel_model, solveLP →
solve_lp).