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 tutorials/ folder of the RAVEN repository.

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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 and omics data

Gene deletions, 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 checkRaven passes; see Installation.

  • Tutorials 2–4 involve editing models in RAVEN-compatible Excel format, using importExcelModel. RAVEN 3 removed importExcelModel, so this step does not run as written on RAVEN 3; see Excel I/O for the replacement, curateModelFromTables, which curates an existing model from .tsv files rather than importing a full model from .xlsx.

  • 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.m companion script containing the completed exercise.

Tip

Python users The reconstruction concepts carry over directly to raven-toolbox, but converting the name to snake_case does not always give the matching raven-toolbox function name; some RAVEN functions map to cobrapy instead (solveLP → model.optimize()), and some have no Python counterpart at all (there is no Excel reader). Look each one up in the API reference, and see RAVEN vs. raven-toolbox for what maps where.

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