Legacy tutorials#

Five tutorials belonged to the original RAVEN 1 paper (Agren et al., 2013). The scripts run with the current version of RAVEN, and the exercises are otherwise unchanged from the original. They use the MATLAB toolbox and progress from a simulation with an existing model to the reconstruction of a genome-scale model from sequence data. The scripts and their data files are in the tutorials/ folder of the RAVEN repository.

#	Tutorial	Content
1	Importing a GEM and running FBA	Load a model, set constraints and an objective, run FBA and visualise fluxes
2	Constructing a functional small model	Build a model from scratch, with exchange reactions and the steady-state assumption
3	Knockouts and omics data	Gene deletions, and a GEM as a scaffold for microarray data
4	Fixing an erroneous model	Quality control: find and fix mass-balance and naming errors
5	Reconstructing a GEM from KEGG	De novo reconstruction from protein sequences with KEGG

Prerequisites#

The tutorials use the MATLAB toolbox. RAVEN is installed and checkRaven passes (Installation).
Tutorials 1 and 2 load their model with importModel (SBML), and Tutorials 3 and 4 load theirs with readYAMLmodel (YAML). curateModelFromTables curates an existing model from .tsv files (Excel I/O).
A section of a script runs in MATLAB when it is highlighted and "Evaluate selection" is chosen from the context menu.
Tutorials 2, 3 and 4 have a companion script *_solutions.m with the completed exercise.

The reconstruction concepts apply to raven-toolbox. The snake_case form of a RAVEN function name is the name of the raven-toolbox function for some functions. Some RAVEN functions map to cobrapy (solveLP to model.optimize()), and some have no Python counterpart (there is no Excel reader). The API reference lists the functions, and Comparison of RAVEN and raven-toolbox shows what maps where.