RAVEN vs. raven-toolbox#

Note

Looking for a specific function? The MATLAB vs Python table pairs every function that exists in both, and links straight to the reference entry for each. This page is the narrative comparison; that page is the lookup.

RAVEN exists as two independent implementations:

RAVEN: the original MATLAB toolbox, which works entirely on its own, including independently of the COBRA Toolbox.
raven-toolbox: the Python package, built on cobrapy, so a model is a cobra.Model and the wider Python ecosystem works on it directly.

They do the same things (homology and KEGG reconstruction, metabolic tasks, gap-filling, context-specific extraction with ftINIT, compartment assignment, model comparison), but one is not a direct translation of the other. Names differ, some capabilities exist on one side only, and a handful of functions answer the same question differently.

This page is the whole comparison. The function-by-function view is the mapping table, generated from both toolboxes’ sources at build time.

Which one should you use?#

Neither is a reduced version of the other, so the right choice usually depends on the rest of your code, not on the toolbox itself.

Use raven-toolbox if the surrounding code is Python, if you want the model to be a cobra.Model that every cobrapy tool accepts without conversion, if you need reproducible environments and CI, or if you need KEGG artefacts built against a stated release rather than whatever is distributed.

Use RAVEN if the surrounding code is MATLAB, if you need to reproduce a model built with tINIT, or if you need dynamic FBA or conversion to the COBRA Toolbox structure.

Either covers the core reconstruction and analysis path. Where both have a function, the mapping table names the pair; where only one does, the sections below say which and why.

Changing the case does not give the matching name#

MATLAB uses camelCase and Python uses snake_case, but changing only the case usually produces a name that does not exist. Much of the API was deliberately renamed as it was ported:

MATLAB	Python
`getBlast`	`run_blast`
`addRxns`	`add_reactions_from_equations`
`fillGaps`	three functions with different algorithms

Check the mapping table rather than guessing. It also records what cobrapy covers instead of raven-toolbox, and what is deliberately absent.

Detail pages#


What each has that the other doesn’t	Capabilities that exist on only one side: cobrapy’s foundation and KEGG artefact generation on the Python side; tINIT, COBRA Toolbox conversion, dynamic FBA, and MATLAB-specific plumbing on the MATLAB side.
Same function, different answer	Where both sides do the same job but hand back a different result: gene-association merging, task-checking runtime, gap-filling’s three-way split, MILP alternate optima, and elemental balance.

Solvers#

	RAVEN (MATLAB)	raven-toolbox (Python)
Configuration	`setRavenSolver('gurobi')`	`cobra.Configuration().solver = 'gurobi'`
Recommended solver	Gurobi (free academic licence)	Gurobi (free academic licence)
Open-source option	GLPK (bundled with RAVEN)	GLPK (bundled with cobrapy)

Genome-scale MILP work (ftINIT extraction in particular) is where the choice matters most; see Installation for the full solver matrix.