2. Model structure and identifiers#

The same model is a struct of parallel arrays in MATLAB and a graph of objects in Python. Knowing which field corresponds to which attribute is most of what you need to translate a script between the two.

Functions on this page#

MATLAB

Python

checkModelStruct

check_model

report problems in a model

sortIdentifiers

sort_identifiers

sort reactions, metabolites and genes by id

getIndexes

DictList.get_by_id cobrapy

look up a reaction, metabolite or gene by id

getIndexes ('metcomps')

parse_name_comp, then a name and compartment filter

resolve a name[comp] token

no equivalent

subsystem_to_str

one subsystem string, whatever the source stored

addIdentifierPrefix, removeIdentifierPrefix

handled on read/write cobrapy

SBML identifier prefixes

ravenCobraWrapper

not needed

convert between RAVEN and COBRA structs

The correspondence#

RAVEN field

cobrapy

Note

model.rxns

model.reactions (ids)

a DictList, indexable by id or position

model.rxnNames

reaction.name

model.mets, model.metNames

model.metabolites, metabolite.name

model.metFormulas

metabolite.formula

model.genes

model.genes

model.grRules

reaction.gene_reaction_rule

same Boolean syntax

model.rxnGeneMat

reaction.genes, gene.reactions

the mapping is navigable from either side

model.lb, model.ub

reaction.bounds

model.rev

derived from the bounds

reaction.reversibility is read-only

model.S

create_stoichiometric_matrix(model)

built on demand, not stored

model.comps, model.metComps

metabolite.compartment, model.compartments

model.subSystems

reaction.subsystem

RAVEN allows several per reaction

model.c

model.objective

an expression, not a coefficient vector

The consequence: in MATLAB the caller keeps parallel arrays aligned, and in Python the objects hold their own references. Deleting a reaction in RAVEN means removing the same row from every reaction-length field, and the matching column of model.S, which is why removeReactions exists rather than a one-line deletion. In cobrapy the object holds its own links, so model.remove_reactions([...]) is enough and there is no state left behind to go stale.

That difference also decides where mistakes become visible. A RAVEN model can be left in a state no function rejects but later functions misread, which is why RAVEN ships a validator (2.4). A cobra.Model cannot reach most of those states at all, because the structure is maintained by the class rather than by the caller.

Setup#

smallYeast.yml from docs/data/.

model = readYAMLmodel('smallYeast.yml');

from raven_toolbox.io import read_yaml_model

model = read_yaml_model("smallYeast.yml")

2.1 The same lookup, two ways#

idx = getIndexes(model, 'PGI', 'rxns');
fprintf('%s: %s\n', model.rxns{idx}, model.rxnNames{idx});
fprintf('genes: %s\n', model.grRules{idx});

PGI: Glucose-6-phosphate isomerase
genes: YBR196C

Most RAVEN functions take or return indices rather than identifiers, and getIndexes converts between the two. The third argument names the field to search, because the same string can be a reaction id in one field and nothing at all in another. It takes 'rxns', 'mets', 'genes', 'metnames' or 'metcomps', plus 'ecrxns', 'ecenzymes' and 'ecgenes' on a GECKO model that carries a model.ec structure.

A miss is an error rather than a zero: getIndexes raises Could not find object 'X' in the model in every mode except 'metnames', which returns an empty vector instead. That exception exists because a metabolite name can occur in several compartments, so 'metnames' returns every matching index rather than one. In 'metcomps' mode a token that matches more than one metabolite (the same name twice in one compartment) is an error as well, naming how many it matched. Asking for several names at once gives back a cell array with one index vector per name; asking for exactly one name unwraps that to a plain vector.

rxn = model.reactions.get_by_id("PGI")
print(f"{rxn.id}: {rxn.name}")
print("genes:", rxn.gene_reaction_rule)
print("also reachable as:", model.reactions.PGI.id)

PGI: Glucose-6-phosphate isomerase
genes: YBR196C
also reachable as: PGI

There is no index to carry around: DictList looks up by id, and the object is the handle. get_by_id raises KeyError on a miss. The attribute form model.reactions.PGI is the same lookup, and works only for ids that happen to be valid Python names, so model.reactions.r_0001 resolves but an id containing a dot or a hyphen has to go through get_by_id. Both forms return the same object, and keeping a reference to it stays valid across edits elsewhere in the model.

2.2 Compartments#

disp(model.comps);                       % compartment ids
i = getIndexes(model, 'G6P_c', 'mets');
disp(model.comps{model.metComps(i)});    % the compartment of one metabolite

    {'c'}
    {'m'}

c

model.metComps holds an index into model.comps rather than the compartment letter, so reading a metabolite's compartment always takes the two steps above. The indirection keeps compartment names editable in one place: renaming a compartment is a single edit to model.compNames, with nothing to update per metabolite.

print(model.compartments)
print(model.metabolites.get_by_id("G6P_c").compartment)

{'m': 'mitochondria', 'c': 'cytosol'}
c

model.compartments maps id to name and is derived from the metabolites, so a compartment exists exactly as long as something is in it.

RAVEN also writes metabolite names as name[comp] in Excel and text exports, and accepts that form on input. Both toolboxes split the token on the last bracketed group, so a name that itself contains brackets still resolves.

% 'metcomps' resolves the name[comp] form; 'metnames' matches names alone
i = getIndexes(model, 'alpha-D-glucose 6-phosphate[c]', 'metcomps');
fprintf('%d %s\n', i, model.mets{i});

14 G6P_c

from raven_toolbox.utils.parse import parse_name_comp

print(parse_name_comp("alpha-D-glucose 6-phosphate[c]"))
print(parse_name_comp("ATP"))

name, comp = parse_name_comp("alpha-D-glucose 6-phosphate[c]")
matches = model.metabolites.query(lambda m: m.name == name and m.compartment == comp, None)
print([m.id for m in matches])

('alpha-D-glucose 6-phosphate', 'c')
('ATP', None)
['G6P_c']

parse_name_comp returns the compartment as None when there is no trailing bracket, so one call handles both forms and the caller decides what a missing compartment means. It only splits the string and never sees a model; the query after it is the lookup that getIndexes performs. An empty list is a miss, and more than one entry means the name is duplicated in that compartment. Note the import path: it is in raven_toolbox.utils.parse, not in the raven_toolbox.utils package namespace, which re-exports only the curation helpers.

2.3 Subsystems#

RAVEN lets a reaction belong to several subsystems, so an entry of model.subSystems is either a single string or a cell array of them. The field as a whole has to pick one of the two shapes; mixing them is what 2.4 catches. cobrapy stores one string per reaction, and subsystem_to_str normalises whichever form a model arrived with.

idx = getIndexes(model, 'PGI', 'rxns');
% nest every entry before nesting one, so the field keeps a single shape
model.subSystems = cellfun(@cellstr, model.subSystems, 'UniformOutput', false);
model.subSystems{idx} = {'Glycolysis', 'Pentose phosphate pathway'};
fprintf('%s\n', strjoin(model.subSystems{idx}, '; '));

Glycolysis; Pentose phosphate pathway

from raven_toolbox.utils.parse import subsystem_to_str

rxn = model.reactions.get_by_id("PGI")
rxn.subsystem = ["Glycolysis", "Pentose phosphate pathway"]
print(subsystem_to_str(rxn.subsystem))
print(subsystem_to_str("Glycolysis"))

Glycolysis;Pentose phosphate pathway
Glycolysis

cobrapy declares Reaction.subsystem a plain string but does not enforce it, so a model that came through RAVEN, or through hand-written YAML, can hold a list there and nothing complains until something tries to concatenate it. subsystem_to_str joins the parts with ; instead of taking the first, so no name is silently dropped, and returns "" for an empty or absent subsystem. Use it wherever a subsystem is printed, compared or written out.

2.4 Check the model#

Both toolboxes provide a validator, and they check different things, because the two representations fail in different ways.

issues = checkModelStruct(model, 'throwErrors', false);
fprintf('%d issue(s)\n', numel(issues));

mixed = model;
mixed.subSystems{idx} = 'Glycolysis';   % a plain string among cell arrays
issues = checkModelStruct(mixed, 'throwErrors', false);
fprintf('%d issue(s), category %s\n', numel(issues), issues(1).category);
fprintf('%s\n', issues(1).message);

0 issue(s)
1 issue(s), category wrong_type
The "subSystems" field must be a cell array of chars, *or* a cell array of cell arrays of chars

checkModelStruct validates the struct itself: that required fields are present, that each holds the type and length it should, and that identifiers are non-empty, unique and legal. On top of that it reports advisory findings: metabolites and genes nothing uses, bounds that contradict each other, a missing or ambiguous objective, malformed gene rules and formulas, and cross-references that point nowhere. Every finding carries a category (missing_field, wrong_type, empty_id, duplicate, invalid_id, invalid_bounds, unused, objective, gpr, invalid_formula, cross_reference or other), a target naming the field or identifier involved, and the full message.

Asking for an output argument, as above, returns the findings and neither prints nor throws. Called without one it reports as it goes, and throwErrors decides whether a structural problem stops the script; advisory findings are warnings either way.

from raven_toolbox.utils import check_model

issues = check_model(model)
print(len(issues), "issue(s)")
for issue in issues[:5]:
    print(f"  {issue.category}: {issue.message}")

0 issue(s)

check_model has no field types to police, because a cobra.Model cannot hold a misshapen field in the first place. What it checks instead is the curation layer cobrapy leaves to the caller: metabolites and genes no reaction uses, reactions with no metabolites, metabolites without a name, two metabolites sharing a name inside one compartment, and an objective that is missing or spread over several reactions. Each ModelIssue carries category, object_id and message, and the function returns a list rather than raising, so a script can filter by category and decide for itself what is fatal.

A 0 here means no curation problem was found, not that the model would also satisfy checkModelStruct. The two lists overlap only on the unused-element and objective checks.

2.5 Sort the identifiers before you commit#

Sorting makes the diff between two versions of a model readable, which is why exportForGit and export_for_git offer it too. Without it, adding one reaction can shift everything after it and turn a one-line change into a whole-file diff.

sortedModel = sortIdentifiers(model);
fprintf('%s\n', strjoin(sortedModel.rxns(1:5)', ', '));

ACO, ACS, ADH1, ALD6, ATPX

sortIdentifiers returns a new struct and leaves the original alone. It permutes reactions, metabolites, genes and compartments together with every field indexed by them, model.S and model.rxnGeneMat included, so the model stays internally consistent.

from raven_toolbox.utils import sort_identifiers

sorted_model = sort_identifiers(model)
print([r.id for r in sorted_model.reactions[:5]])
print("same object:", sorted_model is model)

['ACO', 'ACS', 'ADH1', 'ALD6', 'ATPX']
same object: True

sort_identifiers sorts in place and returns the model it was given, so the name on the left is not a second copy. Pass model.copy() when the original order still matters. Compartments are a plain dict and are left alone here; the writers emit them in order where it counts.

Warning

MATLAB only: converting to and from the COBRA Toolbox RAVEN and the COBRA Toolbox use different field names for the same model. ravenCobraWrapper converts a struct in either direction, inferring which direction from the fields it finds. There is no Python equivalent because there is nothing to convert: a raven-toolbox model is a cobra.Model, which is what every Python COBRA tool already takes.

Warning

What can go wrong

  • Fields drift out of alignment. In MATLAB, editing one reaction-length field by hand without editing the others leaves a model that looks fine and fails later, often far from the edit. checkModelStruct is how you catch it.

  • A field ends up with mixed contents. model.subSystems takes strings or cell arrays of strings, but not both in the same model. Convert the whole field, not one entry.

  • model.rev disagrees with the bounds. RAVEN stores reversibility explicitly, so it can contradict lb and ub after a manual edit, and different functions consult different ones. Python cannot get into that state, because reaction.reversibility is computed from the bounds on every read.

  • Subsystems come back as a list. Code that assumes reaction.subsystem is a string breaks on a model imported from RAVEN. Use subsystem_to_str.

  • A name lookup returns several indices. getIndexes with 'metnames' matches a name in every compartment it occurs in. Use 'metcomps' and the name[comp] form when you mean one specific metabolite.

See also#