3. Reading and writing models#

Get a model in and out of both toolboxes: SBML, RAVEN YAML, Excel, and the directory layout a Git-maintained model repository expects.

Functions on this page#

MATLAB

Python

importModel

read_sbml_model cobrapy

read SBML

exportModel

write_sbml_model cobrapy

write SBML

readYAMLmodel

read_yaml_model

read RAVEN YAML

writeYAMLmodel

write_yaml_model

write RAVEN YAML

exportToExcelFormat

export_to_excel

write the RAVEN Excel format

exportForGit

export_for_git

write a Standard-GEM repository layout

Which of these you need depends on the file, not on the model: all of them produce the same in-memory model, and none of them is the canonical format. What differs is what survives a round trip and how readable the file is, which 3.2 and 3.4 show with examples.

Setup#

This page uses two models from docs/data/: smallYeast.yml (RAVEN YAML, 45 kB) and yeast-GEM.xml (SBML, yeast-GEM v9.1.0).

3.1 Read a model#

modelSmall = readYAMLmodel('smallYeast.yml');
modelYeast = importModel('yeast-GEM.xml');

fprintf('%s %d reactions\n', modelSmall.id, numel(modelSmall.rxns));
fprintf('%s %d reactions\n', modelYeast.id, numel(modelYeast.rxns));

[Warning: The following fields have prefixes removed from all entries. If this is undesired, run importModel with removePrefix as false. Example: importModel('filename.xml',[],false);]
[Warning: The following MIRIAM strings are associated to more than one unique metabolite name: bigg.metabolite/ficytb5 bigg.metabolite/hdd2coa bigg.metabolite/pail_cho bigg.metabolite/pchol_cho bigg.metabolite/succ bigg.metabolite/tchola chebi/CHEBI:138108 chebi/CHEBI:17140 chebi/CHEBI:18097 ...and 23 more]
smallYeast 53 reactions
yeastGEM_v9.1.0 4102 reactions

Both warnings are informational and neither stops the read. The first says importModel stripped the R_, M_, G_ and C_ prefixes SBML requires on identifiers, which it does only when every identifier of that type carries one; pass removePrefix as false to keep them, which is what a comparison against the file's own ids requires. The second reports that the model annotates several differently-named metabolites with the same database identifier. That is a property of yeast-GEM rather than of the reader, and it makes those annotations unusable as a key for matching metabolites across models.

importModel accepts SBML Level 3 Version 1 with FBC version 2 and errors on anything older; see the RAVEN 3 migration guide if a file is rejected.

from cobra.io import read_sbml_model

from raven_toolbox.io import read_yaml_model

small = read_yaml_model("smallYeast.yml")
yeast = read_sbml_model("yeast-GEM.xml")

print(small.id, len(small.reactions), "reactions")
print(yeast.id, len(yeast.reactions), "reactions")

smallYeast 53 reactions
yeastGEM_v9__46__1__46__0 4102 reactions

read_sbml_model is cobrapy's own reader and strips the same SBML prefixes. read_yaml_model is raven-toolbox's, and returns a plain cobra.Model, so the two are interchangeable from here on. It reads gzipped files transparently and accepts both the current YAML layout and the older RAVEN one, so an archived model does not need converting first.

The two ids printed above are not the same, which is 3.2.

3.2 Watch the identifier mangling#

SBML identifiers must be valid XML names, so they cannot contain a dot. Model, reaction and metabolite ids that do are encoded when the file is written, as __ around the character's ASCII code: a . becomes __46__. The encoding is in the file, so what differs between the toolboxes is whether it is undone on the way back in.

disp(modelYeast.id);        % from yeast-GEM.xml
modelFromYaml = readYAMLmodel('yeast-GEM.yml');
disp(modelFromYaml.id);     % same release, unencoded

yeastGEM_v9.1.0
yeastGEM_v9.1.0

importModel decodes every __NN__ back to its character, across reactions, metabolites, compartments, genes, gene rules and the model id, so a round trip through SBML returns the ids you started with.

print("from SBML:", yeast.id)

from_yaml = read_yaml_model("yeast-GEM.yml")
print("from YAML:", from_yaml.id)
print("same size:", len(from_yaml.reactions) == len(yeast.reactions))

from SBML: yeastGEM_v9__46__1__46__0
from YAML: yeastGEM_v9.1.0
same size: True

cobrapy decodes the reaction, metabolite and gene ids but leaves the model id encoded, so the same release read from the two formats reports two different model ids while being the same model. model.name and the annotations are unchanged.

Note

Each toolbox is fast in one format and slow in the other Reading yeast-GEM takes about 17 s from SBML and 73 s from YAML in Python, and about 78 s from SBML and 17 s from YAML in MATLAB, so the ranking is reversed. RAVEN parses YAML itself and goes through libSBML for SBML; cobrapy has the opposite balance. The format therefore follows from what is needed from it, a readable diff or the RAVEN-specific fields YAML preserves, and a script whose run time is dominated by reading the file will often run faster in the other format.

3.3 Write a model#

writeYAMLmodel(modelSmall, 'smallYeast-copy.yml');
exportModel(modelSmall, 'smallYeast.xml');

roundtrip = importModel('smallYeast.xml');
fprintf('%d %d %d\n', numel(roundtrip.rxns), numel(roundtrip.mets), ...
    numel(roundtrip.genes));

[Warning: The following fields have one or more entries that do not start with a letter or _ (conflicting with SBML specifications). Prefixes are added to all entries in those fields:]
Document written
[Warning: The following fields have prefixes removed from all entries. If this is undesired, run importModel with removePrefix as false. Example: importModel('filename.xml',[],false);]
53 52 61

The two warnings are the prefixing rule in both directions: exportModel adds a prefix to a whole field as soon as one id in it starts with something other than a letter or underscore, and importModel takes it off again. Pass neverPrefix as true to suppress the addition, but the resulting file is not valid SBML.

Both writeYAMLmodel and exportModel take sortIds, which sorts a copy before writing and leaves the caller's model untouched.

from cobra.io import write_sbml_model

from raven_toolbox.io import write_yaml_model

write_yaml_model(small, "smallYeast-copy.yml")
write_sbml_model(small, "smallYeast.xml")

roundtrip = read_sbml_model("smallYeast.xml")
print(len(roundtrip.reactions), len(roundtrip.metabolites), len(roundtrip.genes))

53 52 61

write_yaml_model and export_to_excel take sort_ids, which sorts what is written without touching the model. cobrapy's write_sbml_model has no such argument, so a stable SBML diff needs the sort applied first, on a copy: write_sbml_model(sort_identifiers(small.copy()), path).

Both toolboxes write reactions in whatever order the model holds them, so inserting one reaction near the front shifts every line after it. Sorting first confines the diff of a one-reaction change to that reaction.

3.4 Spreadsheets#

The Excel format holds the model as five sheets, covering reactions, metabolites, compartments, genes and the model's own metadata, in a form that can be edited without a toolbox.

exportToExcelFormat(modelSmall, 'fileName', 'smallYeast.xlsx');
fprintf('%s\n', strjoin(sheetnames('smallYeast.xlsx'), ', '));

RXNS, METS, COMPS, GENES, MODEL

exportToExcelFormat writes .xlsx and errors on any other extension, so a bare directory path is rejected rather than interpreted. The RAVEN 3 migration guide covers scripts that pass one. There is no matching importer: curateModelFromTables reads a curated spreadsheet back by applying tabular edits to an existing model, not by building one from scratch.

from raven_toolbox.io import export_to_excel

export_to_excel(small, "smallYeast.xlsx")

from openpyxl import load_workbook

print(load_workbook("smallYeast.xlsx").sheetnames)

['RXNS', 'METS', 'COMPS', 'GENES', 'MODEL']

export_to_excel needs the excel extra (pip install raven-toolbox[excel]) and writes the same five sheets, plus ENZYMES and ENZRXNS for an enzyme-constrained model. Neither toolbox exports a whole model as a set of tab-delimited files any more; the single reaction table that exportForGit writes as its txt format is what remains, and pandas over the model's collections covers the rest.

3.5 Export for a model repository#

A Git-maintained model repository (yeast-GEM, Human-GEM, and the models built from standard-GEM) keeps the same model in several formats under model/, so that a release is usable without a toolbox and a diff is readable in a pull request. Both toolboxes write that layout directly, one subdirectory per format plus a dependencies.txt recording the versions the files were written with.

exportForGit(modelSmall, 'prefix', 'smallYeast', 'path', 'repo', ...
    'formats', {'yml', 'xml'});

written = dir(fullfile('repo', 'model', '**', '*'));
written = written(~[written.isdir]);
fprintf('%s\n', strjoin(sort({written.name}), ', '));

[Warning: The following fields have one or more entries that do not start with a letter or _ (conflicting with SBML specifications). Prefixes are added to all entries in those fields:]
Document written
dependencies.txt, smallYeast.xml, smallYeast.yml

dir reports names, so the subdirectories that contain the files are not visible above: the layout written is repo/model/yml/smallYeast.yml and repo/model/xml/smallYeast.xml. Set subDirs to false to put everything in one folder instead. Identifiers are sorted before writing, with no option to skip it, so successive releases diff cleanly. Left to itself exportForGit writes all five formats, mat, txt, xlsx, xml and yml; formats narrows that. COBRAtext switches the txt table from metabolite names to metabolite ids, and mainBranchFlag makes the export fail unless RAVEN itself is on its main branch, which pins a release to a released toolbox version.

from pathlib import Path

from raven_toolbox.io import export_for_git

root = export_for_git(small, "repo", prefix="smallYeast", formats=("yml", "xml"))
print(sorted(p.relative_to(root).as_posix() for p in root.rglob("*") if p.is_file()))

['dependencies.txt', 'xml/smallYeast.xml', 'yml/smallYeast.yml']

export_for_git returns the model/ directory it wrote, and sorts a copy of the model first, so the files are diff-friendly and the model you passed is unchanged. Its default formats are yml, xml, mat and xlsx; ask for txt explicitly if the repository expects one. sub_dirs=False flattens the layout, and varname sets the variable name inside the .mat file for repositories that pin one.

Warning

What can go wrong

  • The SBML writer refuses an identifier. SBML ids must start with a letter or underscore. Both toolboxes add a prefix to a whole field when any id in it does not, which is why importModel and read_sbml_model strip prefixes on the way back.

  • The model id differs between two copies of the same release. cobrapy decodes __46__-style escapes in reaction, metabolite and gene ids, but not in the model id, so yeast-GEM read from SBML reports yeastGEM_v9__46__1__46__0. Decode it with re.sub(r"__(\d+)__", lambda m: chr(int(m.group(1))), model.id), or compare model.name. Do not match entities on annotations: some database ids are shared by differently named metabolites.

  • Excel export fails with a missing module. The Python side needs openpyxl; install raven-toolbox[excel].

  • A YAML round trip loses a field neither format defines. RAVEN YAML carries the RAVEN model fields; anything a plugin added outside them is not guaranteed to survive. Check with a diff, not by eye.

  • A hand-written SBML release diffs badly. exportForGit and export_for_git sort identifiers for you, but a direct write_sbml_model does not, so a file written that way reorders whenever the model does.

See also#