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raven-python

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Reconstruction, Analysis and Visualisation of Metabolic Networks — in Python.

raven-python is the Python counterpart of the RAVEN Toolbox 2 (MATLAB). It builds on cobrapy for everything cobrapy already does well (simulation, standard analyses, SBML I/O, model manipulation) and adds the functionality that's unique to RAVEN:

  • De novo reconstruction from KEGG and protein homology (BLAST / DIAMOND).
  • Context-specific models from omics data via tINIT / ftINIT, with task-aware gap-filling and the linear-merge MILP reduction.
  • Metabolic-task validation (check_tasks, fitTasks).
  • Connectivity gap-filling against template models.
  • Omics integration — Human Protein Atlas (proteomics + RNA-seq) ingestion.
  • Sub-cellular localisation prediction by MILP, with partial-update mode and pluggable predictors (WoLF PSORT, DeepLoc, …).
  • N-model comparison; reporter metabolites; FSEOF; flux sampling.
  • YAML I/O following the cobra standard, plus geckopy's ec-* enzyme-constrained fields. SIF export. RAVEN-style Excel export.

The status of every RAVEN function (ported, cheatsheet-mapped to cobra, or explicitly not ported) is documented function-by-function in docs/raven_migration.md.

Design principle

The canonical in-memory object is always a cobra.Model. There is no parallel RAVEN struct, no ravenCobraWrapper-style adapter. RAVEN-specific fields that cobra doesn't model natively (rxnMiriams, metDeltaG, rxnConfidenceScores, …) live in cobra's annotation / notes dictionaries. This avoids duplicating cobra's data model and keeps raven-python interoperable with the wider COBRA ecosystem.

Status

raven-python has been validated against MATLAB RAVEN on Human-GEM (5 Hart2015 cell-line models, Jaccard 0.975–0.980 — see docs/humangem_validation.md). The functional scope of the original RAVEN toolbox is covered with two principled omissions:

  • MetaCyc-based reconstruction is not implemented and is flagged for removal from MATLAB RAVEN as well — see IMPROVEMENTS.md under R-MetaCyc.
  • Dynamic FBA is not implemented — several maintained Python packages already cover it (dfba, reframed, mewpy).

What's still open is catalogued in docs/todo.md (visualisation / Phase 6 is the main item).

Installation (development)

git clone https://github.com/SysBioChalmers/raven-python
cd raven-python
pip install -e ".[dev]"

raven-python requires Python ≥ 3.11. Genome-scale (f)tINIT MILPs currently require Gurobi (details on solver portability); toy and unit-test work runs on the open-source GLPK.

Documentation

See docs/README.md for the documentation index.

Relationship to MATLAB RAVEN

raven-python is a derivative work and is released under the same GPL-3.0-or-later license. If you use it in scientific work, please cite the RAVEN 2 paper:

Wang H, Marcišauskas S, Sánchez BJ, Domenzain I, Hermansson D, Agren R, Nielsen J, Kerkhoven EJ. (2018) RAVEN 2.0: A versatile toolbox for metabolic network reconstruction and a case study on Streptomyces coelicolor. PLoS Comput Biol 14(10): e1006541.

License

GPL-3.0-or-later