Extract a tissue- or condition-specific model from a reference GEM plus gene scores derived
from omics data. Two algorithms are provided in {mod}raven_python.init.
Gene scores drive both algorithms. Build them from expression with
{func}raven_python.init.gene_scores_from_expression and turn them into reaction scores via
{func}raven_python.init.score_reactions_from_genes (a GPR walk shared with the omics
adapters — see the omics guide).
- {func}
raven_python.init.run_init— the classic INIT MILP (rewritten in optlang). - {func}
raven_python.init.get_init_model— the full tINIT pipeline (dead-end removal →run_init).
- {func}
raven_python.init.run_ftinit— the single-step ftINIT MILP (continuous indicators for positive-score reactions; binaries only on negatives — the speedup overrun_init). - {func}
raven_python.init.ftinit— the full pipeline: {func}raven_python.init.prep_init_model→ stagedrun_ftinit→ {func}raven_python.init.fill_tasks→ {func}raven_python.init.remove_low_score_genes.
ftINIT's task layer keeps essential metabolic tasks feasible; define and check tasks with the
tasks guide. The parameter defaults (mip_gap, big_m,
force_on, eps, prod_weight, scaling) and their robustness to noisy input are calibrated
in the parameter-calibration study, and the
equivalence to MATLAB RAVEN is established in the
Human-GEM validation.
:::{important}
Genome-scale (f)tINIT MILPs currently require Gurobi for tractable solve times; toy and
unit-test problems run on GLPK. See the
solver benchmark. Metabolomics-based scoring is the one
piece not yet implemented (raises NotImplementedError).
:::