pipeGEM 0.2.0

Processing and integrating data with genome-scale metabolic models (GEM)

PipeGEM v0.2.0

---

PipeGEM is a Python package for analyzing and visualizing multiple genome-scale metabolic models (GEMs). It supports the integration of transcriptomic and proteomic data, metabolic task evaluation, and medium composition into GEMs. Flux analysis is powered by cobrapy.

Documentation: pipegem.readthedocs.io

---

Installation

pip

pip install pipegem

uv

uv add pipegem

uv (development)

git clone https://github.com/qwerty239qwe/pipeGEM.git
cd pipeGEM
uv sync

Documentation build

uv run --locked --extra doc mkdocs build --strict -d ./docs

---

Python API

Single model

import pipeGEM as pg
from pipeGEM.utils import load_model

model = load_model("your_model_path")  # returns a cobra.Model
pmodel = pg.Model(name_tag="model_name", model=model)

print(pmodel)

flux_analysis = pmodel.do_flux_analysis("pFBA")
flux_analysis.plot(
    rxn_ids=['rxn_a', 'rxn_b'],
    file_name='pfba_flux.png'  # pass None to skip saving
)

Multiple models

import pipeGEM as pg
from pipeGEM.utils import load_model

group = pg.Group(
    {
        "group_a": {
            "model_a_dmso": load_model("path_1"),
            "model_a_metformin": load_model("path_2"),
        },
        "group_b": {
            "model_b_dmso": load_model("path_3"),
            "model_b_metformin": load_model("path_4"),
        },
    },
    name_tag="my_group",
    treatments={
        "model_a_dmso": "DMSO",
        "model_b_dmso": "DMSO",
        "model_a_metformin": "metformin",
        "model_b_metformin": "metformin",
    },
)

flux_analysis = group.do_flux_analysis("pFBA")
flux_analysis.plot(rxn_ids=['rxn_a', 'rxn_b'])

Context-specific models from omic data

PipeGEM can reconstruct context-specific GEMs by integrating gene expression data. The example below uses GIMME, but a range of algorithms are available.

import numpy as np
import pipeGEM as pg
from pipeGEM.utils import load_model
from pipeGEM.data import GeneData, synthesis

mod = pg.Model(name_tag="model_name", model=load_model("your_model_path"))

# Generate synthetic transcriptomic data for demonstration
dummy_data = synthesis.get_syn_gene_data(mod, n_sample=3)

gene_data = GeneData(
    data=dummy_data["sample_0"],
    data_transform=lambda x: np.log2(x),
    absent_expression=-np.inf,
)
mod.add_gene_data(
    name_or_prefix="sample_0",
    data=gene_data,
    or_operation="nanmax",  # alternative: "nansum"
    threshold=-np.inf,
    absent_value=-np.inf,
)

gimme_result = mod.integrate_gene_data(
    data_name="sample_0",
    integrator="GIMME",
    high_exp=5 * np.log10(2),
)
context_specific_gem = gimme_result.result_model

Supported integrators: GIMME, iMAT, FASTCORE, SWIFTCORE, MBA, mCADRE, CORDA, ftINIT, RIPTiDe, E-Flux, SPOT, rFASTCORMICS.

Enzyme-constrained models (GECKO)

After attaching enzyme kinetic data, call integrate_enzyme_data to produce an enzyme-constrained model.

mod.add_enzyme_data(enzyme_data)  # EnzymeData object

ec_result = mod.integrate_enzyme_data(method="GECKOLight")  # or "GECKOFull"
ec_model = ec_result.result_model

Logging

PipeGEM is silent by default. To enable progress output, adjust the log level before running analyses:

import logging
import pipeGEM as pg

pg.set_log_level(logging.INFO)  # show progress messages
pg.enable_verbose()             # enable DEBUG output with a StreamHandler to stderr

---

CLI

PipeGEM provides a command-line interface organized around subcommands. To see all available options:

pipeGEM --version
pipeGEM --help

Step 1 — Generate template config files

pipeGEM template -p integration -o ./configs

This creates a configs/ directory containing TOML templates for each required config file.

Step 2 — Edit the configs

Fill in your model paths, data paths, and algorithm parameters in the generated TOML files.

Step 3 — Run a pipeline

Add --dry-run to any command to validate the configs and preview the planned actions without executing them.

# Process a model
pipeGEM process -t configs/model_conf.toml

# Find expression thresholds
pipeGEM threshold -g configs/gene_data_conf.toml -r configs/threshold_conf.toml

# Full context-specific model reconstruction
pipeGEM integrate \
    -g configs/gene_data_conf.toml \
    -t configs/model_conf.toml \
    -r configs/threshold_conf.toml \
    -m configs/mapping_conf.toml \
    -i configs/integration_conf.toml

# Flux analysis
pipeGEM flux -f configs/flux_conf.toml -t configs/model_conf.toml

# Compare models across conditions
pipeGEM compare -c configs/comparison_conf.toml

Note: The legacy -n <pipeline> style is still accepted for backward compatibility but is deprecated. Please migrate to the subcommand style shown above.

---

What's new in 0.2.0

• CLI subcommands — the flat -n <pipeline> interface has been replaced with proper subcommands (integrate, process, threshold, flux, compare, template). The old style still works but emits a deprecation warning.
• --dry-run flag — available on all subcommands; validates configs and prints the planned actions without running the pipeline.
• integrate_enzyme_data() — now fully implemented. Accepts method="GECKOLight" (default) or "GECKOFull".
• Silent by default — all internal print calls have been replaced with structured loggers under the pipeGEM namespace. Use pg.set_log_level or pg.enable_verbose to opt in to output.
• Bug fixes:
  • Model.rename() silently swallowed a TypeError when given a non-string argument — it now raises correctly.
  • PairwiseTester always selected non-parametric methods regardless of the normality test result.
  • data.preprocessing: column drops were incorrectly targeting rows; a row-wise apply was missing axis=1; na_action="" was invalid and replaced with na_action=None.
  • fetch_HPA_data updated to use the current biodbs API (hpa_search).