geckopy

Genome-scale model Enzyme Constraints, using Kinetics and Omics in python.

By combining kcats and proteomics measurement, geckopy allows for improving the modeling capabilities in genome-scale models.

Based on Sánchez et al., 2017.

Citing geckopy: Carrasco et al., 2023.

Check https://github.com/SysBioChalmers/GECKO for the matlab counterpart.

Overview

Load a model.

import geckopy

model = geckopy.io.read_sbml_ec_model("tests/data/eciML1515.xml.gz")
model.optimize()

Add copy number experimental data.

import pandas as pd
from geckopy.experiment import from_copy_number

raw_proteomics = pd.read_csv("tests/data/ecoli_proteomics_schmidt2016S5.tsv")
exp_model = from_copy_number(
    model,
    index=raw_proteomics["uniprot"],
    cell_copies=raw_proteomics["copies_per_cell"],
    stdev=raw_proteomics["stdev"],
    vol=2.3,
    dens=1.105e-12,
    water=0.3,
)
exp_model.optimize()

Add pool constraint.

# add some molecular weights to the proteins if the model does not have them
for prot in ec_model.proteins:
    prot.mw = 330
exp_model.constrain_pool(
    p_measured=12.,
    sigma_saturation_factor=0.8,
    fn_mass_fraction_unmeasured_matched=0.2,
)
print(exp_model.optimize())
print(exp_model.protein_pool_exchange)

Build the documentation

To build the documentation locally, run

cd docs
pip install -r requirements.txt
make ipy2rst  # if there are notebooks for the docs at docs/notebooks
make html

License

Copyright 2021 Ginkgo Bioworks.

Licensed under Apache License, Version 2.0, (LICENSE or http://www.apache.org/licenses/LICENSE-2.0).

Contribution

Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in the work by you, as defined in the Apache-2.0 license, shall be licensed as above, without any additional terms or conditions.