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.
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)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 htmlCopyright 2021 Ginkgo Bioworks.
Licensed under Apache License, Version 2.0, (LICENSE or http://www.apache.org/licenses/LICENSE-2.0).
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