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#!/usr/bin/env python
"""
Regenerate model pickle files.
This should be performed after updating core classes in order to prevent suble
bugs.
"""
import importlib.resources
from collections import OrderedDict
from json import dump as json_dump
from pickle import dump, load
import cobra
from cobra.io import (
load_matlab_model,
load_model,
save_json_model,
save_matlab_model,
save_yaml_model,
write_sbml_model,
)
config = cobra.Configuration()
config.solver = "glpk"
if __name__ == "__main__":
# ecoli
ecoli_model = load_model("iJO1366", cache=False)
with open("iJO1366.pickle", "wb") as outfile:
dump(ecoli_model, outfile, protocol=2)
# salmonella
salmonella = load_model("salmonella", cache=False)
with open("salmonella.media", "rb") as infile:
salmonella.media_compositions = load(infile)
with open("salmonella.pickle", "wb") as outfile:
dump(salmonella, outfile, protocol=2)
# create mini model from textbook
textbook = load_model("textbook", cache=False)
mini = cobra.Model("mini_textbook")
mini.compartments = textbook.compartments
for r in textbook.reactions:
if r.id in (
"GLCpts",
"PGI",
"PFK",
"FBA",
"TPI",
"GAPD",
"PGK",
"PGM",
"ENO",
"PYK",
"EX_glc__D_e",
"EX_h_e",
"H2Ot",
"ATPM",
"PIt2r",
):
mini.add_reaction(r.copy())
mini.reactions.ATPM.upper_bound = mini.reactions.PGI.upper_bound
mini.objective = ["PFK", "ATPM"] # No biomass, 2 reactions
# add in some information from iJO1366
mini.add_reaction(ecoli_model.reactions.LDH_D.copy())
mini.add_reaction(ecoli_model.reactions.EX_lac__D_e.copy())
r = cobra.Reaction("D_LACt2")
mini.add_reaction(r)
mini.reactions.GLCpts.gene_reaction_rule = (
ecoli_model.reactions.GLCptspp.gene_reaction_rule
)
# adjust bounds
for i in ["ATPM", "D_LACt2", "EX_lac__D_e", "LDH_D"]:
mini.reactions.get_by_id(i).upper_bound = mini.reactions.PGI.upper_bound
for i in ["D_LACt2", "LDH_D"]:
mini.reactions.get_by_id(i).lower_bound = mini.reactions.PGI.lower_bound
# set names and annotation
for g in mini.genes:
try:
tg = textbook.genes.get_by_id(g.id)
except KeyError:
continue
g.name = tg.name
g.annotation = tg.annotation
mini.reactions.sort()
mini.genes.sort()
mini.metabolites.sort()
# output to various formats
with open("mini.pickle", "wb") as outfile:
dump(mini, outfile, protocol=2)
save_matlab_model(mini, importlib.resources.files(cobra.data).joinpath("mini.mat"))
save_json_model(
mini, importlib.resources.files(cobra.data).joinpath("mini.json"), pretty=True
)
save_yaml_model(mini, importlib.resources.files(cobra.data).joinpath("mini.yml"))
write_sbml_model(mini, "mini_fbc2.xml")
write_sbml_model(mini, "mini_fbc2.xml.bz2")
write_sbml_model(mini, "mini_fbc2.xml.gz")
write_sbml_model(
mini, importlib.resources.files(cobra.data).joinpath("mini_cobra.xml")
)
raven = load_matlab_model("raven.mat")
with open("raven.pickle", "wb") as outfile:
dump(raven, outfile, protocol=2)
# TODO:these need a reference solutions rather than circular solution checking!
# fva results
fva_result = cobra.flux_analysis.flux_variability_analysis(textbook)
clean_result = OrderedDict()
for key in sorted(fva_result):
clean_result[key] = {k: round(v, 5) for k, v in fva_result[key].items()}
with open("textbook_fva.json", "w") as outfile:
json_dump(clean_result, outfile)
# fva with pfba constraint
fva_result = cobra.flux_analysis.flux_variability_analysis(
textbook, pfba_factor=1.1
)
clean_result = OrderedDict()
for key in sorted(fva_result):
clean_result[key] = {k: round(v, 5) for k, v in fva_result[key].items()}
with open("textbook_pfba_fva.json", "w") as outfile:
json_dump(clean_result, outfile)
# textbook solution
solution = cobra.flux_analysis.parsimonious.pfba(textbook)
with open("textbook_solution.pickle", "wb") as f:
dump(solution, f, protocol=2)
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