/
cosa_minimal_changes_test.py
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/
cosa_minimal_changes_test.py
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"""Contains the function for the minimal number of reactions from wild-type to optimum analysis."""
# IMPORTS #
# Internal
import copy
import cobra
import pulp
from typing import Dict
import matplotlib.pyplot as plt
# External
from cosa_get_all_tcosa_reaction_ids import get_all_tcosa_reaction_ids
from cosa_get_suffix import cosa_get_suffix
from helper import ensure_folder_existence, json_load, json_write, json_zip_load
from optmdfpathway import (
STANDARD_R, STANDARD_T, get_optmdfpathway_base_problem,
add_differential_reactions_constraints, get_z_variable_status,
)
from optimization import perform_variable_minimization, perform_variable_maximization
from cosa_load_model_data import (
MIN_OPTMDF, load_model_data
)
# PUBLIC FUNCTION #
def cosa_minimal_changes_test(anaerobic: bool, disallowed_changed_reaction: str="", c_source: str="glucose") -> None:
"""Performs the minimal number of reactions from wild-type to optimum analysis.
Args:
anaerobic (bool): Is it anaerobic (True)?
disallowed_changed_reaction (str, optional): A reaction which is not allowed to be cofactor switched (only if given). Defaults to "".
c_source (str, optional): Either 'glucose' or 'acetate'. Defaults to "glucose".
"""
all_base_ids, cobra_model, concentration_values_free, concentration_values_paper,\
standardconc_dG0_values, paperconc_dG0_values,\
num_nad_and_nadp_reactions, num_nad_base_ids, num_nadp_base_ids,\
ratio_constraint_data, nad_base_ids, nadp_base_ids, used_growth, zeroed_reaction_ids = load_model_data(anaerobic=anaerobic, expanded=False, c_source=c_source)
biomass_reaction_id = "BIOMASS_Ec_iML1515_core_75p37M"
suffix = cosa_get_suffix(anaerobic, False, c_source)
figures_path = f"./cosa/results{suffix}/figures/"
ensure_folder_existence(figures_path)
if c_source == "glucose":
concentrations = ("PAPERCONCS", "STANDARDCONCS")
else:
concentrations = ("STANDARDCONCS",)
report = ""
for concentrations in concentrations:
print(f"=CONCENTRATION RANGES: {concentrations}=")
report += f"=CONCENTRATION RANGES: {concentrations}=\n"
if concentrations == "STANDARDCONCS":
dG0_values = copy.deepcopy(standardconc_dG0_values)
used_concentration_values = concentration_values_free
elif concentrations == "PAPERCONCS":
dG0_values = copy.deepcopy(paperconc_dG0_values)
used_concentration_values = concentration_values_paper
num_changes_dict = {}
for target in ("OptSubMDF", "OptMDF"):
full_scenario_key = f"{target} target, {'aerobic' if (not anaerobic) else 'anaerobic'} conditions"
num_changes_dict[full_scenario_key] = []
print(f"===OPTIMIZATION TARGET: {target}===")
report += f"===OPTIMIZATION TARGET: {target}===\n"
if concentrations == "PAPERCONCS":
jsondata_flexible = json_zip_load(f"cosa/results{suffix}/runs/{target.upper()}_VIVOCONC_FLEXIBLE.json")
else:
jsondata_flexible = json_zip_load(f"cosa/results{suffix}/runs/{target.upper()}_STANDARDCONC_FLEXIBLE.json")
optmdfpathway_base_problem = get_optmdfpathway_base_problem(
cobra_model=cobra_model,
dG0_values=dG0_values,
metabolite_concentration_values=used_concentration_values,
ratio_constraint_data=ratio_constraint_data,
R=STANDARD_R,
T=STANDARD_T,
extra_constraints=[],
sub_network_ids=get_all_tcosa_reaction_ids(cobra_model),
)
optmdfpathway_base_variables: Dict[str, pulp.LpVariable] = optmdfpathway_base_problem.variablesDict()
error_sum = 0.0
for reaction in cobra_model.reactions:
reaction: cobra.Reaction = reaction
if (not reaction.id.endswith("_TCOSA")):
continue
if f"dG0_{reaction.id}" not in optmdfpathway_base_variables.keys():
continue
if "_ORIGINAL_" in reaction.id:
continue
flux_variable = optmdfpathway_base_variables[reaction.id]
flux_variable.bounds(
reaction.lower_bound,
1e12,
)
error_z_variable = pulp.LpVariable(
name=f"z_{reaction.id}_error",
cat=pulp.LpBinary,
)
if reaction.id.endswith("_ORIGINAL_NAD_TCOSA"):
other_id = reaction.id.replace("_ORIGINAL_NAD_TCOSA", "_VARIANT_NADP_TCOSA")
elif reaction.id.endswith("_VARIANT_NAD_TCOSA"):
other_id = reaction.id.replace("_VARIANT_NAD_TCOSA", "_ORIGINAL_NADP_TCOSA")
elif reaction.id.endswith("_ORIGINAL_NADP_TCOSA"):
other_id = reaction.id.replace("_ORIGINAL_NADP_TCOSA", "_VARIANT_NAD_TCOSA")
elif reaction.id.endswith("_VARIANT_NADP_TCOSA"):
other_id = reaction.id.replace("_VARIANT_NADP_TCOSA", "_ORIGINAL_NAD_TCOSA")
else:
continue
current_z_variable = optmdfpathway_base_variables[f"z_var_{reaction.id}"]
other_z_variable = optmdfpathway_base_variables[f"z_var_{other_id}"]
optmdfpathway_base_problem += flux_variable <= error_z_variable * 1_000
optmdfpathway_base_problem += current_z_variable + other_z_variable <= 1
if reaction.id == disallowed_changed_reaction:
optmdfpathway_base_problem += error_z_variable <= 1e-5
error_sum += error_z_variable
error_sum_var = pulp.LpVariable(
name=f"reaction_error_sum",
cat=pulp.LpContinuous,
)
optmdfpathway_base_problem += error_sum_var == error_sum
growth_rates = jsondata_flexible.keys()
for growth_rate in jsondata_flexible.keys():
growth_rate_float = float(growth_rate.replace(",", "."))
optmdfpathway_base_variables[biomass_reaction_id].bounds(
growth_rate_float,
1e12,
)
if target == "OptSubMDF":
min_target = jsondata_flexible[growth_rate]["values"]["var_B2"]
optmdfpathway_base_variables["var_B"].bounds(
MIN_OPTMDF,
1e12,
)
optmdfpathway_base_variables["var_B2"].bounds(
min_target,
1e12,
)
else:
min_target = jsondata_flexible[growth_rate]["values"]["var_B"]
optmdfpathway_base_variables["var_B"].bounds(
min_target,
1e12,
)
print(f"@ µ of {growth_rate_float} 1/h and minimal {target} of {min_target} kJ/mol:")
report += f"@ µ of {growth_rate_float} 1/h and minimal {target} of {min_target} kJ/mol:\n"
minimization_result = perform_variable_minimization(
optmdfpathway_base_problem,
"reaction_error_sum",
)
num_changes = minimization_result['objective_value']
print(f" Minimal number of NADX/Y changes compared to in vivo distribution is {num_changes}")
report += f" Minimal number of NADX/Y changes compared to in vivo distribution is {num_changes}\n"
print(num_changes_dict[full_scenario_key])
num_changes_dict[full_scenario_key].append(num_changes)
if num_changes > 1e-6:
print(" Affected reactions:")
report += " Affected reactions:\n"
for var_id in minimization_result["values"]:
if not var_id.endswith("_error"):
continue
if minimization_result["values"][var_id] > 1e-6:
report += f" *{var_id}\n"
print(f" *{var_id}")
print(num_changes_dict)
for full_scenario_key in num_changes_dict.keys():
num_changes = num_changes_dict[full_scenario_key]
print(full_scenario_key)
if ("OptMDF" in full_scenario_key) and ("aerobic" in full_scenario_key):
color="red"
elif ("OptMDF" in full_scenario_key) and ("anaerobic" in full_scenario_key):
color="salmon"
elif ("SubMDF" in full_scenario_key) and ("aerobic" in full_scenario_key):
color="blue"
elif ("SubMDF" in full_scenario_key) and ("anaerobic" in full_scenario_key):
color="deepskyblue"
plt.plot(
[float(x.replace(",", ".")) for x in growth_rates], # x
num_changes_dict[full_scenario_key], # y
label=full_scenario_key,
linestyle="-",
color=color,
linewidth=1.0,
)
figurename = f"4_{concentrations}.jpg"
plt.legend(loc="best")
plt.title("Minimal number of NADX/NADY swaps to reach given target")
plt.xlabel("Growth rate [1/h]")
plt.ylabel("Num changes")
plt.xlim(min(growth_rates), max(growth_rates))
plt.savefig(f"{figures_path}{figurename}")
plt.close()
print("")
report += "\n"
if disallowed_changed_reaction != "":
disallowment_addition = "_no_"+disallowed_changed_reaction+"_change"
else:
disallowment_addition = ""
with open(f"./cosa/results{suffix}/figures/4_report{disallowment_addition}.txt", "w") as f:
f.write(report)
# LOGIC #
cosa_minimal_changes_test(anaerobic=False)
cosa_minimal_changes_test(anaerobic=False, c_source="acetate")
# cosa_minimal_changes_test(anaerobic=False, disallowed_changed_reaction="PDH_NADY")
# cosa_minimal_changes_test(anaerobic=False, disallowed_changed_reaction="NADH16pp_NADY")
# cosa_minimal_changes_test(anaerobic=False, disallowed_changed_reaction="FLDR2_NADX")
# cosa_minimal_changes_test(anaerobic=False, disallowed_changed_reaction="ICDHyr_FWD_NADX")
cosa_minimal_changes_test(anaerobic=True)