/
cosa_maximal_reaction_numbers.py
169 lines (154 loc) · 8.38 KB
/
cosa_maximal_reaction_numbers.py
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from tkinter.messagebox import NO
import matplotlib.pyplot as plt
import cobra
import copy
import pulp
import math
from cosa_get_all_tcosa_reaction_ids import get_all_tcosa_reaction_ids
from cosa_get_model_with_nadx_scenario import cosa_get_model_with_nadx_scenario
from cosa_get_suffix import cosa_get_suffix
from helper import json_load, json_write, json_zip_load
from typing import List
from optmdfpathway import (
STANDARD_R, STANDARD_T, get_optmdfpathway_base_problem,
add_differential_reactions_constraints, get_z_variable_status,
get_thermodynamic_bottlenecks
)
from optimization import perform_variable_minimization, perform_variable_maximization
from cosa_load_model_data import (
MIN_OPTMDF, load_model_data
)
from typing import Dict
from helper import ensure_folder_existence
def cosa_maximal_reaction_numbers(anaerobic: bool, expanded: bool, growth_epsilon: float = 0.01) -> None:
suffix = cosa_get_suffix(anaerobic, expanded)
figures_path = f"./cosa/results{suffix}/figures/"
ensure_folder_existence(figures_path)
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=expanded)
biomass_reaction_id = "BIOMASS_Ec_iML1515_core_75p37M"
original_cobra_model = copy.deepcopy(cobra_model)
for concentrations in ("STANDARDCONC",): #"VIVOCONC"):
print(f"CONCENTRATIONS: {concentrations}")
print(f"=CONCENTRATION RANGES: {concentrations}=")
if concentrations == "STANDARDCONC":
dG0_values = copy.deepcopy(standardconc_dG0_values)
used_concentration_values = concentration_values_free
elif concentrations == "VIVOCONC":
dG0_values = copy.deepcopy(paperconc_dG0_values)
used_concentration_values = concentration_values_paper
for target in ("OPTMDF", "OPTSUBMDF"):
print(f"TARGET: {target}")
print(f"===OPTIMIZATION TARGET: {target}===")
for distribution in ("WILDTYPE",): #"FLEXIBLE", "SINGLE_COFACTOR"):
print(f"DISTRIBUTION: {distribution}")
print(f"cosa/results{suffix}/runs/{target}_{concentrations}_{distribution}.json")
jsondata_invivo = json_zip_load(f"cosa/results{suffix}/runs/{target}_{concentrations}_{distribution}.json")
growth_rates = jsondata_invivo.keys()
report = ""
cobra_model = copy.deepcopy(original_cobra_model)
cobra_model = cosa_get_model_with_nadx_scenario(
nadx_scenario=distribution,
cobra_model=cobra_model,
)
reaction_ids = [x.id for x in cobra_model.reactions]
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),
add_optmdf_bottleneck_analysis=False,
)
optmdfpathway_base_variables: Dict[str, pulp.LpVariable] = optmdfpathway_base_problem.variablesDict()
z_sum_var = pulp.LpVariable(
name=f"z_sum_var",
lowBound=-float("inf"),
upBound=float("inf"),
cat=pulp.LpContinuous,
)
z_sum = 0.0
all_tcosa_ids = get_all_tcosa_reaction_ids(cobra_model)
for reaction_id in reaction_ids:
# if not reaction_id.endswith("_TCOSA"):
# continue
# z_active = pulp.LpVariable(
# name=f"z_active_{reaction_id}",
# cat=pulp.LpBinary,
# )
# optmdfpathway_base_problem += -optmdfpathway_base_variables[reaction_id] + z_active * 1e-5 <= 0
# optmdfpathway_base_variables: Dict[str, pulp.LpVariable] = optmdfpathway_base_problem.variablesDict()
if reaction_id in all_tcosa_ids:
if "_VARIANT_" not in reaction_id:
z_sum += optmdfpathway_base_variables[f"z_var_{reaction_id}"]
z_sum_var = pulp.LpVariable(
name=f"z_sum_var",
lowBound=-float("inf"),
upBound=float("inf"),
cat=pulp.LpContinuous,
)
optmdfpathway_base_problem += z_sum_var == z_sum
for growth_rate in growth_rates:
growth_rate_float = float(growth_rate.replace(",", "."))
optmdfpathway_base_variables[biomass_reaction_id].bounds(
growth_rate_float-growth_epsilon,
1e12,
)
if target == "OPTMDF":
min_target = jsondata_invivo[growth_rate]["values"]["var_B"] - 0.001
optmdfpathway_base_variables["var_B"].bounds(
min_target,
1e12,
)
elif target == "OPTSUBMDF":
min_target = jsondata_invivo[growth_rate]["values"]["var_B2"] - 0.001
optmdfpathway_base_variables["var_B"].bounds(
MIN_OPTMDF,
1e12,
)
optmdfpathway_base_variables["var_B2"].bounds(
min_target,
1e12,
)
print(f" @ µ [1/h] of {growth_rate_float} and min {target} of {min_target} kJ/mol")
report += f" @ µ [1/h] of {growth_rate_float} and min {target} of {min_target} kJ/mol\n"
print("RUN")
minimization_result = perform_variable_maximization(
optmdfpathway_base_problem,
"z_sum_var",
)
all_tcosas = get_all_tcosa_reaction_ids(cobra_model)
active_reactions = []
sum_ = 0
for var_name in minimization_result["values"].keys():
if not var_name.startswith("z_var_"):
continue
reac_id = var_name.replace("z_var_", "")
if reac_id not in all_tcosas:
continue
z_value = minimization_result["values"][var_name]
reaction_string = cobra_model.reactions.get_by_id(reac_id).reaction
if z_value > 0.1:
report_line = f"* {reac_id} | {round(dG0_values[reac_id]['dG0'], 3)} kJ/mol | {minimization_result['values'][reac_id]} | {reaction_string}"
active_reactions.append(reac_id)
# print(report_line)
report += report_line + "\n"
sum_ += 1
print("sum", sum_)
report += f"Reached MDF: {minimization_result['values']['var_B']}\n"
report += f"Reached SubMDF: {minimization_result['values']['var_B2']}\n"
report += f"Minimal sum of active reactions: {len(active_reactions)}\n"
# print(f"Reached MDF: {minimization_result['values']['var_B']} kJ/mol")
# print(f"Reached SubMDF: {minimization_result['values']['var_B2']} kJ/mol")
# json_write("x.json", minimization_result)
# input()
aerobic = "anaerobic" if anaerobic else "aerobic"
with open(f"./cosa/max_reacs_{aerobic}_{distribution}_{target}_{concentrations}.txt", "w", encoding="utf-8") as f:
f.write(report)
cosa_maximal_reaction_numbers(anaerobic=False, expanded=False)
cosa_maximal_reaction_numbers(anaerobic=True, expanded=False)