# The MIT License (MIT)
#
# Copyright (c) 2026 Department of Plant and Environmental Science,
# Weizmann Institute of Science.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
"""
A module for I/O operations related to model balancing.
"""
import itertools
import json
from collections import OrderedDict
from importlib.resources import files
from typing import Dict, List, Union
import numpy as np
import pandas as pd
from sbtab import SBtab
from . import Q_
JSON_EXAMPLE_URL = (
"https://raw.githubusercontent.com/liebermeister/model"
"-balancing/master/examples/JSON/"
)
JSON_NAME_MAPPINGS = {
"Keq": ("kinetic_constants", "Keq"),
"Km": ("kinetic_constants", "KM"),
"Ka": ("kinetic_constants", "KA"),
"Ki": ("kinetic_constants", "KI"),
"kcatf": ("kinetic_constants", "Kcatf"),
"kcatr": ("kinetic_constants", "Kcatr"),
"conc_met": ("metabolite_concentrations",),
"conc_enz": ("enzyme_concentrations",),
} # mapping from the variable names in python to the location in the JSON
def read_arguments_from_url(
url: str,
) -> Dict[str, np.array]:
import requests
res = requests.get(url)
data = json.loads(res.content.decode("UTF-8"))
return read_arguments_from_json(data)
def read_adguments_from_filename(
json_fname: str,
) -> Dict[str, np.array]:
# Try to load from package data first (for built-in examples)
try:
data_files = files("model_balancing").joinpath("data")
json_path = data_files.joinpath(json_fname)
with json_path.open("r") as fp:
data = json.load(fp)
return read_arguments_from_json(data)
except (FileNotFoundError, ModuleNotFoundError):
# Fall back to direct file path if package data not found
pass
# Load from filesystem path
with open(json_fname, "rt") as fp:
data = json.load(fp)
return read_arguments_from_json(data)
def read_arguments_from_json(
data,
) -> Dict[str, np.array]:
"""Read the list of model balancing arguments from a JSON.
See our page about the :ref:`JSON specification sheet <json>`.
"""
keq_standard_concentration = Q_(data["standard_concentration"])
args = {}
args["S"] = np.array(data["network"]["stoichiometric_matrix"])
args["A_act"] = np.array(data["network"]["activation_matrix"])
args["A_inh"] = np.array(data["network"]["inhibition_matrix"])
args["fluxes"] = standardize_input_matrix(
data["reaction_fluxes"]["data"]["mean"], data["reaction_fluxes"]["unit"]
)
# Read the kinetic parameters that have 'normal' units:
for p in JSON_NAME_MAPPINGS.keys():
# in the JSON, parameter names have slightly different casing, so we
# use a dictionary for converting between the conventions.
p_json = data
for k in JSON_NAME_MAPPINGS[p]:
p_json = p_json[k]
unit = p_json["unit"]
if len(p_json["combined"]["geom_mean"]) == 0:
args[f"geom_mean_{p}"] = None
args[f"lower_bound_{p}"] = None
args[f"upper_bound_{p}"] = None
args[f"precision_ln_{p}"] = None
continue
if "geom_std" in p_json["combined"]:
args[f"precision_ln_{p}"] = np.diag(
list(
map(
lambda x: np.log(x) ** (-2.0),
itertools.chain.from_iterable(p_json["combined"]["geom_std"]),
)
)
)
elif "prec_ln" in p_json["combined"] and len(p_json["combined"]["prec_ln"]) > 0:
args[f"precision_ln_{p}"] = np.array(p_json["combined"]["prec_ln"])
else:
raise KeyError(f"neither 'geom_std' nor 'prec_ln' provided for {p}")
geom_mean = np.array(p_json["combined"]["geom_mean"])
lb = np.array(p_json["bounds"]["min"])
ub = np.array(p_json["bounds"]["max"])
true_value = np.array(p_json["true"])
if p == "Keq":
# For the equilibrium constants, which are unitless but if the standard
# concentration is not 1M, we need to adjust their values based on what
# it is in the JSON file.
A = np.diag(
np.power(keq_standard_concentration.m_as("M"), args["S"].sum(axis=0))
)
unit = ""
else:
# otherwise, we set A to be the identity matrix, so it would have no
# effect
A = np.eye(geom_mean.shape[0])
args[f"geom_mean_{p}"] = A @ Q_(geom_mean, unit)
args[f"lower_bound_{p}"] = A @ Q_(lb, unit)
args[f"upper_bound_{p}"] = A @ Q_(ub, unit)
args[f"true_value_{p}"] = A @ Q_(true_value, unit)
args["precision_ln_kinetic"] = np.array(data["kinetic_constants"]["all"]["prec_ln"])
args["kinetic_order"] = data["kinetic_constants"]["all"]["names"]
args["kinetic_order_unique"] = list(
OrderedDict(zip(args["kinetic_order"], itertools.repeat(None)))
)
args["metabolite_names"] = data["network"]["metabolite_names"]
args["reaction_names"] = data["network"]["reaction_names"]
args["state_names"] = data["state_names"]
args["rate_law"] = "CM"
return args
[docs]
def to_state_sbtab(
v,
c,
e,
delta_g,
metabolite_names,
reaction_names,
state_names,
) -> SBtab.SBtabDocument:
"""Create a state SBtab.
The state SBtab contains the values of the state-dependent variables,
i.e. flux, concentrations of metabolites, concentrations of enzymes,
and the ΔG' values.
"""
state_sbtabdoc = SBtab.SBtabDocument(name="MB result")
flux_df = pd.DataFrame(v.m_as("mM/s"), columns=state_names)
flux_df.insert(0, "!QuantityType", "rate of reaction")
flux_df.insert(1, "!Reaction", reaction_names)
flux_sbtab = SBtab.SBtabTable.from_data_frame(
flux_df.astype(str),
table_id="Flux",
table_name="Metabolic fluxes",
table_type="QuantityMatrix",
unit="mM/s",
)
state_sbtabdoc.add_sbtab(flux_sbtab)
conc_met_df = pd.DataFrame(c.m_as("mM"), columns=state_names)
conc_met_df.insert(0, "!QuantityType", "concentration")
conc_met_df.insert(1, "!Compound", metabolite_names)
conc_met_sbtab = SBtab.SBtabTable.from_data_frame(
conc_met_df.astype(str),
table_id="MetaboliteConcentration",
table_name="Metabolite concentration",
table_type="QuantityMatrix",
unit="mM",
)
state_sbtabdoc.add_sbtab(conc_met_sbtab)
conc_enz_df = pd.DataFrame(e.m_as("mM"), columns=state_names)
conc_enz_df.insert(0, "!QuantityType", "concentration of enzyme")
conc_enz_df.insert(1, "!Reaction", reaction_names)
conc_enz_sbtab = SBtab.SBtabTable.from_data_frame(
conc_enz_df.astype(str),
table_id="EnzymeConcentration",
table_name="Enzyme concentration",
table_type="QuantityMatrix",
unit="mM",
)
state_sbtabdoc.add_sbtab(conc_enz_sbtab)
gibbs_energy_df = pd.DataFrame(delta_g.m_as("kJ/mol"), columns=state_names)
gibbs_energy_df.insert(0, "!QuantityType", "Gibbs energy of reaction")
gibbs_energy_df.insert(1, "!Reaction", reaction_names)
gibbs_energy_sbtab = SBtab.SBtabTable.from_data_frame(
gibbs_energy_df.astype(str),
table_id="ReactionGibbsFreeEnergy",
table_name="Gibbs free energies of reaction",
table_type="QuantityMatrix",
unit="kJ/mol",
)
state_sbtabdoc.add_sbtab(gibbs_energy_sbtab)
return state_sbtabdoc
[docs]
def to_model_sbtab(
kcatf,
kcatr,
Keq,
Km,
Ka,
Ki,
S,
A_act,
A_inh,
metabolite_names,
reaction_names,
state_names,
) -> SBtab.SBtabDocument:
"""Create a model SBtab.
The model SBtab contains the values of the state-independent variables,
i.e. kcatf, kcatr, Km, Ka, and Ki.
"""
model_sbtabdoc = SBtab.SBtabDocument(name="MB result")
parameter_data = []
parameter_data += [
("equilibrium constant", rxn, "", value, "dimensionless")
for rxn, value in zip(reaction_names, Keq.m_as(""))
]
parameter_data += [
("catalytic rate constant geometric mean", rxn, "", value.m_as("1/s"), "1/s")
for rxn, value in zip(reaction_names, (kcatf * kcatr) ** (0.5))
]
parameter_data += [
("forward catalytic rate constant", rxn, "", value.m_as("1/s"), "1/s")
for rxn, value in zip(reaction_names, kcatf)
]
parameter_data += [
("reverse catalytic rate constant", rxn, "", value.m_as("1/s"), "1/s")
for rxn, value in zip(reaction_names, kcatr)
]
for j, rxn in enumerate(reaction_names):
for i, met in enumerate(metabolite_names):
if S[i, j] != 0:
parameter_data += [
("Michaelis constant", rxn, met, Km[i, j].m_as("mM"), "mM")
]
if A_act[i, j] != 0:
parameter_data += [
("Activation constant", rxn, met, Ka[i, j].m_as("mM"), "mM")
]
if A_inh[i, j] != 0:
parameter_data += [
("Inhibition constant", rxn, met, Ki[i, j].m_as("mM"), "mM")
]
parameter_df = pd.DataFrame(
data=parameter_data,
columns=["!QuantityType", "!Reaction", "!Compound", "!Mode", "!Unit"],
).sort_values("!QuantityType")
parameter_sbtab = SBtab.SBtabTable.from_data_frame(
parameter_df.astype(str),
table_id="Parameter",
table_name="Parameter",
table_type="Quantity",
unit="",
)
parameter_sbtab.change_attribute("StandardConcentration", "M")
model_sbtabdoc.add_sbtab(parameter_sbtab)
return model_sbtabdoc
__all__ = [
"standardize_input_matrix",
"to_state_sbtab",
"to_model_sbtab",
]