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grenaissance/skimpy/mechanisms/generalized_elementary_kinetics.py
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generalized_elementary_kinetics.py
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# -*- coding: utf-8 -*-
"""
.. module:: skimpy
:platform: Unix, Windows
:synopsis: Simple Kinetic Models in Python
.. moduleauthor:: SKiMPy team
[---------]
Copyright 2017 Laboratory of Computational Systems Biotechnology (LCSB),
Ecole Polytechnique Federale de Lausanne (EPFL), Switzerland
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
from sympy import Symbol, exp
from .mechanism import KineticMechanism
from ..utils.tabdict import TabDict
from collections import namedtuple
from ..core.itemsets import make_parameter_set, make_reactant_set
from skimpy.utils.general import make_subclasses_dict
from ..utils.namespace import *
from .utils import stringify_stoichiometry
def make_generalized_elementary_kinetics(stoichiometry, generalized_reactants):
"""
Creates a reversible N-M GeneralizedElementaryKinetics class
GeneralizedElementaryKinetics iplementing the kinetic scheme to account for non ideal effects in large scale kinetic model:
Weilandt, Daniel R., and Vassily Hatzimanikatis. "Particle-based simulation reveals macromolecular
crowding effects on the Michaelis-Menten mechanism." Biophysical Journal (2019).
:param stoichiometry is a list of the reaction stoichioemtry
:param generalized_reactants is a list of reactants that contribute in a powerlaw form:
v = v_0 * exp(beta_forward)
... * (x_1/x_1_0)^(alpha_f_x_1 )
... * (x_n/x_n_0)^(alpha_f_x_n )
with v_0 beeing the reversible Massaction flux
"""
# This refresh all subclasses and fetches already create mechanism classes
ALL_MECHANISM_SUBCLASSES = make_subclasses_dict(KineticMechanism)
new_class_name = "GEEK" \
+ "_{0}".format(stringify_stoichiometry(stoichiometry))
if new_class_name in ALL_MECHANISM_SUBCLASSES.keys():
return ALL_MECHANISM_SUBCLASSES[new_class_name]
class GeneralizedElementaryKinetics(KineticMechanism):
"""
A reversible N-M GEEK class
Implementing the kinetic scheme to account for non ideal effects in large scale kinetic model
Weilandt, Daniel R., and Vassily Hatzimanikatis. "Particle-based simulation reveals macromolecular
crowding effects on the Michaelis-Menten mechanism." Biophysical Journal (2019).
"""
suffix = "_{0}_{1}".format(stringify_stoichiometry(stoichiometry), generalized_reactants)
reactant_list = []
parameter_list = {'vmax_forward': [ODE, MCA, QSSA],
'k_equilibrium': [ODE, MCA, QSSA],
'beta_forward': [ODE, MCA, QSSA],
'beta_reverse': [ODE, MCA, QSSA],}
reactant_parameter_links = {}
reactant_stoichiometry = {}
num_substrates = 1
num_products = 1
for s in stoichiometry:
if s < 0:
substrate = 'substrate{}'.format(num_substrates)
reactant_list.append(substrate)
reactant_stoichiometry[substrate] = float(s)
num_substrates += 1
if s > 0:
product = 'product{}'.format(num_products)
reactant_list.append(product)
reactant_stoichiometry[product] = float(s)
num_products += 1
for gr in generalized_reactants:
alpha_f = 'alpha_forward_{}'.format(gr)
parameter_list[alpha_f] = [ODE, MCA, QSSA]
alpha_r = 'alpha_reverse_{}'.format(gr)
parameter_list[alpha_r] = [ODE, MCA, QSSA]
X_0 = '{}_0'.format(gr)
parameter_list[X_0] = [ODE, MCA, QSSA] # TODO Does this work if multiple times defined
reactant_parameter_links[gr] = (alpha_f, alpha_r, X_0)
Reactants = make_reactant_set(__name__ + suffix, reactant_list)
Parameters = make_parameter_set(__name__ + suffix, parameter_list)
Inhibitors = make_reactant_set(__name__ + suffix, generalized_reactants)
#ElementaryReactions = namedtuple('ElementaryReactions', [])
def __init__(self, name, reactants, parameters=None, inhibitors=None, **kwargs):
# FIXME dynamic linking, separaret parametrizations from model init
# FIXME Reaction has a mechanism, and this is a mechanism
KineticMechanism.__init__(self, name, reactants, parameters, **kwargs)
if inhibitors is not None:
self.inhibitors = inhibitors
else:
self.inhibitors = self.Inhibitors(**dict(zip(generalized_reactants, generalized_reactants)))
def get_qssa_rate_expression(self):
substrates = {k: r for k, r in self.reactants.items()
if k.startswith('substrate')}
products = {k: r for k, r in self.reactants.items()
if k.startswith('product')}
kf = self.parameters.vmax_forward.symbol
Keq = self.parameters.k_equilibrium.symbol
beta_f = self.parameters.beta_forward.symbol
beta_r = self.parameters.beta_reverse.symbol
forward_rate_expression = kf * exp(beta_f)
backward_rate_expression = kf / Keq * exp(beta_r)
for this_type, this_substrate in substrates.items():
s = this_substrate.symbol
forward_rate_expression *= s
for this_type, this_product in products.items():
p = this_product.symbol
backward_rate_expression *= p
for this_gr in self.inhibitors.values():
alpha_f, alpha_r, gr_0 = [self.parameters[p].symbol for p in
self.reactant_parameter_links[this_gr.name]]
# FIXME get this from a proper defined set
gr = this_gr.symbol
forward_rate_expression *= (gr/gr_0)**alpha_f
backward_rate_expression *= (gr/gr_0)**alpha_r
rate_expression = forward_rate_expression - backward_rate_expression
self.reaction_rates = TabDict([('v_net', rate_expression),
('v_fwd', forward_rate_expression),
('v_bwd', backward_rate_expression),
])
expressions = {}
for type, this_substrate in substrates.items():
s = this_substrate.symbol
stoich = self.reactant_stoichiometry[type]
expressions[s] = stoich * rate_expression
for type, this_product in products.items():
p = this_product.symbol
stoich = self.reactant_stoichiometry[type]
expressions[p] = stoich * rate_expression
self.expressions = expressions
self.expression_parameters = self.get_parameters_from_expression(rate_expression)
def update_qssa_rate_expression(self):
substrates = {k: r for k, r in self.reactants.items()
if k.startswith('substrate')}
products = {k: r for k, r in self.reactants.items()
if k.startswith('product')}
for type, this_substrate in substrates.items():
s = this_substrate.symbol
stoich = self.reactant_stoichiometry[type]
self.expressions[s] = stoich * self.reaction_rates['v_net']
for type, this_product in products.items():
p = this_product.symbol
stoich = self.reactant_stoichiometry[type]
self.expressions[p] = stoich * self.reaction_rates['v_net']
""""
Not implemented not necessary for elementary mechanisms
"""
def get_full_rate_expression(self):
raise NotImplementedError
def calculate_rate_constants(self):
raise NotImplementedError
GeneralizedElementaryKinetics.__name__ += GeneralizedElementaryKinetics.suffix
return GeneralizedElementaryKinetics
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