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simple_driven_network.py
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simple_driven_network.py
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import numpy as np
from tqdm import tqdm
class reaction:
def __init__(self, N, educts, products, catalysts, ks):
self.N = N
self.educts = educts
self.products = products
self.catalysts = catalysts
self.k = np.random.choice(ks,1)
def get_flux(self, A):
flux = np.zeros_like(A)
if self.catalysts is None:
Rplus = self.k*A[self.educts].prod()
Rminus = self.k*A[self.products].prod()
else:
Rplus = self.k*A[self.educts].prod()*A[self.catalysts].prod()
Rminus = self.k*A[self.products].prod()*A[self.catalysts].prod()
flux[self.products] = Rplus - Rminus
flux[self.educts] = Rminus - Rplus
return flux
def get_scalar_flux(self, A):
if self.catalysts is None:
Rplus = self.k*A[self.educts].prod()
Rminus = self.k*A[self.products].prod()
else:
Rplus = self.k*A[self.educts].prod()*A[self.catalysts].prod()
Rminus = self.k*A[self.products].prod()*A[self.catalysts].prod()
flux = Rplus - Rminus
return flux
def get_potential_difference(self, A):
mu_educts = np.log(A[self.educts]).sum()
mu_products = np.log(A[self.products]).sum()
delta_G = mu_products - mu_educts
return delta_G
class simple_reaction_network:
# initializers
def __init__(self, N = 25, M = 35, p_bimolecular = 0.3, p_catalytic = 0.5, steps = int(1e6), ks = [1, 1e-1, 1e-2, 1e-3], dt = 0.001, create_reactions = True, noise = 0.0):
self.N = N
self.M = M
self.A = np.zeros( (N,) )
self.dt = dt
self.clamped = dict()
self.noise = noise
self.reactions = []
if create_reactions:
# Create baseline reactions
for i in range(M):
# Create monomolecular reaction
if np.random.rand() < 1.0 - p_bimolecular:
chemicals = np.random.choice(N,2,replace = False)
educt = chemicals[0]
product = chemicals[1]
self.reactions.append(reaction(N, [educt], [product], None, ks))
# Create non-catalytic bimolecular reaction
elif np.random.rand() < 1.0 - p_catalytic:
chemicals = np.random.choice(N,4,replace = False)
educt = chemicals[0:2]
product = chemicals[2:5]
self.reactions.append(reaction(N, educt, product, None, ks))
# Create catalytic bimolecular reaction
else:
chemicals = np.random.choice(N,5,replace = False)
educt = chemicals[0:2]
product = chemicals[2:4]
catalyst = chemicals[4]
self.reactions.append(reaction(N, educt, product, [catalyst], ks))
def clamp_concentration(self, species, concentration):
self.clamped[species] = concentration
def get_potential(self, species):
pot = np.log(self.A[species])
return pot
def get_potentials(self):
pot = np.log(self.A)
return pot
def init_concentrations(self, A0 = None):
if A0 is None:
alpha = np.ones((self.N,))
init_state = np.random.dirichlet(alpha)*self.N
self.A = init_state
else:
self.A = A0
# Enforce clamped variables, if there are any
for key, value in self.clamped.items():
self.A[key] = value
def get_fluxes(self, A):
flux = np.zeros_like(A)
for r in self.reactions:
flux += r.get_flux(A)
return flux
# forward method
def run(self, steps = 100000, log_fluxes = True, show_progress_bar = True):
Aout = np.zeros((steps,self.N))
AoutG = np.zeros((steps,self.N))
for key, value in self.clamped.items():
self.A[key] = value
Aout[0,:] = self.A
AoutG[0,:] = self.A
if log_fluxes:
fluxes = np.zeros( (steps, len(self.reactions)) )
delta_Gs = np.zeros( (steps, len(self.reactions)) )
clamped_fluxes = np.zeros( (steps, len(self.clamped)) )
clamped_potentials = np.zeros( (steps, len(self.clamped)) )
potentials = np.zeros( (steps, self.N) )
clamped_species = list(self.clamped.keys())
if show_progress_bar:
iters = tqdm(range(1,steps))
else:
iters = range(1,steps)
for t in iters:
Flux = self.get_fluxes(self.A)
if log_fluxes:
for i in range(len(self.reactions)):
fluxes[t-1,i] = self.reactions[i].get_scalar_flux(self.A)
for i in range(len(self.reactions)):
delta_Gs[t-1,i] = self.reactions[i].get_potential_difference(self.A)
for i in range(len(clamped_species)):
clamped_fluxes[t-1,i] = Flux[clamped_species[i]]
clamped_potentials[t-1,i] = self.get_potential(clamped_species[i])
potentials[t-1,:] = self.get_potentials()
self.A = self.A + self.dt*Flux
AoutG[t,:] = self.A
if self.noise > 1e-12:
self.A = self.A + self.noise*np.random.randn(self.N)
for key, value in self.clamped.items():
self.A[key] = value
Aout[t,:] = self.A
if log_fluxes:
for i in range(len(self.reactions)):
fluxes[-1,i] = self.reactions[i].get_scalar_flux(self.A)
for i in range(len(self.reactions)):
delta_Gs[-1,i] = self.reactions[i].get_potential_difference(self.A)
for i in range(len(clamped_species)):
clamped_fluxes[-1,i] = Flux[clamped_species[i]]
clamped_potentials[-1,i] = self.get_potential(clamped_species[i])
potentials[-1,:] = self.get_potentials()
return Aout, fluxes, delta_Gs, clamped_fluxes, clamped_potentials, potentials, AoutG
else:
return Aout