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Simulación de Michaelis-Menten.

El siguiente código simula una reacción simple de Michaelis-Menten con una única concentración. En la misma carpeta donde se encuentra el archivo, se generará el archivo example_1_mm.rde, el cual permite posteriormente generar reportes en excell, graficos, etc.

#!python

# -*- coding: utf-8 -*-
import pypsdier
from math import exp

seed_file = "example_1_mm.rde"

# COMON PARAMETERS
# To define: legend, initial concentrations, 
# catalyst Volume, bulk liquid volume and simulation time 
legend = ('PenG',)
IC = (1.3,)   # [mM]
Vc = 0.01 # [mL]
Vb = 40.0  # [mL]
Tsim = 20 #2700. # [s]
p_common = (legend, IC, Vc, Vb, Tsim)

# REACTION PARAMETERS
# To define: reaction function and corresponding parameters
def MichaelisMenten(Cs, E0, params):
  """
  MichaelisMenten(Cs, E0, params)
  Cs = PenG [mM]
  E0 [mM]
  params = k [1/s], K [mM]
  """
  k, K = params
  S, = Cs
  v_S = k*E0*S/(K+S)
  v = (-v_S,)
  return v

def E(t):
  """Variation in enzyme activity"""
  return 0.10*(1.0-exp(.01*t)) # [mM]
params =  41 , 0.13  #[1/s], [mM/s] 
p_reaction  = (MichaelisMenten, E, params)

# DIFUSION PARAMETERS
# To define: Efective Diffusivity and Particle size distribution
De  = (5.30E-10,)  # [m2/s]
H_R = [24.65E-6] # [m]
H_f = [1.0]     # []
p_diffusion = (De, H_R, H_f)

################################################################################
# PDE SOLVE
################################################################################
pypsdier.pde(p_common, p_reaction, p_diffusion, seed_file, pypsdier.get_filename(), dt_save=1)

Updated