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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)
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