def make_plate(params, inducer_conc):
amount = inducer_conc * 1e-6 #milli moles
agar_thickness = 3.12 # mm
init_conc = amount / (w**2 * agar_thickness) #mol/mm^3
init_conc *= 1e6 # mM
A_0 = init_conc
D_N, mu_max, K_mu, gamma, D_A, \
alpha_T, beta_T, K_IT, n_IT, K_lacT, T7_0, \
alpha_R, beta_R, K_IR, n_IR, K_lacR, R_0, \
alpha_G, beta_G, n_A, K_A, n_R, K_R, X_0, G_s = params
## Create our environment
plate = Plate(environment_size)
dist = 4.5 # mm
centre = plate_width / 2
receiver_radius = 2
receiver_pos = [[centre - i * dist, centre] for i in range(1, 4)]
receiver_pos.extend([[centre + i * dist, centre] for i in range(1, 4)])
receiver_pos.extend([[centre, centre + i * dist] for i in range(1, 4)])
receiver_pos.extend([[centre, centre - i * dist] for i in range(1, 4)])
receiver_coordinates = get_node_coordinates(receiver_pos, receiver_radius,
environment_size[0],
environment_size[1], w)
rows = receiver_coordinates[:, 0]
cols = receiver_coordinates[:, 1]
receiver_flags = np.zeros(environment_size)
receiver_flags[rows, cols] = 1
## add nutrient to the plate
U_N = np.ones(environment_size) * N_0
N = Species("N", U_N)
def N_behaviour(t, species, params):
## unpack params
D_N, mu_max, K_mu, gamma, D_A, \
alpha_T, beta_T, K_IT, n_IT, K_lacT, T7_0, \
alpha_R, beta_R, K_IR, n_IR, K_lacR, R_0, \
alpha_G, beta_G, n_A, K_A, n_R, K_R, X_0, G_s = params
#mu = mu_max * np.maximum(0, species['N']) / (K_mu + np.maximum(0,species['N'])) * np.maximum(0,species['X'])
#print(np.max(mu))
mu = dx(t, species) * receiver_flags
#print('mu', mu)
n = D_N * hf.ficks(np.maximum(0, species['N']), w) - mu / gamma
return n
N.set_behaviour(N_behaviour)
plate.add_species(N)
## add one strain to the plate
U_X = np.zeros(environment_size)
U_X[rows, cols] = X_0
strain = Species("X", U_X)
def X_behaviour(t, species, params):
## unpack params
#mu = mu_max * np.maximum(0, species['N']) / (K_mu + np.maximum(0, species['N'])) * np.maximum(0,species['X'])
mu = dx(t, species) * receiver_flags
return mu
strain.set_behaviour(X_behaviour)
plate.add_species(strain)
## add IPTG to plate
#inducer_position = [[int(j * (4.5/w)) for j in i] for i in inducer_positions # convert position to specified dims
U_A = np.zeros(environment_size)
U_A[inducer_position[0], inducer_position[1]] = A_0
A = Species("A", U_A)
def A_behaviour(t, species, params):
## unpack params
D_N, mu_max, K_mu, gamma, D_A, \
alpha_T, beta_T, K_IT, n_IT, K_lacT, T7_0, \
alpha_R, beta_R, K_IR, n_IR, K_lacR, R_0, \
alpha_G, beta_G, n_A, K_A, n_R, K_R, X_0, G_s = params
a = D_A * hf.ficks(np.maximum(0, species['A']), w)
return a
A.set_behaviour(A_behaviour)
plate.add_species(A)
#add T7 to the plate
U_T7 = np.ones(environment_size) * T7_0
T7 = Species("T7", U_T7)
def T7_behaviour(t, species, params):
D_N, mu_max, K_mu, gamma, D_A, \
alpha_T, beta_T, K_IT, n_IT, K_lacT, T7_0, \
alpha_R, beta_R, K_IR, n_IR, K_lacR, R_0, \
alpha_G, beta_G, n_A, K_A, n_R, K_R, X_0, G_s = params
mu = dx(t, species) * receiver_flags
dT7 = (alpha_T * mu *
(1 + (np.maximum(0, species['A']) / K_IT)**n_IT)) / (
1 + (np.maximum(0, species['A']) / K_IT)**n_IT +
K_lacT) + beta_T * mu - mu * np.maximum(0, species['T7'])
return dT7
T7.set_behaviour(T7_behaviour)
plate.add_species(T7)
## add GFP to plate
U_G = np.ones(environment_size) * GFP_0
G = Species("G", U_G)
def G_behaviour(t, species, params):
## unpack params
D_N, mu_max, K_mu, gamma, D_A, \
alpha_T, beta_T, K_IT, n_IT, K_lacT, T7_0, \
alpha_R, beta_R, K_IR, n_IR, K_lacR, R_0,\
alpha_G, beta_G, n_A, K_A, n_R, K_R, X_0, G_s = params
#mu = mu_max * np.maximum(0, species['N']) / (K_mu + np.maximum(0, species['N'])) * np.maximum(0,species['X'])
mu = dx(t, species) * receiver_flags
#T7 = alpha_T + beta_T - alpha_T * K_lacT / (1 + (np.maximum(0, species['A']) / K_IT)**n_IT + K_lacT) + T7_0 * np.exp(-mu * t)
#T7 = alpha_T + beta_T - alpha_T * K_lacT / (1 + (np.maximum(0, species['A']) / K_IT)**n_IT + K_lacT) + T7_0 * np.exp(-mu * t) #rescaled
T7 = np.maximum(0, species['T7'])
#print(np.max(T7))
#R = alpha_R + beta_R - alpha_R * K_lacR / (1 + (np.maximum(0, species['A']) / K_IR)**n_IR + K_lacR) + R_0 * np.exp(-mu * t)
#R = alpha_R + beta_R - alpha_R * K_lacR / (1 + (np.maximum(0, species['A']) / K_IR)**n_IR + K_lacR) + R_0 * np.exp(-mu * 1e8 * t) #rescaled
R = 0 # produces treshold
T7**n_A / (K_A**n_A + T7**n_A)
K_R**n_R / (K_R**n_R + R**n_R)
#dGFP = alpha_G * mu * T7**n_A / (K_A**n_A + T7**n_A) * K_R**n_R / (K_R**n_R + R**n_R) + beta_G * mu - np.maximum(0, species['G']) * mu*G_s
dGFP = alpha_G * mu * T7**n_A / (K_A**n_A +
T7**n_A) + beta_G * mu - np.maximum(
0, species['G']) * mu * G_s
return dGFP
G.set_behaviour(G_behaviour)
plate.add_species(G)
return plate
