Team:iBowu-China/Model equilibrium mini.py

Project

Description Design Results

Parts

Interlab

Notebook Protocols Safety

Human Practices

Integrated HP Public Engagement Entrepreneurship

Modeling

Model Software

Judging form

Team

Team members Collaborations Attributions

simple_model_optimize.py

import numpy as np

import matplotlib.pyplot as plt

from mpl_toolkits.mplot3d import Axes3D

def model(N, A):

mr = 0.161 * 0.784 * N / 2.657

pr = 0.36 * mr / 2.658

mb = (1.13E-16 + 5.269 * 0.784 * N * (0.696 * A * pr)**2 / (1 + (0.696 * A * pr)**2)) / 0.231

pb = 2.297 * mb / 0.181

return pb

x_new = np.arange(0, 35, 1)

y_new = np.arange(0, 50, 0.01)

G = np.arange(1000, 3000, 100)

xx = []

yy = []

index = -1

for g in G:

xx.append([])

yy.append([])

index += 1

for i in x_new:

for j in y_new:

if(model(i, j) > g):

xx[index].append(i)

yy[index].append(j)

break

fig = plt.figure()

ax = fig.add_subplot(111, projection='3d')

for i in range(len(G)):

x = xx[i]

y = [G[i] for j in range(len(x))]

z = yy[i]

ax.plot(x, y, z, color='blue')

#ax.plot_surface(x_new, y_new, z_new, cmap='rainbow', label = "simulated distribution")

ax.set_xlabel("DNA($ng/uL)$")

ax.set_ylabel("GFP($ng/ul$)")

ax.set_zlabel("AHL($nM$)")

ax.grid(True)

plt.show()