In this paper, the best experimental data were obtained from the study of miRNA detection. In the thermostatic amplification reaction, the amplification temperature was optimal at 40°C, and the shortest reaction time was 30 minutes at a product concentration of 1000 ng/μL and a miRNA concentration of 10 nM.
  The shortest time required for the reaction was 45 minutes for sample sizes up to 1 μg in the Cas12a cutting study.
  Based on the conclusions of this study, proposals for rationalisation are made to improve the experimental process of miRNA detection methods.
  Keywords: fitted model, thermostatic amplification reaction, Cas12a cleavage.

  In recent years, myocardial infarction has been occurring frequently and is obviously on the rise, but the detection means of myocardial infarction on the market are expensive and time-consuming, in order to prevent myocardial infarction and detect patients with the disease as early as possible, we use the thermostatic amplification technology and Cas12a cutting into the detection of myocardial infarction.
  For this study, in order to ensure the efficiency of the experiment, we need to obtain the optimal ratio of reactants.
Question 1: Optimal reactant ratios in thermostatic amplification reaction studies.
Question 2: Optimal reactant ratios in Cas12a cutting studies.

Data sources
  In the study of thermostatic amplification technology, H1 concentration, H2 concentration, amplification temperature, reaction time, and amplification product concentration was selected. Cas12a Protein concentration, crRNA concentration, the concentration of the reporters, reaction temperature, sample size, and reaction time was selected for Cas12a cleavage study.
  In order to improve the experimental efficiency and accuracy of the results, data comparisons were made through the above experimental data to reflect the changing law of the experimental process, to control the experimental variables, and to derive the optimal ratio of the reactants. Establishing a data analysis model for the experiment is of great importance to process of the experimental data.

  Amplification temperature and miRNA concentration was assumed to have an effect on the thermostatic amplification reaction. It is assumed that sample size has an effect on Cas12a cutting experiments.

Question 1: Optimal reactant ratios in thermostatic
  amplification reaction studies. The following table was obtained by experimentally obtaining the data and the fitted model were built using Python to plot the graphs.

  In the constant temperature amplification reaction study, the amplification temperature and amplification product concentration data were used to obtain the above graph, and it was concluded that the amplification temperature reached 37℃ to start the reaction, the higher the temperature the higher the concentration of amplification product, and the amplification temperature reached 40℃ to reach the threshold, and the concentration of the amplification product was no longer changed, and it was maintained at 1000ng/μL.

Fitted graphs were plotted using miRNA concentration and reaction time data, the higher the miRNA concentration the shorter the reaction time, the most rapid increase in the reaction rate during the increase in miRNA concentration up to 10nM, and the slowest increase in the reaction rate after exceeding 10nM, it was concluded that a miRNA concentration of 10nM was optimal. Question 2: Optimal reactant ratios in Cas12a cutting studies. The following table was obtained by experimentally obtaining the data and the fitted model were built using Python to plot the graphs.

  In the Cas12a cutting study, a fitted graph was plotted using data on sample size and time required for the reaction, the higher the sample size the shorter the time required for the reaction, with the shortest time required for the reaction at a sample size of 1 μg, concluding that a sample size of 1 μg was optimal.

  Advantage: fitting the model using the data obtained from the   experiment.
Weaknesses: the amount of data is too small for a deeper study.

Using the software:Python
The procedure:
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import matplotlib
%matplotlib inline
df0 = pd.read_exce('experimental data.xlsx').sort_values(by = ['miRNA(nM)'],ascending = True)
df1 = pd.read_excel('experimental data.xlsx').sort_values(by = ['Amplification temperature (°C)'],ascending = True)
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
x1 = df0['miRNA(nM)']
y1 = df0['reaction time (minutes)']
plt.xlabel('miRNA(nM)')
plt.ylabel('reaction time (minutes)')
p1 = np.poly1d(np.polyfit(x1,y1,3))
t1 = np.linspace(1,15,70)
plt.plot(x1,y1,'o',t1,p1(t1),'-')
plt.show()
x2 = df1['amplification temperature (°C)']
y2 = df1['amplification product concentration (ng/μL)']
plt.xlabel('amplification temperature (℃)')
plt.ylabel('amplification product concentration (ng/μL)')
p2 = np.poly1d(np.polyfit(x2,y2,3))
t2 = np.linspace(35,40)
plt.plot(x2,y2,'o',t2,p2(t2),'-')
plt.show()
df2 = pd.read_excel('experimental Data 2.xlsx')
x3 = df2['sample size (μg)']
y3 = df2['time required for reaction (minutes)']
# plt.plot(x3,y3,label = 'Effect of sample size concentration')
plt.xlabel('sample size (μg)')
plt.ylabel('Time required for reaction (minutes)')
# plt.legend(loc = 'upper right')
p3 = np.poly1d(np.polyfit(x3,y3,3))
t3 = np.linspace(0.1,1.5)
plt.plot(x3,y3,'o',t3,p3(t3),'-')
plt.show()