Team:SCUT/Collaborations
Collaboration with Team FAFU-China
Collaboration
Aiming to respond to the requirements of the official, we help the team FAFU-CHINA with some modeling work.
The introduction of FAFU-CHINA’s Project
We use the method of RNA interference to suppress the replication of CSBV and to prevent CSBV infecting larvae. The methods of rearing honeybee larvae and viral inoculation under lab conditions were established. CSBV homologous specific dsRNAs (dsRdRP) were synthesized using T7 RiboMAXTM Express RNAi System Kit. The daRNAs and CSBV were added into the food, dsGFP as control. The second instar larvae were first fed with food containing dsRNAs. Twelve hours later, the larvae were then fed with food containing CSBV. The mRNA level of CSBV were detected by RT-qPCR in dsRNAs treated larvae. In order to apply RNA interference technology to production practice RdRP gene cloned into the L4440 vector, and transformed into E.coli strain HT115 to express dsRdRP. Bacteria containing dsRdRP were fed to CSBV-infected colonies for studying its effect on the prevention and treatment of CSBV.
Modeling Work
In FAFU’s project, it is difficult to measure the quantitative data and determine the amount of dsRNAs which is fed to the larvae. So in the modeling part, we devoted to establishing an accurate mathematical model to simulate the dsRNA expression according to the mechanism of T7 promoter. After the model is built, we can determine the relationship between the concentration of IPTG and the production of dsRNA . Then we can control the amount of dsRNAs which if fed to the larvae by controlling the concentration of IPTG easily.
We know that T7 promoter is a kind of inducible promoter. Hill equation can be used to simulate the effect of T7 promoter. In T7 strength model, the independent variable is the concentration of IPTG, and the dependent variable is the production of dsRNA.
1.The Model Simulating the Change of dsRNA with Time in Different Concentration of IPTG
By observing the pattern of the data, we figure out that the Logistic equation, which is often used to simulate the growth of population, can model the trend best. Thus, we adapt the formula in the following fitting, where a is the concentration of dsRNA in the steady state.
When the concentration of IPTG=0.3mmol/L, the result of curve fitting is:
Coefficients (with 95% confidence bounds):
a = 0.3241 (0.299, 0.3492)
b = 35.95 (-79.99, 151.9)
c = 1.829 (0.3231, 3.334)
Goodness of fit:
SSE: 0.00333
R-square: 0.9676
Adjusted R-square: 0.9567
RMSE: 0.02356
Figure 1. The curve is matched by the formula mentioned above.
a = 0.3474 (0.3261, 0.3686)
b = 46.66 (-63.55, 156.9)
c = 1.828 (0.7647, 2.892)
Goodness of fit:
SSE: 0.00232
R-square: 0.9808
Adjusted R-square: 0.9744
RMSE: 0.01966
Figure 2. The curve is matched by the formula mentioned above.
When the concentration of IPTG=0. 5mmol/L, the result of curve fitting is:
a = 0.3465 (0.3206, 0.3723)
b = 49.63 (-79.79, 179)
c = 1.76 (0.6333, 2.886)
Goodness of fit:
SSE: 0.003338
R-square: 0.9732
Adjusted R-square: 0.9642
RMSE: 0.02359
Figure 3.The curve is matched by the formula mentioned above.
2.The Hill Equation
By the work of first part, it is found that the concentration of dsRNA will become steady after around 4 hours, so we regard the concentration of dsRNA after 4 hours’ culture as that of steady state. Then the Hill equation is applied to model the relationship between the concentration of IPTG and the production of dsRNA, the result of curve fitting is: (where P_max=0.3375 is the maximal data we can get from the data)
XM = 0.1265 (0.08411, 0.1689)
n = 2.239 (0.8187, 3.658)
Goodness of fit:
SSE: 0.003107
R-square: 0.9062
Adjusted R-square: 0.8828
RMSE: 0.02787
Figure 4.The curve is matched by the formula mentioned above.
https://2015.igem.org/Team:FAFU-CHINA/Modeling