Team:NCTU Formosa/Modeling

Modeling

In the modeling part, we discover optimum protein expression time.

We use Hill-function based model :

In order to characterize the actual kinetics of this Hill-function based model that precisely reflects protein expression time, we use the genetic algorithm (GA) in MATLAB.

To achieve this, we needed to focus on the tasks of estimating model parameters from the experimental data in the case of Hill-function based model for parameter inference. These reverse engineering tasks offered focus of the present difficulty, also known as the Estimation of Model Parameters Challenge.

By using the differential function which was derived from these optimum parameters which were calculated by GA can help us to simulate the optimum protein expression.

The graph of simulated protein expression versus time were drawn (Fig.1) (Fig.2) (Fig.3). Thus, we can find the optimum protein expression time. However, the simulated protein expression curve is slower than the experimental curve by one hour. Thus, to find the most exact optimum protein expression time, we infer that subtracting one hour of the optimum protein expression time would be correct.



Figure 1. From this graph, the protein expression reaches peak after growing about 15 hours. This means that the E. Cotector can have maximum efficiency at this point.



Figure 2. From this graph, the protein expression reaches peak after growing about 18 hours. This means that the E. Cotector can have maximum efficiency at this point.



Figure 3. From this graph, the protein expression reaches peak after growing about 16 hours. This means that the E. Cotector can have maximum efficiency at this point.


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