【Introduction】

Our mice experiment has primarily proven the validity of our project. However, just like most scientific immune experiments on animals, the aim of our mice experiment was verification instead of exploring the optimal conditions for the production of our vaccine. In fact, fewer optimization experiments have been done by pure scientific researches, as most scientists care about facts and theories only, whereas exploring the optimal conditions is often viewed as the task of pharmaceutical factories. Yet since igem itself frequently involves industrial fields, which make igem seems like more an engineering competition than a science competition sometimes.
We investigated the methodology of Design of Experiment (DOE) in our project, and realized although most papers claim the wide application of DOE, the popularity of DOE is much lower in scientific fields compared with that in engineered fields. Perhaps the disparity of ideology between science and engineering determines this puzzling phenomenon. Consider the dual qualities of igem, we decided to explore on DOE further and design further experiments for pharmaceutical factories. We have also utilized DOE on our optimization of Bacillus subtilis medium experiment. Various designs have been made, and the overall runs of them all are too large for our laboratory.
But generally factories have adequate time and equipment to fulfill our designs, and different designs meet the requirement of different situations.

【Sweeping factors】

The final effects of the vaccine hinge on various factors, in fact perhaps over ten factors. Yet the more factors, the more runs. In our laboratory, an experiment involving over five factors is hard to design, whatever the method. Fortunately we were designing experiments for pharmaceutical factories, which enabled us to take more factors into account without sacrificing accuracy too much. The first step of any method in DOE is to make a list of controllable factors, and the second step is to find out levels of each factors. In our design, we finally selected eight factor as follows:
The rate of four engineered bacteria, which produce antigen, LTB, KNFα and reporter respectively;(We selected the concentration of antigen as our standard, fixed at 1, and the rates of other three bacteria to engineered bacteria produced antigen provides three independent factor);
The area of the sticky vaccine;
The concentration of bacteria per unit area;
The body temperature of the vaccinees;
The time consumed for culturing the bacteria;
The molecule weight of the antigen;
The ranges of these factor is given as follows:
<表格> Note: The ranges of rates and concentration of engineered bacteria were too large, and thus we used the common logarithms instead of the original values. For example, the low level of LTB was -1, meaning the lowest rate of LTB to antigen was 0.1.