To provide iGEM teams as well as other scientists a cost and time efficient way to construct genetic constructs.

One of the overwhelming problems that synthetic biologists face on a daily basis is the variability in genetic constructs. With our ever persistent drive to find a way to develop a standard set of parts for synthetic biology, it is often overlooked that these “standard” parts do not interact the same way that a nut and a bolt do. Sure the threads of each must match for compatibility, but you are guaranteed that a matching set of nuts and bolts will hold together a table just as well as they will hold together a chair, or a piece of metal machinery, or the engine of your car.

When we place together a promoter with a new gene of interest we do not yet have these compatibility rules to know how a promoter will work with this gene of interest. We, as synthetic biologists, still don’t know that one promoter/gene will work the same way in one strain of E. coli as they will in another E. coli. For synthetic biology to truly emerge as a field that can capitalize in industry, we must find novel ways of engineering biological systems reliably using our own standard parts.

Taguchi method is a statistical method of design of experiment (DOE) that allows information to be obtained from a small number of experiments (1). This method looks at the robustness of a system: how is a system dependent or affected by one part within this system. This is called the Robust Technology Development (2). It is primarily used in industry such as mayonnaise production. For this example a pump, tank, and pipe may be needed to produce mayonnaise of a specific texture and viscosity. These parameters need to be maintained from mayonnaise plant to mayonnaise plant. This is difficult if one plant is in Florida, another in Colorado, and finally in New Jersey. Different plant locations and operating procedures can affect the quality of the product. The goal of the Taguchi Method is to optimize a process based on the mean and variance of these different parameters so that the quality is maintained.

This process was determined by Dr. Geinichi Taguchi, a Japanese researcher whose goal was to create a tool to design an efficient experimental design (2). The goal is to minimize the number experiments; therefore the cost, while still gathering significant data. The following is the philosophy of the method (3):

1. Quality should be designed into a product not inspected into it
2. Quality is best achieved by minimizing the deviation from a target. The product should be designed so that it is immune to uncontrollable environmental factors
3. The cost of quality should be measured as a function of deviation from the standard and the losses should be measured system wide

The method is based on orthogonal arrays which are used to determine the experiments that are to be performed. The arrays are determined by the number of parameters which are referred to as variables and the number of levels of each variable, or states. Each parameter is tested in pairs (3). Once the array is set up, the data from each experiment is used to determine the optimal system setup with minimal variance between systems. Data can be analyzed by ANOVA, bin yield, and Fischer’s exact test or Chi-Squared test as well as other means of statistical analysis (3). In the case of our project, we are looking at how to determine the best system for a genetic circuit. GFP is the gene of interest that is analyzed for ease of gathering and analyzing the data.

The following is the typically procedure that is followed for the Taguchi Method:

1. Define the process objective:
1. The goal is to find a system that has reliable GFP expression regardless of outside noise.
2. Determine the design parameters including what affects the performance measurements, such as temperature, and specify the number of levels the parameters will be varied at.
1. Parameters are promoters, RBS, and terminators.
2. There will be three levels of each parameter.
3. Each of the genetic circuits that are constructed by these parameters will be tested in three different strains of E. coli.
3. Create orthogonal arrays

4. Conduct the experiments
1. Details on this can be found in the Experimental Design section.
5. Complete data analysis

Once the data has been collected, the mean and variance can be calculated by the following equations:

Mean Equation (3)

Variance Equation (3)

Using these two equations, the noise to signal ratio has to be determined:

Noise to signal ratio equation (3)

Experiment

Design experiment to test results of the 27 different circuits in the 3 different strains of E. coli for robustness.

Purpose

Procedure

Standardized Curve Relating OD to Cell Number

1. Grow up cells from each strain of E. coli in LB media along with any required antibiotic resistances
2. Dilute stock cell solution down in 5 ML of LB media so that the initial OD reading is 0.0
3. Grow cells at 37⁰C for 6 – 8 hours checking OD every hour to half hour
4. Measure a sample of cell solution at each time point with Coulture Counter
5. Plot cell number vs. OD for each strain to obtain standardized curve

3A Assembly of Constructs Expressing GFP

1. From the Taguchi Orthogonal Array, determine which genetic circuits to construct
2. Using iGEM’s protocol for 3A assembly

Calculate the GFP Expression/Individual Cell

1. After all three strains are transformed with each construct for a total of 81 constructs to test, use Cary Eclipse Fluorescence Spectrophotometer and OD to gain total GFP expression and number of cells
2. Divide total expression by number of cells to gain GFP expression per cell

Statistically Determine Which Construct has Most Robustness
1. After the amount of GFP expression per cell is calculated for all 81 constructs, use JMP statistical Analysis Software to analyze the data.
2. Using the screening or fit model platform and the built in optimizer in JMP, see which construct is the most robust.
3. Compare the robustness for the 81 constructs against the Taguchi constructs to see if the most robust construct in the 81 constructs array is a part of the Taguchi array.