Case Study
Applying End-to-End Analysis for Developers of Organic Programs
To investigate the analysis potential of our models we start by using the 2017 gold medal iGEM team from Arizona State University (ASU) as a subject. In their project they built a quorum-sensing network which is a type of Cell-to-Cell Signaling system. Next we slice the Cell-to-Cell Signaling model to replicate what the ASU team was studying. We also use covering arrays to sample both the ASU model and the Cell-to-Cell Signaling model. Finally we see whether it is possible to automatically reverse-engineer the ASU model using the tool SPLRevO.
Artifacts for the case study include:
Feature model for the ASU Quorum Sensing Model
Covering Arrays for the ASU and Cell-to-Cell Signaling models
Feature model for the protein slice of the Cell-to-Cell Signaling model
Feature model for the reverse-engineered ASU Quorum Sensing Model
Detail on the reverse-engineering tool SPLRevO
ASU model (Figures 13 & 14)
At the time of our publication the 2017 ASU iGEM team did not have all of their parts (with the subparts) entered into the Registry of Standard Biological Parts. We began manually building the model by referring to their wiki page. After contacting the team to get a more accurate description of each part we were given access to a collection of their parts on a cloud software service called Benchling. We translated the Benchling data into Excel format found here. We used this information to create a feature model representing the ASU experiments. The complete Feature Model represents 90 products, and the ASU team ran a subset of 30 product experiments by adding constraints.
As of publishing our artifacts, ASU has both published their results [1] and made the collection of their parts public (link).
Protein Slice (Figure 16)
If the ASU team wanted to focus on the proteins, which function as the main communication between the sender, receiver, and reporter, they could slice the cell-to-cell signaling model. We use the slice feature of FeatureIDE to get this sliced model. This slice is already included in the FeatureIDE workspace we provide. To generate your own slice:
Find the Cell_to_Cell_Signaling model (or another model of your choice)
Right-click on the .xml model file
Under FeatureIDE select Slice Feature Model
Select the features you want to include in your slice
Our Protein Slice creates 100 products. This is significantly fewer than the total space (7.50169 × 1020), but more than what the ASU team tested (30).
Covering Arrays (CIT Samples)
We have two 2-way covering arrays in this work, one for the ASU model and one for the cell-to-cell signaling model. To generate the samples we used the CASA tool [2]. Further information on CASA can be found here. CASA source files can be directly downloaded here.
Due to the simplicity of the ASU model (no constraints and high commonality) we create the sample on the three features that vary (10 proteins of the sender, 3 proteins of the regulator, and 3 proteins of the activation of the behavior). This generates a sample with 30 tests covering a total of 69 pairs of the features.
The cell-to-cell signaling model was more complicated, for example the cardinality of the terminators (choose 1 or 2 terminators) had to be encoded. To allow the promoter to choose 1, 2, or 3 of its subfeatures, constraints were implemented. This generated a sample with 741 tests, and with constraints
All covering array inputs and outputs can be downloaded here.
[2] Brady J.Garvin, Myra B.Cohen, and Matthew B. Dwyer. Evaluating improvements to a meta-heuristic search for constrained interaction testing. Empirical Software Engineering 16, 1 (2011), 61–102.