Team:OhioState/Engineering
Integrated Human Practices and the Database
The iterative engineering process is the core method used by any iGEM team to create a final product. While we had no traditional design to iterate over and no product to test against the intended implementation conditions, we found ourselves using engineering concepts in our Integrated Human Practices.
In the brainstorm phase, we knew we wanted to make the implementation of genetic biocontainment easier. Our initial thought was to simply test the biosafety parts in the iGEM parts registry and record how well each part functioned. Upon further inspection into the registry, we found that the majority of the parts tagged with ‘biosafety’ were incapable of being a biocontainment system on their own. In fact, many of them were simply promoters or toxins that other teams had used to create functioning systems, and those systems were quite rudimentary. At this point, we realized that our solution needed to be more than just characterization.
Therefore, we expanded our scope to include the rest of the registry and we adjusted our plan to include time for sorting through the larger pool of parts. But this wasn’t our final revision: we brought this concept and the beginnings of our database to our sponsor, the Ohio State University’s Infectious Disease Institute. They helped us expand this plan even further. We added organizational tags and we included non-iGEM biocontainment systems as well. We also had a new feature to implement: Automation.
It seemed entirely possible to create a program to crawl across the internet in search of new biocontainment systems to add to the database, and we knew that this would be an indispensable tool. However, after we started working on this concept, we found it to be beyond the scope of our technical knowledge. In fact, this kind of program is on the bleeding edge of computer science, so we decided to cut it out of our plan. Instead, we focused on making sure our selection was thorough and high-quality. The engineering process
Our integrated human practices became a shining example of the iterative engineering process, starting as a distant idea and becoming a polished product over numerous revisions and ‘backward’ steps. And yet, there is still more that can be done in the realm of genetic biocontainment. It’s our goal that future projects will pick up where we left off in this cycle of improvement.
Experiments for Modeling Data
One of our initial ideas was to develop mathematical models that could predict properties of biocontainment systems based on data from the constituent genes. In order to determine the feasibility and relevance of this, we talked to several experts in the field. All agreed that it was both possible and would be a useful, new tool.
For our design, we picked a set of 12 promoters and 10 death mechanisms, selecting both commonly used genes and those that are used in more niche circumstances, such as temperature and nitrate based promoters.
|
Promoter |
Death Mechanism |
||
|
Description |
Gene |
Description |
Gene |
|
Nitrate based promoter |
amilCP |
Death Pathway |
mazF |
|
Rhamnose inducible, glucose repressible promoter (pRha) |
pRha |
Suicide Mechanism |
t |
|
Tightly-regulated lactose-inducible promoter |
PlacIQ |
Holes in the membrane |
hokD |
|
acid induced promoter |
asr |
Inhibits DNA replication |
parE |
|
ethanol/stress induced |
YJL052W |
Puts holes into the membrane |
ghoT |
|
mercury inducible |
MerRT |
Degrades both RNA and DNA |
1BRS |
|
cI repressor and cI regulated promoter. The repressor protein denatures at 42 C |
lambda PR |
Holes in the membrane |
nucD |
|
Interrupts DNA gyrase and kills the cell |
ccdB |
||
Plasmid Diagrams
We would clone these promoters into a pRU1156 plasmid for the promoter and pZA2[PLac:GFP] for the toxins using Gibson Assembly, both of which are available on AddGene. Then, we would individually characterize each one with GFP expression rates for promoters and survival rates for toxins.
We would then pass this on to our modeling team who would predict kill switch escape rates for several combinations of one promoter and one cell-death gene. After, we will combine the previously characterized promoter and toxin to create a kill switch. Then the killswitched would be characterized for escape frequency This would be done multiple times with different combinations of promoter and toxin..This would provide us data with which we could compare our modeling predictions and refine the models used to predict kill switches.
This process would be repeated as many times as possible given time and resource constraints in order to improve our model’s accuracy to the best of our ability. If we were unsuccessful in getting consistent results from the model for the kill switches, we would look into other modeling methods, such as mutation frequencies, or create different models for different types of promoters/death mechanisms. If we encounter problems with cloning we would look to our many resources in microbiology who could help us identify the problem, potentially fix it, or find a new avenue to pursue.
If the model is successful we would expand the experiments to cover many more uncommon or niche promoters and toxins and use those results to further refine the model.
Human Practices Modeling
To exemplify the potential value of genetically modified organisms in solving real world issues, our team decided to tackle an important issue in developing countries: malnutrition. Specifically, we took population data and calculated the vitamin A supply necessary to satisfy the recommended daily intake of the Zimbabwean people. Then, we showed how genetically modified maize, which is a staple of the Zimbabwean diet, could be used to satisfy the vitamin A requirements in Zimbabwe, which is a country where approximately ¼ of the population has a vitamin A deficiency.