To prevent future iGEM teams from reinventing the wheel, we made multiple contributions to the iGEM community. Together with our partnership team, iGEM team Vienna, we have created a guide with tips & tricks for the Matlab add-on SimBiology to make it easier to work in this program for future iGEM teams without experience in this program. Furthermore, we contributed with our lab work to existing iGEM Parts. We hope future iGEM teams can use our guide and information added to the Part Registry Pages to limit their investigation time for these subjects. Furthermore, we hope that they may elaborate on them for other next-generation iGEM teams
To get a better understanding of the different components within our system, we tested the sensor part and the reporter part separately by means of various experiments. The TtrS/R system was characterized as described by Daeffler et al. [1]. To achieve this, the system was transformed into E. coli BL21 (DE3) cells and subsequently small and large cultures were made. These cultures were induced with various concentrations of IPTG, and then used to perform sfGFP and mCherry fluorescence measurements in a spectrophotometer. With the data of these measurements, a dose-response curve (DRC) could be generated, depicting the sfGFP signal at different inducer concentrations of IPTG. With this DRC, we proved that we reconstructed the TtrS/R system successfully. More information on this contribution can be found on the Part Registry Pages BBa_K2507002 and BBa_K2507003. The DNA sequence used for our TtrS/R system deviated a bit from the sequence found on those Part Registry Pages, however, it contained the same amino acid sequence.
Comparison with another iGEM team
The iGEM team SHSBNU China had previously attempted to characterize this system. However, this iGEM team was not able to generate such a DRC. After analyzing our own results, which can be found on Results segment TtrS/R, and the results of Daeffler et al.[1], we assumed the troubles that SHSBNU faced might be the result of TtrR overexpression. Overexpression of TtrR will result in sfGFP expression even though the TtrS binding molecule, tetrathionate, is not present. Due to this behavior the sensor will lose its sensitivity to tetrathionate, which can be observed in the results of iGEM team SHSBNU 2017. For this reason, we used the pLtetO-1 promoter, making the system sufficiently adjustable and sensitive for our design to work.
Characterization ARG
For characterization of the reporter part of our system ultrasound measurements were executed. These ultrasound measurements were based on literature [2]. Previously, solely detection via floatation has been used to characterize ARG. However, we chose to focus on the application of the ARG system in our project, and therefore we characterized the ARG proteins via ultrasound measurements.
The ARG plasmid was transformed into BL21 (DE3) cells and small and large cultures were made. Large cultures were then induced with various concentrations of IPTG, and incorporated in an ultrasound measureable phantom. The phantoms were measured for gas vesicle expression using a Verasonic ultrasound system with a 22-14 transducer. The ultrasound images can be found on Results segment ARG Reporting Mechanism. These results really expand the usage of this part and can therefore be implemented in sensors.
Our contribution can be found on the Part Registry Page BBa_K2699000. A different DNA sequence was used, however, our DNA sequence resulted in an identical amino acid sequence.
Daeffler, K., Galley, J., Sheth, R., Ortiz‐Velez, L., Bibb, C., Shroyer, N., Britton, R. and Tabor, J., 2017. Engineering bacterial thiosulfate and tetrathionate sensors for detecting gut inflammation. Molecular Systems Biology, [online] 13(4), p.923.
Bourdeau R, Lee-Gosselin A, Lakshmanan A, Farhadi A, Kumar S, Nety S et al. Acoustic reporter genes for noninvasive imaging of microorganisms in mammalian hosts. Nature [Internet]. 2018 [cited 9 May 2021];553(7686):86-90.
Model Guide
Tips and Tricks - Symbiology
We decided to make our model in Matlab add-on SimBiology. We discovered that the SimBiology guides that can be found on the internet often lack specific information for the use of SimBiology and decided to take matters into our own hands and create our own guide for this add-on. We used the available iGEM webinars that can be found online as a basis and used our own experience with Matlab to compile a list of useful tips that can not be found in the guides that are currently available. By combining our knowledge and experience with team iGEM Vienna we managed to establish an elaborate, well-rounded list of tips & tricks that we deem essential for working with SimBiology in the context of iGEM. This way, we want to help future iGEM teams that want to use SimBiology for their kinetic model as well. We decided to split the tips and tricks into several categories, to make it easier to look up tips on a specific part of the program. All tips are gathered in the PDF file that can be found below.
