Team:Fudan-TSI/Model Software
Modeling
In our modeling, we successfully simulated the function of our mutagenesis system, and contributed to improve our experimental setup.
We divided our system into 3 sub-models: induced expression model, reverse transcription model and Cre recombination model. We utilized deterministic and stochastic techniques with parameters derived from our experiments or published papers.
Our modeling established theoretical basis for our experiments:
1) The determination of simultaneous or separate expression of reverse transcriptase and Cre is guided by models revealing their different working concentration.
2) We estimated the optimal expression level and induction time needed to achieve maximal recombination efficiency.
3) We modeled the effect of Cre degradation on recombination.
4) We demonstrated that mutations accumulate accompanying E. coli growth.
Modeling acted as a shortcut of answering questions concerning experimental setup and revealed new insights into our system. Thus, we believe that our modeling work is very competitive for the best modeling prize.
We divided our system into 3 sub-models: induced expression model, reverse transcription model and Cre recombination model. We utilized deterministic and stochastic techniques with parameters derived from our experiments or published papers.
Our modeling established theoretical basis for our experiments:
1) The determination of simultaneous or separate expression of reverse transcriptase and Cre is guided by models revealing their different working concentration.
2) We estimated the optimal expression level and induction time needed to achieve maximal recombination efficiency.
3) We modeled the effect of Cre degradation on recombination.
4) We demonstrated that mutations accumulate accompanying E. coli growth.
Modeling acted as a shortcut of answering questions concerning experimental setup and revealed new insights into our system. Thus, we believe that our modeling work is very competitive for the best modeling prize.
Software
Our software simplifies the primer design process for target-specific mutagenesis via reverse transcriptase (RT). We called it tRNA primer designer. Studies have shown that tRNA functions as the primer for in vivo reverse transcription initiation: the 5' end of the tRNA interacts with RT, and the 3' end matches with the mRNA encoding the target.
The software consists of 4 parts: reverse transcriptase selection, target sequence input, designed-tRNA visualization, and primer output. Although we only test MMLV RT experimentally, the software can adjust its designing method based on the properties of well-studied RT from 3 species, MMLV, HIV-1 and RSV. Users could design their tRNA primers even for eukaryotic experiments. In addition, we calculate and output the tRNA acceptor stem annealing temperature, as this might be used as an indicator for likelihood to success.
The software consists of 4 parts: reverse transcriptase selection, target sequence input, designed-tRNA visualization, and primer output. Although we only test MMLV RT experimentally, the software can adjust its designing method based on the properties of well-studied RT from 3 species, MMLV, HIV-1 and RSV. Users could design their tRNA primers even for eukaryotic experiments. In addition, we calculate and output the tRNA acceptor stem annealing temperature, as this might be used as an indicator for likelihood to success.
Hardware
To track bacteria growth on the plate and observe the fluorescence recovery from nonsense mutation due to continuous mutagenesis, we devised this hardware - the Fluorescence Tracker. It provides continuous, hands-off recording of the growth of plate colonies as well as fluorescent protein expression. For users of our mutagenesis system, with the help of our hardware, they could plate, and then monitor all plates together to increase the likelihood of spotting bacteria colonies with recovered fluorescence at the earliest time point. After discussions with our PI, we improved our hardware by adding remote access through TeamViewer, which allows visualizing the dynamic changes on smartphones. Although the current hardware is only suitable for monitoring fluorescence recovery, it could be easily modified to monitor bacteria colonies growing out of any antibiotic plate. Our hardware allows us to come to lab knowing that a plate with desired colonies is waiting for us.