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| - | [on main Modeling page]
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| - | Modeling
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| - | We utilized the CRISPR/Cas system with gRNA engineered to recognize genes from infectious bacteria using bacterial specific phages delivery system. Specifically, we target Cholera as a proof of principle. To target the Cholerae genome, our team focus on (1) cutting efficiency of the gRNA-CRISPR/Cas9 system and (2) the delivering efficiency of the phage delivery system.
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| - | There are two important features we have to look at:
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| - | 1. On the Intracellular level, in a single Cholera cell, the CRISPR/Cas9-gRNA system is transcripted, translated. The complexes then cleaves the Cholera native genome, leading to cell death.
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| - | 2. On the population level, the phagemids deliver the CRISPR/Cas9-gRNA plasmid from the probiotics to
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| - | Our Mathematical Model and computer simulation provide a great way to describe the functioning and operation of our system. Enjoy!
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| - | [on main Population page]
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| - | Population Level
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| - | This simulation provides a simple and intuitive visualization of the effects of the probiotics onto the pathogenic Cholerae population.
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| - | Initially, we plan to test on Phagemids M13 and then CTXphi, which is more specific to Cholerae. (Please refer to our experiment design page). Our lab had ordered Cholerae for this experiment. However, the limited time did not allow us to test this. After Wiki Freeze, we still expect to continue the experiment.
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| - | Nevertheless, this is where this population simulation comes in and shows the potential application of our bio-system.
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| - | [population subpage1]
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| - | Design - This part describes the design of the simulation, how we come up with it and its relation to our wet lab experiment
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| - | Our design start with a relatively stable probiotics population (green) and a growing pathogenic Cholerae population (black). The probiotics should be able to sense the presence of Cholerae with a sensing mechanism. (We discussed with Colombia iGEM Team on collaboration on this subject, as they were working on the sensing specificity for Cholerae. We invite you to visit their wiki and check out their project.)
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| - | Ultimately, we design the probiotics with our genetically modified plasmid with gRNA-Cas9 system to be a stable non-pathogenic colony growing in harmony with the human intestinal flora.
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| - | • [mode 0] In the absence of Cholerae, the probiotics would not release phages.
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| - | • [mode 1] In the presence of Cholerae, the probiotics would release phages with the CRISPR system plasmid.
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| - | The molecules released by Cholerae induces the production of phages (the build up of phage capsid protein), the packaging of our CRISPR system plasmid, and the release of such phages.
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| - | Once the phages get in contact with Cholerae cells, the plasmid is released into the Cholerae cytosol. Thus, the cleaving system goes to work and the bad Cholerae die! (More on our system mechanism, please visit our project mechanism page.)
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| - | Once the Cholerae and its presence-sensing-signal died out, the probiotics return back to [mode 0] and stop releasing phages.
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| - | [population subpage2]
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| - | Simulation
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| - | We use GRO - The cell programming language, a shareware developed by The Klavins Lab at the University of Washington. (http://depts.washington.edu/soslab/gro/)
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| - | Green population := the probiotics
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| - | Black population := the pathogens (V. Cholerae)
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| - | Blue signal := signal emitted by the pathogens, indicating its presence
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| - | Pink signal := the phages emitted by the probiotics, carrying the CRISPR plasmid
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| - | ###Please insert the gif and the link to the video of the simulation#####
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| - | [population subpage3]
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| - | Model result
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| - | The delay in signal disappearance indicates the significance of choosing appropriate phages for the delivery system with appropriate speed, effectiveness, specificity, and degradation rate.
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| - | [Cellular page]
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| - | Cellular Level
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| - | The model focus on simulating our experiment, with the decrease YFP signal observed as a signal of cell death.
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| - | Each of the components in our system is transcripted with a separate promoter. Our further goal with the model is to determine the more efficient promoter by varying transcription rate.
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| - | [cell~ subpage1]
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| - | Design - This part describes the design of the model.
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| - | [cell~ subpage2]
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| - | Model - A more mathematical view on our system. Depending perspectives, this text can be seen as ‘very extensive’ or ‘very short and preliminary’, but please decide for yourself. Enjoy!
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| - | We utilize MatLab and SimBiology to build this intracellular modeling system.
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| - | [cell~ subpage3]
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| - | Model result
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| - | [Trivia page]
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| - | Trivia
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| - | All of the Mathematical Modelling works was done by Tra. She did not have any experience on programming prior to iGEM. Such dedication and diligence! It was thanks to the help and instructions from Dr. Ma and Taek that she was able to complete both the simulation and the model.
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| - | Tra’s biggest lesson learnt from modelling experience, besides from knowing how to build mathematical models and programming, is to always back up her files in several places.
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| - | Tra saves everything, all her works (and photos), in a flash drive. She always carry it with her, so there is no way she could lose the drive and its files.
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| - | Two nights before the Wiki Freeze deadline, she was working late at our lab building. It was a beautiful night. The cold front brought nice chill to the Texas heat. The starless night sky was brightly lit up with the lights from the construction site next to our lab. It was midnight. Tra quickly packed up her bag and bike home.
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| - | She got home, opened her bag, and found her USB broken in half. The electrodes were teared off. Her USB was crushed inside her bag. Let’s not discussed how that happened. But she tried a great deal with her EE friend to save the USB and savages the data inside. Sadly, at 4AM, the day before the deadline, they have to say goodbye to the USB. It was a great lost.
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| - | The models and simulation you saw in this wiki was rebuilt from the vestiges.
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| - | Cheers!
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