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<specialh4>Transcription Rate Sensitivity Analysis</specialh4> <br><br><br> | <specialh4>Transcription Rate Sensitivity Analysis</specialh4> <br><br><br> |
Revision as of 16:15, 19 October 2016
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The first stage of our modelling process was to construct a single cell in silico model of our circuit. Our model was built using mass action kinetics in Simbiology (Matlab toolbox) and built up reaction by reaction
Once built, the model was first used to test and compare the time taken for four different growth regulators (GP0.4, GP2, Leucine B Auxotrophy and Chloramphenicol acetyl transferase antibiotic resistance) that we shortlisted in order to determine which of them would work the fastest.
This allowed us to optimize our assembly experiments allowing us to achieve a faster route to a working prototype circuit. We decided to focus our attention on the GP2 and GP0.4 systems as they were shown to work faster.
We performed sensitivity analysis on each of our circuit designs. We did these for the parameters that we can change in lab (transcriptions rates via promoter strength, translation rates via RBS strength, copy numbers and degradation rates via the inclusion of degradation tags).
Our next process was to create a framework in which we could balance our circuit in silico. To do this we ran parameter sweeps for each of the transcription rates, translation rates and degradation rates that were indicated by the sensitivity analysis. We used known numbers from biobrick parts as we wanted our model to use the materials available to us in the lab whenever possible.
This data was then used this data in our population level models to balance the system and predetermine the ratio of cocultures.
We constructed the four quorum systems that we considered viable choices for our system (cin, rhl, lux and las) to allow us to directly compare the expected behaviour and plan our growth module experiments accordingly. We designed the overall model for the Rhl and Cin systems (Chen et al., 2015) as they have been previously shown to operate with minimal crosstalk.
We used numbers obtained from Chen et al for C4 and C14 production. This is a high level production term that ignores parts of the central dogma. We made this assumption due to the limited data on the enzymatic kinetics of the autoinducer synthases.
Using STAR (Short Transcription Activating RNA) technology, we were able to develop a novel method of comparing the sizes of two populations from their quorum signal concentrations.
We used RNAstruct developed by Matthews Lab to help aid the development of the ANTISTAR.
This software allowed us to determine the secondary structure and free energy to optimize the way in which our ANTISTAR sequence was designed. This was done so that our ANTISTAR sequence would have as high an affinity to the STAR sequence as was possible.
We modelled 4 different growth regulator systems in silico in order to assess the speed and effectiveness of each case.
Auxotrophy (LeuB)
Antibiotic resistance (Chloramphenicol resistance)
Gene product 2
Gene Product 0.4
Works Cited
Chen, Y., Kim, J., Hirning, A., Josi, K. and Bennett, M. (2015). Emergent genetic oscillations in a synthetic microbial consortium. Science, 349(6251), pp.986-989.
Zhang, D. and Winfree, E. (2009). Control of DNA Strand Displacement Kinetics Using Toehold Exchange. J. Am. Chem. Soc., 131(47), pp.17303-17314.