Difference between revisions of "Team:Imperial College/SingleCell"

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<div class="col-lg-10 col-centered text-justify"><p><br><br><br>This year one of our mathematical model extensively describes the intracellular interactions of our Genetically Engineered Artificial Ratio (G.E.A.R.) system. The model was built using Simbiology, a MATLAB toolbox. The model has provided us with timescale information and it’s results allowed us to review our assembly strategies in the wet lab. Experiments conducted in the wet lab fed back into the model and improved the accuracy of our parameters. We are very proud to present you our model, worth weeks of very hard work!<br><br><br></p></div>
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<p><specialh3>Single Cell Modelling Overview </specialh3><br><br>
 
<p><specialh3>Single Cell Modelling Overview </specialh3><br><br>
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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.
 
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.
 
<br><br>
 
<br><br>
Once, the models were built they were 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.
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We first separated the models into 3 modules: Quorum Communication, STAR-antiSTAR Comparator and Growth Regulation.<br><br>
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 predicted by our single cell model to work the fastest.
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We performed sensitivity analysis on each of our circuit designs. We only analysed 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). This allows us to identify which parameters should be tweaked in order to balance our system in order to set the population ratios that we want.
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<p><br><br>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.
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<br><br><specialh3>Assumptions</specialh3>
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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.
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<br><br><specialh3>Rhl</specialh3>
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The Rhl system consists of an autoinducer synthase RhlI (that produces C4 AHL) and a transcriptional regulator RhlR that dimerises to activate a pRhl promoter which activates STAR transcription.
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<br><br><specialh3>Cin</specialh3>
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The Cin system consists of an autoinducer synthase CinI (that produces C14 AHL) and a transcriptional regulator CinR that dimerises to activate a pCin promoter which induces Anti-STAR production.
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<p><br><br><specialh3>Reactions</specialh3>
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<p align='center'><br><br> Communication module:</p>
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\[\varnothing\rightarrow C14\]
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\[\varnothing\rightarrow C4\]
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\[RNA_{pol} + g_{C4R} \rightarrow RNA_{pol} + g_{C4R} + mRNA_{C4R}\
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\[RNA_{pol} + g_{C14R} \rightarrow RNA_{pol} + g_{C14R} + mRNA_{C4R}\
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\[mRNA_{C4R}\rightarrow C4R\]
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\[mRNA_{C14R}\rightarrow C14R\]
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\[C14+C14R \rightarrow C14_{complex}\]
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\[C4+C4R \rightarrow C4_{complex}\]
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\[C4\rightarrow\varnothing\]
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\[mRNA_{C4R}\rightarrow\varnothing\]
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\[C4R\rightarrow\varnothing\]
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\[C4_{complex}\rightarrow\varnothing\]
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\[C4_{dimer}\rightarrow\varnothing\]
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\[C14\rightarrow\varnothing\]
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\[mRNA_{C14R}\rightarrow\varnothing\]
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\[C14R\rightarrow\varnothing\]
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\[C14_{complex}\rightarrow\varnothing\]
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\[C14_{dimer}\rightarrow\varnothing\]
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<p><br><br><specialh3>Simulation Results</specialh3>
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</p>
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<p>
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<br><br>The results indicate that the species within this module reach steady states at time points unique to that species.
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<center>
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<br> <img src='https://static.igem.org/mediawiki/2016/d/d2/T--Imperial_College--C4.png'> <p><br> <b> Figure 1:</b> Production of C4 AHLs and C14 AHLs  against time <br><br></p>
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<center>
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<br> <img src='hhttps://static.igem.org/mediawiki/2016/8/8b/T--Imperial_College--C4RvsC14R.png'> <p><br> <b> Figure 2:</b> Production of C4R and C14R regulatory proteins against time  <br><br></p>
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<br> <img src='https://static.igem.org/mediawiki/2016/f/fe/T--Imperial_College--C4ComplexvsC14Complex.png'> <p><br> <b> Figure 3:</b>C4:C4R and C14:C14R complex formation against time  <br><br></p>
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Revision as of 00:03, 20 October 2016

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Single Cell Modelling Overview

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.

We first separated the models into 3 modules: Quorum Communication, STAR-antiSTAR Comparator and Growth Regulation.



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.

Assumptions 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.

Rhl The Rhl system consists of an autoinducer synthase RhlI (that produces C4 AHL) and a transcriptional regulator RhlR that dimerises to activate a pRhl promoter which activates STAR transcription.

Cin The Cin system consists of an autoinducer synthase CinI (that produces C14 AHL) and a transcriptional regulator CinR that dimerises to activate a pCin promoter which induces Anti-STAR production.



Reactions



Communication module:

\[\varnothing\rightarrow C14\] \[\varnothing\rightarrow C4\] \[RNA_{pol} + g_{C4R} \rightarrow RNA_{pol} + g_{C4R} + mRNA_{C4R}\ \[RNA_{pol} + g_{C14R} \rightarrow RNA_{pol} + g_{C14R} + mRNA_{C4R}\ \[mRNA_{C4R}\rightarrow C4R\] \[mRNA_{C14R}\rightarrow C14R\] \[C14+C14R \rightarrow C14_{complex}\] \[C4+C4R \rightarrow C4_{complex}\] \[C4\rightarrow\varnothing\] \[mRNA_{C4R}\rightarrow\varnothing\] \[C4R\rightarrow\varnothing\] \[C4_{complex}\rightarrow\varnothing\] \[C4_{dimer}\rightarrow\varnothing\] \[C14\rightarrow\varnothing\] \[mRNA_{C14R}\rightarrow\varnothing\] \[C14R\rightarrow\varnothing\] \[C14_{complex}\rightarrow\varnothing\] \[C14_{dimer}\rightarrow\varnothing\]



Simulation Results



The results indicate that the species within this module reach steady states at time points unique to that species.



Figure 1: Production of C4 AHLs and C14 AHLs against time



Figure 2: Production of C4R and C14R regulatory proteins against time



Figure 3:C4:C4R and C14:C14R complex formation against time

TEXT GOES HERE

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.