Team:Imperial College/SingleCell

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Single Cell Model

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

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).

Transcription Rate Sensitivity Analysis


Figure 1: Sensitivity analysis for the transcription rates in the GP2 and GP0.4 models



Translation Rate Sensitivity Analysis


Figure 2: Sensitivity analysis for the translation rates in the GP2 and GP0.4 models



Degredation Rate Sensitivity Analysis


Figure 3: Sensitivity analysis for the degradation rates in the GP2 and GP0.4 models



Copy Number Sensitivity Analysis


Figure 4: Sensitivity analysis for the copy numbers in the GP2 and GP0.4 models



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.



Figure 5: Parameter sweep for the transcription rate of C4R (k_mC4R) encompassing the anderson promoter library





This data was then used this data in our population level models to balance the system and predetermine the ratio of cocultures.



Communication module
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.

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

Figure 6: Production of C4 AHL and C14 AHL against time



Figure 7: Production of C4R and C14R against time



Figure 8: C4 and C14 Complex formation against time

Comparator module
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.

Figure 9: Secondary structure of our ANTISTAR sequence

To calculate the kinetics of the RNA interactions that occur within this module we adapted a method developed by Eric Winfree known as DNA strand displacement kinetics (Zhang and Winfree, 2009)

Figure: Sensitivity analysis for the degradation rates in the GP2 and GP0.4 models

Figure 3: Sensitivity analysis for the degradation rates in the GP2 and GP0.4 models

Figure 3: Sensitivity analysis for the degradation rates in the GP2 and GP0.4 models

Growth regulator module
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.