Team:Imperial College/SingleCell
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Click on the circuit to discover how we modeled each section
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.
Example
This data was then used this data in our population level models to balance the system and predetermine the ratio of cocultures.
We constructed four quorum systems that we considered viable choices for our system. We designed the model for the Rhl and Cin systems [Reference Bennett paper] as they have been previously shown to operate with minimal crosstalk.
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Using STAR 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 we 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 STAR as was possible.
[RNA structure Diagram of ANTISTAR and STAR ANTISTAR]
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 [REFERENCE].
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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
