Difference between revisions of "Team:Imperial College/SingleCell"
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<figcaption>Figure 2: Sensitivity analysis for the translation rates in the GP2 and GP0.4 models</figcaption> | <figcaption>Figure 2: Sensitivity analysis for the translation rates in the GP2 and GP0.4 models</figcaption> | ||
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<figcaption>Figure 3: Sensitivity analysis for the degradation rates in the GP2 and GP0.4 models</figcaption> | <figcaption>Figure 3: Sensitivity analysis for the degradation rates in the GP2 and GP0.4 models</figcaption> | ||
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<figcaption>Figure 4: Sensitivity analysis for the copy numbers in the GP2 and GP0.4 models </figcaption> | <figcaption>Figure 4: Sensitivity analysis for the copy numbers in the GP2 and GP0.4 models </figcaption> | ||
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<figcaption>Figure 5: Parameter sweep for the transcription rate of C4R (k_mC4R) encompassing the anderson promoter library</figcaption> | <figcaption>Figure 5: Parameter sweep for the transcription rate of C4R (k_mC4R) encompassing the anderson promoter library</figcaption> | ||
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Revision as of 11:30, 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.
