Team:Imperial College/SingleCell
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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).
Walkthrough Documentation
To search for species to co-culture together, you will first be directed to this page:
Simply enter the names of the species you are looking to culture together. The population fields must be filled in and begin with a single capitalized letter for an abbreviated genus followed by the species or strain name (eg. E. coli) to successfully run a search. When typing into these fields a drop-down, autocomplete list of suggestions of species previously entered to ALICE will appear.
Relevant co-culture entries will appear first after the search is executed. This will be followed by relevant monoculture entries ordered by first ascending doubling time. If doubling times are the same, monocultures will be ordered by ascending temperature and then by ascending pH. Finally, an option to search again will be found at the bottom of results:
If no relevant co-cultures can be found, you will be notified and no co-culture entries will be displayed. If no relevant monocultures are found for a species, none will be displayed, and you will also be notified. If no monocultures of either species can be found, nothing will be displayed for either species, and you will be notified of this for both of them. However, if monocultures of one species are found, these will still be displayed.
The submission page for monoculture entries will display the following entry form:
There is extensive validation of the fields of this submission form. You will not be able to submit an entry until all required fields are completed. Only valid values or numbers can be entered into numerical fields such as temperature, doubling time and pH. Additionally, the species field must begin with a single capitalized letter for an abbreviated genus followed by the species or strain name (eg. E. coli). When typing into the species field a drop-down, autocomplete list of suggestions of species previously entered to ALICE will appear. Finally, the entry will only be successfully submitted if the URL field is filled in with a valid Google spreadsheet URL.
In order for your optical density data spreadsheet obtained from experiments to be displayed correctly as a graph when your submitted entry is searched up, please follow the instructions on this page form correctly, especially with respect to never deleting or changing the Google spreadsheet's location after submission and the correct configuration of the spreadsheet:
The top-left cell of the sheet WILL NOT affect what is displayed in the graph at all. The left-most column will contain the time points (in minutes) of the x-axis. Each column to the right will contain optical density measurements that will be plotted for each time point, as part of a single growth curve. The first row of these columns contains the labels in the legend for each growth curve.
When submitting an entry ensure the Google spreadsheet this URL links to is shared so that anyone with the link can view it and that the optical density data obtained from experiments is formatted correctly. When monoculture entries are displayed, the graphs that are displayed with entries that are searched up are created from the Google spreadsheet data in the Google spreadsheet URL that was submitted with each monoculture entry.
The following submission form will be displayed on the page to submit co-culture entries:
There is extensive validation of the fields of this submission form as well. You will not be able to submit an entry until all required fields are completed and valid. Only numbers can be entered into numerical fields such as temperature, doubling time, pH, inoculation ratio, hours of stable population ratio and hours after which population 1 was inoculated. All these fields apart from temperature must be positive numbers, and only numbers greater than 1 can be entered into the inoculation ratio field. Additionally, the population fields must begin with a single capitalized letter for an abbreviated genus followed by the species or strain name (eg. E. coli). When typing into these fields a drop-down, autocomplete list of suggestions of species previously entered to ALICE will appear.
As there are a multitude of ways to measure co-culture growth, data is not required to be submitted with a co-culture entry. However, an image file uploaded online at MediaFire can be optionally submitted to visualize growth data for an entry. To do this, include a valid URL of a direct link to an image file uploaded on MediaFire on the appropriate field of your submission form.
To get the direct link URL of an image uploaded on MediaFire, simply click on the image file, click share and click for more sharing options to copy its direct link URL. The corresponding image of this URL will be displayed along with the protocol and growth conditions of a co-culture entry when the entry is searched up. Again, the file's location should not be changed after its URL is submitted.
Figure 1: Sensitivity analysis for the transcription rates in the GP2 and GP0.4 models
Figure 2: Sensitivity analysis for the translation rates in the GP2 and GP0.4 models
Figure 3: Sensitivity analysis for the degradation rates in the GP2 and GP0.4 models
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.
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.
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
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
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
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.
