Team:Imperial College London/Extras/Collaboration
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<h1>Collaboration</h1> | <h1>Collaboration</h1> | ||
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<p><b>The Imperial College London team collaborated with the <a href="https://2011.igem.org/Team:WITS-CSIR_SA" target="_blank"><b>WITS-CSIR_SA</b></a> iGEM team. Aspects that were similar in both projects included rewiring of chemotaxis in <i>E. coli</i>. However, the design was different for both projects. We have tried to rewire the chemotaxis of <i>E. coli</i> using structurally similar chemoreceptors that use the endogenous molecular mechanism of chemotaxis, whereas the WITS-CSIR_SA team used the novel concept of riboswitches to rewire the chemotaxis pathway in <i>E. coli</i>. We collaborated in both wetlab and modelling aspects.</b></p> | <p><b>The Imperial College London team collaborated with the <a href="https://2011.igem.org/Team:WITS-CSIR_SA" target="_blank"><b>WITS-CSIR_SA</b></a> iGEM team. Aspects that were similar in both projects included rewiring of chemotaxis in <i>E. coli</i>. However, the design was different for both projects. We have tried to rewire the chemotaxis of <i>E. coli</i> using structurally similar chemoreceptors that use the endogenous molecular mechanism of chemotaxis, whereas the WITS-CSIR_SA team used the novel concept of riboswitches to rewire the chemotaxis pathway in <i>E. coli</i>. We collaborated in both wetlab and modelling aspects.</b></p> | ||
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Revision as of 13:26, 11 October 2011
Collaboration
The Imperial College London team collaborated with the WITS-CSIR_SA iGEM team. Aspects that were similar in both projects included rewiring of chemotaxis in E. coli. However, the design was different for both projects. We have tried to rewire the chemotaxis of E. coli using structurally similar chemoreceptors that use the endogenous molecular mechanism of chemotaxis, whereas the WITS-CSIR_SA team used the novel concept of riboswitches to rewire the chemotaxis pathway in E. coli. We collaborated in both wetlab and modelling aspects.
1. Wet lab collaboration
Chemotaxis assays are quite difficult to perform correctly and therefore know-how, tips and tricks are essential to know in order to get chemotaxis assays working. In sight of this, a number of skype conversations occurred and a number of relevant scientific papers were exchanged that have given important clues for both teams on how to proceed with the testing for chemotaxis. Due to the distance, short time frame and different BioBricks used to rewire chemotaxis, we did not characterise a BioBrick of WITS-CSIR_SA, nor did they characterise one of our BioBricks. Our dry lab team however, have modelled part of their design.
Figure 1. The members of the WITS-CSIR_SA team (picture courtesy of the WITS-CSIR team).
1.1 Know-how exchange
Both teams have used qualitative assays to analyse chemotactic movement. Even though the setup of the assays used by each team were slightly different, the basic principles remained the same. Both teams have discussed the type of media that should be used to grow cells. Some sources claim that it is necessary for chemotaxis to have overnight cultures grown in Tryptone broth, whereas other sources state that overnight growth in LB broth is sufficient. Both teams have agreed that growing overnight cultures in LB does not affect the ability of the bacteria to swim.
Another aspect of qualitative assays is semi-solid agar. Both teams have used semi-solid agar based on M9 minimal medium. Another important factor in observing chemotaxis is visualisation. The WITS-CSIR_SA team have added GFP into their construct, so visualisation of their qualitative assay was easy. However, since our construct does not contain GFP, visualisation was more difficult. WITS-CSIR_SA team have suggested to use Tetrazolium Chloride, a chemical that is metabolised by cells to produce a red pigment, and is often used as an indicator of cellular respiration.
In terms of quantitative assays, we have suggested to the WITS-CSIR_SA team that they combine a commonly-used capillary assay coupled to subsequent data collection using a flow cytometer for testing chemotactic movement as opposed to using colony forming units for the quantification of bacterial populations.
2. Dry Lab collaboration
The modellers of the Imperial College London team had several Skype meetings with the WITS-CSIR iGEM team to discuss the design of the chemotaxis modelling, and the theories behind our models. In order to share our modelling ideas and exchange some of our models, we modelled the theophylline riboswitch pathway for the WITS-CSIR_SA team. The modeling results are shown below.
2.1 Modelling results
The WITS-CSIR_SA iGEM team intend to use a riboswitch to reprogram the chemotactic behavior of E. coli. The project includes engineering the bacteria to be move towards theophylline[1]. CheZ is an important protein controlling the chemotaxis of bacteria, and WITS-CSIR_SA have used a theophylline riboswitch to control the expression of CheZ in CheZ mutants in order to engineer the bacterial movement towards theophylline[1]. They are using a riboswitch sensitive to theophylline to control the expression of CheZ. In the absence of theophylline, the start codon is covered so the translation cannot occur. In the presence of theophylline, the shape of the aptamer (riboswitch) changes and the start codon is exposed[1]. Thus, the higher the concentration of the theophylline, the more compound will enter the cell and the higher the expression of CheZ and the higher the level of directed movement will be[1]. The theophylline riboswitch can be modelled in three differential equations (Equation 1)[2].
M, CheZ and T stand for the concentration of CheZ mRNA, the concentration of protein CheZ and the concentration of theophylline, respectively. The constants α, β, γ, ξ and δ are all positive, and respectively denote the CheZ-promoter transcription rate, the CheZ-mRNA translation rate and the mRNA, CheZ and theophylline degradation-plus-dilution-rates. ζ(Text) is the theophylline transport rate per unit CheZ concentration. It is a function of the number of theophylline receptors and the external theophylline concentration. The functions ϕ(T) and Θ(T) denote the theophylline-governed regulation at the transcription and translation levels respectively (Equation 2 below [2]). KΦ is the equilibrium constant at transcriptional level and KΘ is the equilibrium constant at translation level [2].
Varying the parameter ζ(Text) of the above model could help us understand how the number of receptors and external theophylline concentration affect the intracellular concentration of theothylline and hence the expression level of CheZ. The results are shown in Figure 2 and Figure 3 below. In addition, the response curve of CheZ against the theophylline concentration with different theophylline transport rate was illustrated in Figure 4.
Figure 2. Expression of CheZ vs. time.The expression of CheZ increases with an increasing transport rate. With a higher theophylline transport rate bacteria will produce more CheZ and therefore display a higher level of directed movement. (Modelling by Imperial College London iGEM team 2011).
Figure 3. Intracellular theophylline vs. time. The intracellular theophylline level increases with transport rate. This means that we can enhance CheZ expression by increasing the number of theophylline chemoreceptors. (Modelling by Imperial College London iGEM team 2011).
Figure 4. Response curve of CheZ vs. intracellular theophylline. The CheZ response curve shows that the transport rate can be used to tune the CheZ response, the threshold intracellular concentration of theophylline required to trigger CheZ response increases with transport rate. (Modelling by Imperial College London iGEM team 2011).
In conclusion, from modelling this system we know that the concentration of CheZ and intracellular theophylline increase with increasing theophylline transport rate. Also, the expression of CheZ can be tuned by this transport rate. Therefore, it is believed that the CheZ level and the level of directed movement of bacteria can be increased by expressing more theophylline transporters in the cell membrane.
2.2 Parameters
2.3 Matlab code
2.4 References
[1] Topp S, Gallivan JP (2007) Guiding bacteria with small molecules and RNA. J Am Chem Soc 129(21): 6807-6811.
[2] Santillan M, Mackey MC (2005) Dynamic behavior of the B12 riboswitch. Phys Biol 2: 29-35, Doi: 10.1088/1478-3967/2/1/004.
[3] Beisel CL and Smolke CD (2009) Design principles for riboswitch function. PLoS Comput Biol 5(4): e1000363, doi:10.1371/journal.pcbi.1000363.