Difference between revisions of "Team:CityU HK/Collaborations"

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Revision as of 17:23, 14 September 2015

<!DOCTYPE html>

Partnership 

          In our collaboration work with the Chinese University of Hong Kong iGEM team, we were given the task to assist them with their project by doing a simulation that look into the interaction between GFP-nanobody to different concentrations of GFP.
          It is our honor to be able to collaborate and participate in their project. A wonderful experience we have gained through...... 

Background

The addition of magnetosomes together to GFP-nanobodies for the immunoprecipitation of GFP.

Task

           Simulate the binding dynamics of a fixed concentration of magnetosome and GFP-nanobodies in different initial concentration of antigens. 

Initial condition and Parameters

Calculation

Software - SimBiology

          SimBiology is a toolbox in Matlab that enable us to model and simulate the dynamics of the association and dissociation between molecules. Most of the function can be utilized through its GUI (Graphical User Interface).

          Once the model is constructed (i.e. the mathematics relationships between molecules are set up, parameters such as association constant, molarity of reagent, etc. are provided), we can simulate the reaction or even scan through one or more variable. The “scan” function enables us to simulate the dynamic of the system when some parameters are at different values. 

Model

          Figure 1 show the model constructed in SimBiology. The blue rounded rectangle represents the reagents, including antigens, GFP-nanobody and GFP-nanobody-antigen complex. The yellow circle represents the binding reaction. The arrow points toward GFP-nanobody-antigen complex indicate that the complex is the product of the forward reaction. The double arrow above the yellow circle “binding” indicates that the reaction is reversible. 

          Mass action, which assume the reaction rate is proportional to the concentration of the reagent, is used to model the reaction.

          Forward reaction (association) rate: 
                    Kon *[GFP-nanobody]*[antigens]

          Reverse reaction (dissociation) rate:  
                    Koff *[GFP-nanobody-antigens-complex]

          Net reaction rate:               
                    Kon *[GFP-nanobody]*[antigens] - Koff *[GFP-nanobody-antigens-complex]


Note: Kon, Koff are the reaction rate constant described in the parameters table.

Simulation Result
          By using the scan function of SimBiology, we simulate the dynamic of the system with the initial concentration of antigen ranging from 0 to 1.6E-6M with an interval of 2E-7M. 

          From figure 2, we can see that when the molarity of antigen below that of GFP-nanobody (7.78 x10-7M), it becomes the limiting reagent, and the final molarity of the nanobody-antigen complex equals the initial molarity of antigen, vice versa.

          Another observation is that, as the molarity of antigen increase, the reaction (i.e. the formation of nanobody-antigen complex) goes equilibrium quicker. This can be explained by the increased forward reaction rate, which depends on the molarity of GFP-nanobody and antigen as well. 

Reference

Marta H. Kubala†Kovtun†, Kirill Alexandrov* andBrett M. Collins*Oleksiy. (2010 September). Structural and thermodynamic analysis of the GFP:GFP-nanobody complex. Protein Science, Volume 19, Issue 12, Pages 2389-2401.