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Practical 3: Simple Kinetic Modelling in ScrumPy

This practical relates to lectures 2, 3 and 4. Additional explanation is also available in the relevant section of the ScrumPy documentation.

3.0 Preparation

1. Create a directory in your home area called "Practical_3"

   $ mkdir Practical_3

2. cd into this and create 4 more directories: :Tcourse", "Revers", "Irrev", and "Cycle"

   $ cd Practical_3
   $ mkdir Tcourse Revers Irrev Cycle

3.1 Time course to Steady State

1. Download the TwoEnzTcourse.spy model (right click for saving) and put it in the "Tcourse" directory.

2. cd into the Tcourse directory.

3. The model corresponds to the two enzyme system you investigated with Gnuplot in Practical 1. Launch ScrumPy and load the model. Let's see it approach steady state from a starting point of [B] = 0.5 mM.

   •    1 >>> m = ScrumPy.Model()
        2 >>> dm = m.AddDynMonitor()
        3 >>> dm.SetPlotX("Time")
        4 >>> dm.AddToPlot("B")
        5 >>> m.Simulate(StepSize=0.025,NSteps=50)
     

   dm is a data object that contains all the results from the simulation, and has methods for plotting and saving the data associated with it (dir(dm) will tell you what these are).

If you repeat the Simulate instruction, another 50 simulation steps will be appended to the data, and the graph. Type m["B"] to find the current value of [B]. How does it compare with the value from the Gnuplot exercise?

1. To see how the individual reaction rates were behaving during the approach to steady state, we can add them to the graph from dm:

   •    1 >>> dm.AddToPlot(["R1","R2"])
     

2. You can now repeat the investigations of Practical 1, 2.8. For example, to see the time course from [B]=2.0, type:

   •    1 >>> m["B"] = 2.0
        2 >>> m.Simulate(StepSize=0.025,NSteps=50)
        3 >>> m["B"]
     

3. When you want a new graph, or to revert to the original values in the model, you can do this with:

   •    1 >>> m.Reload()
        2 
        3 >>> dm=m.AddDynMonitor()
     

   then continue as before.
4. Finally, reload the model, set Keq1 to 0.25 and simulate to the steady state. How do you explain your result?
5. As you can see, when a model has a stable steady state, most of the interesting effects are on the rates and concentrations at steady state. Hence for the rest of the practical, and for much of the course, we will be focusing more on directly calculating the steady state.

3.2 Steady-States with Reversible Kinetics

1. Download the TwoReacRever.spy model (right click for saving) and put it in the "Revers" directory.

2. cd into the Revers directory (cd ../Revers from Tcourse), launch ScrumPy and load the model.

3. Identify the kinetics, Parameter and initial value assignments.
4. On the basis of the network structure, what predictions can you make about relationships of steady-state fluxes ?
5. Investigate the effect of varying the Vmax value of the first reaction, e.g.

   •    1 >>> results = m.AddStatMonitor()
        2 >>> for n in range(20):
        3         m["Vmax1"] += 1
        4         m.FindSS()
        5 
        6 >>> results.SetPlotX("Vmax1")
        7 >>> results.AddToPlot("R1")
     

6. Repeat with some other parameters that could be tested in the lab - e.g. change some concentrations, calculate values of the external metabolites that will result in no steady-state flux.

3.3 Steady-States with Irreversible Kinetics

1. Download the TwoReacIrrev.spy model and put it in the "Irrevs" directory.

2. Repeat the investigation in 3.2 on this model.
3. How many results are in the data set ?
4. What are the smilarities and differences in behaviour ?
5. What conclusions can you draw ?

3.4 Steady-States of a Moiety Conserved Cycle

1. Download the SimpleCycle.spy model and put it in the "Cycle" directory.

2. Proceed as 3.2 (but this time you need to vary the concentration of the external metabolite, x_A).
3. Now investigate the effect of varying the conserved total. Print the dependency solutions of the model, using this loop:

   1 >>> for sol in m.Evaluator.DepReport.sol:
   2         print sol, "=", m.Evaluator.DepReport.sol[sol]

This will display how moieties are conserved in the model, e.g. if metabolites A and B form a conserved moiety we get the dependency solution A + B = k, where k is some constant. (This functionality is not part of the current ScrumPy version, but will be in future versions.)

1. Now break the conservation relationship and repeat the first part of 3.2
2. What conclusions can you draw ?