= Practical 4: Feedback Inhibition: simulation and Control Analysis =
Insert Xref to associated slides.

== MCA of a 4 step pathway ==
 1.
 Create a new directory and download the [[http://mudsharkstatic.brookes.ac.uk/AccliPhot/Workshop2/Models/feedback4.spy|feedback4.spy]] model.

  . This is a 4 step pathway with end product inhibition on the first step.


 1. Identify the Vmax parameters of each reactions.

 1. Calculate the flux control coefficient of every Vmax over every flux in the system:
 {{{#!python
>>> m.FindSS(10)  # get initial steady-state
>>> for v in VmaxNames:
        print m.ScaledSensits(v, m.sm.cnames)
}}}


(you will need to specify {{{VmaxNames}}} - don't just cut and paste)

 1.
 What relationship can you identify about the effect that any one {{{Vmax}}} value has on all reaction fluxes. Explain why this relationship exists.


 1. Assume that this is a metabolic engineering problem. Try and achieve a 10 fold increase in flux by manipulating only the Vmax values. Can you do this whilst minimising the impact on the concentration of the intermediates ?
 1.
 The effect of end-product inhibition on control coefficients. Here we wish to scan across a very wide range of values for the inhibition constant of the first reaction, so we will increase it proportionately, and plot on a log scale. (In the code below, {{{x *= y}}} is a convenient short hand for {{{ x  = x*y}}}).

 {{{#!python
>>> from Data import DataSets
>>> ds = DataSets.DataSet(ItemNames  = ["K1_S", "CJ1", "CJ4"])
>>> m["K1_S"] = 1e-5
>>> for n in range(50):
        m["K1_S"] *= 1.2
        K1_S = m["K1_S"]
        m.FindSS(10)
        CJ1 = m.ScaledSensits("VM1", ["R1"])
        CJ4 = m.ScaledSensits("VM4", ["R1"])
        ds.NewRow([K1_S]+CJ1+CJ4)

>>> ds.Plotter.SetLog()# or ds.SetLogAxis()
>>> ds.AddToPlot(["CJ1","CJ4"])
}}}


(Note: you only need to import !DataSets once per session.)