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| {{attachment:Slides.pdf|Slides.pdf|title=""}} If the slides cannot been seen above, please download them [[http://mudsharkstatic.brookes.ac.uk/AccliPhot/Workshop2/Slides/ScrumPyMCA/Slides.pdf|here]] |
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| 1. Create a new directory and download the [[http://mudsharkstatic.brookes.ac.uk/AccliPhot/Workshop2/Models/feedback4.spy|feedback4.spy]] model. |
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| 1. Create a new directory and download the [[ http://mudsharkstatic.brookes.ac.uk/Delhi2013/Models/feedback4.spy | feedback4.spy ]] model. This is a 4 step pathway with end product inhibition on the first step. |
. This is a 4 step pathway with end product inhibition on the first step. |
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| 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. What relationship can you identify about the effect that any one {{{Vmax}}} value has on all reaction fluxes. Explain why this relationship exists. |
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| 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}}}). | 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}}}). |
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| >>> from Data import Datasets | >>> from Data import DataSets |
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| m["K1_S"] *= 1.4 K1_S = m["K1_S"] CJ1 = m.ScaledSensits("VM1", ["R1"]) CJ4 = m.ScaledSensits("VM4", ["R1"]) ds.NewRow([K1_S]+CJ1+CJ4) |
m["K1_S"] *= 1.4 K1_S = m["K1_S"] CJ1 = m.ScaledSensits("VM1", ["R1"]) CJ4 = m.ScaledSensits("VM4", ["R1"]) ds.NewRow([K1_S]+CJ1+CJ4]) |
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| >>> ds.Plotter.SetLog() | >>> ds.Plotter.SetLog()# or ds.SetLogAxis() |
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2 Metabolic Control Analysis with ScrumPy
If the slides cannot been seen above, please download them here
MCA of a 4 step pathway
Create a new directory and download the feedback4.spy model.
- This is a 4 step pathway with end product inhibition on the first step.
- Identify the Vmax parameters of each reactions.
- Calculate the flux control coefficient of every Vmax over every flux in the system:
(you will need to specify VmaxNames - don't just cut and paste)
What relationship can you identify about the effect that any one Vmax value has on all reaction fluxes. Explain why this relationship exists.
- 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 ?
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).
1 >>> from Data import DataSets 2 >>> ds = DataSets.DataSet(ItemNames = ["K1_S", "CJ1", "CJ4"]) 3 >>> m["K1_S"] = 1e-5 4 >>> for n in range(40): 5 m["K1_S"] *= 1.4 6 K1_S = m["K1_S"] 7 CJ1 = m.ScaledSensits("VM1", ["R1"]) 8 CJ4 = m.ScaledSensits("VM4", ["R1"]) 9 ds.NewRow([K1_S]+CJ1+CJ4]) 10 11 >>> ds.Plotter.SetLog()# or ds.SetLogAxis() 12 >>> ds.AddToPlot(["CJ1","CJ4"])
(Note: you only need to import DataSets once per session.)