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• Pune2016
• P2

Practical 2

Note

This practical requires a small update to ScrumPy.

1. Download this file

2. Unpack it: tar zxf patch.tgz

3. cd into the newly created directory: cd patch

4. Become root.

5. Execute the "patchit" script: ./patchit

That's it !

Analysing a genome scale model of Salmonella

In this practical we (you !) will be replicating some the analysis that was described in the previous lecture . In order to do this you will need to download the files associated with the model:

1. Download the file P2.tgz into the area in which you have been using for your other practicals.

2. This is a compressed archive file and you will need to extract the files before they can be used:

    $ tar -zxf P2.tgz 

3. This will generate a directory, S.Typhim, containing two sub-directories: Model and Analysis. Model contains the model definition files and an additional python module (in Model/Tools). Analysis contains the python modules you will need for this practical.
4. For the sake of the practical we have made a few simplifications and the model and results will not be identical to those in the lecture. The aim of the practical is to illustrate the techniques used.

Part A: Analysing the response to varying ATP demand.

• (1. Before starting take a look at this basic tutorial on LP analysis.)

• Change directory to the relevant area:

  • $ cd S.Typhim/Analysis/ATPScan

• Start ScrumPy and load the model:

  •  >>> m = ScrumPy.Model("../../Model/MetaSal.spy") 

  • (If you wish to avoid a bit of typing, leave the model name blank and use the file selector to find the model file instead.)
• Examine the files that are now presented - how much can you recognise from previous work in this course?
• Now import the module called ATPScan

  •  >>> import ATPScan 

• The module contains a function, also called ATPScan that will generate a data set (as shown in previous lectures) containing the lp solutions over a range of imposed ATP demand values, e.g.:

•  >>> results = ATPScan.ATPScan(m, 0, 10, 50) 

• This will generate a dataset containing 50 solutions for the model with the imposed ATPase flux varying between 0 and 10 flux units.
• The ATPScan module also contains three functions to aid in the analysis of results:

  •  GetChangers(results, tol=1e-6)  A list of reactions whose flux value changes by more than tol (default = 1e-6).

     GetSwitchers(results)           A list of reactions that carry no flux at some point.

     GetRanges(results, tol=1e-6)    A dictionary mapping reactions to the amount of change in flux over the range of ATP demand.

  • For example, to plot the reactions that at some point carry no flux (after setting the x-axis to the rate of ATPase):

    •  >>> results.SetPlotX("ATPase") 

       >>> results.AddToPlot(ATPScan.GetSwitchers(results)) 

  • Use these functions to identify which fluxes show the greatest response to changes in ATP demand

Part B: Impact of single and double reaction knockouts

1. cd into Analysis/Knockouts

2. Start ScrumPy and the load the model as before.

3. Import the KnockOutImpacts module.

4. This defines a single function also called KnockOutImpacts that returns a dictionary recording the impact of remove each reaction from the model (relative change in objective value) eg:

   •  >>> res = KnockOutImpacts.KnockOutImpacts(m)

5. Use this to
   • a) Identify the lethal knockouts. b) Identify the non lethal knockouts that have the greatest impact. c) From b investigate the impact of dual knockouts, e.g.:

     •  >>> lp.SetFixedFlux({Reac1:0,Reac2:0}) 

        >>> lp.Solve()  etc. Remember to clear the constraint before proceeding:

        >>> lp.ClearFluxConstraints([Reac1,Reac2]) 

     • (Generate lp by: >>> lp = KnockOutImpacts.BuildLP.BuildLP(m)  )