Self

Practical 7

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 P7.tgz into the area in whch 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 P7.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. Change directory to the relevant area:
    • $ cd S.Typhim/Analysis/ATPScan

  2. 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.)
  3. Examine the files that are now presented - how much can you recognise from previous work in this course?
  4. Now import the module called ATPScan
    •  >>> import ATPScan 

  5. 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.:

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

  7. Will generate a dataset containing 50 solutions for the model with the imposed ATPase flux varying between 0 and 10 flux units.
  8. 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:
      •  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.ClearFluxConstraint([Reac1,Reac2]) 

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

None: Meetings/C1NetWork1/Prac7 (last edited 2014-12-18 17:28:31 by mark)