= 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 [[http://mudsharkstatic.brookes.ac.uk/C1Net/Wshop1/L10.pdf|previous lecture]] .  In order to do this you will need to download the files associated with the model:

 1.
 Download the file [[http://mudsharkstatic.brookes.ac.uk/C1Net/Wshop1/P7.tgz|P7.tgz]] into the area in whch you have been using for your other practicals.


 1. This is a compressed archive file and you will need to extract the files before they can be used:
 {{{ $ tar -zxf P7.tgz }}}


 1. 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.

 1. 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}}}



 1.
 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.)


 1. Examine the files that are now presented - how much can you recognise from previous work in this course?

 1. Now import the module called ATPScan
  .
  {{{ >>> import ATPScan }}}



 1.
 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.:


 1.
 {{{ >>> results = ATPScan.ATPScan(m, 0, 10, 50) }}}


 1. Will generate a dataset containing 50 solutions for the model with the imposed ATPase flux varying between 0 and 10 flux units.

 1. 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
 1.
 Start ScrumPy and the load the model as before.

 1.
 Import the {{{KnockOutImpacts}}} module.

 1.
 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)}}}

 1. 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) }}} )