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= Workshop: Metabolic Modelling and its Applications = = Practical ... =
== Analysing a genome scale metabolic model of Arabidopsis thaliana ==
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:
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=== 27 - 30 June 2016 ===

== Tribhuvan University, Kirtipur, Kathmandu, Nepal ==

 The workshop is organised by Tribhuvan University and the Society for Biological Applications Nepal (SBAN).

 A brief introduction to the workshop is available on the website for the [[http://sbanepal.com/events/|SBAN]].

==== Application ====
  
  To apply for the workshop, complete the registration form an the [[http://sbanepal.com/events/|SBAN website]]. If the workshop is over-subscribed, successful applicants will be selected and notified.		  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.
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==== Instructions for workshop attendees ====  1. This is a compressed archive file and you will need to extract the files before they can be used:
 {{{ $ tar -zxf P7.tgz }}}
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 You are required to bring a laptop to the workshop. The metabolic modelling package used in the course, [[http://mudshark.brookes.ac.uk/ScrumPy|!ScrumPy]], will be made available to you. The two options for installing !ScrumPy on your computer are to directly install the package on a suitable Linux operating system, or to run a virtual machine under other operating system. Instructions are [[Meetings/Pune2016/ScrumPyVM|here]]. (TO BE UPDATED)
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==== Timetable ====  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.
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 To help plan your attendance, the provisional workshop timetable is given below. The precise content and order of topics may be changed - even during the workshop itself!  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) }}} )

Practical ...

Analysing a genome scale metabolic model of Arabidopsis thaliana

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/Nepal2016 (last edited 2016-09-16 13:21:02 by mark)