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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 discussed in the 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 (TBA) 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, A.thaliana, 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: Investigating the effect of knocking out the Calvin cycle enzymes ==
 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) }}} )




== Part B: Analysing the response to varying input of light. ==
 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

Practical ...

Analysing a genome scale metabolic model of Arabidopsis thaliana

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

  1. Download the file (TBA) 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, A.thaliana, 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: Investigating the effect of knocking out the Calvin cycle enzymes

  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)  )

Part B: Analysing the response to varying input of light.

  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

None: Meetings/Nepal2016 (last edited 2016-09-16 13:21:02 by mark)