Practical 8

Analysing a genome scale metabolic model of A. 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 AraGSM.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 AraGSM.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 our the Calvin cycle enzymes from GSM of A. thaliana

1. cd into Analysis/Knockouts

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

3. Import the KnockOut module.

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

5. lp = BuildLP.BiomassLP(m)
6. lp.Solve()

   • wild_type_solution = lp.GetPrimSol()

   • Observe the flux values of the Calvin Cycle enzymes

7. Load the KnockOutEffects from the KnockOut module and provide the reaction to knockout in the argument

   • eg. mutant_solution = KnockOutEffects.KnockOut(m, 'SEDOHEPTULOSE-BISPHOSPHATASE-RXN_Plas', lp)

8. Investigate the differences in wild_type_solution and mutant_solution and interpret the results.
9. from Util import Set
10. new_active_reactions = Set.Complement(mutant_solution, wild_type_solution)
11. Similarly , find the reactions that were inactive as a effect of knock out.
12. Identify the reactions that have change in the flux values and interpret the results.

13. Repeat Step 5-9 for the FBPase, phosphoribulokinase, G3Pdh and SBPase, FBPase dual knock out mutant.

Part B : Analysing the response to varying input of photon flux

1. Change directory to the relevant area:

   • $ cd A.thaliana/Analysis/LightScan

2. Start ScrumPy and load the model:

   •  >>> m = ScrumPy.Model("../../Model/AraTopLevel.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 LightScan

   •  >>> import LightScan