Differences between revisions 1 and 38 (spanning 37 versions)

Practical 5: Identifying pathways for TAG synthesis in Phaeodactylum tricornutum

Part 1

Here, we will investigate the genome-scale metabolic model of P. tricornutum to identify pathways for TAG synthesis. See Villanova et al (2021). Front. Plant Sci. 12:642199. doi: 10.3389/fpls.2021.642199

Download the archive containing the model from here and extract the files.This will generate a new directory,"srcs", containing two sub-directories: "Model" and "Analysis". Start ScrumPy.
Load the Model:
1. m = ScrumPy.Model("../Model/Phaeo.spy")
We can now generate a linear programming object:
1. lp = m.GetLP()
And specify minimising total flux as the objective:
1. lp.SetObjective(m.sm.cnames)
With the constraint that we must generate 1 mole of TAG:
1. lp.SetFixedFlux({"TAG_Exp_tx":-1})
We can now solve te lp:
1. lp.Solve()
And obtain the solution:
1. sol = lp.GetPrimSol()

Sol is a dictionary, mapping reactions to fluxes, satisfying our constraints and objectives. Examine its properties. e.g. what transport processes are involved?for r in sol:

if "_tx" in r:
- print(r, sol[r])

Reaction and metabolite names are derived from MetaCyc so you can use thses to find out more about individual reactions in the solution. NB: the _Cyto suffix is added to differentiate compartmentalisation in the model and is not part of MetaCyc identifier, and should be removed before searching on MetaCyc.

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+= Practical 5: Identifying pathways for TAG synthesis in Phaeodactylum tricornutum =
== Part 1 ==
Here, we will investigate the genome-scale metabolic model of ''P. tricornutum'' to identify pathways for TAG synthesis''. ''See'' Villanova et al (2021). Front. Plant Sci. 12:642199. doi: 10.3389/fpls.2021.642199''

 1. Download the archive containing the model from [[http://mudsharkstatic.brookes.ac.uk/Nottingham2024/P5.zip|here]] and extract the files.This will generate a new directory,"srcs", containing two sub-directories: "Model" and "Analysis". Start !ScrumPy.
 1. Load the Model:
  1. m = !ScrumPy.Model("../Model/Phaeo.spy")
 1. We can now generate a linear programming object:
  1. lp = m.GetLP()
 1. And specify minimising total flux as the objective:
  1. lp.!SetObjective(m.sm.cnames)
 1. With the constraint that we must generate 1 mole of TAG:
  1. lp.!SetFixedFlux({"TAG_Exp_tx":-1})
 1. We can now solve te lp:
  1. lp.Solve()
 1. And obtain the solution:
  1. sol = lp.!GetPrimSol()

Sol is a dictionary, mapping reactions to fluxes, satisfying our constraints and objectives. Examine its properties. e.g. what transport processes are involved?for r in sol:

 . if "_tx" in r:
  . print(r, sol[r])

Reaction and metabolite names are derived from [[https://metacyc.org|MetaCyc]] so you can use thses to find out more about individual reactions in the solution. NB: the `_Cyto suffix` is added to differentiate compartmentalisation in the model and is not part of !MetaCyc identifier, and should be removed before searching on !MetaCyc.

 . ''' '''
 <<BR>>

== Part 2 - Constraint Scanning ==
=== See Demo and Tomorrow ===