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 here and extract the files.This will generate a new directory,"srcs", containing two sub-directories: "Model" and "Analysis". Start ScrumPy.

  2. Load the Model:

    1. m = ScrumPy.Model("../Model/Phaeo.spy")

  3. We can now generate a linear programming object:
    1. lp = m.GetLP()
  4. And specify minimising total flux as the objective:

    1. lp.SetObjective(m.sm.cnames)

  5. With the constraint that we must generate 1 mole of TAG:

    1. lp.SetFixedFlux({"TAG_Exp_tx":-1})

  6. We can now solve te lp:
    1. lp.Solve()
  7. 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:

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.

Part 2 - Constraint Scanning

See Demo and Tomorrow