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 the 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:
• 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.

Part 2 - Constraint Scanning

Part 1 describes how to examine a single lp solution. Now we can move on to exploring multiple solutions and examine how Phaeo can rearange its metabolism in response to increasing TAG demand. The "Analysis" directory contains a simple Python module, LipidScan.py, to facilitate this.

Having started ScrumPy and loaded the model as in Part 1, we import the LipidScan module:

• >>> import LipidScan

(The model must be loaded first)

We can now generate some results:

• >>> res = !LipidScan.LipidScan(m)

"res" is a DataSet, (matrix-like) object that contains 100 lp solutions, for the model, subject to a varying demand for TAG and satisfyng demand for biomass precursors. We are only interested in the reactions that change:

• >>> chs = LipidScan.Changers(res)

Commonly, we start by examining the transport processes, which can be conveniently plotted:

• >>> res.!SetPlotX("TAG_Exp_tx") >>> for ch in chs:

  • if "_tx" in ch:

    • res.AddToPlot(ch)

This more than is immediately convenient, so we can remove the plasted transporters, to leave only the cytosolic transporters:

• >>> res.RemoveMatchesFromPlot("_Plas")

At which point an interpretable pattern starts to emerge.

Over to you!