Differences between revisions 32 and 42 (spanning 10 versions)
⇤ ← Revision 32 as of 2024-12-11 08:12:09 →
Size: 5288
Editor: mark
Comment:
← Revision 42 as of 2024-12-12 09:03:16 → ⇥
Size: 2900
Editor: mark
Comment:
Deletions are marked like this. Additions are marked like this.
Line 2: Line 2:
== Part 1 ==
Line 4: Line 5:
1. Download the archive containing the model from [[http://mudsharkstatic.brookes.ac.uk/Nottingham2024/P5.zip|here]] and extract the files.  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 the lp:
  1. lp.Solve()
 1. And obtain the solution:
  1. sol = lp.!GetPrimSol()
Line 6: Line 19:
 a. Start !ScrumPy in the folder containing the .spy files and load the top-level model file Phaeo.spy to create a model object. Note that the model is created in a modular fashion, and the top-level file will load the different components of the model and each module will be in a separate tab.
 a. Can you explain why there are more modules in this model compared to Campylobacter model?		 Sol is a dictionary, mapping reactions to fluxes, satisfying our constraints and objectives. Examine its properties. e.g. what transport processes are involved?
Line 9: Line 21:
2. Set up and solve an LP problem where the objective is to minimise total flux (see previous practical), while producing 1 unit flux of TAG (Hint: use !SetFixedFlux() function on reaction ‘TAG_synthesis_Cyto’).  . for r in sol:
Line 11: Line 23:
 a. What is the source of energy in your LP solution ?
 a. Examine the source of carbons.		  . if "_tx" in r:
  . print(r, sol[r])
Line 14: Line 26:
3. Now we will perform lipid scan analysis under mixotrophic condition. We will perform this analysis under various growth conditions so better to write the steps into python method, for re-usability. 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.
Line 16: Line 28:
The code that you will be using is stored in module “!LipidScan.py” in the “Analysis” directory. (Note: Model directory contains the model definition files i.e .spy files and analysis contains the python modules, i.e., the ` .py` files you will need for this practical)  . ''' ''' <<BR>>
Line 18: Line 30:
The module "!LipidScan.py" contains two functions. Following is their code: == 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.
Line 20: Line 33:
'''a. BuildLP ''''''function''' Having started !ScrumPy and loaded the model as in Part 1, we import the !LipidScan module:
Line 22: Line 35:
{{{#!python
def BuildLP(m):
        lp = m.GetLP()
        lp.SetObjective(m.sm.cnames)
        lp.SetFluxBounds({"RIBULOSE-BISPHOSPHATE-CARBOXYLASE-RXN_Plas":(0,400.0)})
        if "GLYCEROL_Cyto_tx" in m.sm.cnames:
                lp.SetFluxBounds({"GLYCEROL_Cyto_tx":(0,20)})
        return lp
}}}
'''b. !LipidScan ''''''function'''		  . >>> import !LipidScan
Line 33: Line 37:
{{{#!python
def LipidScan(m,lp=None,lo=1.0,hi=20.0):
        ds = DataSets.DataSet()
        ranges = numpy.arange(lo,hi)
        if lp == None:
                lp = BuildLP(m)
        for t in ranges:
                lp.SetFixedFlux({"TAG_synthesis_Cyto":t})
                lp.Solve()
                if lp.GetStatusMsg() == "optimal":
                        sol = lp.GetPrimSol()
                        ds.UpdateFromDic(sol)
        ds.SetPlotX("TAG_synthesis_Cyto")
        ds.AddToPlot("RIBULOSE-BISPHOSPHATE-CARBOXYLASE-RXN_Plas")
        return ds
}}}
To use these methods you need to import the "!LipidScan" module. On ScrumPy window execute the following statements.		 (The model must be loaded first)
Line 51: Line 39:
{{{#!python
import sys
sys.path.append('../Analysis')
import LipidScan
}}}
Now the methods in the "!LipidScan" module can be used.		 We can now generate some results:
Line 58: Line 41:
 a. Generate LP problem where the objective is to minimise total flux. Constrain the maximum Rubisco flux and glycerol transporter flux to 400 and 20 respectively (make use of !SetFluxBounds() function).  . >>> res = [[LipidScan|!LipidScan]].LipidScan(m)
Line 60: Line 43:
{{{#!python
lp = LipidScan.BuildLP(m)
}}}
 . b. Solve this LP repeatedly (using for loop) while increasing flux in TAG synthesis reaction in range between 1 to 20. Save each of the solution in a dataset.		 "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:
Line 65: Line 45:
{{{#!python
ds = LipidScan.LipidScan(m, lp=lp)
}}}
 . c. Examine the flux pattern in Rubisco reaction with respect to increasing flux in TAG synthesis. What is the maximum flux in Rubisco reaction?		  . >>> chs = !LipidScan.Changers(res)
Line 70: Line 47:
{{{#!python
ds.SetPlotX("TAG_synthesis_Cyto") #setting x-axis
ds.AddToPlot("RIBULOSE-BISPHOSPHATE-CARBOXYLASE-RXN_Plas")
}}}
 . d. Add inorganic carbon transporters (Hint: “CO2_Cyto_tx” and “HCO3_Cyto_tx”) and organic carbon transporter (“GLYCEROL_Cyto_tx”) to the plot ''' '''
 . e. What is the maximum flux in TAG synthesis? ''' '''		 Commonly, we start by examining the transport processes, which can be conveniently plotted:
Line 77: Line 49:
4. As you would have noticed TAG synthesis in above example is through mixotrophic mode (i.e model uses light energy and organic carbon, glycerol, for lipid production).  . >>> res.!SetPlotX("TAG_Exp_tx") >>> for ch in chs:
  . if "_tx" in ch:
   . res.!AddToPlot(ch)
Line 79: Line 53:
As you remember from the lecture, ''P. tricornutum'' can grow under phototrophic condition too (i.e in the absence of glycerol). You will simulate the model in autotrophic condition. For this, constrain the flux in glycerol transporter to zero. ''' ''' This more than is immediately convenient, so we can remove the plasted transporters, to leave only the cytosolic transporters:
Line 81: Line 55:
{{{#!python
lp = LipidScan.BuildLP(m)
lp.SetFixedFlux({"GLYCEROL_Cyto_tx":0})
res = LipidScan.LipidScan(m, lp=lp)
}}}
 * Plot reactions as above. Examine the difference in flux patterns.'
 * What is the maximum feasible flux in TAG synthesis under phototrophic condition?
 * Is is higher or lower than that in mixotrophic condition (in question 3)?		  . >>> res.!RemoveMatchesFromPlot("_Plas")
Line 90: Line 57:
5. Find the reactions that are active in mixotrophic condition but not in phototrophic condition? At which point an interpretable pattern starts to emerge.
Line 92: Line 59:
{{{#!python
from ScrumPy.Util import Set
Set.Complement(ds.cnames,res.cnames)
}}}
Can you identify which pathways these reactions belong to? Refer to network diagram in lecture slides for convenience or visit !MetaCyc. Note that `_Cyto suffix` is added to differentiate compartmentalisation in the model and is not part of !MetaCyc identifier.

https://mudsharkstatic.brookes.ac.uk/Nottingham2022/P6/ ''' '''		 Over to you!

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" 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!

None: Meetings/Nottingham2024/Prac5 (last edited 2024-12-12 09:03:16 by mark)