

In [ ]:

    
from bokeh.io import output_notebook
output_notebook()









    





    
        
        Loading BokehJS ...



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from cameo import models
from marsi.cobra.flux_analysis import sensitivity_analysis



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model = models.bigg.iJO1366

Sensitivity analysis for L-Serine

In this example, the ammount of produced serine is increased in steps. The biomass production will decrease with increased accumulation of Serine. This is a scenario where an metabolite analog would compete with Serine and the cell needs to increase the production of Serine to compete for biomass production and enzyme activity.



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ser__L = model.metabolites.ser__L_c



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result = sensitivity_analysis(model, ser__L, is_essential=True, steps=10,
                              biomass=model.reactions.BIOMASS_Ec_iJO1366_core_53p95M)



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result.data_frame









    Out[ ]:







  
    
      
      fraction
      ser__L
      BIOMASS_Ec_iJO1366_core_53p95M
    
  
  
    
      0
      0.00
      0.000000
      0.982372
    
    
      1
      0.11
      2.327074
      0.879966
    
    
      2
      0.22
      4.654149
      0.776675
    
    
      3
      0.33
      6.981223
      0.673384
    
    
      4
      0.44
      9.308298
      0.567309
    
    
      5
      0.55
      11.635373
      0.459727
    
    
      6
      0.66
      13.962447
      0.352145
    
    
      7
      0.77
      16.289522
      0.244563
    
    
      8
      0.88
      18.616597
      0.129905
    
    
      9
      0.99
      20.943671
      0.010895



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result.plot(width=700, height=500)

We can also see how does this affects other variables.



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result = sensitivity_analysis(model, ser__L, is_essential=True, steps=10, 
                              variables=[model.reactions.SERAT, model.reactions.SUCOAS],
                              biomass=model.reactions.BIOMASS_Ec_iJO1366_core_53p95M)



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result.data_frame









    Out[ ]:







  
    
      
      fraction
      ser__L
      BIOMASS_Ec_iJO1366_core_53p95M
      SERAT
      SUCOAS
    
  
  
    
      0
      0.00
      0.000000
      0.982372
      0.243502
      -3.285931
    
    
      1
      0.11
      2.327074
      0.879966
      0.218119
      -2.187337
    
    
      2
      0.22
      4.654149
      0.776675
      0.192516
      -1.107405
    
    
      3
      0.33
      6.981223
      0.673384
      0.166913
      -0.027474
    
    
      4
      0.44
      9.308298
      0.567309
      0.140620
      0.297758
    
    
      5
      0.55
      11.635373
      0.459727
      0.113953
      0.241292
    
    
      6
      0.66
      13.962447
      0.352145
      0.087286
      0.184826
    
    
      7
      0.77
      16.289522
      0.244563
      0.060619
      0.000000
    
    
      8
      0.88
      18.616597
      0.129905
      0.032200
      0.068182
    
    
      9
      0.99
      20.943671
      0.010895
      0.002701
      0.005718



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result.plot(width=700, height=500)

The same analysis can be done with different simulation methods (e.g. lMOMA).



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from cameo.flux_analysis.simulation import lmoma



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result = sensitivity_analysis(model, ser__L, is_essential=True, steps=10, simulation_method=lmoma,
                              biomass=model.reactions.BIOMASS_Ec_iJO1366_core_53p95M)



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result.plot(width=700, height=500)

Sensitivity analysis for Pyruvate

In this example, the pyruvate of succinate is decreased. This is a scenario where the cells are evolved with a toxic compound and the consumption turnover of that compound decreases.



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pyr = model.metabolites.pyr_c



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result = sensitivity_analysis(model, pyr, is_essential=False, steps=10,
                              biomass=model.reactions.BIOMASS_Ec_iJO1366_core_53p95M)



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result.plot(width=700, height=500)



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result = sensitivity_analysis(model, pyr, is_essential=False, steps=10, simulation_method=lmoma,
                              biomass=model.reactions.BIOMASS_Ec_iJO1366_core_53p95M)



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result.data_frame









    Out[ ]:







  
    
      
      fraction
      pyr
      BIOMASS_Ec_iJO1366_core_53p95M
    
  
  
    
      0
      0.00
      0.000000
      0.982372
    
    
      1
      0.11
      0.153405
      0.908226
    
    
      2
      0.22
      0.365068
      0.831883
    
    
      3
      0.33
      0.523172
      0.754858
    
    
      4
      0.44
      0.681276
      0.677833
    
    
      5
      0.55
      0.839380
      0.600808
    
    
      6
      0.66
      0.997485
      0.523783
    
    
      7
      0.77
      1.155566
      0.446758
    
    
      8
      0.88
      1.008145
      0.359887
    
    
      9
      0.99
      1.533426
      0.036982

	fraction	ser__L	BIOMASS_Ec_iJO1366_core_53p95M
0	0.00	0.000000	0.982372
1	0.11	2.327074	0.879966
2	0.22	4.654149	0.776675
3	0.33	6.981223	0.673384
4	0.44	9.308298	0.567309
5	0.55	11.635373	0.459727
6	0.66	13.962447	0.352145
7	0.77	16.289522	0.244563
8	0.88	18.616597	0.129905
9	0.99	20.943671	0.010895