1.1 Sensitivity Analysis

from bokeh.io import output_notebook
output_notebook()

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

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.

ser__L = model.metabolites.ser__L_c

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

result.data_frame

	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

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

We can also see how does this affects other variables.

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)

result.data_frame

	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

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

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

from cameo.flux_analysis.simulation import lmoma

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

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.

pyr = model.metabolites.pyr_c

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

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

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

result.data_frame

	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