1. findCPcore¶
Import core modules¶
In order to use findCPcore library you have to import the following core modules.
[1]:
import sys
from findCPcore import CobraMetabolicModel
Read metabolic model¶
Read input metabolic model in Systems Biology Markup Language (SBML) format. SBML is a XML-based standard for systems biology model’s exchange.
Allowed files formats are: * xml * json * yml
[2]:
model = CobraMetabolicModel("aureus.xml")
Get model info¶
The following methods allow us to print on the command line data from the model.
Model info¶
[3]:
model.print_model_info()
MODEL INFO
-------------------------------------------------------
MODEL: MODEL1507180070
REACTIONS: 743
METABOLITES: 655
GENES: 619
COMPARTMENTS: c
e
Metabolites¶
[4]:
model.print_metabolites()
MODEL: MODEL1507180070 - NUMBER OF METABOLITES: 655
METABOLITE | COMPARTMENT | REACTION ID
-------------------------------------------------------
10fthf_c | c | biomass_SA_7a
| | biomass_SA_8a
| | FTHFL
| | biomass_SA_7b
| | MTHFC
| | AICART
| | GARFT
12dgr_EC_c | c | biomass_SA_2a
| | biomass_SA_2b
| | biomass_SA_3b
| | biomass_SA_lipids_only
|
Reactions¶
[5]:
model.print_reactions()
MODEL: MODEL1507180070 - NUMBER OF REACTIONS: 743
REACTION ID | UPPER BOUND | LOWER BOUND | REACTION
-------------------------------------------------------
3M2OBLOXRD | 999999.0 | -999999.0 | 3mob_c + h_c + lpam_c <=> 2mpdhl_c + co2_c
3M2OPLOXRD | 999999.0 | -999999.0 | 3mop_c + h_c + lpam_c <=> 2mbdhl_c + co2_c
4M2OPLOXRD | 999999.0 | -999999.0 | 4mop_c + h_c + lpam_c <=> 3mbdhl_c + co2_c
6PGALSZ | 999999.0 | 0.0 | h2o_c + lac6p_c --> dgal6p_c + glc_DASH_D_c
6PHBG | 999999.0 | 0.0 | h2o_c + salc6p_c --> 2hymeph_c + g6p_c
ABTAr | 999999.0 | 0.0 | 4abut_c + akg_c --> glu_DASH_L_c + sucsal_c
ACACT1r | 999999.0
Genes¶
[6]:
model.print_genes()
MODEL: MODEL1507180070 - NUMBER OF GENES: 619
GENE ID | GENE NAME | REACTION ID | GPR RELATION
-------------------------------------------------------
SA0008 | SA0008 | HISDr | SA0008
SA0009 | SA0009 | SERTRS | SA0009
SA0011 | SA0011 | HSERTA | SA0011
SA0016 | SA0016 | ADSS | SA0016
SA0036 | SA0036 | GPDDA2 | SA0036 or SA0820 or SA1542 or SA0969 or SA0220
| | GPDDA5 | SA0036 or SA0820 or SA1542 or SA0969 or SA0220
| | GPDDA3 | SA0036 or SA0820 or SA1542 or SA0969 or SA0220
|
Dead-end metabolites¶
Dead-end metabolites are those metabolites which are not consumed or not produced by any reaction of a given compartment of the model including exchange reactions.
Finding Dead-end metabolites¶
find_dem(). The method dem() returns a python dict with the following content:[7]:
model.find_dem()
model.dem()
[7]:
{'c': [<Metabolite 2pglyc_c at 0x7fe3dae1c110>,
<Metabolite adcobdam_c at 0x7fe3dae4e190>,
<Metabolite 2ombz_c at 0x7fe42cece250>,
<Metabolite fdxox_c at 0x7fe40fdde750>,
<Metabolite nop_c at 0x7fe40fd90910>,
<Metabolite acmama_c at 0x7fe3dae48950>,
<Metabolite adprib_c at 0x7fe3dae4e950>,
<Metabolite DGDG_SA_c at 0x7fe3dae42990>,
<Metabolite udcp_c at 0x7fe40fd3cb10>,
<Metabolite Sptmyn_c at 0x7fe3dae42b50>,
<Metabolite mhpglu_c at 0x7fe40fd86b50>,
<Metabolite 12dgr_EC_c at 0x7fe3dae24b90>,
<Metabolite o2s_c at 0x7fe40fd90bd0>,
<Metabolite Stmyn_c at 0x7fe3dae42bd0>,
<Metabolite drib_c at 0x7fe40fdd2c50>,
<Metabolite mi1p_DASH_D_c at 0x7fe40fd86c50>,
<Metabol
Dead-end metabolites can be printed on the command line with print_dem() method.
[8]:
model.print_dem()
MODEL: MODEL1507180070 - NUMBER OF DEM: 2 - COMPARTMENT: ALL
METABOLITE | COMPARTMENT | REACTION ID
-------------------------------------------------------
2pglyc_c | c | PGLYCP
adcobdam_c | c | ADCYRS
2ombz_c | c | URFGTT
fdxox_c | c | OOR3r
nop_c | c | NOPD
acmama_c | c | AMAA
adprib_c | c | ADPRDP
DGDG_SA_c | c | biomass_SA_lipids_only
| | biomass_SA_2a
udcp_c | c | UDCPKr
Sptmyn_c | c | Spt3AdT
mhpglu_c | c | MHPGLUT
12dgr_E
Dead-end metabolites printed can be limited to a specific compartment. Available compartments are given by model.compartments() method.
[9]:
model.print_dem(compartment="e")
MODEL: MODEL1507180070 - NUMBER OF DEM: 2 - COMPARTMENT: e
METABOLITE | COMPARTMENT | REACTION ID
-------------------------------------------------------
nh4_e | e | NH4t4
| | EX_nh4_e
lcts_e | e | LACpts
| | EX_lcts_e
mnl_e | e | EX_mnl_e
| | MNLpts
man_e | e | EX_man_e
| | MANpts
mobd_e | e | MOBDabc
| | EX_mobd_e
ni2_e | e | NIabc
| | EX_ni2_e
gua_e | e
Removing dead-end metabolites¶
Method remove_dem() removes dead-end metabolites from the model. Once dead-end metabolites are deleted, some reactions might not produce a metabolite anymore so the method deletes also these reactions and loops again until no dead-end metabolite is found:
while number of metabolites does not change:
delete all dead-end metabolites
for reaction that produced or consumed dead-end metabolites:
if reaction produces or consumes 0 metabolites [and is not exchange nor demand]:
delete reaction
find dead-end metabolites
The reactions that are deleted on the method can be modified by the following params:
delete_exchange :
True : all the reactions that are produce or consume 0 metabolites are deleted whether they are exchange/demand or not
False : (default) deleted according to ‘keep_all_incomplete_reactions’ param.
keep_all_incomplete_reactions :
False: if a reaction is a cobra boundary reaction (calculated by heuristics) that reaction can’t be deleted.
True: (default) if a reaction initially doesn’t produce or consume any metabolite that reaction can’t be deleted.
[10]:
print("Metabolites: ", len(model.metabolites()), "\nReactions: ", len(model.reactions()))
Metabolites: 655
Reactions: 743
[11]:
model.remove_dem()
[12]:
print("Metabolites: ", len(model.metabolites()), "\nReactions: ", len(model.reactions()))
Metabolites: 486
Reactions: 739
Dead reactions¶
Dead reactions are those reactions with upper and lower flux equal to zero.
Finding Dead reactions¶
Dead reactions of the model are calculated with the method dead_reactions(), which returns a list of cobra.core.reactions with dead reactions.
[13]:
model.dead_reactions()
[13]:
[<Reaction SA_biomass_1a at 0x7fe40f8359d0>,
<Reaction biomass_SA_2a at 0x7fe40f7025d0>,
<Reaction biomass_SA_2b at 0x7fe40f702a50>,
<Reaction biomass_SA_3a at 0x7fe40f702ed0>,
<Reaction biomass_SA_3b at 0x7fe40f702c90>,
<Reaction biomass_SA_4a at 0x7fe40f7160d0>,
<Reaction biomass_SA_5a at 0x7fe40f716050>,
<Reaction biomass_SA_6a at 0x7fe40f716fd0>,
<Reaction biomass_SA_6b at 0x7fe40f721fd0>,
<Reaction biomass_SA_7a at 0x7fe40f730f90>,
<Reaction biomass_SA_7b at 0x7fe40f6bff90>,
<Reaction biomass_SA_lipids_only at 0x7fe40f721f90>,
<Reaction biomass_SA_nuc_only at 0x7fe40f710c90>,
<Reaction biomass_SA_only_AA at 0x7fe40f730fd0>]
Chokepoint reactions¶
Chokepoint reactions are those reactions that are the only consumer or producer of a given metabolite that isn’t a dead-end metabolite.
Finding chokepoint reactions¶
Chokepoint reactions are calculated with the method find_chokepoints(). The method chokepoints() returns a list of tuples of types (cobra.core.reactions, cobra.core.metabolites) representing a chokepoint reactions object and the metabolite it only produces/consumes.
[15]:
# Read initial model again
model = CobraMetabolicModel("aureus.xml")
model.find_chokepoints()
model.chokepoints()
[15]:
[(<Reaction PAPA_SA at 0x7fe40f2e4a10>,
<Metabolite 12dgr_SA_c at 0x7fe40fd65590>),
(<Reaction PROD2 at 0x7fe40f249cd0>, <Metabolite 1pyr5c_c at 0x7fe40fd65bd0>),
(<Reaction DHDPRy at 0x7fe40f4d1d90>,
<Metabolite 23dhdp_c at 0x7fe40fdb1310>),
(<Reaction DHDPS at 0x7fe40f4d34d0>, <Metabolite 23dhdp_c at 0x7fe40fdb1310>),
(<Reaction DHAD1 at 0x7fe40f4ce8d0>, <Metabolite 23dhmb_c at 0x7fe40fde34d0>),
(<Reaction KARA1i at 0x7fe40f3ce910>,
<Metabolite 23dhmb_c at 0x7fe40fde34d0>),
(<Reaction DHAD2 at 0x7fe40f4d1490>, <Metabolite 23dhmp_c at 0x7fe40fde3910>),
(<Reaction KARA2i at 0x7fe40f3ceb10>,
<Metabolite 23dhmp_c at 0x7fe40fde3910>),
(<Reaction DHPPDA at 0x7fe40f4d9950>,
<Met
Dead reactions can be excluded from the computation of chokepoints with the exclude_dead_reactions=True parameter. This is strongly recommended as dead reactions are considered forward reactions by default an this can lead to misinterpretations.
[16]:
model.find_chokepoints(exclude_dead_reactions=True)
model.chokepoints()
[16]:
[(<Reaction PAPA_SA at 0x7fe40f2e4a10>,
<Metabolite 12dgr_SA_c at 0x7fe40fd65590>),
(<Reaction PROD2 at 0x7fe40f249cd0>, <Metabolite 1pyr5c_c at 0x7fe40fd65bd0>),
(<Reaction DHDPRy at 0x7fe40f4d1d90>,
<Metabolite 23dhdp_c at 0x7fe40fdb1310>),
(<Reaction DHDPS at 0x7fe40f4d34d0>, <Metabolite 23dhdp_c at 0x7fe40fdb1310>),
(<Reaction DHAD1 at 0x7fe40f4ce8d0>, <Metabolite 23dhmb_c at 0x7fe40fde34d0>),
(<Reaction KARA1i at 0x7fe40f3ce910>,
<Metabolite 23dhmb_c at 0x7fe40fde34d0>),
(<Reaction DHAD2 at 0x7fe40f4d1490>, <Metabolite 23dhmp_c at 0x7fe40fde3910>),
(<Reaction KARA2i at 0x7fe40f3ceb10>,
<Metabolite 23dhmp_c at 0x7fe40fde3910>),
(<Reaction DHPPDA at 0x7fe40f4d9950>,
<Met
Chokepoint reactions can also be printed on the command line with print_chokepoints.
[17]:
model.print_chokepoints()
MODEL: MODEL1507180070 - NUMBER OF CHOKEPOINTS: 448
METABOLITE ID | METABOLITE NAME | REACTION ID | REACTION NAME
------------------------------------------------------------
12dgr_SA_c | 1,2-Daicylglycerol (Saureus) | PAPA_SA | Phosphatidate phosphatase
1pyr5c_c | 1-Pyrroline-5-carboxylate | PROD2 | Proline dehydrogenase
23dhdp_c | 2,3-Dihydrodipicolinate | DHDPRy | dihydrodipicolinate reductase (NADPH)
23dhdp_c | 2,3-Dihydrodipicolinate | DHDPS | dihydrodipicolinate synthase
23dhmb_c | (R)-2,3-Dihydroxy-3-methylbutanoate | DHAD1 |
Flux Balance Analysis¶
Flux Balance Analysis (FBA) is a mathematical procedure used to calculate the growth rate of a metabolic model considering an objective function to maximize and the reactions flux as constraints.
The method get_growth() calculates the objective value (growth rate) that maximizes the objective function. This method uses cobra.core.model.slim_optimize and was obtained from cobra.flux_analysis.deletion.
The objective value obtained with FBA can be accessed with objective_value().
[18]:
model.get_growth()
model.objective_value()
[18]:
0.1580502916027849
The objective function that is maximized during FBA can be accessed by objective()
[19]:
model.objective()
[19]:
'1.0*biomass_SA_8a - 1.0*biomass_SA_8a_reverse_4ce77'
This objective function can be changed by another reaction of the model with set_objective(). This method receives the id of the reaction that will be set as the new objective value.
[20]:
model.set_objective("DHAD1")
model.objective()
[20]:
'1.0*DHAD1 - 1.0*DHAD1_reverse_39dca'
Flux Variability Analysis¶
Flux Vatiability Analysis (FVA) is a mathematical procedure used to calculate the ‘’minimum and maximum flux for reactions in the network while maintaining some state of the network, e.g., supporting 90% of maximal possible biomass production rate’’.
fva() runs Flux Variability Analysis on the model. This method runs cobra.flux_analysis.variability and as so it allows the same parameters (see the previous link):Method fva() returns an error list if there was an error while running FVA or an empty list [] otherwise.
[21]:
model.fva(threshold=0.95)
[21]:
[]
[22]:
model.fva(threshold=0.95, verbose=True)
FLUX VARIABILITY ANALYSIS: MODEL1507180070
REACTION: 3-Methyl-2-oxobutanoate:lipoamide oxidoreductase(decarboxylating and acceptor-2-methylpropanoylating)
fva ranges: [ 0.0 , 0.0 ]
REACTION: 3-Methyl-2-oxopentanoate:lipoamide oxidoreductase(decarboxylating and acceptor-2-methylpropanoylating)
fva ranges: [ 0.0 , 0.0 ]
REACTION: 4-Methyl-2-oxopentanoate:lipoamide oxidoreductase(decarboxylating and acceptor-2-methylpropanoylating)
fva ranges: [ 0.0 , 0.0 ]
REACTION: 6-phospho-beta-galactosidase
fva ranges: [ 0.0 , 0.0 ]
REACTION: 6-phospho-beta-glucosidase
fva ranges: [ 0.0 , 0.0 ]
RE
The result obtained with FVA can be accessed with get_fva(). This method returns a list of tuples: (cobra.core.reaction, [float] maximum flux, [float] minimum flux)
[23]:
model.get_fva()
[23]:
[(<Reaction 3M2OBLOXRD at 0x7fe40fc3d2d0>, 0.0, 0.0),
(<Reaction 3M2OPLOXRD at 0x7fe40fc3d790>, 0.0, 0.0),
(<Reaction 4M2OPLOXRD at 0x7fe40fc3d390>, 0.0, 0.0),
(<Reaction 6PGALSZ at 0x7fe40fca2ed0>, 0.0, 0.0),
(<Reaction 6PHBG at 0x7fe40fca2f90>, 0.0, 0.0),
(<Reaction ABTAr at 0x7fe40f6df490>, 0.0, 0.0),
(<Reaction ACACT1r at 0x7fe40fcb6d50>,
0.00032511320071648854,
-2065.6249999999704),
(<Reaction ACACT2r at 0x7fe40fcb6850>, 0.0, -2065.624999999973),
(<Reaction ACACT3r at 0x7fe40fcb6e90>, 0.0, -2065.624999999973),
(<Reaction ACACT4r at 0x7fe40fcb6cd0>, 0.0, -2065.624999999973),
(<Reaction ACACT5r at 0x7fe40fcb6b90>, 0.0, -2065.624999999973),
(<Reaction ACACT6r at 0x7fe40fcb6b
Updating reaction’s flux with FVA¶
findCritical adds an extra parameter to this method which is update_flux. If True the method fva() updates the model reaction’s flux with the maximum and minimum flux values obtained with FVA (see the example below).
[24]:
# Print initial reactions of the model with initial flux values.
model.print_reactions()
MODEL: MODEL1507180070 - NUMBER OF REACTIONS: 743
REACTION ID | UPPER BOUND | LOWER BOUND | REACTION
-------------------------------------------------------
3M2OBLOXRD | 999999.0 | -999999.0 | 3mob_c + h_c + lpam_c <=> 2mpdhl_c + co2_c
3M2OPLOXRD | 999999.0 | -999999.0 | 3mop_c + h_c + lpam_c <=> 2mbdhl_c + co2_c
4M2OPLOXRD | 999999.0 | -999999.0 | 4mop_c + h_c + lpam_c <=> 3mbdhl_c + co2_c
6PGALSZ | 999999.0 | 0.0 | h2o_c + lac6p_c --> dgal6p_c + glc_DASH_D_c
6PHBG | 999999.0 | 0.0 | h2o_c + salc6p_c --> 2hymeph_c + g6p_c
ABTAr | 999999.0 | 0.0 | 4abut_c + akg_c --> glu_DASH_L_c + sucsal_c
ACACT1r | 999999.0
[25]:
# Update reactions flux values with FVA
model.fva(update_flux=True)
[25]:
[]
[26]:
# Print reactions of the model with refined reactions flux values.
model.print_reactions()
MODEL: MODEL1507180070 - NUMBER OF REACTIONS: 743
REACTION ID | UPPER BOUND | LOWER BOUND | REACTION
-------------------------------------------------------
3M2OBLOXRD | 0.0 | 0.0 | 3mob_c + h_c + lpam_c --> 2mpdhl_c + co2_c
3M2OPLOXRD | 0.0 | 0.0 | 3mop_c + h_c + lpam_c --> 2mbdhl_c + co2_c
4M2OPLOXRD | 0.0 | 0.0 | 4mop_c + h_c + lpam_c --> 3mbdhl_c + co2_c
6PGALSZ | 0.0 | 0.0 | h2o_c + lac6p_c --> dgal6p_c + glc_DASH_D_c
6PHBG | 0.0 | 0.0 | h2o_c + salc6p_c --> 2hymeph_c + g6p_c
ABTAr | 0.0 | 0.0 | 4abut_c + akg_c --> glu_DASH_L_c + sucsal_c
ACACT1r | -3.26642
Essential genes¶
Essential genes are those genes that cause a zero growth rate when knocked out.
Finding essential genes¶
Essential genes are calculated with the method find_essential_genes_1(). This method returns an error list if there was an error during the computing or an empty list [] otherwise.
[27]:
model.find_essential_genes_1()
[27]:
[]
If essential genes were computed with the method above, they can be accessed with essential_genes(). This method return a list of cobra.core.gene with essential genes.
[28]:
model.essential_genes()
[28]:
{<Gene SA0183 at 0x7fe40fb95450>,
<Gene SA0512 at 0x7fe40fb7a550>,
<Gene SA0728 at 0x7fe40fbe8690>,
<Gene SA0729 at 0x7fe40fbe8710>,
<Gene SA0823 at 0x7fe40fbe0fd0>,
<Gene SA0910 at 0x7fe40fbdae10>,
<Gene SA0911 at 0x7fe40fbdafd0>,
<Gene SA0912 at 0x7fe40fbdab50>,
<Gene SA0913 at 0x7fe40fbda050>,
<Gene SA0937 at 0x7fe40fbd4e50>,
<Gene SA0938 at 0x7fe40fbd4ad0>,
<Gene SA0994 at 0x7fe40fbccf50>,
<Gene SA0995 at 0x7fe40fbccb10>,
<Gene SA0996 at 0x7fe40fbcc650>,
<Gene SA1088 at 0x7fe40fbc7f10>,
<Gene SA1089 at 0x7fe40fbc79d0>,
<Gene SA1244 at 0x7fe40fc30850>,
<Gene SA1245 at 0x7fe40fc30a10>,
<Gene SA1255 at 0x7fe40fc30510>,
<Gene SA1521 at 0x7fe40fc14310>,
<Gene SA1585 at 0x7
Essential reactions¶
Essential reactions are those genes that cause a zero growth rate when knocked out.
Finding essential reactions¶
Essential reactions are calculated with the method find_essential_reactions_1(). This method returns an error list if there was an error during the computing or an empty list [] otherwise.
[29]:
model.find_essential_reactions_1()
[29]:
[]
If essential genes were computed with the method above, they can be accessed with essential_reactions(). This method return a dict with keys cobra.core.reaction with all the reactions of the model, and values float with the result of computing FBA with the reaction knocked-out.
[30]:
model.essential_reactions()
[30]:
{<Reaction TECA1S at 0x7fe40f1d7410>: 100.00000000000126,
<Reaction HETZK at 0x7fe40f410bd0>: 100.00000000000126,
<Reaction YUMPS at 0x7fe40f164990>: 100.00000000000126,
<Reaction NO3R1 at 0x7fe40f3179d0>: 100.00000000000126,
<Reaction GLYD at 0x7fe40f45ce10>: 100.00000000000126,
<Reaction GMHEPPA at 0x7fe40f465e90>: 100.00000000000126,
<Reaction THRAr at 0x7fe40f1edad0>: 100.00000000000126,
<Reaction ADPRDP at 0x7fe40f525890>: 100.00000000000126,
<Reaction SUCBZL at 0x7fe40f1bd950>: 100.00000000000126,
<Reaction EX_ser_DASH_L_e at 0x7fe40f493850>: 100.00000000000126,
<Reaction biomass_SA_4a at 0x7fe40f164e90>: 100.00000000000126,
<Reaction KAS8 at 0x7fe40f3e8e90>: 100.00000000000
Essential genes reactions¶
Essential genes reactions are those reactions that are knocked-out when an essential gene is knocked-out.
Finding essential genes reactions¶
Essential genes reactions are calculated with the method find_essential_genes_reactions(). This method returns an error list if there was an error during the computing or an empty list [] otherwise.
[31]:
model.find_essential_genes_reactions()
[31]:
[]
If essential genes reactions were computed with the method above, they can be accessed with essential_genes_reactions(). This method returns a dict with keys cobra.core.reaction with all the reactions of the model, and values a list of cobra.core.genes with the essential genes that cause the knock-out of the reaction.
[32]:
model.essential_genes_reactions()
[32]:
{<Reaction CYTBD at 0x7fe40f4c4890>: [<Gene SA0913 at 0x7fe40fbda050>,
<Gene SA0938 at 0x7fe40fbd4ad0>,
<Gene SA0912 at 0x7fe40fbdab50>,
<Gene SA0911 at 0x7fe40fbdafd0>,
<Gene SA0910 at 0x7fe40fbdae10>,
<Gene SA0937 at 0x7fe40fbd4e50>],
<Reaction AKGDb at 0x7fe40f52c3d0>: [<Gene SA1244 at 0x7fe40fc30850>],
<Reaction P5CD at 0x7fe40f2e4910>: [<Gene SA2341 at 0x7fe40fc4e9d0>],
<Reaction SUCOAS at 0x7fe40f1bde90>: [<Gene SA1089 at 0x7fe40fbc79d0>,
<Gene SA1088 at 0x7fe40fbc7f10>],
<Reaction PROD2 at 0x7fe40f249cd0>: [<Gene SA1585 at 0x7fe40fc0f9d0>],
<Reaction AKGDa at 0x7fe40f52c510>: [<Gene SA1245 at 0x7fe40fc30a10>],
<Reaction FUM at 0x7fe40f4a98d0>: [<Gene SA1669 at 0x7fe4
2. FacadeUtils¶
The module FacadeUtils from findCPcore provides specific methods for the generation of spreadsheets regarding the computation of chokepoint reactions taking into account Flux Variability Analysis and Dead End Metabolites.
Generate chokepoint summary spreadsheet¶
run_summary_model generates a spreadsheet file from a model file with the following data:Method declaration:
run_summary_model(self, model_path, print_f, arg1, arg2, objective=None, fraction=1.0)
model_path: Path of the SBML metabolic model file.print_f: Callback function to inform of the computation progression. The function must have a declaration like the following:custom_function_name(message, arg1, arg2)arg1: First parameter to pass to the function if any.arg2: Second parameter to pass to the function if any.objective: Reactions id to be used as objective function.fraction: Fraction of optmimum to be used with FVA.[34]:
def callback_print_ignore(message, arg1, arg2):
pass
def callback_print_logger(message, arg1, arg2):
logger = arg1
logger.info(message)
def callback_print(message, arg1, arg2):
print(arg1 + message)
facadeUtils = FacadeUtils()
spreadsheet = facadeUtils.run_summary_model("aureus.xml", callback_print, "LOG:", None)
facadeUtils.save_spreadsheet("output.xls", spreadsheet)
LOG:Reading model...
LOG:Generating models...
LOG:Searching Dead End Metabolites (D.E.M.)...
LOG:Searching chokepoint reactions...
LOG:Searching essential reactions...
LOG:Searching essential genes...
LOG:Searching essential genes reactions...
LOG:Removing Dead End Metabolites (D.E.M.)...
LOG:Searching essential reactions...
LOG:Searching new chokepoint reactions...
LOG:Searching essential genes...
LOG:Searching essential genes reactions...
LOG:Running Flux Variability Analysis...
LOG:Searching Dead End Metabolites (D.E.M.)...
LOG:Searching new chokepoint reactions...
LOG:Searching essential genes...
LOG:Searching essential genes reactions...
LOG:Searching essential reactions...
LOG:Removing
Generate chokepoint growth sensibility spreadsheet¶
The method run_sensibility_analysis produces a spreadsheet file from a model file with an analysis of varying rhe values of fraction of optimum with FVA effects on the size of the folloing sets: - Reversible Reactions (RR) - Non-reversible Reactions (NR) - Dead Reactions (DR) - Chokepoint reactions (CP)
Method declaration
run_sensibility_analysis(self, model_path, print_f, arg1, arg2, objective=None)
model_path: Path of the SBML metabolic model file.print_f: Callback function to inform of the computation progession. The function must have a declaration like the following:custom_function_name(message, arg1, arg2)arg1: First parameter to pass to the function if any.arg2: Second parameter to pass to the function if any.objective: Reactions id to be used as objective function.[35]:
def callback_print(message, arg1, arg2):
print(message)
facadeUtils = FacadeUtils()
spreadsheet = facadeUtils.run_sensibility_analysis("aureus.xml", callback_print, None, None)
facadeUtils.save_spreadsheet("output.xls", spreadsheet)
Reading model...
Running Flux Variability Analysis with fraction: 0.0
Running Flux Variability Analysis with fraction: 0.1
Running Flux Variability Analysis with fraction: 0.2
Running Flux Variability Analysis with fraction: 0.3
Running Flux Variability Analysis with fraction: 0.4
Running Flux Variability Analysis with fraction: 0.5
Running Flux Variability Analysis with fraction: 0.6
Running Flux Variability Analysis with fraction: 0.7
Running Flux Variability Analysis with fraction: 0.8
Running Flux Variability Analysis with fraction: 0.9
Running Flux Variability Analysis with fraction: 1.0
[35]:
(True, 'output.xls')
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