# Working with SBML Ids

Usually basico uses the COPASI display names, to work with model elements. That way a consistent naming scheme between the COPASI graphical user interface, and the scripts can be easily maintained. However, for someone inspecting an SBML model, it might be convenient to also look at the SBML ids and identify elements that way. For this reason the data frames returned for compartments, events, parameters, species and reactions now also contain a column sbml_id.

Lets start as usual with the common imports:

[1]:

import sys
if '../..' not in sys.path:
sys.path.append('../..')
from basico import *
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline


Next lets load a model from the BioModels Database, and look at the elments:

[2]:

load_biomodel(64);


Now we have not just the element name availabe, but also their respective sbml_id:

[3]:

get_species()[['sbml_id', 'initial_concentration']]

[3]:

sbml_id initial_concentration
name
High energy phosphates P 6.310000
Glucose 6 Phosphate G6P 2.450000
Triose-phosphate TRIO 0.960000
Acetaldehyde ACE 0.170000
2-phosphoglycerate P2G 0.120000
1,3-bisphosphoglycerate BPG 0.000000
Glucose in Cytosol GLCi 0.087000
Fructose 6 Phosphate F6P 0.620000
Phosphoenolpyruvate PEP 0.070000
Pyruvate PYR 1.850000
Fructose-1,6 bisphosphate F16P 5.510000
3-phosphoglycerate P3G 0.900000
ATP concentration ATP 2.509190
AMP concentration AMP 0.299190
Extracellular Glucose GLCo 50.000000
Glycogen Glyc 0.000000
Trehalose Trh 0.000000
CO2 CO2 1.000000
Succinate SUCC 0.000000
Ethanol ETOH 50.000000
Glycerol GLY 0.150000
sum of AXP conc SUM_P 4.100000
F2,6P F26BP 0.020000

similarly we can get the elements by SBML id as well:

[4]:

get_species(sbml_id='ATP')

[4]:

compartment type unit initial_concentration initial_particle_number initial_expression expression concentration particle_number rate particle_number_rate key sbml_id
name
ATP concentration cytosol assignment mmol/l 2.50919 1.511070e+21 ( [High energy phosphates] - [ADP concentratio... NaN NaN NaN NaN Metabolite_21 ATP

Whereas in COPASI each element has a concentration and a particle number, in SBML usually elements deal only with concentrations and amounts. To make it easy to access them, it is convenient to add the expressions for the amount to the model, so that they can be accessed at any point in time. For that a utility function exists. If use_sbml_ids is specified, the sbml id of the species will be used in the name (i.e: amount(sbml_id)), otherwise it will be named amount(display name). In case ignore_fixed is specified, no expressions for fixed species will be created, and similarly assignment expressions can be ignored:

[5]:

add_amount_expressions(use_sbml_ids=True, ignore_fixed=True)


lets look at the expressions created, we see it is just the concentration multiplied with the compartment size the species is in:

[6]:

get_parameters(name='amount(')[['initial_value', 'expression']]

[6]:

initial_value expression
name
amount(GLCi) 0.087000 [Glucose in Cytosol] * Compartments[cytosol].V...
amount(G6P) 2.450000 [Glucose 6 Phosphate] * Compartments[cytosol]....
amount(F6P) 0.620000 [Fructose 6 Phosphate] * Compartments[cytosol]...
amount(F16P) 5.510000 [Fructose-1,6 bisphosphate] * Compartments[cyt...
amount(TRIO) 0.960000 [Triose-phosphate] * Compartments[cytosol].Volume
amount(BPG) 0.000000 [1,3-bisphosphoglycerate] * Compartments[cytos...
amount(P3G) 0.900000 [3-phosphoglycerate] * Compartments[cytosol].V...
amount(P2G) 0.120000 [2-phosphoglycerate] * Compartments[cytosol].V...
amount(PEP) 0.070000 [Phosphoenolpyruvate] * Compartments[cytosol]....
amount(PYR) 1.850000 [Pyruvate] * Compartments[cytosol].Volume
amount(ACE) 0.170000 [Acetaldehyde] * Compartments[cytosol].Volume
amount(P) 6.310000 [High energy phosphates] * Compartments[cytoso...
amount(ATP) 2.509190 [ATP concentration] * Compartments[cytosol].Vo...
amount(AMP) 0.299190 [AMP concentration] * Compartments[cytosol].Vo...

the run_time_course function now also takes a parameter to use sbml id’s if they are present (it will still use the display names in case an element has no sbml id.

[7]:

run_time_course(use_sbml_id=True)

[7]:

Time
0.00 6.310000 2.450000 0.960000 1.200000 0.170000 0.120000 0.000000 0.087000 0.620000 0.070000 ... 0.120000 0.070000 1.850000 0.170000 6.310000 1.200000 0.390000 2.509190 1.291619 0.299190
0.01 6.530115 2.491309 2.325367 1.055557 0.011908 0.051758 0.000412 0.097408 0.351027 0.075957 ... 0.051758 0.075957 3.176736 0.011908 6.530115 1.055557 0.534443 2.669381 1.191352 0.239267
0.02 6.481760 2.428287 2.355642 1.010053 0.012240 0.039849 0.000327 0.097234 0.341946 0.062268 ... 0.039849 0.062268 3.864468 0.012240 6.481760 1.010053 0.579947 2.633713 1.214334 0.251953
0.03 6.419771 2.357805 2.326751 1.033128 0.013500 0.040591 0.000326 0.099157 0.328112 0.062661 ... 0.040591 0.062661 4.355273 0.013500 6.419771 1.033128 0.556872 2.588384 1.243002 0.268613
0.04 6.465266 2.295451 2.288924 1.076765 0.015151 0.043923 0.000361 0.098315 0.323283 0.068018 ... 0.043923 0.068018 4.792030 0.015151 6.465266 1.076765 0.513235 2.621609 1.222048 0.256343
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
0.96 6.470716 1.124734 0.799562 1.550632 0.195576 0.049470 0.000391 0.094281 0.127878 0.084961 ... 0.049470 0.084961 9.734328 0.195576 6.470716 1.550632 0.039368 2.625605 1.219506 0.254889
0.97 6.463801 1.120309 0.798489 1.550554 0.195101 0.049284 0.000388 0.094464 0.127152 0.084495 ... 0.049284 0.084495 9.698833 0.195101 6.463801 1.550554 0.039446 2.620535 1.222730 0.256734
0.98 6.457177 1.116112 0.797470 1.550473 0.194615 0.049106 0.000385 0.094640 0.126464 0.084049 ... 0.049106 0.084049 9.663914 0.194615 6.457177 1.550473 0.039527 2.615684 1.225809 0.258507
0.99 6.450834 1.112132 0.796503 1.550390 0.194118 0.048936 0.000383 0.094809 0.125811 0.083622 ... 0.048936 0.083622 9.629595 0.194118 6.450834 1.550390 0.039610 2.611044 1.228747 0.260209
1.00 6.444763 1.108357 0.795584 1.550304 0.193613 0.048772 0.000380 0.094971 0.125191 0.083214 ... 0.048772 0.083214 9.595898 0.193613 6.444763 1.550304 0.039696 2.606606 1.231551 0.261843

101 rows × 34 columns

[8]:

df = run_time_course()


so lets plot just the amounts we got:

[9]:

amount_columns = list(df.columns)
amount_columns = [name for name in amount_columns if 'amount(' in name]

[10]:

df[amount_columns].plot();


of course the added global parameters can be easily removed:

[11]:

remove_amount_expressions()


and now we can plot the concentrations:

[14]:

run_time_course(use_sbml_id=True).plot();

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