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:

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:

load_biomodel(64);

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

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

	sbml_id	initial_concentration
name
High energy phosphates	P	6.310000
Glucose 6 Phosphate	G6P	2.450000
Triose-phosphate	TRIO	0.960000
NAD	NAD	1.200000
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
NADH	NADH	0.390000
ATP concentration	ATP	2.509190
ADP concentration	ADP	1.291619
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:

get_species(sbml_id='ATP')

	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:

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:

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

	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(NAD)	1.200000	[NAD] * Compartments[cytosol].Volume
amount(NADH)	0.390000	[NADH] * Compartments[cytosol].Volume
amount(ATP)	2.509190	[ATP concentration] * Compartments[cytosol].Vo...
amount(ADP)	1.291619	[ADP 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.

run_time_course(use_sbml_id=True)

	P	G6P	TRIO	NAD	ACE	P2G	BPG	GLCi	F6P	PEP	...	Values[amount(P2G)]	Values[amount(PEP)]	Values[amount(PYR)]	Values[amount(ACE)]	Values[amount(P)]	Values[amount(NAD)]	Values[amount(NADH)]	Values[amount(ATP)]	Values[amount(ADP)]	Values[amount(AMP)]
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

df = run_time_course()

so lets plot just the amounts we got:

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

df[amount_columns].plot();

of course the added global parameters can be easily removed:

remove_amount_expressions()

and now we can plot the concentrations:

run_time_course(use_sbml_id=True).plot();