crnt4sbml.CRNT

class crnt4sbml.CRNT(path)[source]

Class for managing CRNT methods.

__init__(path)[source]

Initialization of CRNT class.

Parameters:path (string) – String representation of the path to the XML file.

Example

>>> import crnt4sbml
>>> network = crnt4sbml.CRNT("path/to/sbml_file.xml")

Methods

__init__(path) Initialization of CRNT class.
basic_report() Prints out basic CRNT properties of the network.
get_physiological_range([for_what]) Obtains physiological ranges.
get_low_deficiency_approach() Initializes and creates an object for the class LowDeficiencyApproach for the CRNT object constructed.
get_mass_conservation_approach() Initializes and creates an object for the class MassConservationApproach for the CRNT object constructed.
get_semi_diffusive_approach() Initializes and creates an object for the class SemiDiffusiveApproach for the CRNT object constructed.
get_general_approach() Initializes and creates an object for the class GeneralApproach for the CRNT object constructed.
get_advanced_deficiency_approach() Placeholder for Advanced Deficiency Approach.
get_c_graph() Allows access to the class C-graph for the constructed CRNT object.
print_c_graph() Prints the reactions and reaction labels for the network.
print_biological_reaction_types() Prints the reactions, reaction labels, and biological reaction type for the network.
plot_c_graph() Generates a matplotlib plot for the C-graph of the network using the networkx.draw function with circular and Kamada Kawai layout.
plot_save_c_graph() Saves the matplotlib plot for the C-graph of the network using the networkx.draw function with circular and Kamada Kawai layout to the file network_cgraph.png
get_network_graphml() Writes the NetworkX Digraph to the file network.graphml.
basic_report()[source]

Prints out basic CRNT properties of the network. Fig1Ci.xml for the provided example.

Example

>>> import crnt4sbml
>>> network = crnt4sbml.CRNT("path/to/Fig1Ci.xml")
>>> network.basic_report()
    Number of species: 7
    Number of complexes: 9
    Number of reactions: 9
    Network deficiency: 2
get_advanced_deficiency_approach()[source]

Placeholder for Advanced Deficiency Approach. Future version of crnt4sbml will include the implementation of the Higher Deficiency Algorithm.

get_c_graph()[source]

Allows access to the class C-graph for the constructed CRNT object. Returns C-graph object for the provided CRNT object.

Example

>>> import crnt4sbml
>>> network = crnt4sbml.CRNT("path/to/sbml_file.xml")
>>> c_graph = network.get_c_graph()
get_general_approach()[source]

Initializes and creates an object for the class GeneralApproach for the CRNT object constructed.

Example

>>> import crnt4sbml
>>> network = crnt4sbml.CRNT("path/to/sbml_file.xml")
>>> GA = network.get_general_approach()
get_low_deficiency_approach()[source]

Initializes and creates an object for the class LowDeficiencyApproach for the CRNT object constructed.

Example

>>> import crnt4sbml
>>> network = crnt4sbml.CRNT("path/to/sbml_file.xml")
>>> approach = network.get_low_deficiency_approach()
get_mass_conservation_approach()[source]

Initializes and creates an object for the class MassConservationApproach for the CRNT object constructed. Fig1Ci.xml for the provided example.

Example

>>> import crnt4sbml
>>> network = crnt4sbml.CRNT("path/to/Fig1Ci.xml")
>>> approach = network.get_mass_conservation_approach()
    Creating Equilibrium Manifold ...
    Elapsed time for creating Equilibrium Manifold: 2.060944
get_network_graphml()[source]

Writes the NetworkX Digraph to the file network.graphml. Note that this generation only includes the names of the nodes, edges, and edge reaction names, it does not include other list attributes of the nodes and edges.

Example

>>> import crnt4sbml
>>> network = crnt4sbml.CRNT("path/to/sbml_file.xml")
>>> network.get_network_graphml()
static get_physiological_range(for_what=None)[source]

Obtains physiological ranges.

Parameters:for_what (string) – Accepted values: “concentration”, “complex formation”, “complex dissociation”, “catalysis”, or “flux”
Returns:
  • concentration (tuple) – (5e-1,5e5) pM
  • complex formation (tuple) – (1e-8,1e-4) pM^-1s^-1
  • complex dissociation (tuple) – (1e-5,1e-3) s^-1
  • catalysis (tuple) – (1e-3,1) s^-1
  • flux (tuple) – (0, 55) M s^-1

Example

>>> import crnt4sbml
>>> network = crnt4sbml.CRNT("path/to/sbml_file.xml")
>>> network.get_physiological_range("concentration")
get_semi_diffusive_approach()[source]

Initializes and creates an object for the class SemiDiffusiveApproach for the CRNT object constructed.

Example

>>> import crnt4sbml
>>> network = crnt4sbml.CRNT("path/to/semi_diffusive_sbml_file.xml")
>>> approach = network.get_semi_diffusive_approach()
plot_c_graph()[source]

Generates a matplotlib plot for the C-graph of the network using the networkx.draw function with circular and Kamada Kawai layout.

Example

>>> import crnt4sbml
>>> network = crnt4sbml.CRNT("path/to/sbml_file.xml")
>>> network.plot_c_graph()
plot_save_c_graph()[source]

Saves the matplotlib plot for the C-graph of the network using the networkx.draw function with circular and Kamada Kawai layout to the file network_cgraph.png

Example

>>> import crnt4sbml
>>> network = crnt4sbml.CRNT("path/to/sbml_file.xml")
>>> network.plot_save_c_graph()
print_biological_reaction_types()[source]

Prints the reactions, reaction labels, and biological reaction type for the network. Fig1Ci.xml for the provided example.

Example

>>> import crnt4sbml
>>> network = crnt4sbml.CRNT("path/to/Fig1Ci.xml")
>>> network.print_biological_reaction_types()
    Reaction graph of the form
    reaction -- reaction label -- biological reaction type:
    s1+s2 -> s3  --  re1 -- complex formation
    s3 -> s1+s2  --  re1r -- complex dissociation
    s3 -> s6+s2  --  re2 -- catalysis
    s6+s7 -> s16  --  re3 -- complex formation
    s16 -> s6+s7  --  re3r -- complex dissociation
    s16 -> s7+s1  --  re4 -- catalysis
    s1+s6 -> s15  --  re5 -- complex formation
    s15 -> s1+s6  --  re5r -- complex dissociation
    s15 -> 2*s6  --  re6 -- catalysis
print_c_graph()[source]

Prints the reactions and reaction labels for the network. Fig1Ci.xml for the provided example.

Example

>>> import crnt4sbml
>>> network = crnt4sbml.CRNT("path/to/Fig1Ci.xml")
>>> network.print_c_graph()
    Reaction graph of the form
    reaction -- reaction label:
    s1+s2 -> s3  --  re1
    s3 -> s1+s2  --  re1r
    s3 -> s6+s2  --  re2
    s6+s7 -> s16  --  re3
    s16 -> s6+s7  --  re3r
    s16 -> s7+s1  --  re4
    s1+s6 -> s15  --  re5
    s15 -> s1+s6  --  re5r
    s15 -> 2*s6  --  re6