psctb.analyse._symca package

Submodules

psctb.analyse._symca._symca module

class psctb.analyse._symca._symca.Symca(mod, auto_load=False, internal_fixed=False, ignore_steady_state=False, keep_zero_elasticities=True)

Bases: object

A class that performs Symbolic Metabolic Control Analysis.

This class takes pysces model as an input and performs symbolic inversion of the E matrix using Sympy by calculating the determinant and adjoint matrices of this E matrix.

Parameters:

mod : PysMod

The pysces model on which to perform symbolic control analysis.

auto_load : boolean

If true

Returns

——

Attributes:

ematrix

esL

es_matrix

fluxes

fluxes_dependent

fluxes_independent

kmatrix

lmatrix

nmatrix

num_ind_fluxes

num_ind_species

scaled_k

scaled_k0

scaled_l

scaled_l0

species

species_dependent

species_independent

subs_fluxes

Methods

do_symca
load_session
path_to
save_results
save_session

do_symca(internal_fixed=None, auto_save_load=False)

ematrix

esL

es_matrix

fluxes

fluxes_dependent

fluxes_independent

kmatrix

lmatrix

load_session(file_name=None)

nmatrix

num_ind_fluxes

num_ind_species

path_to(path)

save_results(file_name=None, separator=', ', fmt='%.9f')

save_session(file_name=None)

scaled_k

scaled_k0

scaled_l

scaled_l0

species

species_dependent

species_independent

subs_fluxes

psctb.analyse._symca.ccobjects module

class psctb.analyse._symca.ccobjects.CCBase(mod, name, expression, ltxe)

Bases: object

The base object for the control coefficients and control patterns

Attributes:

latex_expression

latex_name

value

The value property.

latex_expression

latex_name

value

The value property. Calls self._calc_value() when self._value is None and returns self._value

class psctb.analyse._symca.ccobjects.CCoef(mod, name, expression, denominator, ltxe)

Bases: psctb.analyse._symca.ccobjects.CCBase

The object the stores control coefficients. Inherits from CCBase

Attributes:

abs_value

latex_expression

latex_expression_full

latex_name

latex_numerator

value

The value property.

Methods

do_par_scan
highlight_patterns

abs_value

do_par_scan(parameter, scan_range, scan_type='percentage', init_return=True, par_scan=False, par_engine='multiproc', force_legacy=False)

highlight_patterns(width=None, height=None, show_dummy_sinks=False, show_external_modifier_links=False, pos_dic=None)

latex_expression

latex_expression_full

latex_name

latex_numerator

class psctb.analyse._symca.ccobjects.CPattern(mod, name, expression, denominator, parent, ltxe)

Bases: psctb.analyse._symca.ccobjects.CCBase

docstring for CPattern

Attributes:

latex_expression

latex_expression_full

latex_name

latex_numerator

percentage

value

The value property.

latex_expression

latex_expression_full

latex_name

latex_numerator

percentage

psctb.analyse._symca.ccobjects.cctype(obj)

psctb.analyse._symca.ccobjects.get_state(mod, do_state=False)

psctb.analyse._symca.symca_toolbox module

class psctb.analyse._symca.symca_toolbox.SymcaToolBox

Bases: object

The class with the functions used to populate SymcaData. The project is structured in this way to abstract the ‘work’ needed to build the various matrices away from the SymcaData class.

Methods

adjugate_matrix(matrix)	Returns the adjugate matrix which is the transpose of the cofactor matrix.
build_cc_matrix(j, jind, sind, jdep, sdep)	Produces the matrices j_cci, j_ccd, s_cci and s_ccd which holds the symbols for the independent and dependent flux control coefficients and the independent and dependent species control coefficients respectively
det_bareis(matrix)	Adapted from original det_bareis function in Sympy 0.7.3.
get_es_matrix(mod, nmatrix, fluxes, species)	Gets the esmatrix.
get_es_matrix_no_mca(mod, nmatrix, fluxes, …)	Gets the esmatrix.
get_fluxes_vector(mod)	Gets the dependent and independent fluxes (in the correct order)
get_nmatrix(mod)	Returns a sympy matrix made from the N matrix in a Pysces model where the elements are in the same order as they appear in the k and l matrices in pysces.
get_species_vector(mod)	Returns a vector (sympy matrix) with the species in the correct order
invert(matrix, path_to)	Returns the numerators of the inverted martix separately from the common denominator (the determinant of the matrix)
maxima_factor(expression, path_to)	This function is equivalent to the sympy.cancel() function but uses maxima instead
scale_matrix(all_elements, mat, inds)	Scales the k or l matrix.
simplify_matrix(matrix)	Replaces floats with ints and puts elements with fractions on a single demoninator.
solve_dep(cc_i_num, scaledk0, scaledl0, …)	Calculates the dependent control matrices from the independent control matrix CC_i_solution
substitute_fluxes(all_fluxes, kmatrix)	Substitutes equivalent fluxes in the kmatrix (e.i.

build_inner_dict
build_outer_dict
fix_expressions
generic_populate
get_fix_denom
get_fix_denom_jannie
get_num_ind_fluxes
get_num_ind_species
make_CC_dot_dict
make_inner_dict
make_internals_dict
populate_with_fake_elasticities
populate_with_fake_fluxes
populate_with_fake_ss_concentrations
spawn_cc_objects

static adjugate_matrix(matrix)

Returns the adjugate matrix which is the transpose of the cofactor matrix.

Contains code adapted from sympy. Specifically:

cofactorMatrix() minorEntry() minorMatrix() cofactor()

static build_cc_matrix(j, jind, sind, jdep, sdep)

Produces the matrices j_cci, j_ccd, s_cci and s_ccd which holds the symbols for the independent and dependent flux control coefficients and the independent and dependent species control coefficients respectively

static build_inner_dict(cc_object)

static build_outer_dict(symca_object)

static det_bareis(matrix)

Adapted from original det_bareis function in Sympy 0.7.3. cancel() and expand() are removed from function to speed up calculations. Maxima will be used to simplify the result

Original docstring below:

Compute matrix determinant using Bareis’ fraction-free algorithm which is an extension of the well known Gaussian elimination method. This approach is best suited for dense symbolic matrices and will result in a determinant with minimal number of fractions. It means that less term rewriting is needed on resulting formulae.

static fix_expressions(cc_num, common_denom_expr, lmatrix, species_independent, species_dependent)

static generic_populate(mod, function, value=1)

static get_es_matrix(mod, nmatrix, fluxes, species)

Gets the esmatrix.

Goes down the columns of the nmatrix (which holds the fluxes) to get the rows of the esmatrix.

Nested loop goes down the rows of the nmatrix (which holds the species) to get the columns of the esmatrix

so the format is

ecReationN0_M0 ecReationN0_M1 ecReationN0_M2 ecReationN1_M0 ecReationN1_M1 ecReationN1_M2 ecReationN2_M0 ecReationN2_M1 ecReationN2_M2

static get_es_matrix_no_mca(mod, nmatrix, fluxes, species)

Gets the esmatrix.

Goes down the columns of the nmatrix (which holds the fluxes) to get the rows of the esmatrix.

Nested loop goes down the rows of the nmatrix (which holds the species) to get the columns of the esmatrix

so the format is

ecReationN0_M0 ecReationN0_M1 ecReationN0_M2 ecReationN1_M0 ecReationN1_M1 ecReationN1_M2 ecReationN2_M0 ecReationN2_M1 ecReationN2_M2

static get_fix_denom(lmatrix, species_independent, species_dependent)

get_fix_denom_jannie(species_independent, species_dependent)

static get_fluxes_vector(mod)

Gets the dependent and independent fluxes (in the correct order)

static get_nmatrix(mod)

Returns a sympy matrix made from the N matrix in a Pysces model where the elements are in the same order as they appear in the k and l matrices in pysces.

We need this to make calculations easier later on.

static get_num_ind_fluxes(mod)

static get_num_ind_species(mod)

static get_species_vector(mod)

Returns a vector (sympy matrix) with the species in the correct order

static invert(matrix, path_to)

Returns the numerators of the inverted martix separately from the common denominator (the determinant of the matrix)

static make_CC_dot_dict(cc_objects)

static make_inner_dict(cc_container, cc_container_name)

static make_internals_dict(cc_sol, cc_names, common_denom_expr, path_to)

static maxima_factor(expression, path_to)

This function is equivalent to the sympy.cancel() function but uses maxima instead

static populate_with_fake_elasticities(mod)

static populate_with_fake_fluxes(mod)

static populate_with_fake_ss_concentrations(mod)

static scale_matrix(all_elements, mat, inds)

Scales the k or l matrix.

The procedure is the same for each matrix:

(D^x)^(-1) * y * D^(x_i)

Inverse diagonal The matrix to be The diagonal of of the x where scaled. i.e. the the independent x x is either the k or l matrix where x is the species or the species or the fluxes fluxes

static simplify_matrix(matrix)

Replaces floats with ints and puts elements with fractions on a single demoninator.

static solve_dep(cc_i_num, scaledk0, scaledl0, num_ind_fluxes, path_to)

Calculates the dependent control matrices from the independent control matrix CC_i_solution

static spawn_cc_objects(mod, cc_names, cc_sol, common_denom_exp, ltxe)

static substitute_fluxes(all_fluxes, kmatrix)

Substitutes equivalent fluxes in the kmatrix (e.i. dependent fluxes with independent fluxes or otherwise equal fluxes)

Module contents