6.1.1.1.1.4. pytfa.analysis.variability

Variability analysis

6.1.1.1.1.4.1. Module Contents

6.1.1.1.1.4.1.1. Functions

find_bidirectional_reactions(va, tolerance=1e-08)

Returns the ids of reactions that can both carry net flux in the forward or

find_directionality_profiles(tmodel, bidirectional, max_iter=10000.0, solver='optlang-glpk')

Takes a ThermoModel and performs enumeration of the directionality profiles

_bool2str(bool_list)

turns a list of booleans into a string

_variability_analysis_element(tmodel, var, sense)

variability_analysis(tmodel, kind='reactions', proc_num=BEST_THREAD_RATIO)

Performs variability analysis, gicven a variable type

parallel_variability_analysis(tmodel, kind='reactions', proc_num=BEST_THREAD_RATIO)

WIP.

calculate_dissipation(tmodel, solution=None)

6.1.1.1.1.4.1.2. Attributes

CPU_COUNT

BEST_THREAD_RATIO

pytfa.CPU_COUNT
pytfa.BEST_THREAD_RATIO
pytfa.find_bidirectional_reactions(va, tolerance=1e-08)

Returns the ids of reactions that can both carry net flux in the forward or backward direction.

Parameters

va

A variability analysis, pandas Dataframe like so:

maximum minimum

6PGLter -8.330667e-04 -8.330667e-04 ABUTt2r 0.000000e+00 0.000000e+00 ACALDt 0.000000e+00 0.000000e+00

Returns

pytfa.find_directionality_profiles(tmodel, bidirectional, max_iter=10000.0, solver='optlang-glpk')

Takes a ThermoModel and performs enumeration of the directionality profiles

Parameters
  • tmodel

  • max_iter

Returns

pytfa._bool2str(bool_list)

turns a list of booleans into a string

Parameters

bool_list – ex: ‘[False True False False True]’

Returns

‘01001’

pytfa._variability_analysis_element(tmodel, var, sense)
pytfa.variability_analysis(tmodel, kind='reactions', proc_num=BEST_THREAD_RATIO)

Performs variability analysis, gicven a variable type

Parameters
  • tmodel

  • kind

  • proc_num

Returns

pytfa.parallel_variability_analysis(tmodel, kind='reactions', proc_num=BEST_THREAD_RATIO)

WIP.

Parameters
  • tmodel

  • kind

  • proc_num

Returns

pytfa.calculate_dissipation(tmodel, solution=None)