CBMPy Module Reference

CBMPy: CBCommon module

PySCeS Constraint Based Modelling (http://cbmpy.sourceforge.net) Copyright (C) 2009-2015 Brett G. Olivier, VU University Amsterdam, Amsterdam, The Netherlands

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see <http://www.gnu.org/licenses/>

Author: Brett G. Olivier Contact email: bgoli@users.sourceforge.net Last edit: $Author: bgoli $ ($Id: CBCommon.py 346 2015-08-03 14:09:32Z bgoli $)

class cbmpy.CBCommon.ComboGen[source]

Generate sets of unique combinations

cbmpy.CBCommon.checkChemFormula(cf, quiet=False)[source]

Checks whether a string conforms to a Chemical Formula C3Br5 etc, returns True/False. Please see the SBML Level 3 specification and http://wikipedia.org/wiki/Hill_system for more information.

  • cf a string that contains a formula to check
  • quiet [default=False] do not print error messages
cbmpy.CBCommon.checkId(s)[source]

Checks the validity of the string to see if it conforms to a C variable. Returns true/false

  • s a string
cbmpy.CBCommon.extractGeneIdsFromString(g)[source]

Extract and return a list of gene names from a gene association string formulation

  • g a COBRA style gene association string
cbmpy.CBCommon.fixId(s, replace=None)[source]

Checks a string (Sid) to see if it is a valid C style variable. first letter must be an underscore or letter, the rest should be alphanumeric or underscore.

  • s the string to test
  • replace [None] default is to leave out offensive character, otherwise replace with this one
cbmpy.CBCommon.parseGeneAssociation(gs)[source]

Parse a COBRA style gene association into a nested list.

  • gs a string containing a gene association
cbmpy.CBCommon.processSpeciesChargeChemFormulaAnnot(s, getFromName=False, overwriteChemFormula=False, overwriteCharge=False)[source]

Disambiguate the chemical formula from either the Notes or the overloaded name

  • s a species object
  • getFromName [default=False] whether to try strip the chemical formula from the name (old COBRA style)
  • overwriteChemFormula [default=False]
  • overwriteCharge [default=False]

CBMPy: CBConfig module

PySCeS Constraint Based Modelling (http://cbmpy.sourceforge.net) Copyright (C) 2009-2015 Brett G. Olivier, VU University Amsterdam, Amsterdam, The Netherlands

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see <http://www.gnu.org/licenses/>

Author: Brett G. Olivier Contact email: bgoli@users.sourceforge.net Last edit: $Author: bgoli $ ($Id: CBConfig.py 445 2016-04-18 21:44:38Z bgoli $)

cbmpy.CBConfig.current_version()[source]

Return the current CBMPy version as a string

cbmpy.CBConfig.current_version_tuple()[source]

Return the current CBMPy version as a tuple (x, y, z)

CBMPy: CBCPLEX module

PySCeS Constraint Based Modelling (http://cbmpy.sourceforge.net) Copyright (C) 2009-2015 Brett G. Olivier, VU University Amsterdam, Amsterdam, The Netherlands

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see <http://www.gnu.org/licenses/>

Author: Brett G. Olivier Contact email: bgoli@users.sourceforge.net Last edit: $Author: bgoli $ ($Id: CBCPLEX.py 390 2015-10-05 13:44:45Z bgoli $)

cbmpy.CBCPLEX.cplx_FluxVariabilityAnalysis(fba, selected_reactions=None, pre_opt=True, tol=None, objF2constr=True, rhs_sense='lower', optPercentage=100.0, work_dir=None, quiet=True, debug=False, oldlpgen=False, markupmodel=True, default_on_fail=False, roundoff_span=10, method='o')[source]

Perform a flux variability analysis on an fba model:

  • fba an FBA model object
  • selected reactions [default=None] means use all reactions otherwise use the reactions listed here
  • pre_opt [default=True] attempt to presolve the FBA and report its results in the ouput, if this is disabled and objF2constr is True then the rid/value of the current active objective is used
  • tol [default=None] do not floor/ceiling the objective function constraint, otherwise round of to tol
  • rhs_sense [default=’lower’] means objC >= objVal the inequality to use for the objective constraint can also be upper or equal
  • optPercentage [default=100.0] means the percentage optimal value to use for the RHS of the objective constraint: optimal_value*(optPercentage/100.0)
  • work_dir [default=None] the FVA working directory for temporary files default = cwd+fva
  • debug [default=False] if True write out all the intermediate FVA LP’s into work_dir
  • quiet [default=False] if enabled, supress CPLEX output
  • objF2constr [default=True] add the model objective function as a constraint using rhs_sense etc. If this is True with pre_opt=False then the id/value of the active objective is used to form the constraint
  • markupmodel [default=True] add the values returned by the fva to the reaction.fva_min and reaction.fva_max
  • default_on_fail [default=False] if pre_opt is enabled replace a failed minimum/maximum with the solution value
  • roundoff_span [default=10] number of digits is round off (not individual min/max values)
  • method [default=’o’] choose the CPLEX method to use for solution, default is automatic. See CPLEX reference manual for details
    • ‘o’: auto
    • ‘p’: primal
    • ‘d’: dual
    • ‘b’: barrier (no crossover)
    • ‘h’: barrier
    • ‘s’: sifting
    • ‘c’: concurrent

Returns an array with columns: Reaction, Reduced Costs, Variability Min, Variability Max, abs(Max-Min), MinStatus, MaxStatus and a list containing the row names.

cbmpy.CBCPLEX.cplx_MinimizeNumActiveFluxes(fba, selected_reactions=None, pre_opt=True, tol=None, objF2constr=True, rhs_sense='lower', optPercentage=100.0, work_dir=None, quiet=False, debug=False, objective_coefficients=None, return_lp_obj=False, populate=None, oldlpgen=False)[source]

Minimize the sum of active fluxes, updates the model with the values of the solution and returns the value of the MILP objective function (not the model objective function which remains unchanged). If population mode is activated output is as described below:

Min: sum(Bi)
Bi = 0 -> Ci Ji = 0
Such that:
NJi = 0 Jbio = opt
where:
Binary Bi

Arguments:

  • fba an FBA model object
  • selected reactions [default=None] means use all reactions otherwise use the reactions listed here
  • pre_opt [default=True] attempt to presolve the FBA and report its results in the ouput, if this is diabled and objF2constr is True then the vid/value of the current active objective is used
  • tol [default=None] do not floor/ceiling the objective function constraint, otherwise round of to tol
  • rhs_sense [default=’lower’] means objC >= objVal the inequality to use for the objective constraint can also be upper or equal Note this does not necessarily mean the upper or lower bound, although practically it will. If in doubt use equal
  • optPercentage [default=100.0] means the percentage optimal value to use for the RHS of the objective constraint: optimal_value * (optPercentage/100.0)
  • work_dir [default=None] the MSAF working directory for temporary files default = cwd+fva
  • debug [default=False] if True write out all the intermediate MSAF LP’s into work_dir
  • quiet [default=False] if enabled supress CPLEX output
  • objF2constr [default=True] add the model objective function as a constraint using rhs_sense etc. If this is True with pre_opt=False then the id/value of the active objective is used to form the constraint
  • objective_coefficients [default=None] a dictionary of (reaction_id : float) pairs that provide the are introduced as objective coefficients to the absolute flux value. Note that the default value of the coefficient (non-specified) is +1.
  • return_lp_obj [default=False] off by default when enabled it returns the CPLEX LP object
  • populate [default=None] enable search algorithm to find multiple (sub)optimal solutions. Set with a tuple of (RELGAP=0.0, POPULATE_LIMIT=20, TIME_LIMIT=300) suggested values only. - RELGAP [default=0.0] relative gap to optimal solution - POPULATE_LIMIT [default=20] terminate when so many solutions have been found - TIME_LIMIT [default=300] terminate search after so many seconds important with higher values of POPULATION_LIMIT
  • with_reduced_costs [default=’uncsaled’] can be ‘scaled’, ‘unscaled’ or anything else which is None

With outputs:

  • mincnt the objective function value OR
  • mincnt, cpx the objective function and cplex model OR
  • populate_data, mincnt a population data set OR
  • populate_data, mincnt, cpx both the cps object and population data set

depending on selected flags.

cbmpy.CBCPLEX.cplx_MinimizeSumOfAbsFluxes(fba, selected_reactions=None, pre_opt=True, tol=None, objF2constr=True, rhs_sense='lower', optPercentage=100.0, work_dir=None, quiet=False, debug=False, objective_coefficients=None, return_lp_obj=False, oldlpgen=False, with_reduced_costs=None, method='o')[source]

Minimize the sum of absolute fluxes sum(abs(J1) + abs(J2) + abs(J3) ... abs(Jn)) by adding two constraints per flux and a variable representing the absolute value:

Min: Ci abs_Ji
Ji - abs_Ji <= 0 Ji + abs_Ji >= 0
Such that:
NJi = 0 Jopt = opt

returns the value of the flux minimization objective function (not the model objective function which remains unchanged from)

Arguments:

  • fba an FBA model object
  • selected reactions [default=None] means use all reactions otherwise use the reactions listed here
  • pre_opt [default=True] attempt to presolve the FBA and report its results in the ouput, if this is disabled and objF2constr is True then the vid/value of the current active objective is used
  • tol [default=None] do not floor/ceiling the objective function constraint, otherwise round of to tol
  • rhs_sense [default=’lower’] means objC >= objVal the inequality to use for the objective constraint can also be upper or equal
  • optPercentage [default=100.0] means the percentage optimal value to use for the RHS of the objective constraint: optimal_value*(optPercentage/100.0)
  • work_dir [default=None] the MSAF working directory for temporary files default = cwd+fva
  • debug [default=False] if True write out all the intermediate MSAF LP’s into work_dir
  • quiet [default=False] if enabled supress CPLEX output
  • objF2constr [default=True] add the model objective function as a constraint using rhs_sense etc. If this is True with pre_opt=False then the id/value of the active objective is used to form the constraint
  • objective_coefficients [default=None] a dictionary of (reaction_id : float) pairs that provide the are introduced as objective coefficients to the absolute flux value. Note that the default value of the coefficient (non-specified) is +1.
  • return_lp_obj [default=False] off by default when enabled it returns the CPLEX LP object
  • with_reduced_costs [default=None] if not None should be ‘scaled’ or ‘unscaled’
  • method [default=’o’] choose the CPLEX method to use for solution, default is automatic. See CPLEX reference manual for details
    • ‘o’: auto
    • ‘p’: primal
    • ‘d’: dual
    • ‘b’: barrier (no crossover)
    • ‘h’: barrier
    • ‘s’: sifting
    • ‘c’: concurrent

With outputs:

  • fba an update instance of a CBModel. Note that the FBA model objective function value is the original value set as a constraint
cbmpy.CBCPLEX.cplx_MultiFluxVariabilityAnalysis(lp, selected_reactions=None, tol=1e-10, rhs_sense='lower', optPercentage=100.0, work_dir=None, debug=False)[source]

Perform a flux variability analysis on a multistate LP

  • lp a multistate LP
  • selected reactions [default=None] means use all reactions otherwise use the reactions listed here
  • pre_opt [default=True] attempt to presolve the FBA and report its results in the ouput
  • tol [default=1e-10] do floor/ceiling the objective function constraint, otherwise floor/ceil to tol
  • rhs_sense [default=’lower’] means objC >= objVal the inequality to use for the objective constraint can also be upper or equal
  • optPercentage [default=100.0] means the percentage optimal value to use for the RHS of the objective constraint: optimal_value*(optPercentage/100.0)
  • work_dir [default=None] the FVA working directory for temporary files default = cwd+fva
  • debug [default=False] if True write out all the intermediate FVA LP’s into work_dir
  • bypass [default=False] bypass everything and only run the min/max on lp

and returns an array with columns:

Reaction, Reduced Costs, Variability Min, Variability Max, abs(Max-Min), MinStatus, MaxStatus

and a list containing the row names.

cbmpy.CBCPLEX.cplx_SolveMILP(c, auto_mipgap=False)[source]

Solve and MILP

  • auto_mipgap auto decrease mipgap until mipgap == absmipgap
cbmpy.CBCPLEX.cplx_WriteFVAtoCSV(pid, fva, names, Dir=None, fbaObj=None)[source]

Takes the resuls of a FluxVariabilityAnalysis method and writes it to a nice csv file. Note this method has been refactored to CBWrite.WriteFVAtoCSV().

  • pid filename_base for the CSV output
  • fva FluxVariabilityAnalysis() OUTPUT_ARRAY
  • names FluxVariabilityAnalysis() OUTPUT_NAMES
  • Dir [default=None] if set the output directory for the csv files
  • fbaObj [default=None] if supplied adds extra model information into the output tables
cbmpy.CBCPLEX.cplx_analyzeModel(f, lpFname=None, return_lp_obj=False, with_reduced_costs='unscaled', with_sensitivity=False, del_intermediate=False, build_n=True, quiet=False, oldlpgen=False, method='o')[source]

Optimize a model and add the result of the optimization to the model object (e.g. reaction.value, objectiveFunction.value). The stoichiometric matrix is automatically generated. This is a common function available in all solver interfaces. By default returns the objective function value

  • f an instantiated PySCeSCBM model object
  • lpFname [default=None] the name of the intermediate LP file. If not specified no LP file is produced
  • return_lp_obj [default=False] off by default when enabled it returns the CPLEX LP object
  • with_reduced_costs [default=’unscaled’] calculate and add reduced cost information to mode this can be: ‘unscaled’ or ‘scaled’ or anything else which is interpreted as ‘None’. Scaled means s_rcost = (r.reduced_cost*rval)/obj_value
  • with_sensitivity [default=False] add solution sensitivity information (not yet implemented)
  • del_intermediate [default=False] redundant except if output file is produced and deleted (not useful)
  • build_n [default=True] generate stoichiometry from the reaction network (reactions/reagents/species)
  • quiet [default=False] suppress cplex output
  • method [default=’o’] choose the CPLEX method to use for solution, default is automatic. See CPLEX reference manual for details
    • ‘o’: auto
    • ‘p’: primal
    • ‘d’: dual
    • ‘b’: barrier (no crossover)
    • ‘h’: barrier
    • ‘s’: sifting
    • ‘c’: concurrent
cbmpy.CBCPLEX.cplx_constructLPfromFBA(fba, fname=None)[source]

Create a CPLEX LP in memory. - fba an FBA object - fname optional filename if defined writes out the constructed lp

cbmpy.CBCPLEX.cplx_fixConSense(operator)[source]

Fixes the sense of inequality operators, returns corrected sense symbol

  • operator the operator to check
cbmpy.CBCPLEX.cplx_func_GetCPXandPresolve(fba, pre_opt, objF2constr, quiet=False, oldlpgen=False, with_reduced_costs='unscaled', method='o')[source]

This is a utility function that does a presolve for FVA, MSAF etc. Generates properly formatted empty objects if pre_opt == False

  • pre_opt a boolean
  • fba a CBModel object
  • objF2constr add objective function as constraint
  • quiet [default=False] supress cplex output
  • with_reduced_costs [default=’unscaled’] can be ‘scaled’ or ‘unscaled’
  • method [default=’o’] choose the CPLEX method to use for solution, default is automatic. See CPLEX reference manual for details
    • ‘o’: auto
    • ‘p’: primal
    • ‘d’: dual
    • ‘b’: barrier (no crossover)
    • ‘h’: barrier
    • ‘s’: sifting
    • ‘c’: concurrent

Returns: pre_sol, pre_oid, pre_oval, OPTIMAL_PRESOLUTION, REDUCED_COSTS

cbmpy.CBCPLEX.cplx_func_SetObjectiveFunctionAsConstraint(cpx, rhs_sense, oval, tol, optPercentage)[source]
Take the objective function and “optimum” value and add it as a constraint
  • cpx a cplex object
  • oval the objective value
  • tol [default=None] do not floor/ceiling the objective function constraint, otherwise round of to tol
  • rhs_sense [default=’lower’] means objC >= objVal the inequality to use for the objective constraint can also be upper or equal
  • optPercentage [default=100.0] means the percentage optimal value to use for the RHS of the objective constraint: optimal_value*(optPercentage/100.0)
cbmpy.CBCPLEX.cplx_getCPLEXModelFromLP(lptFile, Dir=None)[source]

Load a LPT (CPLEX format) file and return a CPLX LP model

  • lptfile an CPLEX LP format file
  • Dir an optional directory
cbmpy.CBCPLEX.cplx_getDualValues(c)[source]

Get the get the dual values of the solution

  • c a CPLEX LP

Output is a dictionary of {name : value} pairs

cbmpy.CBCPLEX.cplx_getModelFromLP(lptFile, Dir=None)[source]

Load a LPT (CPLEX format) file and return a CPLX LP model

  • lptfile an CPLEX LP format file
  • Dir an optional directory
cbmpy.CBCPLEX.cplx_getModelFromObj(fba)[source]

Return a CPLEX object from a FBA model object (via LP file)

cbmpy.CBCPLEX.cplx_getOptimalSolution(c)[source]

From a CPLX model extract a tuple of solution, ObjFuncName and ObjFuncVal

cbmpy.CBCPLEX.cplx_getOptimalSolution2(c, names)[source]

From a CPLX model extract a tuple of solution, ObjFuncName and ObjFuncVal

cbmpy.CBCPLEX.cplx_getReducedCosts(c, scaled=False)[source]

Extract ReducedCosts from LP and return as a dictionary ‘Rid’ : reduced cost

  • c a cplex LP object
  • scaled scale the reduced cost by the optimal flux value
cbmpy.CBCPLEX.cplx_getSensitivities(c)[source]

Get the sensitivities of each constraint on the objective function with inpt

  • c a CPLEX LP

Output is a tuple of bound and objective sensitivities where the objective sensitivity is described in the CPLEX reference manual as:

... the objective sensitivity shows each variable, its reduced cost and the range over
which its objective function coefficient can vary without forcing a change
in the optimal basis. The current value of each objective coefficient is
also displayed for reference.

- *objective coefficient sensitivity* {flux : (reduced_cost, lower_obj_sensitivity, coeff_value, upper_obj_sensitivity)}
- *rhs sensitivity* {constraint : (low, value, high)}
- *bound sensitivity ranges* {flux : (lb_low, lb_high, ub_low, ub_high)}
cbmpy.CBCPLEX.cplx_getShadowPrices(c)[source]

Returns a dictionary of shadow prices containing ‘Rid’ : (lb, rhs, ub)

  • c a cplex LP object
cbmpy.CBCPLEX.cplx_getSolutionStatus(c)[source]

Returns one of:

  • LPS_OPT: solution is optimal;
  • LPS_FEAS: solution is feasible;
  • LPS_INFEAS: solution is infeasible;
  • LPS_NOFEAS: problem has no feasible solution;
  • LPS_UNBND: problem has unbounded solution;
  • LPS_UNDEF: solution is undefined.
  • LPS_NONE: no solution
cbmpy.CBCPLEX.cplx_runInputScan(fba, exDict, wDir, input_lb=-10.0, input_ub=0.0, writeHformat=False, rationalLPout=False)[source]

scans all inputs

cbmpy.CBCPLEX.cplx_setFBAsolutionToModel(fba, lp, with_reduced_costs='unscaled')[source]

Sets the FBA solution from a CPLEX solution to an FBA object

  • fba and fba object
  • lp a CPLEX LP object
  • with_reduced_costs [default=’unscaled’] calculate and add reduced cost information to mode this can be: ‘unscaled’ or ‘scaled’ or anything else which is interpreted as None. Scaled is: s_rcost = (r.reduced_cost*rval)/obj_value
cbmpy.CBCPLEX.cplx_setMIPGapTolerance(c, tol)[source]

Sets the the relative MIP gap tolerance

cbmpy.CBCPLEX.cplx_setObjective(c, pid, expr=None, sense='maximize', reset=True)[source]

Set a new objective function note that there is a major memory leak in c.variables.get_names() whch is used when reset=True. If this is a problem use cplx_setObjective2 which takes names as an input:

  • c a CPLEX LP object
  • pid the r_id of the flux to be optimized
  • expr a list of (coefficient, flux) pairs
  • sense ‘maximize’/’minimize’
  • reset [default=True] reset all objective function coefficients to zero
cbmpy.CBCPLEX.cplx_setObjective2(c, pid, names, expr=None, sense='maximize', reset=True)[source]

Set a new objective function. This is a workaround function to avoid the e is a major memory leak in c.variables.get_names() whch is used in cplx_setObjective() when reset=True.

cbmpy.CBCPLEX.cplx_setOutputStreams(lp, mode='default')[source]

Sets the noise level of the solver, mode can be one of:

  • None silent i.e. no output
  • ‘file’ set solver to silent and output logs to CPLX_RESULT_STREAM_FILE cplex_output.log
  • ‘iostream’ set solver to silent and output logs to CPLX_RESULT_STREAM_IO csio
  • ‘default’ or anything else noisy with full output closes STREAM_IO and STREAM_FILE (default)
cbmpy.CBCPLEX.cplx_singleGeneScan(fba, r_off_low=0.0, r_off_upp=0.0, optrnd=8, altout=False)[source]

Perform a single gene deletion scan

  • fba a model object
  • r_off_low the lower bound of a deactivated reaction
  • r_off_upp the upper bound of a deactivated reaction
  • optrnd [default=8] round off the optimal value
  • altout [default=False] by default return a list of gene:opt pairs, alternatively (True) return an extended result set including gene groups, optima and effect map
cbmpy.CBCPLEX.cplx_writeLPsolution(fba_sol, objf_name, fname, Dir=None, separator=', ')[source]

This function writes the optimal solution, produced wth cplx_getOptimalSolution to file

  • fba_sol a dictionary of Flux : value pairs
  • objf_name the objective function flux id
  • fname the output filename
  • Dir [default=None] use directory if not None
  • separator [default=’,’] the column separator
cbmpy.CBCPLEX.cplx_writeLPtoLPTfile(c, filename, title=None, Dir=None)[source]

Write out a CPLEX model as an LP format file

cbmpy.CBCPLEX.getReducedCosts(fba)[source]

Get a dictionary of reduced costs for each reaction/flux

cbmpy.CBCPLEX.setReducedCosts(fba, reduced_costs)[source]

For each reaction/flux, sets the attribute “reduced_cost” from a dictionary of reduced costs

  • fba an fba object
  • reduced_costs a dictionary of {reaction : value} pairs

CBMPy: CBDataStruct module

PySCeS Constraint Based Modelling (http://cbmpy.sourceforge.net) Copyright (C) 2009-2015 Brett G. Olivier, VU University Amsterdam, Amsterdam, The Netherlands

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see <http://www.gnu.org/licenses/>

Author: Brett G. Olivier Contact email: bgoli@users.sourceforge.net Last edit: $Author: bgoli $ ($Id: CBDataStruct.py 331 2015-07-01 14:36:41Z bgoli $)

class cbmpy.CBDataStruct.MIRIAMannotation[source]

The MIRIAMannotation class MIRIAM annotations: Biological Qualifiers

addIDorgURI(qual, uri)[source]

Add a URI directly into a qualifier collection:

addMIRIAMannotation(qual, entity, mid)[source]

Add a qualified MIRIAM annotation or entity:

  • qual a Biomodels biological qualifier e.g. “is” “isEncodedBy”
  • entity a MIRIAM resource entity e.g. “ChEBI”
  • mid the entity id e.g. CHEBI:17158
checkEntity(entity)[source]

Check an entity entry, this is a MIRIAM resource name: “chEBI”. The test is case insensitive and will correct the case of wrongly capitalised entities automatically. If the entity is not recognised then a list of possible candidates based on the first letters of the input is displayed.

  • entity a MIRIAM resource entity e.g. “ChEBI”
checkEntityPattern(entity)[source]

For an entity key compile the pattern to a regex, if necessary.

  • entity a MIRIAM resource entity
checkId(entity, mid)[source]

Check that a entity id e.g. CHEBI:17158

  • mid the entity id e.g. CHEBI:17158
deleteMIRIAMannotation(qual, entity, mid)[source]

Deletes a qualified MIRIAM annotation or entity:

  • qual a Biomodels biological qualifier e.g. “is” “isEncodedBy”
  • entity a MIRIAM resource entity e.g. “ChEBI”
  • mid the entity id e.g. CHEBI:17158
getAllMIRIAMUris()[source]

Return a dictionary of qualifiers that contain ID.org URL’S

getAndViewUrisForQualifier(qual)[source]

Retrieve all url’s associated with qualifier and attempt to open them all in a new browser tab

  • qual the qualifier e.g. “is” or “isEncoded”
getMIRIAMUrisForQualifier(qual)[source]

Return all list of urls associated with qualifier:

  • qual the qualifier e.g. “is” or “isEncoded”
viewURL(url)[source]

This will try to open the URL in a new tab of the default webbrowser

  • url the url
class cbmpy.CBDataStruct.StructMatrix(array, ridx, cidx, row=None, col=None)[source]

This class is specifically designed to store structural matrix information give it an array and row/col index permutations it can generate its own row/col labels given the label src.

getColsByIdx(*args)[source]

Return the columns referenced by index (1,3,5)

getColsByName(*args)[source]

Return the columns referenced by label (‘s’,’x’,’d’)

getIndexes(axis='all')[source]

Return the matrix indexes ([rows],[cols]) where axis=’row’/’col’/’all’

getLabels(axis='all')[source]

Return the matrix labels ([rows],[cols]) where axis=’row’/’col’/’all’

getRowsByIdx(*args)[source]

Return the rows referenced by index (1,3,5)

getRowsByName(*args)[source]

Return the rows referenced by label (‘s’,’x’,’d’)

setCol(src)[source]

Assuming that the col index array is a permutation (full/subset) of a source label array by supplying that src to setCol maps the row labels to cidx and creates self.col (col label list)

setRow(src)[source]

Assuming that the row index array is a permutation (full/subset) of a source label array by supplying that source to setRow it maps the row labels to ridx and creates self.row (row label list)

class cbmpy.CBDataStruct.StructMatrixLP(array, ridx, cidx, row=None, col=None, rhs=None, operators=None)[source]

Adds some stuff to StructMatrix that makes it LP friendly

getCopy(attr_str, deep=False)[source]

Return a copy of the attribute with name attr_str. Uses the copy module copy.copy or copy.deepcopy

  • attr_str a string of the attribute name: ‘row’, ‘col’
  • deep [default=False] try to do a deepcopy. Use with caution see copy module docstring for details

CBMPy: CBGUI module

PySCeS Constraint Based Modelling (http://cbmpy.sourceforge.net) Copyright (C) 2009-2015 Brett G. Olivier, VU University Amsterdam, Amsterdam, The Netherlands

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see <http://www.gnu.org/licenses/>

Author: Brett G. Olivier Contact email: bgoli@users.sourceforge.net Last edit: $Author: bgoli $ ($Id: CBGUI.py 305 2015-04-23 15:18:31Z bgoli $)

cbmpy.CBGUI.createReaction(mod)[source]

Load the QT4 reaction creator widget

  • mod a PySCeS CBMPy model instance
cbmpy.CBGUI.loadCBGUI(mod, version=2)[source]

Load an FBA model instance into the quick editor to view or change basic model properties

  • mod a PySCeS CBMPy model instance
cbmpy.CBGUI.openFileName(work_dir=None)[source]

Load the QT4 file open selection dialogue

  • work_dir the optional initial directory
cbmpy.CBGUI.saveFileName(work_dir=None)[source]

Load the QT4 file save selection dialogue

  • work_dir the optional initial directory

CBMPy: CBModel module

PySCeS Constraint Based Modelling (http://cbmpy.sourceforge.net) Copyright (C) 2009-2014 Brett G. Olivier, VU University Amsterdam, Amsterdam, The Netherlands

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see <http://www.gnu.org/licenses/>

Author: Brett G. Olivier Contact email: bgoli@users.sourceforge.net Last edit: $Author: bgoli $ ($Id: CBModel.py 430 2016-04-08 15:21:09Z bgoli $)

class cbmpy.CBModel.Compartment(cid, name=None, size=1, dimensions=3, volume=None)[source]

A compartment

containsReactions()[source]

Lists the species contained in this compartment

containsSpecies()[source]

Lists the species contained in this compartment

getDimensions()[source]

Get the compartment dimensions

getSize()[source]

Get the compartment size

setDimensions(dimensions)[source]

Get the compartment dimensions

  • dimensions set the new compartment dimensions
setSize(size)[source]

Set the compartment size

  • size the new compartment size
class cbmpy.CBModel.Fbase[source]

Base class for CB Model objects

addMIRIAMannotation(qual, entity, mid)[source]

Add a qualified MIRIAM annotation or entity:

  • qual a Biomodels biological qualifier e.g. “is” “isEncodedBy”
  • entity a MIRIAM resource entity e.g. “ChEBI”
  • mid the entity id e.g. CHEBI:17158 or fully qualifies url (if only_qual_uri)
addMIRIAMuri(qual, uri)[source]

Add a qualified MIRIAM annotation or entity:

clone()[source]

Return a clone of this object. Cloning performs a deepcop on the object which will also clone any objects that exist as attributes of this object, in other words an independent copy of the original. If this is not the desired behaviour override this method when subclassing or implement your own.

deleteAnnotation(key)[source]

Unsets (deltes) an objects annotation with key

  • key the annotation key
deleteMIRIAMannotation(qual, entity, mid)[source]

Deletes a qualified MIRIAM annotation or entity:

  • qual a Biomodels biological qualifier e.g. “is” “isEncodedBy”
  • entity a MIRIAM resource entity e.g. “ChEBI”
  • mid the entity id e.g. CHEBI:17158
getAnnotation(key)[source]

Return the object annotation associated with:

  • key the annotation key
getAnnotations()[source]

Return the object annotation dictionary

getCompartmentId()[source]

Return the compartment id where this element is located

getId()[source]

Return the object ID.

getMIRIAMannotations()[source]

Returns a dictionary of all MIRIAM annotations associated with this object or None of there are none defined.

getMetaId()[source]

Return the object metaId.

getName()[source]

Return the object name.

getNotes()[source]

Return the object’s notes

getPid()[source]

Return the object ID.

getSBOterm()[source]

Return the SBO term for this object.

hasAnnotation(key)[source]

Returns a boolean representing the presence/absence of the key in the objext annotation

  • key the annotation key
serialize(protocol=0)[source]

Serialize object, returns a string by default

  • protocol [default=0] serialize to a string or binary if required,

    see pickle module documentation for details

serializeToDisk(filename, protocol=0)[source]

Serialize to disk using pickle protocol:

  • filename the name of the output file

  • protocol [default=0] serialize to a string or binary if required,

    see pickle module documentation for details

setAnnotation(key, value)[source]

Set an objects annotation as a key : value pair.

  • key the annotation key
  • value the annotation value
setCompartmentId(compartment)[source]

Set the compartment id where this element is located

setId(fid)[source]

Sets the object Id

  • fid a valid c variable style id string
setMetaId(mid=None)[source]

Sets the object Id

  • mid [default=None] a valid c variable style metaid string, if None it will be set as meta+id
setName(name)[source]

Set the object name:

  • name the name string
setNotes(notes)[source]

Sets the object’s notes:

  • notes the note string, should preferably be (X)HTML for SBML
setPid(fid)[source]

Sets the object Id

  • fid a valid c variable style id string
setSBOterm(sbo)[source]

Set the SBO term for this object.

  • sbo the SBOterm with format: SBO:nnnnnnn”
class cbmpy.CBModel.FluxBound(fid, reaction, operation, value)[source]

A reaction fluxbound

getType()[source]

Returns the type of FluxBound: ‘lower’, ‘upper’, ‘equality’ or None

getValue()[source]

Returns the current value of the attribute (input/solution)

setReactionId(react)[source]

Sets the reaction attribute of the FluxBound

setValue(value)[source]

Sets the attribute ‘’value’‘

class cbmpy.CBModel.FluxObjective(pid, reaction, coefficient=1)[source]

A weighted flux that appears in an objective function

NOTE: reaction is a string containing a reaction id

class cbmpy.CBModel.Gene(gid, label=None, active=True)[source]

Contains all the information about a gene (or gene+protein construct depending on your philosophy)

TODO: I will change the whole Gene/GPR structure to a dictionary data structure on the model which should simplify this all significantly.

getLabel()[source]

Returns the gene label

isActive()[source]

Returns whether the gene is active or not

resetActivity()[source]

Reset the gene to its default activity state

setActive()[source]

Set the gene to be active

setInactive()[source]

Set the gene to be inactive

setLabel(label)[source]

Sets the gene label

class cbmpy.CBModel.GeneProteinAssociation(gpid, protein)[source]

This class associates genes to proteins. TODO: I will change the whole Gene/GPR structure to a dictionary data structure on the model which should simplify this all significantly.

addAssociation(assoc)[source]

Add a gene/protein association expression

addGeneref(geneid)[source]

Add a gene reference to the list of gene references

  • geneid a valid model Gene id
buildEvalFunc()[source]

Builds a function which evaluates the gene expressions and evaluates to an integer uisng the following rules:

  • True –> 1
  • False –> 0
  • and –> *
  • or –> +
createAssociationAndGeneRefs(assoc, altlabels=None)[source]

Evaluate the gene/protein association and add the genes necessary to evaluate it Note that this GPR should be added to a model with cmod.addGPRAssociation() before calling this method

  • assoc the COBRA style gene protein association
  • altlabels [default=None] a dictionary containing a label<–>id mapping
deleteGeneref(gid)[source]

Deletes a gene reference

  • geneid a valid model Gene id
evalAssociation()[source]

Returns an integer value representing the logical associations or None.

getActiveGenes()[source]

Return a list of active gene objects

getAssociationStr(use_labels=False)[source]

Return the gene association string, alternatively return string with labels

  • use_lablels [default=False] return the gene association string with labels rather than geneId’s (FBCv2 issue)
getGene(gid)[source]

Return a gene object with id

getGeneIds()[source]

Return a list of gene id’s

getGeneLabels()[source]

Return a list of gene labels associated with this GPRass

getGenes()[source]

Return a list of gene objects associated with this GPRass

getProtein()[source]

Return the protein associated with this set of genes

isProteinActive()[source]

This returns a boolean which indicates the result of evaluating the gene association. If the result is positive then the protein is expressed and True is returned, otherwise if the expression evaluates to a value of 0 then the protein is not expressed and False is returned.

setAllGenesActive()[source]

Activate all genes in association

setAllGenesInactive()[source]

Deactivates all genes in association

setGeneActive(gid)[source]

Set a gene to be inactive

setGeneInactive(gid)[source]

Set a gene to be inactive

class cbmpy.CBModel.Group(pid)[source]

Container for SBML groups

addMember(obj)[source]

Add member CBMPy object(s) to the group

  • obj either a single, tuple or list of CBMPy objects
addSharedMIRIAMannotation(qual, entity, mid)[source]

Add a qualified MIRIAM annotation or entity to the list of members (all) rather than the group itself:

  • qual a Biomodels biological qualifier e.g. “is” “isEncodedBy”
  • entity a MIRIAM resource entity e.g. “ChEBI”
  • mid the entity id e.g. CHEBI:17158 or fully qualifies url (if only_qual_uri)
assignAllSharedPropertiesToMembers(overwrite=False)[source]

Assigns all group shared properties (notes, annotations, MIRIAM annotations, SBO) to the group members.

  • overwrite [default=False] overwrite the target notes if they are defined
assignSharedAnnotationToMembers()[source]

This function merges or updates the group member objects annotations with the group shared annotation.

assignSharedMIRIAMannotationToMembers()[source]

This function merges or updates the group member objects MIRIAM annotations with the group shared MIRIAM annotation.

assignSharedNotesToMembers(overwrite=False)[source]

Assigns the group shared notes to the group members.

  • overwrite [default=False] overwrite the target notes if they are defined
assignSharedSBOtermsToMembers(overwrite=False)[source]

Assigns the group shared member SBO term to the group members.

  • overwrite [default=False] overwrite the target SBO term if it is defined
clone()[source]

Return a clone of this object. Note the for Groups this is a shallow copy, in that the reference objects themselves are not cloned only the group (and attributes)

deleteMember(oid)[source]

Deletes a group member with group id.

  • oid group member id
getKind()[source]

Return the group kind

getMemberIDs(as_set=False)[source]

Return the ids of the member objects.

  • as_set return id’s as a set rather than a list
getMembers(as_set=False)[source]

Return the member objects of the group.

  • as_set return objects as a set rather than a list
getSharedAnnotations()[source]

Return a dictionary of the shared member annotations (rather than the group attribute).

getSharedMIRIAMannotations()[source]

Return a dictionary of the shared member MIRIAM annotations (rather than the group attribute).

getSharedNotes()[source]

Return the shared member notes (rather than the group attribute).

getSharedSBOterm()[source]

Return the shared member SBO term (rather than the group attribute).

setKind(kind)[source]

Sets the kind or type of the group, this must be one of: ‘collection’, ‘partonomy’, ‘classification’.

  • kind the kind
setSharedAnnotation(key, value)[source]

Sets the list of members (all) annotation as a key : value pair.

  • key the annotation key
  • value the annotation value
setSharedNotes(notes)[source]

Sets the group of objects notes attribute (all):

  • notes the note string, should preferably be (X)HTML for SBML
setSharedSBOterm(sbo)[source]

Set the SBO term for the the members of the group (all).

  • sbo the SBOterm with format: “SBO:<7 digit integer>”
class cbmpy.CBModel.GroupMemberAttributes[source]

Contains the shared attributes of the group members (equivalent to SBML annotation on ListOfMembers)

class cbmpy.CBModel.Model(pid)[source]

Container for constraint based model, adds methods for manipulating:

  • objectives
  • constraints
  • reactions
  • species
  • compartments
  • groups
  • parameters
  • N a structmatrix object
addCompartment(comp)[source]

Add an instantiated Compartment object to the CBM model

  • comp an instance of the Compartment class
addFluxBound(fluxbound, fbexists=None)[source]

Add an instantiated FluxBound object to the FBA model

  • fluxbound an instance of the FluxBound class
addGPRAssociation(gpr, update_idx=True)[source]

Add a GeneProteinAssociation instance to the model

  • gpr an instantiated GeneProteinAssociation object
addGene(gene)[source]

Add an instantiated Gene object to the FBA model

  • gene an instance of the G class
addGroup(obj)[source]

Add an instantiated group object to the model

  • obj the Group instance
addMIRIAMannotation(qual, entity, mid)[source]

Add a qualified MIRIAM annotation or entity:

  • qual a Biomodels biological qualifier e.g. “is” “isEncodedBy”
  • entity a MIRIAM resource entity e.g. “ChEBI”
  • mid the entity id e.g. CHEBI:17158
addModelCreator(firstname, lastname, organisation=None, email=None)[source]

Add a model creator to the list of model creators, only the first and fmaily names are mandatory:

  • firstname
  • lastname
  • organisation [default=None]
  • email [default=None]
addObjective(obj, active=False)[source]

Add an instantiated Objective object to the FBA model

  • obj an instance of the Objective class
  • active [default=False] flag this objective as the active objective (fba.activeObjIdx)
addParameter(par)[source]

Add an instantiated Parameter object to the model

  • par an instance of the Parameter class
addReaction(reaction)[source]

Adds a reaction object to the model

  • reaction an instance of the Reaction class
addSpecies(species)[source]

Add an instantiated Species object to the FBA model

  • species an instance of the Species class
addUserConstraint(pid, fluxes=None, operator='=', rhs=0.0)[source]

Add a user defined constraint to FBA model, this is additional to the automatically determined Stoichiometric constraints.

  • pid user constraint name/id, use None for auto-assign
  • fluxes a list of (coefficient, reaction id) pairs where coefficient is a float
  • operator is one of = > < >= <=
  • rhs a float
buildStoichMatrix(matrix_type='numpy', only_return=False)[source]

Build the stoichiometric matrix N and additional constraint matrix CN (if required)

  • matrix_type [default=’numpy’] the type of matrix to use to generate constraints
    • numpy a NumPy matrix default
    • sympy a SymPy symbolic matrix, if available note the denominator limit can be set in CBModel.__CBCONFIG__['SYMPY_DENOM_LIMIT'] = 10**12
    • scipy_csr create using NumPy but store as SciPy csr_sparse
  • only_return [default=False] IMPORTANT only returns the stoichiometric matrix and constraint matrix (if required), does not update the model
changeAllFluxBoundsWithValue(old, new)[source]

Replaces all flux bounds with value “old” with a new value “new”:

  • old value
  • new value
clone()[source]

Return a clone of this object.

createCompartment(cid, name=None, size=1, dimensions=3, volume=None)[source]

Create a new compartment and add it to the model if the id does not exist

  • cid compartment id
  • name [None] compartment name
  • size [1] compartment size
  • dimensions [3] compartment size dimensions
  • volume [None] compartment volume
createGeneAssociationsFromAnnotations(annotation_key='GENE ASSOCIATION', replace_existing=True)[source]

Add genes to the model using the definitions stored in the annotation key. If this fails it tries some standard annotation keys: GENE ASSOCIATION, GENE_ASSOCIATION, gene_association, gene association.

  • annotation_key the annotation dictionary key that holds the gene association for the protein/enzyme
  • replace_existing [default=True] replace existing annotations, otherwise only new ones are added
createGeneProteinAssociation(protein, assoc, gid=None, name=None, gene_pattern=None, update_idx=True, altlabels=None)[source]

Create and add a gene protein relationship to the model, note genes are mapped on protein objects which may or may not be reactions

  • protein in this case the reaction
  • assoc the COBRA style gene protein association
  • gid the unique id
  • name the optional name
  • gene_pattern deprecated, not needed anymore
  • update_idx update the model gene index, not used
  • altlabels [default=None] alternative labels for genes, default uses geneIds
createGroup(gid)[source]

Create an empty group with

  • gid the unique group id
createObjectiveFunction(rid, coefficient=1, osense='maximize', active=True, delete_current_obj=True)[source]

Create a single variable objective function:

  • rid The
  • coefficient [default=1]
  • osense [default=’maximize’]
  • active [default=True]
  • delete_current_obj [default=True]
createReaction(rid, name=None, reversible=True, create_default_bounds=True, silent=False)[source]

Create a new blank reaction and add it to the model:

  • id the unique reaction ID
  • name the reaction name
  • reversible [default=True] the reaction reversibility. True is reversible, False is irreversible
  • create_default_bounds create default reaction bounds, irreversible 0 <= J <= INF, reversable -INF <= J <= INF
createReactionBounds(reaction, lb_value, ub_value)[source]

Create a new lower bound for a reaction: value <= reaction

  • reaction the reaction id
  • lb_value the value of the lower bound
  • ub_value the value of the upper bound
createReactionLowerBound(reaction, value)[source]

Create a new lower bound for a reaction: value <= reaction

  • reaction the reaction id
  • value the value of the bound
createReactionReagent(reaction, metabolite, coefficient, silent=False)[source]

Add a reagent to an existing reaction, both reaction and metabolites must exist

  • reaction a reaction id
  • metabolite a species/metabolite id
  • coefficient the reagent coefficient
createReactionUpperBound(reaction, value)[source]

Create a new upper bound for a reaction: reaction <= value

  • reaction the reaction id
  • value the value of the bound
createSingleGeneEffectMap()[source]

This takes a model and analyses the logical gene expression patterns. This only needs to be done once, the result is a dictionary that has boolean effect patterns as keys and the (list of) genes that give rise to those patterns as values. This map is used by the single gene deletion method for further analysis.

Note this dictionary can also be stored and retrieved separately as long as the model structure is not changed i.e. the gene associations themselves or order of reactions (stored as the special entry ‘keyJ’).

Stored as self.__single_gene_effect_map__

createSpecies(sid, boundary=False, name='', value=nan, compartment=None, charge=None, chemFormula=None)[source]

Create a new species and add it to the model:

  • id the unique species id
  • boundary [default=False] whether the species is a variable (False) or is a boundary parameter (fixed)
  • name [default=’‘] the species name
  • value [default=nan] the value not currently used
  • compartment [default=None] the compartment the species is located in
  • charge [default=None] the species charge
  • chemFormula [default=None] the chemical formula
deleteAllFluxBoundsWithValue(value)[source]

Delete all flux bounds which have a specified value:

  • value the value of the flux bound(s) to delete
deleteBoundsForReactionId(rid, lower=True, upper=True)[source]

Delete bounds connected to reaction, rid

  • rid a valid reaction id
  • upper [default=True] delete the upper bound
  • lower [default=True] delete the lower bound
deleteGroup(gid)[source]

Delete a group with

  • gid the unique group id
deleteNonReactingSpecies(simulate=True)[source]

Deletes all species that are not reagents (do not to take part in a reaction). Warning this deletion is permanent and greedy (not selective). Returns a list of (would be) deleted species

  • simulate [default=True] only return a list of the speciesId’s that would have been deleted if False
deleteObjective(objective_id)[source]

Delete objective function:

objective_id the id of the objective function. If objective_id is given as ‘active’ then the active objective is deleted.
deleteReactionAndBounds(rid)[source]

Delete all reaction and bounds connected to reaction

  • rid a valid reaction id
deleteSpecies(sid)[source]

Deletes a species object with id

  • sid the species id
exportFVAdata()[source]

Export the fva data as an array and list of reaction id’s

findFluxesForConnectedSpecies(metab)[source]

Returns a list of (reaction, flux value) pairs that this metabolite appears as a reagent of

  • metab the metabolite name
getActiveObjective()[source]

Returns the active objective object.

getAllFluxBounds()[source]

Returns a dictionary of all flux bounds [id:value]

getAllGeneActivities()[source]

Returns a dictionary of genes (if defined) and whether they are active or not

getAllGeneProteinAssociations(use_labels=False)[source]

Returns a dictionary of genes associated with each protein

  • use_labels use V2 gene labels rather than ID’s
getAllProteinActivities()[source]

Returns a dictionary of reactions (if genes and GPR’s are defined) and whether they are active or not

getAllProteinGeneAssociations(use_labels=False)[source]

Returns a dictionary of the proteins associated with each gene

  • use_labels use V2 gene labels rather than ID’s
getBoundarySpeciesIds(rid=None)[source]

Return all boundary species associated with reaction

  • rid [default=None] by default return all boundary species in a model, alternatively a string containing a reaction id or list of reaction id’s
getCompartment(cid)[source]

Returns a compartment object with cid

  • cid compartment ID
getCompartmentIds(substring=None)[source]

Returns a list of compartment Ids, applies a substring search if substring is defined

  • substring search for this pattern anywhere in the id
getDescription()[source]

Returns the model description which was stored in the SBML <notes> field

getExchangeReactionIds()[source]

Returns id’s of reactions where the ‘is_exchange’ attribute set to True. This is by default reactions that contain a boundary species.

getExchangeReactions()[source]

Returns reaction instances where the ‘is_exchange’ attribute set to True. This is by default reactions that contain a boundary species.

getFluxBoundByID(fid)[source]

Returns a FluxBound with id

  • fid the fluxBound ID
getFluxBoundByReactionID(rid, bound)[source]

Returns a FluxBound instance

  • rid the reaction ID
  • bound the bound: ‘upper’, ‘lower’, ‘equality’
getFluxBoundIds(substring=None)[source]

Returns a list of fluxbound Ids, applies a substring search if substring is defined

  • substring search for this pattern anywhere in the id
getFluxBoundsByReactionID(rid)[source]

Returns all FluxBound instances connected to a reactionId as a tuple of valid (lower, upper, None) or (None, None, equality) or alternatively invalid (lower, upper, equality).

  • rid the reaction ID

under evaluation

getFluxesAssociatedWithSpecies(metab)[source]

Returns a list of (reaction, flux value) pairs that this metabolite appears as a reagent in

  • metab the metabolite name
getGPRassociation(gpr_id)[source]

Returns a gene protein association object that has the identifier:

  • gpr_id the gene protein identifier
getGPRforReaction(rid)[source]

Return the GPR associated with the reaction id:

  • rid a reaction id
getGene(g_id)[source]

Returns a gene object that has the identifier:

  • gid the gene identifier
getGeneByLabel(label)[source]

Given a gene label return the corresponding Gene object

  • label
getGeneIdFromLabel(label)[source]

Given a gene label it returns the corresponding Gene id or None

  • label
getGeneIds(substring=None)[source]

Returns a list of gene Ids, applies a substring search if substring is defined

  • substring search for this pattern anywhere in the id
getGeneLabels(substring=None)[source]

Returns a list of gene labels (locus tags), applies a substring search if substring is defined

  • substring search for this pattern anywhere in the id
getGroup(gid)[source]

Return a group with

  • gid the unique group id
getGroupIds()[source]

Delete all group ids

getIrreversibleReactionIds()[source]

Return a list of irreversible reaction Id’s

getModelCreators()[source]

Return model creator information

getObjFuncValue()[source]

Returns the objective function value

getObjectiveIds(substring=None)[source]

Returns a list of objective function Ids, applies a substring search if substring is defined

  • substring search for this pattern anywhere in the id
getReaction(rid)[source]

Returns a reaction object with id

  • rid reaction ID
getReactionActivity(rid)[source]

If there is a GPR and genes associated with the reaction ID then return either active=True or inactive=False Note if there is no gene associated information then this will return active.

  • rid a reaction id
getReactionBounds(rid)[source]

Get the bounds of a reaction, returns a tuple of rid, lowerbound value, upperbound value and equality value (None means bound does not exist).

  • rid the reaction ID
getReactionIds(substring=None)[source]

Returns a list of reaction Ids, applies a substring search if substring is defined

  • substring search for this pattern anywhere in the id
getReactionLowerBound(rid)[source]

Returns the lower bound of a reaction (it it exists) or None

  • rid the reaction ID
getReactionNames(substring=None)[source]

Returns a list of reaction names, applies a substring search if substring is defined

  • substring search for this pattern anywhere in the name
getReactionUpperBound(rid)[source]

Returns the upper bound of a reaction (it it exists) or None

  • rid the reaction ID
getReactionValues(only_exchange=False)[source]

Returns a dictionary of ReactionID : ReactionValue pairs:

  • only_exchange [default=False] only return the reactions labelled as exchange
getReversibleReactionIds()[source]

Return a list of reversible reaction Id’s

getSolutionVector(names=False)[source]

Return a vector of solution values

  • names [default=False] if True return a solution vector and list of names
getSpecies(sid)[source]

Returns a species object with sid

  • sid a specied ID
getSpeciesIds(substring=None)[source]

Returns a list of species Ids, applies a substring search if substring is defined

  • substring search for this pattern anywhere in the id
renameObjectIds(prefix=None, suffix=None, target='all', ignore=None)[source]

This method is designed for target=”all” other use may result in incomplete models.

  • prefix [None] if supplied add as a prefix
  • suffix [None] if supplied add as a suffix
  • target [‘all’] specify what class of objects to rename
  • ‘species’
  • ‘reactions’
  • ‘bounds’
  • ‘objectives’
  • ‘all’
  • ignore [default=None] a list of id’s to ignore
resetAllGenes(update_reactions=False)[source]

Resets all genes to their default activity state (normally on)

  • update_reactions [default=False] update the associated reactions fluxbounds from the gene deletion bounds if they exist
resetAllInactiveGPRBounds()[source]

Resets all reaction bounds modified by the cmod.setAllInactiveGeneReactionBounds() method to their previous values

setAllFluxBounds(bounds)[source]

DEPRECATED! use setFluxBoundsFromDict()

Sets all the fluxbounds present in bounds

  • bounds a dictionary of [fluxbound_id : value] pairs (not per reaction!!!)
setAllInactiveGPRBounds(lower=0.0, upper=0.0)[source]

Set all reactions that are inactive (as determined by gene and gpr evaluation) to bounds:

  • lower [default=0.0] the new lower bound
  • upper [default=0.0] the new upper bound
setAllProteinActivities(activites, lower=0.0, upper=0.0)[source]

Given a dictionary of activities [rid : boolean] pairs set all the corresponding reactions:

  • activities a dictionary of [rid : boolean] pairs
  • lower [default=0.0] the lower bound of the deactivated flux
  • upper [default=0.0] the upper bound of the deactivated flux
setBoundValueByName(rid, value, bound)[source]

Deprecated use setReactionBound

Set a reaction bound
  • rid the reactions id
  • value the new value
  • bound this is either ‘lower’ or ‘upper’
setCreatedDate(date=None)[source]

Set the model created date tuple(year, month, day, hour, minute, second)

  • date [default=None] default is now (automatic) otherwise (year, month, day, hour, minute, second) e.g. (2012, 09, 24, 13, 34, 00)
setDescription(html)[source]

Sets the model description which translates into the SBML <notes> field.

  • html any valid html or the empty string to clear ‘’
setFluxBoundsFromDict(bounds)[source]

Sets all the fluxbounds present in bounds

  • bounds a dictionary of [fluxbound_id : value] pairs (not per reaction!!!)
setGeneActive(g_id, update_reactions=False)[source]

Effectively restores a gene by setting it’s active flag

  • g_id a gene ID
  • update_reactions [default=False] update the associated reactions fluxbounds from the gene deletion bounds if they exist
setGeneInactive(g_id, update_reactions=False, lower=0.0, upper=0.0)[source]

Effectively deletes a gene by setting it’s inactive flag while optionally updating the GPR associated reactions

  • g_id a gene ID
  • update_reactions [default=False] update the associated reactions fluxbounds
  • lower [default=0.0] the deactivated reaction lower bound
  • upper [default=0.0] the deactivated reaction upper bound
setModifiedDate(date=None)[source]

Set the model modification date: tuple(year, month, day, hour, minute, second)

  • date [default=None] default is now (automatic) otherwise (year, month, day, hour, minute, second) e.g. (2012, 09, 24, 13, 34, 00)
setObjectiveFlux(rid, coefficient=1, osense='maximize', delete_objflx=True)[source]

Set single target reaction flux for the current active objective function.

  • rid a string containing a reaction id
  • coefficient [default=1] an objective flux coefficient
  • osense the optimization sense must be maximize or minimize
  • delete_objflx [default=True] delete all existing fluxObjectives in the active objective function
setPrefix(prefix, target)[source]

This is alpha stuff, target can be:

  • ‘species’
  • ‘reactions’
  • ‘constraints’
  • ‘objectives’
  • ‘all’
setReactionBound(rid, value, bound)[source]

Set a reaction bound

  • rid the reactions id
  • value the new value
  • bound this is either ‘lower’ or ‘upper’, or ‘equal’
setReactionBounds(rid, lower, upper)[source]

Set both the upper and lower bound of a reaction:

  • rid the good old reaction id
  • lower the lower bound value
  • upper the upper bound value
setReactionLowerBound(rid, value)[source]

Set a reactions lower bound (if it exists)

  • rid the reactions id
  • value the new value
setReactionUpperBound(rid, value)[source]

Set a reactions upper bound (if it exists)

  • rid the reaction id
  • value the new value
setSuffix(suffix, target)[source]

This is alpha stuff, target can be:

  • ‘species’
  • ‘reactions’
  • ‘constraints’
  • ‘objectives’
  • ‘all’
sortReactionsById()[source]

Sorts the reactions by Reaction.id uses the python string sort

sortSpeciesById()[source]

Sorts the reaction list by Reaction.id uses the python string sort

splitEqualityFluxBounds()[source]

Splits any equalit flux bounds into lower and upper bounds.

testGeneProteinAssociations()[source]

This method will test the GeneProtein associations and return a list of protein, association pairs

undeleteObjective(objective_id)[source]

Undeltes a deleted objective function:

  • objective_id the id of an objeective function
undeleteReactionAndBounds(rid)[source]

Undelete a reaction and bounds deleted with the deleteReactionAndBounds method

  • rid a deleted reaction id

Please note this method is still experimental ;-)

updateNetwork(lower=0.0, upper=0.0)[source]

Update the reaction network based on gene activity. If reaction is deactivated then lower and upper bounds are used

  • lower [default=0.0] deactivated lower bound
  • upper [default=0.0] deactivated upper bound
class cbmpy.CBModel.Objective(pid, operation)[source]

An objective function

addFluxObjective(fobj)[source]

Adds a FluxObjective instance to the Objective

createFluxObjectives(fluxlist)[source]

Create and add flux objective objects to this objective function.

  • fluxlist a list of one or more (‘coefficient’, ‘rid’) pairs
deleteAllFluxObjectives()[source]

Delete all flux objectives

getFluxObjective(foid)[source]

Return the flux objective with id.

  • foid the flux objective id returns either an object or a list if there are multiply defined flux objectives
getFluxObjectiveData()[source]

Returns a list of ObjectiveFunction components as (coefficient, flux) pairs

getFluxObjectiveForReaction(rid)[source]

Returns the FluxObjective associated with the suplied rid. If there is more than fluxObjective associated with a reaction (illegal) then a list of fluxObjectives is returned.

rid a reaction id
getFluxObjectiveIDs()[source]

Returns a list of ObjectiveFlux ids, for the reaction id’s use getFluxObjectiveReactions() or for coefficient, fluxobjective pairs use getFluxObjectiveData()

getFluxObjectiveReactions()[source]

Returns a list of reactions that are used as FluxObjectives

getFluxObjectives()[source]

Returns the list of FluxObjective objects.

getOperation()[source]

Returns the operation or sense of the objective

getValue()[source]

Returns the current value of the attribute (input/solution)

setOperation(operation)[source]

Sets the objective operation (sense)

  • operation [default=’maximize’] one of ‘maximize’, ‘maximise’, ‘max’, ‘minimize’, ‘minimise’, ‘min’
setValue(value)[source]

Sets the attribute ‘’value’‘

class cbmpy.CBModel.Parameter(pid, value, name=None, constant=True)[source]

Holds parameter information

addAssociation(assoc)[source]

Add a fluxbound ID’s to associate with this object

deleteAssociation(assoc)[source]

Delete the fluxbound id associated with this object

getAssociations()[source]

Return the FluxBounds ID’s associated with this object

getValue()[source]

Returns the current value of the attribute (input/solution)

setValue(value)[source]

Sets the attribute ‘’value’‘

class cbmpy.CBModel.Reaction(pid, name=None, reversible=True)[source]

Holds reaction information

addReagent(reag)[source]

Adds an instantiated Reagent object to the reaction

changeId(pid)[source]

Changes the Id of the reaction and updates associated FluxBounds

changeReagentCoefficientForSpecies(s_id, coefficient)[source]

Change the coefficient of reagent which refers to s_id. If there is more than one reagent that refers to this species return a warning and a list of reagents otherwise None.

  • s_id a species/metabolite id
  • coefficient the new coefficient
createReagent(metabolite, coefficient)[source]

Create a new reagent and add it to the reaction:

  • metabolite the metabolite name
  • coefficient the

—negative coefficient is a substrate – positive coefficient is a product

Will fail if a species reference already exists

deactivateReaction(lower=0.0, upper=0.0)[source]

Deactivates a reaction by setting its bounds to lower and upper. Restore with reactivateReaction()

  • lower [default=0.0] bound
  • upper [default=0.0] bound
deleteReagentWithSpeciesRef(species)[source]

Delete a reagent that refers to the species id:

  • species a species/metabolite id
getEquation(reverse_symb='=', irreverse_symb='>', use_names=False)[source]

Return a pretty printed string containing the reaction equation

  • reverse_symb [default = ‘=’] the symbol to use for reversible reactions
  • irreverse_symb [default = ‘>’] the symbol to use for irreversible reactions
  • use_names [defualt = False] use species names rather than id’s
getFVAdata(roundnum=None, silent=True)[source]

Returns the data generated by CBSolver.FluxVariabilityAnalysis() for this reaction as a tuple of (Flux, FVAmin, FVAmax, span) where span is abs(FVAmax - FVAmin). FVAmin or FVAmax is None this indicates no solution to that particular optimization (infeasible).

  • roundnum [default=None] the integer number of roundoff decimals the default is no rounding
  • silent [default=True] supress output to stdout
getLowerBound()[source]

Get the value of the reactions lower bound

getProductIds(use_names=False)[source]

Returns a list of the reaction products, species identifiers

  • use_names [defualt = False] use species names rather than id’s
getReagent(rid)[source]

Return the one or more reagent objects which have rid:

  • rid a reagent rid
getReagentObjIds()[source]

Returns a list of the reagent id’s. For the name of the reagents/metabolites use <reaction>.getSpeciesIds()

getReagentRefs()[source]

Returns a list of the reagents/metabolites

getReagentWithSpeciesRef(species)[source]

Return the reagent object which refers to the species id:

  • species the species/metabolite id
getSpeciesIds()[source]

Returns a list of the reagents/metabolites

getSpeciesObj()[source]

Returns a list of the species objects that are reagents

getStoichiometry(use_names=False, altout=False)[source]

Returns a list of (coefficient, species) pairs for this reaction

  • use_names [default = False] use species names rather than id’s
  • altout [default = False] returns a dictionary
getSubstrateIds(use_names=False)[source]

Returns a list of the reaction substrates, species identifiers

  • use_names [defualt = False] use species names rather than id’s
getUpperBound()[source]

Get the value of the reactions upper bound

getValue()[source]

Returns the current value of the flux.

reactivateReaction()[source]

Activates a reaction deactivated with deactivateReaction

setLowerBound(value)[source]

Set the value of the reactions lower bound

  • value a floating point value
setStoichCoefficient(sid, value)[source]

Sets the stoichiometric coefficient of a reagent that refers to a metabolite. Note negative coefficients are substrates while positive ones are products. At this point zero coefficients are not allowed

  • sid the species/metabolite id
  • value a floating point value != 0
setUpperBound(value)[source]

Set the value of the reactions upper bound

  • value a floating point value
setValue(value)[source]

Sets the attribute value in this case the flux.

undeleteReagentWithSpeciesRef(species)[source]

Attempts to unDelete reagent deleted with deleteReagent() that refers to the species id:

  • species a species/metabolite id
class cbmpy.CBModel.Reagent(reid, species_ref, coef)[source]
Has a reactive species id and stoichiometric coefficient:
  • negative = substrate
  • positive = product
  • species_ref a reference to a species obj
getCoefficient()[source]

Returns the reagent coefficient

getRole()[source]

Returns the reagents role, “substrate”, “product” or None

getSpecies()[source]

Returns the metabolite/species that the reagent reference refers to

setCoefficient(coef)[source]

Sets the reagent coefficient and role, negative coefficients are substrates and positive ones are products

  • coeff the new coefficient
setSpecies(spe)[source]

Sets the metabolite/species that the reagent reference refers to

class cbmpy.CBModel.Species(pid, boundary=False, name=None, value=nan, compartment=None, charge=None, chemFormula=None)[source]

Holds species/metabolite information

getCharge()[source]

Returns the species charge

getChemFormula()[source]

Returns the species chemical formula

getReagentOf()[source]

Returns a list of reaction id’s that this metabolite occurs in

getValue()[source]

Returns the current value of the attribute (input/solution)

isReagentOf()[source]

Returns a dynamically generated list of reactions that this species occurs as a reagent

setBoundary()[source]

Sets the species so it is a boundary metabolite or fixed which does not occur in the stoichiometric matrix N

setCharge(charge)[source]

Sets the species charge:

  • charge a signed double but generally a signed int is used
setChemFormula(cf)[source]

Sets the species chemical formula

  • cf a chemical formula e.g. CH3NO2
setReagentOf(rid)[source]

Adds the supplied reaction id to the reagent_of list (if it isn’t one already)

  • rid a valid reaction id
setValue(value)[source]

Sets the attribute ‘’value’‘

unsetBoundary()[source]

Unsets the species boundary attribute so that the metabolite is free and therefore occurs in the stoichiometric matrix N

CBMPy: CBModelTools module

PySCeS Constraint Based Modelling (http://cbmpy.sourceforge.net) Copyright (C) 2009-2015 Brett G. Olivier, VU University Amsterdam, Amsterdam, The Netherlands

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see <http://www.gnu.org/licenses/>

Author: Brett G. Olivier Contact email: bgoli@users.sourceforge.net Last edit: $Author: bgoli $ ($Id: CBModelTools.py 431 2016-04-13 10:06:59Z bgoli $)

CBMPy: CBMultiCore module

PySCeS Constraint Based Modelling (http://cbmpy.sourceforge.net) Copyright (C) 2009-2015 Brett G. Olivier, VU University Amsterdam, Amsterdam, The Netherlands

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see <http://www.gnu.org/licenses/>

Author: Brett G. Olivier Contact email: bgoli@users.sourceforge.net Last edit: $Author: bgoli $ ($Id: CBMultiCore.py 305 2015-04-23 15:18:31Z bgoli $)

cbmpy.CBMultiCore.grouper(3, 'abcdefg', 'x') --> ('a', 'b', 'c'), ('d', 'e', 'f'), ('g', 'x', 'x')[source]
cbmpy.CBMultiCore.runMultiCoreFVA(fba, selected_reactions=None, pre_opt=True, tol=None, objF2constr=True, rhs_sense='lower', optPercentage=100.0, work_dir=None, quiet=True, debug=False, oldlpgen=False, markupmodel=True, procs=2)[source]

Run a multicore FVA where:

  • fba is an fba model instance
  • procs [default=2] number of processing threads (optimum seems to be about the number of physical cores)
cbmpy.CBMultiCore.runMultiCoreMultiEnvFVA(lp, selected_reactions=None, tol=None, rhs_sense='lower', optPercentage=100.0, work_dir=None, debug=False, procs=2)[source]

Run a multicore FVA where:

  • lp is a multienvironment lp model instance
  • procs [default=2] number of processing threads (optimum seems to be about the number of physical cores)

CBMPy: CBMultiEnv module

PySCeS Constraint Based Modelling (http://cbmpy.sourceforge.net) Copyright (C) 2009-2015 Brett G. Olivier, VU University Amsterdam, Amsterdam, The Netherlands

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see <http://www.gnu.org/licenses/>

Author: Brett G. Olivier Contact email: bgoli@users.sourceforge.net Last edit: $Author: bgoli $ ($Id: CBMultiEnv.py 305 2015-04-23 15:18:31Z bgoli $)

CBMPy: CBNetDB module

PySCeS Constraint Based Modelling (http://cbmpy.sourceforge.net) Copyright (C) 2009-2015 Brett G. Olivier, VU University Amsterdam, Amsterdam, The Netherlands

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see <http://www.gnu.org/licenses/>

Author: Brett G. Olivier Contact email: bgoli@users.sourceforge.net Last edit: $Author: bgoli $ ($Id: CBNetDB.py 426 2016-04-05 09:24:56Z bgoli $)

class cbmpy.CBNetDB.DBTools[source]

Tools to work with SQLite DB’s (optimized, no SQL required).

checkEntryInColumn(table, col, rid)[source]

Check if an entry exists in a table

  • table the table name
  • col the column name
  • rid the row to search for
closeDB()[source]

Close the DB connection and reset the DBTools instance (can be reconnected)

connectSQLiteDB(db_name, work_dir=None)[source]

Connect to a sqlite database.

  • db_name the name of the sqlite database
  • work_dir the optional database path
createDBTable(table, sqlcols)[source]

Create a database table if it does not exist:

  • table the table name
  • sqlcols a list containing the SQL definitions of the table columns: <id> <type> for example [‘gene TEXT PRIMARY KEY’, ‘aa_seq TEXT’, ‘nuc_seq TEXT’, ‘aa_len INT’, ‘nuc_len INT’]

Effectively writes CREATE TABLE “table” (<id> <type>, gene TEXT PRIMARY KEY, aa_seq TEXT, nuc_seq TEXT, aa_len INT, nuc_len INT) % table

dumpTableToTxt(table, filename)[source]

Save a table as tab separated txt file

  • table the table to export
  • filename the filename of the table dump
executeSQL(sql)[source]

Execute a SQL command:

  • sql a string containing a SQL command
fetchAll(sql)[source]

Raw SQL query e.g. ‘SELECT id FROM gene WHERE gene=”G”’

getCell(table, col, rid, cell)[source]

Get the table cell which correspond to rid in column. Returns the value or None

  • table the database table
  • col the column id
  • rid the row index id
  • cell the column of the cell you want tp extract
getColumns(table, cols)[source]

Fetch the contents of one or more columns of data in a table

  • table the database table
  • cols a list of one or more column id’s
getRow(table, col, rid)[source]

Get the table row(s) which correspond to rid in column. Returns the row(s) as a list, if the column is the primary key this is always a single entry.

  • table the database table
  • col the column id
  • rid the row index id
getTable(table, colOut=False)[source]

Returns an entire database table

  • table the table name
  • colOut optionally return a tuple of (data,ColNames)
insertData(table, data, commit=True)[source]
Insert data into a table: “INSERT INTO %s (gene, aa_seq, nuc_seq, aa_len, nuc_len) VALUES (?, ?, ?, ?, ?)” % tablename,
(str(ecg), str(prot2), str(gene2), int(len(prot2)), int(len(gene2))) )
  • table the DB table name
  • data a dictionary of {id:value} pairs
  • commit whether to commit the data insertions
updateData(table, col, rid, data, commit=True)[source]

Update already defined data

  • table the table name
  • col the column name
  • rid the row id to update
  • data a dictionary of {id:value} pairs
  • commit whether to commit the data updates

UPDATE COMPANY SET ADDRESS = ‘Texas’ WHERE ID = 6;

class cbmpy.CBNetDB.KeGGSequenceTools(url, db_name, work_dir)[source]

Using the KeGG connector this class provides tools to construct an organims specific sequence database

class cbmpy.CBNetDB.KeGGTools(url)[source]

Class that holds useful methods for querying KeGG via a SUDS provided soap client

fetchSeqfromKeGG(k_gene)[source]

Given a gene name try and retrieve the gene and amino acid sequence

class cbmpy.CBNetDB.MIRIAMTools[source]

Tools dealing with MIRIAM annotations

class cbmpy.CBNetDB.RESTClient[source]

Class that provides the basis for application specific connectors to REST web services

Close()[source]

Close the currently active connection

Connect(root)[source]

Establish HTTP connection to

  • root the site root “www.google.com”
Get(query)[source]

Perform an http GET using:

  • query e.g.
  • reply_mode [default=’‘] this is the reply mode

For example “/semanticSBML/annotate/search.xml?q=ATP”

GetLog()[source]

Return the logged history

Log(txt)[source]

Add txt to logfile history

  • txt a string
class cbmpy.CBNetDB.SemanticSBML[source]

REST client for connecting to SemanticSBML services

parseXMLtoText(xml)[source]

Parse the xml output by quickLookup() into a list of URL

  • xml XML returns from SemanticSBML
quickLookup(txt)[source]

Do a quick lookpup for txt using SemanticSBML (connectic if required) and return results. Returns a list of identifiers.org id’s in descending priority (as return)

  • txt the string to lookup
viewDataInWebrowser(maxres=10)[source]

Attempt to view #maxres results returned by SemanticSBML in the default browser

  • maxres default maximum number of results to display.

CBMPy: CBPlot module

PySCeS Constraint Based Modelling (http://cbmpy.sourceforge.net) Copyright (C) 2009-2015 Brett G. Olivier, VU University Amsterdam, Amsterdam, The Netherlands

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see <http://www.gnu.org/licenses/>

Author: Brett G. Olivier Contact email: bgoli@users.sourceforge.net Last edit: $Author: bgoli $ ($Id: CBPlot.py 305 2015-04-23 15:18:31Z bgoli $)

cbmpy.CBPlot.plotFluxVariability(fva_data, fva_names, fname, work_dir=None, title=None, ySlice=None, minHeight=None, maxHeight=None, roundec=None, autoclose=True, fluxval=True, type='png')[source]

Plots and saves as an image the flux variability results as generated by CBSolver.FluxVariabilityAnalysis.

  • fva_data FluxVariabilityAnalysis() FVA OUTPUT_ARRAY
  • fva_names FluxVariabilityAnalysis() FVA OUTPUT_NAMES
  • fname filename_base for the CSV output
  • work_dir [default=None] if set the output directory for the csv files
  • title [default=None] the user defined title for the graph
  • ySlice [default=None] this sets an absolute (fixed) limit on the Y-axis (+- ySlice)
  • minHeight [default=None] the minimum length that defined a span
  • maxHeight [default=None] the maximum length a span can obtain, bar will be limited to maxHeight and coloured yellow
  • roundec [default=None] an integer indicating at which decimal to round off output. Default is no rounding.
  • autoclose [default=True] autoclose plot after save
  • fluxval [default=True] plot the flux value
  • type [default=’png’] the output format, depends on matplotlib backend e.g. ‘png’, ‘pdf’, ‘eps’

CBMPy: CBQt4 module

Constraint Based Modelling in Python (http://pysces.sourceforge.net/getNewReaction) Copyright (C) 2009-2015 Brett G. Olivier, VU University Amsterdam, Amsterdam, The Netherlands

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see <http://www.gnu.org/licenses/>

Author: Brett G. Olivier Contact email: bgoli@users.sourceforge.net Last edit: $Author: bgoli $ ($Id: CBQt4.py 305 2015-04-23 15:18:31Z bgoli $)

cbmpy.CBQt4.createReaction(mod)[source]

Create a reaction using the graphical Reaction Creator

  • mod a CBMPy model object

CBMPy: CBRead module

PySCeS Constraint Based Modelling (http://cbmpy.sourceforge.net) Copyright (C) 2009-2015 Brett G. Olivier, VU University Amsterdam, Amsterdam, The Netherlands

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see <http://www.gnu.org/licenses/>

Author: Brett G. Olivier Contact email: bgoli@users.sourceforge.net Last edit: $Author: bgoli $ ($Id: CBRead.py 416 2016-02-23 16:12:23Z bgoli $)

cbmpy.CBRead.readCOBRASBML(fname, work_dir=None, return_sbml_model=False, delete_intermediate=False, fake_boundary_species_search=False, output_dir=None)[source]

Read in a COBRA format SBML Level 2 file with FBA annotation where and return either a CBM model object or a (cbm_mod, sbml_mod) pair if return_sbml_model=True

  • fname is the filename
  • work_dir is the working directory
  • return_sbml_model [default=False] return a a (cbm_mod, sbml_mod) pair
  • delete_intermediate [default=False] delete the intermediate SBML Level 3 FBC file
  • fake_boundary_species_search [default=False] after looking for the boundary_condition of a species search for overloaded id’s <id>_b
  • output_dir [default=None] the directory to output the intermediate SBML L3 files (if generated) default to input directory
cbmpy.CBRead.readExcel97Model(xlname, write_sbml=True, sbml_level=3, return_dictionaries=False)[source]

Reads a model encoded as an Excel97 workbook and returns it as a CBMPy model object and SBML file. Note the workbook must be formatted exactly like those produced by cbm.writeModelToExcel97(). Note that reactions have to be defined in both the reaction and network_react sheets to be included in the model.

  • xlpath the filename of the Excel workbook
  • return_model [default=True] construct and return the CBMPy model
  • write_sbml [default=True] write the SBML file to fname
  • return_dictionaries [default=False] return the dictionaries constructed when reading the Excel file (in place of the model)
  • sbml_level [default=3] write the SBML file as either SBML L2 FBA or SBML L3 FBC file.
cbmpy.CBRead.readSBML2FBA(fname, work_dir=None, return_sbml_model=False, fake_boundary_species_search=False)[source]

Read in an SBML Level 2 file with FBA annotation where:

  • fname is the filename
  • work_dir is the working directory if None then only fname is used
  • return_sbml_model [default=False] return a a (cbm_mod, sbml_mod) pair
  • fake_boundary_species_search [default=False] after looking for the boundary_condition of a species search for overloaded id’s <id>_b
cbmpy.CBRead.readSBML3FBC(fname, work_dir=None, return_sbml_model=False, xoptions={'validate': False})[source]

Read in an SBML Level 3 file with FBC annotation where and return either a CBM model object or a (cbm_mod, sbml_mod) pair if return_sbml_model=True

  • fname is the filename
  • work_dir is the working directory
  • return_sbml_model [default=False] return a a (cbm_mod, sbml_mod) pair
  • xoptions special load options enable with option = True - nogenes do not load/process genes - noannot do not load/process any annotations - validate validate model and display errors and warnings before loading
cbmpy.CBRead.readSK_FVA(filename)[source]

Read Stevens FVA results (opt.fva) file and return a list of dictionaries

cbmpy.CBRead.readSK_vertex(fname, bigfile=True, fast_rational=False, nformat='%.14f', compression=None, hdf5file=None)[source]

Reads in Stevens vertex analysis file:

  • fname the input filename (.all file that results from Stevens pipeline)
  • bigfile [default=True] this option is now always true and is left in for backwards compatability
  • fast_rational [default=False] by default off and uses SymPy for rational–>float conversion, when on uses float decomposition with a slight (2th decimal) decrease in accuracy
  • nformat [default=’%.14f’] the number format used in output files
  • compression [default=None] compression to be used in hdf5 files can be one of [None, ‘lzf’, ‘gz?’, ‘szip’]
  • hdf5file [default=None] if None then generic filename ‘_vtx_.tmp.hdf5’ is uses otherwise <hdf5file>.hdf5

and returns an hdf5 filename of the results with a single group named data which countains datasets

  • vertices
  • rays
  • lin

where all vectors are in terms of the column space of N.

cbmpy.CBRead.readSK_vertexOld(fname, bigfile=False, fast_rational=False, nformat='%.14f', compresslevel=3)[source]

Reads in Stevens vertex analysis file and returns, even more optimized for large datasets than the original.

  • a list of vertex vectors
  • a list of ray vectors
  • the basis of the lineality space as a list of vectors

all vectors in terms of the column space of N

CBMPy: CBReadtxt module

PySCeS Constraint Based Modelling (http://cbmpy.sourceforge.net) Copyright (C) 2009-2015 Brett G. Olivier, VU University Amsterdam, Amsterdam, The Netherlands

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see <http://www.gnu.org/licenses/>

Author: Brett G. Olivier Contact email: bgoli@users.sourceforge.net Last edit: $Author: bgoli $ ($Id: CBReadtxt.py 386 2015-09-28 14:05:35Z bgoli $)

cbmpy.CBReadtxt.readCSV(model_file, bounds_file=None, biomass_flux=None, model_id='FBAModel', reaction_prefix='R_', has_header=False)[source]

This function loads a CSV file and translates it into a Python object:

- *model_file* the name of the CSV file that contains the model
- *bounds_file* the name of the CSV file that contains the flux bounds
- *biomass_flux* the name of the reaction that is the objective function
- *reaction_prefix* [default='R _'] the prefix to add to input reaction ID's
- *has_header* [default=False] if there is a header row in the csv file

CBMPy: CBSolver module

PySCeS Constraint Based Modelling (http://cbmpy.sourceforge.net) Copyright (C) 2009-2015 Brett G. Olivier, VU University Amsterdam, Amsterdam, The Netherlands

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see <http://www.gnu.org/licenses/>

Author: Brett G. Olivier Contact email: bgoli@users.sourceforge.net Last edit: $Author: bgoli $ ($Id: CBSolver.py 305 2015-04-23 15:18:31Z bgoli $)

CBMPy: CBTools module

PySCeS Constraint Based Modelling (http://cbmpy.sourceforge.net) Copyright (C) 2009-2015 Brett G. Olivier, VU University Amsterdam, Amsterdam, The Netherlands

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see <http://www.gnu.org/licenses/>

Author: Brett G. Olivier Contact email: bgoli@users.sourceforge.net Last edit: $Author: bgoli $ ($Id: CBTools.py 414 2016-02-17 08:21:03Z bgoli $)

cbmpy.CBTools.addFluxAsActiveObjective(f, reaction_id, osense, coefficient=1)[source]

Adds a flux as an active objective function

  • reaction_id a string containing a reaction id
  • osense objective sense must be maximize or minimize
  • coefficient the objective funtion coefficient [default=1]
cbmpy.CBTools.addGenesFromAnnotations(fba, annotation_key='GENE ASSOCIATION', gene_pattern=None)[source]

THIS METHOD IS DERPRECATED PLEASE USE cmod.createGeneAssociationsFromAnnotations()

Add genes to the model using the definitions stored in the annotation key

  • fba and fba object
  • annotation_key the annotation dictionary key that holds the gene association for the protein/enzyme
  • gene_pattern deprecated, not needed anymore
cbmpy.CBTools.addSinkReaction(fbam, species, lb=0.0, ub=1000.0)[source]

Adds a sink reactions that consumes a model species so that X –>

  • fbam an fba model object
  • species a valid species name
  • lb lower flux bound [default = 0.0]
  • ub upper flux bound [default = 1000.0]
cbmpy.CBTools.addSourceReaction(fbam, species, lb=0.0, ub=1000.0)[source]

Adds a source reactions that produces a model species so that –> X

  • fbam an fba model object
  • species a valid species name
  • lb lower flux bound [default = 0.0]
  • ub upper flux bound [default = 1000.0]

Note reversiblity is determined by the lower bound, default 0 = irreversible. If negative then reversible.

cbmpy.CBTools.addStoichToFBAModel(fm)[source]

Build stoichiometry: this method has been refactored into the model class - cmod.buildStoichMatrix()

cbmpy.CBTools.checkExchangeReactions(fba, autocorrect=True)[source]

Scan all reactions for exchange reactions (reactions containing a boundary species), return a list of inconsistent reactions or correct automatically.

  • fba a CBMPy model
  • autocorrect [default=True] correctly set the “is_exchange” attribute on a reaction
cbmpy.CBTools.checkFluxBoundConsistency(fba)[source]

Check flux bound consistency checks for multiply defined bounds, bounds without a reaction, inconsistent bounds with respect to each other and reaction reversbility. Returns a dictionary of bounds/reactions where errors occur.

cbmpy.CBTools.checkIds(fba, items='all')[source]

Checks the id’s of the specified model attributes to see if the name is legal and if there are duplicates. Returns a list of items with errors.

  • fba a CBMPy model instance
  • items [default=’all’] ‘all’ means ‘species,reactions,flux_bounds,objectives’ of which one or more can be specified
cbmpy.CBTools.checkProducibility(mod, metabolites=None, reactions=None, retOnlyZeroEntr=False, zeroLimit=1e-11)[source]

Check for blocked metabolites by adding a sink reaction and maximizing its output. If no metabolites are defined all metabolites are used by default. Returns a dictionary of metabolite id and sink flux pairs:

  • mod a CBMPy model
  • metabolites [default=[]] if not specified by default uses all metabolites defined in model
  • reactions [default=[]] if defined, the reagents of each reaction listed here will be tested
  • retOnlyZeroEntr [default=False] default returns all results, if this is try only blocked metabolites are returned
  • zeroLimit [default=1.0e-11] values smaller than this are considered to be zero

This function was contributed by Willi Gottstein, Amsterdam, 2015.

cbmpy.CBTools.checkProducibilityMetabolites(mod, metabolites=None, retOnlyZeroEntr=False, zeroLimit=1e-11)[source]

Check for blocked metabolites by adding a sink reaction and maximizing its output. If no metabolites are defined all metabolites are used by default. Returns a dictionary of metabolite id and sink flux pairs:

  • mod a CBMPy model
  • metabolites [default=[]] if not specified by default uses all metabolites defined in model
  • reactions [default=[]] if defined, the reagents of each reaction listed here will be tested
  • retOnlyZeroEntr [default=False] default returns all results, if this is try only blocked metabolites are returned
  • zeroLimit [default=1.0e-11] values smaller than this are considered to be zero

This function was contributed by Willi Gottstein, Amsterdam, 2015.

cbmpy.CBTools.checkProducibilityReactions(mod, reactions=None, retOnlyZeroEntr=False, zeroLimit=1e-11)[source]

Check for blocked metabolites by adding a sink reaction to each reaction reagent and maximizing its output. Returns a dictionary of reagent/metabolite id and sink flux pairs:

  • mod a CBMPy model
  • reactions [default=[]] if defined, the reagents of each reaction listed here will be tested
  • retOnlyZeroEntr [default=False] default returns all results, if this is try only blocked metabolites are returned
  • zeroLimit [default=1.0e-11] values smaller than this are considered to be zero

This function was contributed by Willi Gottstein, Amsterdam, 2015.

cbmpy.CBTools.checkReactionBalanceElemental(f, Rid=None, zero_tol=1e-12)[source]

Check if the reaction is balanced using the chemical formula

  • f the FBA object
  • Rid [default = None] the reaction to check, defaults to all
  • zero_tol [default=1.0e-12] the floating point zero used for elemental balancing

This function is derived from the code found here: http://pyparsing.wikispaces.com/file/view/chemicalFormulas.py

cbmpy.CBTools.checkSuffixes(aList, suf1, suf2)[source]

Check whether there are strings in aList with the suffixes suf1 and suf2, respectively used in the function getReaByMetSuf

cbmpy.CBTools.createTempFileName()[source]

Return a temporary filename

cbmpy.CBTools.createZipArchive(zipname, files, move=False, compression='normal')[source]

Create a zip archive which contains one or more files

  • zipname the name of the zip archive to create (fully qualified)
  • files either a valid filename or a list of filenames (fully qualified)
  • move [default=False] attempt to delete input files after zip-archive creation
  • compression [default=’normal’] normal zip compression, set as None for no compression only store files (zlib not required)
cbmpy.CBTools.deSerialize(s)[source]

Deserializes a serialised object contained in a string

cbmpy.CBTools.exportArray2CSV(arr, fname)[source]

Export an array to fname.csv

  • arr the an array like object
  • fname the output filename
  • sep [default=’,’] the column separator
cbmpy.CBTools.exportArray2TXT(arr, fname)[source]

Export an array to fname.txt

  • arr the an array like object
  • fname the output filename
  • sep [default=’,’] the column separator
cbmpy.CBTools.exportLabelledArray(arr, fname, names=None, sep=', ', fmt='%f')[source]

Write a 2D array type object to file

  • arr the an array like object
  • names [default=None] the list of row names
  • fname the output filename
  • sep [default=’,’] the column separator
  • fmt [default=’%s’] the output number format
cbmpy.CBTools.exportLabelledArray2CSV(arr, fname, names=None)[source]

Export an array with row names to fname.csv

  • arr the an array like object
  • fname the output filename
  • names [default=None] the list of row names
cbmpy.CBTools.exportLabelledArray2TXT(arr, fname, names=None)[source]

Export an array with row names to fname.txt

  • arr the an array like object
  • names [default=None] the list of row names
  • fname the output filename
cbmpy.CBTools.exportLabelledArrayWithHeader(arr, fname, names=None, header=None, sep=', ', fmt='%f')[source]

Export an array with row names and header

  • arr the an array like object
  • names [default=None] the list of row names
  • header [default=None] the list of column names
  • fname the output filename
  • sep [default=’,’] the column separator
  • fmt [default=’%s’] the output number format
  • appendlist [default=False] if True append the array to fname otherwise create a new file
cbmpy.CBTools.exportLabelledArrayWithHeader2CSV(arr, fname, names=None, header=None)[source]

Export an array with row names and header to fname.csv

  • arr the an array like object
  • fname the output filename
  • names [default=None] the list of row names
  • header [default=None] the list of column names
cbmpy.CBTools.exportLabelledArrayWithHeader2TXT(arr, fname, names=None, header=None)[source]

Export an array with row names and header to fname.txt

  • arr the an array like object
  • names the list of row names
  • header the list of column names
  • fname the output filename
cbmpy.CBTools.exportLabelledLinkedList(arr, fname, names=None, sep=', ', fmt='%s', appendlist=False)[source]

Write a 2D linked list [[...],[...],[...],[...]] and optionally a list of row labels to file:

  • arr the linked list
  • fname the output filename
  • names [default=None] the list of row names
  • sep [default=’,’] the column separator
  • fmt [default=’%s’] the output number format
  • appendlist [default=False] if True append the array to fname otherwise create a new file
cbmpy.CBTools.findDeadEndMetabolites(fbam)[source]

Finds dead-end (single reaction) metabolites rows in N with a single entry), returns a list of (metabolite, reaction) ids

cbmpy.CBTools.findDeadEndReactions(fbam)[source]

Finds dead-end (single substrate/product) reactions (cols in N with a single entry), returns a list of (metabolite, reaction) ids

cbmpy.CBTools.fixReversibility(fbam, auto_correct=False)[source]

Set fluxbound lower bound from reactions reversibility information.

  • fbam and FBAModel instance
  • auto_correct (default=False) if True automatically sets lower bound to zero if required, otherwise prints a warning if false.
cbmpy.CBTools.getBoundsDict(fbamod, substring=None)[source]

Return a dictionary of reactions&bounds

cbmpy.CBTools.getExchBoundsDict(fbamod)[source]

Return a dictionary of all exchange reactions (as determined by the is_exchange attribute of Reaction)

  • fbamod a CBMPy model
cbmpy.CBTools.getModelGenesPerReaction(fba, gene_pattern=None, gene_annotation_key='GENE ASSOCIATION')[source]

Parse a BiGG style gene annotation string using default gene_pattern=’((W*w*W*))’ or (<any non-alphanum><any alphanum><any non-alphanum>)

Old eColi specific pattern ‘(bw*W)’

It is advisable to use the model methods directly rather than this function

cbmpy.CBTools.getReaByMetSuf(fba_mod, suf1, suf2, retSpec=False)[source]
  • can be used to determine all reactions in which at least two species with different suffixes are involved
  • e.g. getReaByMetSuf(fba_mod, ‘_e’, ‘_c’) returns all reactions IDs between the extracellular compartment (suffix

‘_e’) and the cytosol (suffix ‘_c)’.

INPUT: fba_mod: a model instance suf1: suffix one (string) suf2: suffix two (string)

OUTPUT: if retSpec=True, a dictionary of reaction IDs and their associated species are returned if retSpec=False, a list with reaction IDs is returned

cbmpy.CBTools.loadObj(filename)[source]

Loads a serialised Python pickle from filename.dat returns the Python object(s)

cbmpy.CBTools.merge2Models(m1, m2, ignore=None, ignore_duplicate_ids=False)[source]

Merge 2 models, this method does a raw merge of model 2 into model 1 without any model checking. Component id’s in ignore are ignored in both models and the first objective of model 1 is arbitrarily set as active. Compartments are also merged and a new “OuterMerge” compartment is also created.

In all cases duplicate id’s are tracked and ignored, essentially using the object id encountered first - usually that of model 1. Duplicate checking can be disabled by setting the ignore_duplicate_ids flag.

  • m1 model 1
  • m2 model 2
  • ignore [[]] do not merge these id’s
  • ignore_duplicate_ids [False] default behaviour that can be enabled

In development: merging genes and gpr’s.

cbmpy.CBTools.processBiGGannotationNote(fba, annotation_key='note')[source]

Parse the HTML formatted reaction information stored in the BiGG notes field. This function is being deprecated and replaced by CBTools.processSBMLAnnotationNotes()

  • requires an annotation_key which contains a BiGG HTML fragment
cbmpy.CBTools.processBiGGchemFormula(fba)[source]

Disambiguates the overloaded BiGG name NAME_CHEMFORMULA into

  • species.name NAME
  • species.chemFormula CHEMFORMULA
cbmpy.CBTools.processExchangeReactions(fba, key)[source]

Extract exchange reactions from model using key and return:

  • a dictionary of all exchange reactions without medium reactions
  • a dictionary of medium exchange reactions (negative lower bound)
cbmpy.CBTools.processSBMLAnnotationNotes(fba, annotation_key='note')[source]

Parse the HTML formatted reaction information stored in the SBML notes field currently processes BiGG and PySCeSCBM style annotations it looks for the the annotation indexed with the annotation_key

  • annotation_key [default=’note’] which contains a HTML/XHTML fragment in BiGG/PySCeSCBM format
cbmpy.CBTools.removeFixedSpeciesReactions(f)[source]

This function is a hack that removes reactions which only have boundary species as reactants and products. These are typically gene associations encoded in the Manchester style and there is probably a better way of working around this problem ...

  • f an instantiated fba model object
cbmpy.CBTools.roundOffWithSense(val, osense='max', tol=1e-08)[source]

Round of a value in a way that takes into consideration the sense of the operation that generated it

  • val the value
  • osense [default=’max’] the sense
  • tol [default=1e-8] the tolerance of the roundoff factor
cbmpy.CBTools.scanForReactionDuplicates(f, ignore_coefficients=False)[source]

This method uses uses a brute force apprach to finding reactions with matching stoichiometry

cbmpy.CBTools.scanForUnbalancedReactions(f, output='all')[source]

Scan a model for unbalanced reactions, returns a tuple of dictionaries balanced and unbalanced:

  • f an FBA model instance
  • output [default=’all’] can be one of [‘all’,’charge’,’element’]
  • charge return all charge un balanced reactions
  • element return all element un balanced reactions
cbmpy.CBTools.setSpeciesPropertiesFromAnnotations(fbam, overwriteCharge=False, overwriteChemFormula=False)[source]

This will attempt to set the model Species properties from the annotation. With the default options it will only replace missing data. With ChemicalFormula this is easy to detect however charge may have an “unknown value” of 0. Setting the optional values to true will replace any existing value with any valid annotation.

  • overwriteChemFormula [default=False]
  • overwriteCharge [default=False]
cbmpy.CBTools.splitReversibleReactions(fba, selected_reactions=None)[source]

Split a (set of) reactions into reversible reactions returns a copy of the original model

R1: A = B R1f: A -> B R1r: B -> A

  • fba an instantiated CBMPy model object
  • selected_reactions if a reversible reaction id is in here split it
cbmpy.CBTools.splitSingleReversibleReaction(fba, rid, fwd_id=None, rev_id=None)[source]

Split a single reversible reaction into two irreversible reactions, returns the original reversible reaction and bounds while deleting them from model.

R1: A = B R1_fwd: A -> B R1_rev: B -> A

  • fba an instantiated CBMPy model object
  • rid a valid reaction id
  • fwd_id [default=None] the new forward reaction id, defaults to rid_fwd
  • rev_id [default=None] the new forward reaction id, defaults to rid_rev
cbmpy.CBTools.storeObj(obj, filename, compress=False)[source]

Stores a Python obj as a serialised binary object in filename.dat

  • obj a python object
  • filename the base filename
  • compress [False] use gzip compression not implemented
cbmpy.CBTools.stringReplace(fbamod, old, new, target)[source]

This is alpha stuff, target can be:

  • ‘species’
  • ‘reactions’
  • ‘constraints’
  • ‘objectives’
  • ‘all’

CBMPy: CBWrite module

PySCeS Constraint Based Modelling (http://cbmpy.sourceforge.net) Copyright (C) 2009-2015 Brett G. Olivier, VU University Amsterdam, Amsterdam, The Netherlands

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see <http://www.gnu.org/licenses/>

Author: Brett G. Olivier Contact email: bgoli@users.sourceforge.net Last edit: $Author: bgoli $ ($Id: CBWrite.py 427 2016-04-05 09:28:55Z bgoli $)

cbmpy.CBWrite.BuildHformatFluxBounds(fba, infinity_replace=None, use_rational=False)[source]

Build and return a csio that contains the flux bounds in H format

  • fba a PySCeS-CBM FBA object
  • infinity_replace [default=None] if defined this is the abs(value) of +-<infinity>
cbmpy.CBWrite.BuildLPConstraints(fba, use_rational=False)[source]

Build and return a csio that contains constraint constructed from the StoichiometeryLP object

  • fba an fba model object which has a stoichiometry
  • use_rational write rational number output [default=False]
cbmpy.CBWrite.BuildLPConstraintsMath(fba, use_rational=False)[source]

Build and return a csio that contains the constaints in LP format Strict refers to dS/dt => 0 and dS/dt <= 0

cbmpy.CBWrite.BuildLPConstraintsRelaxed(fba)[source]

Build and return a csio that contains the constaints in LP format Relaxed refers to dS/dt >= 0

cbmpy.CBWrite.BuildLPConstraintsStrict(fba, use_rational=False)[source]

Build and return a csio that contains the constaints in LP format Strict refers to dS/dt = 0

cbmpy.CBWrite.BuildLPFluxBounds(fba, use_rational=False)[source]

Build and return a csio that contains the flux bounds in LP format

cbmpy.CBWrite.BuildLPUserConstraints(fba, use_rational=False)[source]

Build and return a csio that contains constraint constructed from the StoichiometeryLP object

  • fba an fba model object which has a stoichiometry
  • use_rational write rational number output [default=False]
cbmpy.CBWrite.WriteFVAdata(fva, names, fname, work_dir=None, roundec=None, scale_min=False, appendfile=False, info=None)[source]

INFO: this method will be deprecated please update your scripts to use “writeFVAdata()”

cbmpy.CBWrite.WriteFVAtoCSV(id, fva, names, Dir=None, fbaObj=None)[source]

INFO: this method will be deprecated please update your scripts to use “writeFVAtoCSV()”

cbmpy.CBWrite.WriteModelHFormatFBA(fba, work_dir=None, use_rational=False, fullLP=True, format='%s', infinity_replace=None)[source]

INFO: this method will be deprecated please update your scripts to use “writeModelHFormatFBA2()”

cbmpy.CBWrite.WriteModelHFormatFBA2(fba, fname=None, work_dir=None, use_rational=False, fullLP=True, format='%s', infinity_replace=None)[source]

INFO: this method will be deprecated please update your scripts to use “writeModelHFormatFBA2()”

cbmpy.CBWrite.WriteModelLP(fba, work_dir=None, fname=None, multisymb=' ', format='%s', use_rational=False, constraint_mode=None, quiet=False)[source]

INFO: this method will be deprecated please update your scripts to use “writeModelLP()”

cbmpy.CBWrite.WriteModelLPOld(fba, work_dir=None, multisymb=' ', lpt=True, constraint_mode='strict', use_rational=False, format='%s')[source]

INFO: this method will be deprecated please update your scripts to use “writeModelLPOld()”

cbmpy.CBWrite.WriteModelRaw(fba, work_dir=None)[source]

INFO: this method will be deprecated please update your scripts to use “writeModelRaw()”

cbmpy.CBWrite.convertExcelToFloat(num)[source]

Converts an Excel “number” to a float

  • num a number
cbmpy.CBWrite.convertFloatToExcel(num, roundoff)[source]

Converts a float to Excel compatible “number”

  • num a number
  • roundoff the number of roundoff digits for round()
cbmpy.CBWrite.exportModel(fba, fname=None, fmt='lp', work_dir=None, use_rational='both')[source]

Export the FBA model in different formats:

  • fba the FBA model
  • fname [default=None] the exported filename if None then fba.getPid() is used
  • fmt [default=’lp’] the export format can be one of: ‘lp’ (CPLEX), ‘hformat’ (Polyhedra), ‘all’ (both)
  • use_rational [default=’both’] if all or hformat is specified should hformat files be written using rational math or not. The default both is the legacy behaviour and writes both.

Note that ‘hformat’ ignores ‘fname’ and only uses fba.getPid() this is a legacy behaviour

cbmpy.CBWrite.generateBGID(num, prefix)[source]
Create a BGID generator, which is <prefix><num> where perfix is two letters num is padded to 6 figures
  • num the starting number
  • prefix the two letter prefix
cbmpy.CBWrite.printFBASolution(fba, include_all=False)[source]

Prints the FBA optimal solution to the screen.

  • fba an FBA model object
  • include_all include all variables
cbmpy.CBWrite.writeCOBRASBML(fba, fname, directory=None)[source]

Takes an FBA model object and writes it to file as a COBRA compatible :

  • fba an fba model object
  • fname the model will be written as XML to fname
  • directory [default=None] if defined it is prepended to fname
cbmpy.CBWrite.writeFVAdata(fvadata, names, fname, work_dir=None, roundec=None, scale_min=False, appendfile=False, info=None)[source]

Takes the resuls of a FluxVariabilityAnalysis method and writes it to a nice csv file. Note this method replaces the glpk/cplx_WriteFVAtoCSV methods. Data is output as a csv file with columns: FluxName, FVA_MIN, FVA_MAX, OPT_VAL, SPAN

  • fvadata FluxVariabilityAnalysis() FVA OUTPUT_ARRAY
  • names FluxVariabilityAnalysis() FVA OUTPUT_NAMES
  • fname filename_base for the CSV output
  • work_dir [default=None] if set the output directory for the csv files
  • roundec [default=None] an integer indicating at which decimal to round off output. Default is no rounding.
  • scale_min [default=False] normalise each flux such that that FVA_MIN = 0.0
  • appendfile [default=False] instead of opening a new file try and append the data
  • info [default=None] a string added to the results as an extra column, useful with appendfile
cbmpy.CBWrite.writeFVAtoCSV(fvadata, names, fname, Dir=None, fbaObj=None)[source]

Takes the resuls of a FluxVariabilityAnalysis method and writes it to a nice csv file. Note this method replaces the glpk/cplx_WriteFVAtoCSV methods.

  • fvadata FluxVariabilityAnalysis() OUTPUT_ARRAY
  • names FluxVariabilityAnalysis() OUTPUT_NAMES
  • fname filename_base for the CSV output
  • Dir [default=None] if set the output directory for the csv files
  • fbaObj [default=None] if supplied adds extra model information into the output tables
cbmpy.CBWrite.writeMinDistanceLPwithCost(fname, fbas, work_dir=None, ignoreDistance=[], constraint_mode='strict')[source]

For backwards compatability only

cbmpy.CBWrite.writeModelHFormatFBA(fba, work_dir=None, use_rational=False, fullLP=True, format='%s', infinity_replace=None)[source]

Write an FBA-LP in polynomial H-Format file. This version has been replaced by writeModelHFormatFBA2() but is kept for backwards compatability.

  • fba a PySCeS-CBM FBA object
  • Work_dir [default=None] the output directory
  • use_rational [default=false] use rational numbers in output (requires sympy)
  • fullLP [default=True] include the default objective function as a maximization target
  • format [default=’%s’] the number format string
  • infinity_replace [default=None] if defined this is the abs(value) of +-<infinity>
cbmpy.CBWrite.writeModelHFormatFBA2(fba, fname=None, work_dir=None, use_rational=False, fullLP=True, format='%s', infinity_replace=None)[source]

Write an FBA-LP in polynomial H-Format file. This is an improved version of WriteModelHFormatFBA() which it replaces. Note that if a SymPy matrix is used as input then use_rational is automatically enabled.

  • fba a PySCeS-CBM FBA object
  • fname [default=None] the output filename, fba.getPid() if not defined
  • Work_dir [default=None] the output directory
  • use_rational [default=false] use rational numbers in output (requires sympy)
  • fullLP [default=True] include the default objective function as a maximization target
  • format [default=’%s’] the number format string
  • infinity_replace [default=None] if defined this is the abs(value) of +-<infinity>
cbmpy.CBWrite.writeModelInfoToFile(fba, fname, Dir=None, separator=', ', only_exchange=False, met_type='all')[source]

This function writes a CBModel to file

  • fba an instance of an PySCeSCBM model
  • fname the output filename
  • Dir [default=None] use directory if not None
  • separator [default=’,’] the column separator
  • only_exchange [default=False] only output fluxes labelled as exchange reactions
  • type [default=’all’] only output certain type of species: ‘all’,’boundary’ or ‘variable’
cbmpy.CBWrite.writeModelLP(fba, work_dir=None, fname=None, multisymb=' ', format='%s', use_rational=False, constraint_mode=None, quiet=False)[source]

Writes an FBA object as an LP in CPLEX LP format

  • fba an instantiated FBAmodel instance
  • work_dir directory designated for output
  • fname the file name [default=fba.getPid()]
  • multisymb the multiplication symbol (default: <space>)
  • format the number format of the output
  • use_rational output rational numbers [default=False]
  • quiet [default=False] supress information messages
cbmpy.CBWrite.writeModelLPOld(fba, work_dir=None, multisymb=' ', lpt=True, constraint_mode='strict', use_rational=False, format='%s')[source]

Writes a fba as an LP/LPT

  • fba an instantiated FBAmodel instance
  • work_dir directory designated for output
  • multisymb the multiplication symbol (default: <space>)
  • lpt the file format (default: True for lpt) or False for lp
cbmpy.CBWrite.writeModelRaw(fba, work_dir=None)[source]

Writes a fba (actually just dumps it) to a text file.

  • fba an instantiated FBAmodel instance
  • work_dir directory designated for output
cbmpy.CBWrite.writeModelToCOMBINEarchive(mod, fname=None, directory=None, sbmlname=None, withExcel=True, vc_given='CBMPy', vc_family='Software', vc_email='None', vc_org='cbmpy.sourceforge.net', add_cbmpy_annot=True, add_cobra_annot=True)[source]

Write a model in SBML and Excel format to a COMBINE archive using the following information:

  • mod a model object
  • fname the output base filename, archive will be <fname>.zip
  • directory [default=None] created the combine archive ‘directory’
  • sbmlname [default=’None’] If sbmlname is defined then SBML file is <sbmlname>.xml otherwise sbml will be <fname>.xml.
  • withExcel [default=True] include a human readable Excel spreadsheet version of the model
  • vc_given [default=’CBMPy’] first name
  • vc_family [default=’Software’] family name
  • vc_email [default=’None’] email
  • vc_org [default=’None’] organisation
  • add_cbmpy_annot [default=True] add CBMPy KeyValueData annotation. Replaces <notes>
  • add_cobra_annot [default=True] add COBRA <notes> annotation
cbmpy.CBWrite.writeModelToExcel97(fba, filename, roundoff=6)[source]

Exports the model as an Excel 97 spreadsheet

  • fba a CBMPy model instance
  • filename the filename of the workbook
  • roundoff [default=6] the number of digits to round off to
cbmpy.CBWrite.writeOptimalSolution(fba, fname, Dir=None, separator=', ', only_exchange=False)[source]

This function writes the optimal solution to file

  • fba an instance of an PySCeSCBM model
  • fname the output filename
  • Dir [default=None] use current directory if not None
  • separator [default=’,’] the column separator
  • only_exchange [default=False] only output fluxes labelled as exchange reactions
cbmpy.CBWrite.writeProteinCostToCSV(fba, fname)[source]

Writes the protein costs ‘CBM_PEPTIDE_COST’ annotation toa csv file.

  • fba an instantiated FBA object
  • fname the exported file name
cbmpy.CBWrite.writeReactionInfoToFile(fba, fname, Dir=None, separator=', ', only_exchange=False)[source]

This function writes a CBModel to file

  • fba an instance of an PySCeSCBM model
  • fname the output filename
  • Dir [default=None] use directory if not None
  • separator [default=’,’] the column separator
  • only_exchange [default=False] only output fluxes labelled as exchange reactions
cbmpy.CBWrite.writeSBML2FBA(fba, fname, directory=None, sbml_level_version=None)[source]

Takes an FBA model object and writes it to file as SBML L2 with FBA annotations. Note if you want to write BiGG/FAME style annotations then you must use sbml_level_version=(2,1)

  • fba an fba model object
  • fname the model will be written as XML to fname
  • sbml_level_version [default=None] a tuple containing the SBML level and version e.g. (2,1)

This is a utility wrapper for the function CBXML.sbml_writeSBML2FBA

cbmpy.CBWrite.writeSBML3FBC(fba, fname, directory=None, gpr_from_annot=False, add_groups=False, add_cbmpy_annot=True, add_cobra_annot=False, xoptions={'fbc_version': 1, 'validate': False, 'compress_bounds': True})[source]

Takes an FBA model object and writes it to file as SBML L3 FBC:

  • fba an fba model object
  • fname the model will be written as XML to fname
  • directory [default=None] if defined it is prepended to fname
  • gpr_from_annot [default=True] if enabled will attempt to add the gene protein associations from the annotations if no gene protein association objects exist
  • add_cbmpy_annot [default=True] add CBMPy KeyValueData annotation. Replaces <notes>
  • add_cobra_annot [default=True] add COBRA <notes> annotation
  • xoptions extended options
    • validate [default=False] validate the output SBML file
    • compress_bounds [default=False] try compress output flux bound parameters
cbmpy.CBWrite.writeSBML3FBCV2(fba, fname, directory=None, gpr_from_annot=False, add_groups=False, add_cbmpy_annot=True, add_cobra_annot=False, validate=False, compress_bounds=True)[source]

Takes an FBA model object and writes it to file as SBML L3 FBCv2 :

  • fba an fba model object
  • fname the model will be written as XML to fname
  • directory [default=None] if defined it is prepended to fname
  • gpr_from_annot [default=False] if enabled will attempt to add the gene protein associations from the annotations
  • add_groups [default=False] add SBML3 groups (if supported by libSBML)
  • add_cbmpy_annot [default=True] add CBMPy KeyValueData annotation. Replaces <notes>
  • add_cobra_annot [default=False] add COBRA <notes> annotation
  • validate [default=False] validate the output SBML file
  • compress_bounds [default=True] try compress output flux bound parameters
cbmpy.CBWrite.writeSensitivitiesToCSV(sensitivities, fname)[source]

Write out a sensitivity report using the objective sensitivities and bound sensitivity dictionaries created by e.g. cplx_getSensitivities().

  • sensitivity tuple containing
  • obj_sens dictionary of objective coefficient sensitivities (per flux)
  • rhs_sens dictionary of constraint rhs sensitivities (per constraint)
  • bound_sens dictionary of bound sensitivities (per flux)
  • fname output filename e.g. fname.csv
cbmpy.CBWrite.writeSolutions(fname, sols=[], sep=', ', extra_output=None, fba=None)[source]

Write 2 or more solutions where a solution is a dictionary of flux:value pairs:

  • fname the export filename
  • sols a list of dictionaries containing flux:value pairs (e.g. output by cmod.getReactionValues())
  • sep [default=’,’] the column separator
  • extra_output [default=None] add detailed information to output e.g. reaction names by giving a CBModel object as an argument to extra_output.
  • fba an fba model that canbe used for extra_output
cbmpy.CBWrite.writeSpeciesInfoToFile(fba, fname, Dir=None, separator=', ', met_type='all')[source]

This function writes a CBModel to file

  • fba an instance of an PySCeSCBM model
  • fname the output filename
  • Dir [default=None] use directory if not None
  • separator [default=’,’] the column separator
  • met_type [default=’all’] only output certain type of species: ‘all’,’boundary’ or ‘variable’
cbmpy.CBWrite.writeStoichiometricMatrix(fba, fname=None, work_dir=None, use_rational=False, fullLP=True, format='%s', infinity_replace=None)[source]

Write an FBA-LP in polynomial H-Format file. This is an improved version of WriteModelHFormatFBA() which it replaces but is kept for backwards compatability.

  • fba a PySCeS-CBM FBA object
  • fname [default=None] the output filename, fba.getPid() if not defined
  • Work_dir [default=None] the output directory
  • use_rational [default=false] use rational numbers in output (requires sympy)
  • fullLP [default=True] include the default objective function as a maximization target
  • format [default=’%s’] the number format string
  • infinity_replace [default=None] if defined this is the abs(value) of +-<infinity>

CBMPy: CBWx module

PySCeS Constraint Based Modelling (http://cbmpy.sourceforge.net) Copyright (C) 2009-2015 Brett G. Olivier, VU University Amsterdam, Amsterdam, The Netherlands

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see <http://www.gnu.org/licenses/>

Author: Brett G. Olivier Contact email: bgoli@users.sourceforge.net Last edit: $Author: bgoli $ ($Id: CBWx.py 378 2015-09-14 16:32:38Z bgoli $)

class cbmpy.CBWx.HtmlWindowMod(*args, **kwargs)[source]

Overrides ‘OnLinkClicked’ to open links in external browser

cbmpy.CBWx.circlePoints(totalPoints=4, startAngle=0, arc=360, circleradius=1, centerxy=(0, 0), direction='forward', evenDistribution=True)[source]

Returns a list of points evenly spread around a circle:

  • totalPoints how many points
  • startAngle where to start
  • arc how far to go
  • circleradius radius
  • centerxy origin
  • direction ‘forward’ or ‘backward’
  • evenDistribution True/False

This code has been adapted from the Flash example that can be found here: http://www.lextalkington.com/blog/2009/12/generate-points-around-a-circles-circumference/

CBMPy: CBXML module

PySCeS Constraint Based Modelling (http://cbmpy.sourceforge.net) Copyright (C) 2009-2015 Brett G. Olivier, VU University Amsterdam, Amsterdam, The Netherlands

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see <http://www.gnu.org/licenses/>

Author: Brett G. Olivier Contact email: bgoli@users.sourceforge.net Last edit: $Author: bgoli $ ($Id: CBXML.py 443 2016-04-18 21:28:42Z bgoli $)

class cbmpy.CBXML.MLStripper[source]

Class for stripping a string of HTML/XML used from: http://stackoverflow.com/questions/753052/strip-html-from-strings-in-python

cbmpy.CBXML.sbml_convertCOBRASBMLtoFBC(fname, outname=None, work_dir=None, output_dir=None)[source]

Read in a COBRA SBML Level 2 file and return the name of the created SBML Level 3 with FBC file that is created in the output directory

  • fname is the filename
  • outname the name of the output file. If not specified then <filename>.l3fbc.xml is used as default
  • work_dir [default=None] is the working directory
  • output_dir [default=None] is the output directory (default is work_dir)

This method is based on code from libSBML (http://sbml.org) in the file “convertCobra.py” written by Frank T. Bergmann.

cbmpy.CBXML.sbml_convertSBML3FBCToCOBRA(fname, outname=None, work_dir=None, output_dir=None)[source]

Read in a SBML Level 3 file and return the name of the created COBRA file that is created in the output directory

  • fname is the filename
  • outname the name of the output file. If not specified then <filename>.cobra.xml is used as default
  • work_dir [default=None] is the working directory
  • output_dir [default=None] is the output directory (default is work_dir)

This method is based on code from libSBML (http://sbml.org) in the file “convertFbcToCobra.py” written by Frank T. Bergmann.

cbmpy.CBXML.sbml_createAssociationFromAST(node, out)[source]

Converts a GPR string ‘((g1 and g2) or g3)’ to an association via a Python AST. In future I will get rid of all the string elements and work only with associations and AST’s.

  • node a Python AST note (e.g. body)
  • out a new shiny FBC V2 GeneProductAssociation
cbmpy.CBXML.sbml_createModelL2(fba, level=2, version=1)[source]

Create an SBML model and document:

  • fba a PySCeSCBM model instance
  • level always 2
  • version always 1

and returns:

  • model an SBML model
cbmpy.CBXML.sbml_exportSBML2FBAModel(document, filename, directory=None, return_doc=False, remove_note_body=False)[source]

Writes an SBML model object to file. Note this is an internal SBML method use sbml_writeSBML2FBA() to write an FBA model:

  • model a libSBML model instance
  • filename the output filename
  • directory [default=None] by default use filename otherwise join, <dir><filename>
  • return_doc [default=False] return the SBML document used to write the XML
cbmpy.CBXML.sbml_getCVterms(sb, model=False)[source]

Get the MIRIAM compliant CV terms and return a MIRIAMAnnotation or None

  • sb a libSBML SBase derived object
  • model is this a BQmodel term
cbmpy.CBXML.sbml_getGeneRefs(association, out)[source]

Walk through a gene association and extract GeneRefs inspired by Frank

cbmpy.CBXML.sbml_readCOBRANote(s)[source]

Parses a COBRA style note from a XML string

  • s an XML string
cbmpy.CBXML.sbml_readCOBRASBML(fname, work_dir=None, return_sbml_model=False, delete_intermediate=False, fake_boundary_species_search=False, output_dir=None, speciesAnnotationFix=True)[source]

Read in a COBRA format SBML Level 2 file with FBA annotation where and return either a CBM model object or a (cbm_mod, sbml_mod) pair if return_sbml_model=True

  • fname is the filename
  • work_dir is the working directory
  • return_sbml_model [default=False] return a a (cbm_mod, sbml_mod) pair
  • delete_intermediate [default=False] delete the intermediate SBML Level 3 FBC file
  • fake_boundary_species_search [default=False] after looking for the boundary_condition of a species search for overloaded id’s <id>_b
  • output_dir [default=None] the directory to output the intermediate SBML L3 files (if generated) default to input directory
  • speciesAnnotationFix [default=True]
cbmpy.CBXML.sbml_readKeyValueDataAnnotation(annotations)[source]

Reads KeyValueData annotation (http://pysces.sourceforge.net/KeyValueData) and returns a dictionary of key:value pairs

cbmpy.CBXML.sbml_readSBML2FBA(fname, work_dir=None, return_sbml_model=False, fake_boundary_species_search=False)[source]

Read in an SBML Level 2 file with FBA annotation where and return either a CBM model object or a (cbm_mod, sbml_mod) pair if return_sbml_model=True

  • fname is the filename
  • work_dir is the working directory (only used if not None)
  • return_sbml_model [default=False] return a a (cbm_mod, sbml_mod) pair
  • fake_boundary_species_search [default=False] after looking for the boundary_condition of a species search for overloaded id’s <id>_b
cbmpy.CBXML.sbml_readSBML3FBC(fname, work_dir=None, return_sbml_model=False, xoptions={})[source]

Read in an SBML Level 3 file with FBC annotation where and return either a CBM model object or a (cbm_mod, sbml_mod) pair if return_sbml_model=True

  • fname is the filename
  • work_dir is the working directory
  • return_sbml_model [default=False] return a a (cbm_mod, sbml_mod) pair
  • xoptions special load options enable with option = True - nogenes do not load/process genes - noannot do not load/process any annotations - validate validate model and display errors and warnings before loading
cbmpy.CBXML.sbml_setCVterms(sb, uridict, model=False)[source]

Add MIRIAM compliant CV terms to a sbml object from a CBM object

  • sb a libSBML SBase derived object
  • uridict a dictionary of uri’s as produced by getAllMIRIAMUris()
  • model is this a BQmodel term [deprecated attribute, ignored and autodetected]
cbmpy.CBXML.sbml_setCompartmentsL3(model, fba)[source]

Sets the model compartments.

  • model a libSBML model instance
  • fba a PySCeSCBM model instance
cbmpy.CBXML.sbml_setDescription(model, fba)[source]

Sets the model description as a <note> containing txt in an HTML paragraph on the model object.

  • model a libSBML model instance
  • fba a PySCeSCBM model instance
cbmpy.CBXML.sbml_setGroupsL3(cs, fba)[source]

add groups to the SBML model

  • cs a CBMLtoSBML instance
  • fba a CBMPy model instance
cbmpy.CBXML.sbml_setNotes3(obj, s)[source]

Formats the CBMPy notes as an SBML note and adds it to the SBMl object

  • obj an SBML object
  • s a string that should be added as a note
cbmpy.CBXML.sbml_setReactionsL2(model, fba, return_dict=False)[source]

Add the FBA instance reactions to the SBML model

  • model an SBML model instance
  • fba a PySCeSCBM model instance
  • return_dict [default=False] if True do not add reactions to SBML document instead return a dictionary description of the reactions
cbmpy.CBXML.sbml_setReactionsL3Fbc(fbcmod, fba, return_dict=False, add_cobra_anno=False, add_cbmpy_anno=True, fbc_version=1)[source]

Add the FBA instance reactions to the SBML model

  • fbcmod a CBM2SBML instance
  • fba a PySCeSCBM model instance
  • return_dict [default=False] if True do not add reactions to SBML document instead return a dictionary description of the reactions
  • add_cbmpy_anno [default=True] add CBMPy KeyValueData annotation. Replaces <notes>
  • add_cobra_anno [default=False] add COBRA <notes> annotation
  • fbc_version [default=1] writes either FBC v1 (2013) or v2 (2015)
cbmpy.CBXML.sbml_setSpeciesL2(model, fba, return_dicts=False)[source]

Add the species definitions to the SBML object:

  • model [default=’‘] a libSBML model instance or can be None if return_dicts == True
  • fba a PySCeSCBM model instance
  • return_dicts [default=False] only returns the compartment and species dictionaries without updated the SBML

returns:

  • compartments a dictionary of compartments (except when give return_dicts argument)
cbmpy.CBXML.sbml_setSpeciesL3(model, fba, return_dicts=False, add_cobra_anno=False, add_cbmpy_anno=True, substance_units=True)[source]

Add the species definitions to the SBML object:

  • model and SBML model instance or can be None if return_dicts == True
  • fba a PySCeSCBM model instance
  • return_dicts [default=False] only returns the compartment and species dictionaries without updating the SBML
  • add_cbmpy_anno [default=True] add CBMPy KeyValueData annotation. Replaces <notes>
  • add_cobra_anno [default=False] add COBRA <notes> annotation
  • substance_units [default=True] defines the species in amounts rather than concentrations (necessary for default mmol/gdw.h)

returns:

  • compartments a dictionary of compartments (except when given return_dicts argument)
cbmpy.CBXML.sbml_setUnits(model, units=None, give_default=False, L3=True)[source]

Adds units to the model:

  • model a libSBML model instance
  • units [default=None] a dictionary of units, if None default units are used
  • give_default [default=False] if true method returns the default unit dictionary
  • L3 [default=True] use the L3 defaults
cbmpy.CBXML.sbml_setValidationOptions(D, level)[source]

set the validation level of an SBML document

  • D an SBML document
  • level the level of consistency check can be either one of:
  • ‘normal’ basic id checking only
  • ‘full’ all checks enabled
cbmpy.CBXML.sbml_validateDocument(D, fullmsg=False)[source]

Validates and SBML document returns three dictionaries, errors, warnings, other and a boolean indicating an invalid document:

  • D and SBML document
  • fullmsg [default=False] optionally display the full error message
cbmpy.CBXML.sbml_writeAnnotationsAsCOBRANote(annotations)[source]

Writes the annotations dictionary as a COBRA compatible SBML <note>

cbmpy.CBXML.sbml_writeCOBRASBML(fba, fname, directory=None)[source]

Takes an FBA model object and writes it to file as a COBRA compatible :

  • fba an fba model object
  • fname the model will be written as XML to fname
  • directory [default=None] if defined it is prepended to fname
cbmpy.CBXML.sbml_writeKeyValueDataAnnotation(annotations)[source]

Writes the key:value annotations as a KeyValueData annotation (http://pysces.sourceforge.net/KeyValueData)

cbmpy.CBXML.sbml_writeSBML2FBA(fba, fname, directory=None, sbml_level_version=None)[source]

Takes an FBA model object and writes it to file as SBML L3 FBA:

  • fba an fba model object
  • fname the model will be written as XML to fname
  • directory [default=None] if defined it is prepended to fname
  • sbml_level_version [default=None] a tuple containing the SBML level and version e.g. (2,4) (ignored)
cbmpy.CBXML.sbml_writeSBML3FBC(fba, fname, directory=None, sbml_level_version=(3, 1), autofix=True, return_fbc=False, gpr_from_annot=False, add_groups=False, add_cbmpy_annot=True, add_cobra_annot=False, xoptions={})[source]

Takes an FBA model object and writes it to file as SBML L3 FBC:

  • fba an fba model object
  • fname the model will be written as XML to fname
  • directory [default=None] if defined it is prepended to fname
  • sbml_level_version [default=(3,1)] a tuple containing the SBML level and version e.g. (3,1)
  • autofix convert <> to <=>=
  • return_fbc return the FBC converter instance
  • gpr_from_annot [default=False] if enabled will attempt to add the gene protein associations from the annotations if no gene protein association objects exist
  • add_cbmpy_annot [default=True] add CBMPy KeyValueData annotation. Replaces <notes>
  • add_cobra_annot [default=True] add COBRA <notes> annotation
  • xoptions extended options
    • fbc_version [default=1] write SBML3FBC using version 1 (2013) or version 2 (2015)
    • validate [default=False] validate the output SBML file
    • compress_bounds [default=False] try compress output flux bound parameters
cbmpy.CBXML.setCBSBOterm(sbo, obj)[source]

Given an SBOterm from libSBML, add it to a CBMPy object

  • sbo the sbo term string
  • obj the CBMPy Fbase derived object
cbmpy.CBXML.xml_addSBML2FBAFluxBound(document, rid, operator, value, fbid=None)[source]

Adds an SBML3FBA flux bound to the document:

  • document a minidom XML document created by xml_createSBML2FBADoc
  • rid the reaction id
  • operator one of [‘greater’,’greaterEqual’,’less’,’lessEqual’,’equal’,’>’,’>=’,’<’,’<=’,’=’]
  • value a float which will be cast to a string using str(value)
  • fbid the flux bound id, autogenerated by default
cbmpy.CBXML.xml_addSBML2FBAObjective(document, objective, active=True)[source]

Adds an objective element to the documents listOfObjectives and sets the active attribute:

  • document a minidom XML document created by xml_createSBML2FBADoc
  • objective a minidom XML objective element created with xml_createSBML2FBAObjective
  • active [default=True] a boolean flag specifiying whether this objective is active
cbmpy.CBXML.xml_createListOfFluxObjectives(document, fluxObjectives)[source]

Create a list of fluxObjectives to add to an Objective:

  • document a minidom XML document created by xml_createSBML2FBADoc
  • fluxobjs a list of (rid, coefficient) tuples
cbmpy.CBXML.xml_createSBML2FBADoc()[source]

Create a ‘document’ to store the SBML2FBA annotation, returns:

  • DOC a minidom document
cbmpy.CBXML.xml_createSBML2FBAObjective(document, oid, sense, fluxObjectives)[source]

Create a list of fluxObjectives to add to an Objective:

  • document a minidom XML document created by xml_createSBML2FBADoc
  • oid the objective id
  • sense a string containing the objective sense either: maximize or minimize
  • fluxObjectives a list of (rid, coefficient) tuples
cbmpy.CBXML.xml_getSBML2FBAannotation(fba, fname=None)[source]

Takes an FBA model object and returns the SBML3FBA annotation as an XML string:

  • fba an fba model object
  • fname [default=None] if supplied the XML will be written to file fname
cbmpy.CBXML.xml_stripTags(html)[source]

Strip a string of HTML/XML, returns a string

  • html the string containing html
cbmpy.CBXML.xml_viewSBML2FBAXML(document, fname=None)[source]

Print a minidom XML document to screen or file, arguments:

  • document a minidom XML document
  • fname [default=None] by default print to screen or write to file fname

PyscesStoich

PySCeS stoichiometric analysis classes.

class cbmpy.PyscesStoich.MathArrayFunc[source]

PySCeS array functions - used by Stoich

MatrixFloatFix(mat, val=1.e-15)[source]

Clean an array removing any floating point artifacts defined as being smaller than a specified value. Processes an array inplace

Arguments:

mat: the input 2D array val [default=1.e-15]: the threshold value (effective zero)

MatrixValueCompare(matrix)[source]

Finds the largest/smallest abs(value) > 0.0 in a matrix. Returns a tuple containing (smallest,largest) values

Arguments:

matrix: the input 2D array

SwapCol(res_a, r1, r2)[source]

Swap two columns using BLAS swap, arrays can be (or are upcast to) type double (d) or double complex (D). Returns the colswapped array

Arguments:

res_a: the input array r1: the first column to be swapped r2: the second column to be swapped

SwapCold(res_a, c1, c2)[source]

Swaps two double (d) columns in an array using BLAS DSWAP. Returns the colswapped array.

Arguments:

res_a: input array c1: column index 1 c2: column index 2

SwapColz(res_a, c1, c2)[source]

Swaps two double complex (D) columns in an array using BLAS ZSWAP. Returns the colswapped array.

Arguments:

res_a: input array c1: column index 1 c2: column index 2

SwapElem(res_a, r1, r2)[source]

Swaps two elements in a 1D vector

Arguments:

res_a: the input vector r1: index 1 r2: index 2

SwapRow(res_a, r1, r2)[source]

Swaps two rows using BLAS swap, arrays can be (or are upcast to) type double (d) or double complex (D). Returns the rowswapped array.

Arguments:

res_a: the input array r1: the first row index to be swapped r2: the second row index to be swapped

SwapRowd(res_a, c1, c2)[source]

Swaps two double (d) rows in an array using BLAS DSWAP. Returns the rowswapped array.

Arguments:

res_a: input array c1: row index 1 c2: row index 2

SwapRowz(res_a, c1, c2)[source]

Swaps two double complex (D) rows in an array using BLAS ZSWAP. Returns the rowswapped array.

Arguments:

res_a: input array c1: row index 1 c2: row index 2

assertRank2(*arrays)[source]

Check that we are using a 2D array

Arguments:

*arrays: input array(s)

castCopyAndTranspose(type, *arrays)[source]

Cast numeric arrays to required type and transpose

Arguments:

type: the required type to cast to *arrays: the arrays to be processed

commonType(*arrays)[source]

Numeric detect and set array precision (will be replaced with new scipy.core compatible code when ready)

Arguments:

*arrays: input arrays

class cbmpy.PyscesStoich.Stoich(input)[source]

PySCeS stoichiometric analysis class: initialized with a stoichiometric matrix N (input)

AnalyseK()[source]

Evaluate the stoichiometric matrix and calculate the nullspace using LU decomposition and backsubstitution . Generates the MCA K and Ko arrays and associated row and column vectors

Arguments: None

AnalyseL()[source]

Evaluate the stoichiometric matrix and calculate the left nullspace using LU factorization and backsubstitution. Generates the MCA L, Lo, Nr and Conservation matrix and associated row and column vectors

Arguments: None

BackSubstitution(res_a, row_vector, column_vector)[source]

Jordan reduction of a scaled upper triangular matrix. The returned array is now in the form [I R] and can be used for nullspace determination. Modified row and column tracking vetors are also returned.

Arguments:

res_a: unitary pivot upper triangular matrix row_vector: row tracking vector column_vector: column tracking vector

GetUpperMatrix(a)[source]

Core analysis algorithm; an input is preconditioned using PivotSort_initial and then cycles of PLUfactorize and PivotSort are run until the factorization is completed. During this process the matrix is reordered by column swaps which emulates a full pivoting LU factorization. Returns the pivot matrix P, upper factorization U as well as the row/col tracking vectors.

Arguments:

a: a stoichiometric matrix

GetUpperMatrixUsingQR(a)[source]

GetUpperMatrix(a)

Core analysis algorithm; an input is preconditioned using PivotSort_initial and then cycles of PLUfactorize and PivotSort are run until the factorization is completed. During this process the matrix is reordered by column swaps which emulates a full pivoting LU factorization. Returns the pivot matrix P, upper factorization U as well as the row/col tracking vectors.

Arguments:

a: a stoichiometric matrix

K_split_R(R_a, row_vector, column_vector)[source]

Using the R factorized form of the stoichiometric matrix we now form the K and Ko matrices. Returns the r_ipart,Komatrix,Krow,Kcolumn,Kmatrix,Korow,info

Arguments:

R_a: the Gauss-Jordan reduced stoichiometric matrix row_vector: row tracking vector column_vector: column tracking vector

L_split_R(Nfull, R_a, row_vector, column_vector)[source]

Takes the Gauss-Jordan factorized N^T and extract the L, Lo, conservation (I -Lo) and reduced stoichiometric matrices. Returns: lmatrix_col_vector, lomatrix, lomatrix_row, lomatrix_co, nrmatrix, Nred_vector_row, Nred_vector_col, info

Arguments:

Nfull: the original stoichiometric matrix N R_a: gauss-jordan factorized form of N^T row_vector: row tracking vector column_vector: column tracking vector

PLUfactorize(a_in)[source]

Performs an LU factorization using LAPACK D/ZGetrf. Now optimized for FLAPACK interface. Returns LU - combined factorization, IP - rowswap information and info - Getrf error control.

Arguments:

a_in: the matrix to be factorized

PivotSort(a, row_vector, column_vector)[source]

This is a sorting routine that accepts a matrix and row/colum vectors and then sorts them so that: there are no zero rows (by swapping with first non-zero row) The abs(largest) pivots are moved onto the diagonal to maintain numerical stability. Row and column swaps are recorded in the tracking vectors.

Arguments:

a: the input array row_vector: row tracking vector column_vector: column tracking vector

PivotSort_initial(a, row_vector, column_vector)[source]

This is a sorting routine that accepts a matrix and row/colum vectors and then sorts them so that: the abs(largest) pivots are moved onto the diagonal to maintain numerical stability i.e. the matrix diagonal is in descending max(abs(value)). Row and column swaps are recorded in the tracking vectors.

Arguments:

a: the input array row_vector: row tracking vector column_vector: column tracking vector

SVD_Rank_Check(matrix=None, factor=1.0e4, resultback=0)[source]

Calculates the dimensions of L/L0/K/K) by way of SVD and compares them to the Guass-Jordan results. Please note that for LARGE ill conditioned matrices the SVD can become numerically unstable when used for nullspace determinations

Arguments:

matrix [default=None]: the stoichiometric matrix default is self.Nmatrix factor [default=1.0e4]: factor used to calculate the ‘zero pivot’ mask = mach_eps*factor resultback [default=0]: return the SVD results, U, S, vh

ScalePivots(a_one)[source]

Given an upper triangular matrix U, this method scales the diagonal (pivot values) to one.

Arguments:

a_one: an upper triangular matrix U

SplitLU(plu, row, col, t)[source]

PLU takes the combined LU factorization computed by PLUfactorize and extracts the upper matrix. Returns U.

Arguments:

plu: LU factorization row: row tracking vector col: column tracking vector t [default=None)]: typecode argument (currently not used)

class cbmpy.PyscesStoich.StructMatrix(array, ridx, cidx, row=None, col=None)[source]

This class is specifically designed to store structural matrix information give it an array and row/col index permutations it can generate its own row/col labels given the label src.

getColsByIdx(*args)[source]

Return the columns referenced by index (1,3,5)

getColsByName(*args)[source]

Return the columns referenced by label (‘s’,’x’,’d’)

getIndexes(axis='all')[source]

Return the matrix indexes ([rows],[cols]) where axis=’row’/’col’/’all’

getLabels(axis='all')[source]

Return the matrix labels ([rows],[cols]) where axis=’row’/’col’/’all’

getRowsByIdx(*args)[source]

Return the rows referenced by index (1,3,5)

getRowsByName(*args)[source]

Return the rows referenced by label (‘s’,’x’,’d’)

setCol(src)[source]

Assuming that the col index array is a permutation (full/subset) of a source label array by supplying that src to setCol maps the row labels to cidx and creates self.col (col label list)

setRow(src)[source]

Assuming that the row index array is a permutation (full/subset) of a source label array by supplying that source to setRow it maps the row labels to ridx and creates self.row (row label list)

CBMPy: MultiCoreFVA module

PySCeS Constraint Based Modelling (http://cbmpy.sourceforge.net) Copyright (C) 2009-2015 Brett G. Olivier, VU University Amsterdam, Amsterdam, The Netherlands

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see <http://www.gnu.org/licenses/>

Author: Brett G. Olivier Contact email: bgoli@users.sourceforge.net Last edit: $Author: bgoli $ ($Id: _multicorefva.py 378 2015-09-14 16:32:38Z bgoli $)

CBMPy: MultiCoreEnvFVA module

PySCeS Constraint Based Modelling (http://cbmpy.sourceforge.net) Copyright (C) 2009-2015 Brett G. Olivier, VU University Amsterdam, Amsterdam, The Netherlands

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see <http://www.gnu.org/licenses/>

Author: Brett G. Olivier Contact email: bgoli@users.sourceforge.net Last edit: $Author: bgoli $ ($Id: _multicoreenvfva.py 378 2015-09-14 16:32:38Z bgoli $)