def gxFBA(cobramodel_1,cobramodel_2,gene_expressions,GPRlist,maxflux = 500, exprType = 2, verbose = True,FVAcondition = 2, wait = False,treshold = 0.5,objectiveweights = 'log',dumpmodel = False):
if verbose:
print 'Performing gxFBA. Verbose = True.'
print 'Provided gene expression ratios:'
print gene_expressions
if exprType == 1:
print 'Input gene expression ratios handled as absolute ratios.'
else:
print 'Input gene expression ratios handled as log ratios.'
if exprType == 1:
logvals = {gene:log(gene_expressions[gene],2) for gene in gene_expressions}
ratios = gene_expressions
if verbose:
print 'Calculated log ratios:'
print logvals
else:
logvals = gene_expressions
ratios = {gene:2**gene_expressions[gene] for gene in gene_expressions}
#Local variables:
eps2 = 0.00001
##1) Generate the wild-type flux distribution v_i_wt for the starting condition:
cobramodel_1.optimize(solver='gurobi')
wildtype = cobramodel_1.solution.x_dict
if verbose:
print 'generated wild-type flux distribution.'
#print cobramodel_1.solution.x_dict
for key,value in sorted(cobramodel_1.solution.x_dict.items()):
print key,value
if wait:
hold()
###2) Perform FVA with nutritional constraints for condition 1 or 2 (Default: condition 2) and minimum biomass flux = 0 to calculate the upper and lower fluxes each
### reaction can carry.
if FVAcondition == 1:
FVA_result = cobra.flux_analysis.variability.flux_variability_analysis(cobramodel_1,fraction_of_optimum = 0)
else:
FVA_result = cobra.flux_analysis.variability.flux_variability_analysis(cobramodel_2,fraction_of_optimum = 0)
if verbose:
print 'Performed FVA for condition',FVAcondition
for key,value in sorted(FVA_result.items()):
print key,value
#print FVA_result
#Reset the objective function for condition 2:
for reaction in cobramodel_2.reactions:
reaction.objective_coefficient = 0
### Calculate mean possible flux value for each reaction under condition 2, and the flux average for all active reactions under condition 1
wt_avg = {key:np.mean([FVA_result[key]['minimum'],FVA_result[key]['maximum']]) for key in FVA_result}
active_fluxes = [fluxvalue for fluxvalue in wildtype.values() if fluxvalue > eps2]
wildtype_averageflux = np.mean(active_fluxes) #average wildtype flux
if verbose:
print 'FVA average fluxes:'
#print wt_avg
for key,value in wt_avg.items():
print key,value
print 'Total number of reactions in wildtype model:',len(wildtype.values())
print 'Number of active reactions in wildtype FBA solution:',len(active_fluxes)
print 'Average wildtype flux (Average of flux value for all fluxes in wildtype FBA solution):',wildtype_averageflux
genemap = defaultdict(list)
for gene in gene_expressions:
if gene in GPRlist:
for rx in GPRlist[gene]:
genemap[rx].append(gene)
if verbose:
print 'Gene map:'
print genemap
if wait:
hold()
T = []
##3) Identify the set of reactions (called T) for which an mRNA expression value can be associated.
if verbose:
print 'Identifying reactions eligible for gx-regulation.'
for reaction in cobramodel_2.reactions:
#Skip reactions for which gene expression data is unavailable
if reaction.id not in genemap:
if verbose:
print reaction.id,'not in genemap. Skipping reaction.'
continue
#Skip reactions for which the maximal up/down-regulation is below the set treshold:
expressionratios = [ratios[gene] for gene in genemap[reaction.id] ]
if max([abs(ratio-1) for ratio in expressionratios]) < treshold:
if verbose:
print reaction.id,': Level of regulation below treshold. Skipping reaction.'
continue
#Do not include the reaction if the average flux is too high:
if wt_avg[reaction.id] >= maxflux:
if verbose:
print reaction.id,': FVA average flux too high. Skipping reaction.'
continue
#Do not include the reaction if it cannot carry any flux (as per FVA):
if abs(wt_avg[reaction.id]) < eps2:
if verbose:
print reaction.id,': Reaction always inactive under specified conditions. Skipping reaction.'
continue
#Do not include the reaction if it is not irreversible under the given constraints:
bounds = FVA_result[reaction.id].values()
irreversible = not min(bounds) < 0 < max(bounds)
if not irreversible:
if verbose:
print reaction.id,': Reaction may carry flux in both directions under specified conditions. Skipping reaction.'
continue
#Do not include the reaction if gene expression data is inconsistent (both up- and down-regulation):
exprvalues = [logvals[gene] for gene in genemap[reaction.id] ]
signs = [cmp(logvalue,0) for logvalue in exprvalues]
if not (signs[1:] == signs[:-1]): #If all the signs are not the same value. Alternative: if not (signs.count(signs[0]) == signs(x))
if verbose:
print 'Gene expression information for reaction indicates mixed regulation (up/down). Skipping reaction.'
continue
#If none of the above checks failed, add the reaction to the list:
if verbose:
print 'Adding reaction',reaction.id,'to T.'
T.append(reaction)
##4) For each reaction in T; define a new upper bound UB = Ci_mRNA * v_i_wt if up-regulated or a new lower bound LB =
## Ci_mRNA * v_i_wt if downregulated, where Ci_mRNA is the mRNA expression ratio.
if verbose:
print 'Calculating new bounds and objective function coefficients:'
def reactionInfo(reaction_id):
print 'Ci_mRNA:',Ci_mRNA
print 'Wild-type flux:',wildtype[reaction.id]
print 'Original reaction bounds:'
print 'Lower:',cobramodel_1.reactions.get_by_id(reaction.id).lower_bound,'Upper:',cobramodel_1.reactions.get_by_id(reaction.id).upper_bound
print 'New reaction bounds:'
print 'Lower:',reaction.lower_bound,'Upper:',reaction.upper_bound
print 'Objective coefficient:',reaction.objective_coefficient
for reaction in T:
if wait:
hold()
logvalues = [logvals[gene] for gene in genemap[reaction.id] ]
expressionratios = [ratios[gene] for gene in genemap[reaction.id] ]
sign = cmp(logvalues[0],0)
if verbose:
print 'Processing reaction:',reaction.id
print 'Logvalues:',logvalues
print 'Ratios:',expressionratios
if sign > 0:
Ci_mRNA = 2**max(logvalues) ## For reactions dependent on several genes (several mRNA values), use the maximal up/down-regulation value.
if wildtype[reaction.id] == 0:
reaction.upper_bound = Ci_mRNA * wildtype_averageflux
else:
reaction.upper_bound = Ci_mRNA * wildtype[reaction.id]
#Set the objective function coefficient: Ci = log(Ci_mRNA) / (v_i average):
if objectiveweights == 'log':
reaction.objective_coefficient = max(logvalues) / wt_avg[reaction.id]
elif objectiveweights =='logabs':
reaction.objective_coefficient = max(logvalues) / wt_avg[reaction.id]
elif objectiveweights == 'ratios':
reaction.objective_coefficient = Ci_mRNA / wt_avg[reaction.id]
elif objectiveweights == 'pureratios':
reaction.objective_coefficient = Ci_mRNA
elif sign < 0:
Ci_mRNA = 2**min(logvalues) ## For reactions dependent on several genes (several mRNA values), use the maximal up/down-regulation value.
if wildtype[reaction.id] == 0:
pass
else:
reaction.lower_bound = Ci_mRNA * wildtype[reaction.id]
#Set the new objective function coefficient: Ci = log(Ci_mRNA) / (v_i average):
if objectiveweights == 'log':
reaction.objective_coefficient = min(logvalues) / wt_avg[reaction.id]
elif objectiveweights =='logabs':
reaction.objective_coefficient = abs(min(logvalues)) / wt_avg[reaction.id]
elif objectiveweights =='ratios':
reaction.objective_coefficient = Ci_mRNA / wt_avg[reaction.id]
elif objectiveweights == 'pureratios':
reaction.objective_coefficient = Ci_mRNA
if verbose:
reactionInfo(reaction.id)
cobramodel_2.reactions.get_by_id('R5_akgdh').lower_bound = 0.65
cobramodel_2.reactions.get_by_id('R5_akgdh').upper_bound = 1.3
cobramodel_2.reactions.get_by_id('R2_lowergly').lower_bound = 0.8
cobramodel_2.reactions.get_by_id('R2_lowergly').upper_bound = 3.42857
cobramodel_2.reactions.get_by_id('R2_lowergly').objective_coefficient = 1.5
cobramodel_2.reactions.get_by_id('R5_akgdh').objective_coefficient = 0.5
cobramodel_2.reactions.get_by_id('R1_uppergly').objective_coefficient = 1.5
cobramodel_2.reactions.get_by_id('R3_citsyn').objective_coefficient = 0.5
if verbose:
print 'New objective function:'
#print {reaction.id:reaction.objective_coefficient for reaction in T}
print {reaction.id:reaction.objective_coefficient for reaction in cobramodel_2.reactions}
print 'New lower bounds:',[reaction.lower_bound for reaction in cobramodel_2.reactions]
print 'New upper bounds:',[reaction.upper_bound for reaction in cobramodel_2.reactions]
print 'Performing gx-FBA.'
#Solve the gx-FBA problem:
cobramodel_2.optimize(solver='gurobi')
if verbose:
print 'Flux solution:'
print cobramodel_2.solution.x_dict
print 'Objective value:',cobramodel_2.solution
#Using Gurobi manually:
gurobisolution = QPmindist.gurobiFBA(cobramodel_2)
gurobisolutiondict = QPmindist.getgurobisolutiondict(gurobisolution)
print 'gurobi solution:'
print gurobisolutiondict
print 'Objective value:',gurobisolution.Objval
#Check that mass conservation is preserved:
print 'Total metabolite fluxes:'
for metabolite in cobramodel_2.metabolites:
print metabolite.id,metabolitefluxsum(metabolite.id,cobramodel_2),metabolitefluxsum(metabolite.id,cobramodel_2, abs = True)
#print 'ATP fluxes:',metabolitefluxes('atp_c',cobramodel_2)
#print ''
print 'Solution:'
print cobramodel_2.solution.x
vectorToText(cobramodel_2.solution.x,'flux.txt')
print 'Flux solution dumped to flux.txt'
#vectorToText([reaction.objective_coefficient for reaction in cobramodel_2.reactions],'objective.txt')
vectorToText([reaction.objective_coefficient for reaction in cobramodel_2.reactions],'objective.txt', decimal = ',')
vectorToText([reaction.lower_bound for reaction in cobramodel_1.reactions],'FBA-lb.txt')
vectorToText([reaction.upper_bound for reaction in cobramodel_1.reactions],'FBA-ub.txt')
vectorToText([reaction.lower_bound for reaction in cobramodel_2.reactions],'gxFBA-lb.txt')
vectorToText([reaction.upper_bound for reaction in cobramodel_2.reactions],'gxFBA-ub.txt')
vectorToText([reaction.objective_coefficient for reaction in cobramodel_2.reactions],'A.txt', decimal = ',', linestart = 'C:')
#vectorToText([reaction.lower_bound for reaction in cobramodel_1.reactions],'A.txt',append = True, linestart = 'LB:')
#vectorToText([reaction.upper_bound for reaction in cobramodel_1.reactions],'A.txt', append = True,linestart = 'UB:')
vectorToText([reaction.lower_bound for reaction in cobramodel_2.reactions],'A.txt', append = True, linestart = 'LB:')
vectorToText([reaction.upper_bound for reaction in cobramodel_2.reactions],'A.txt.', append = True, linestart = 'UB:')
vectorToText(cobramodel_2.solution.x,'A.txt.', append = True, linestart = 'V:')
if dumpmodel:
write_cobra_model_to_sbml_file(cobramodel_2,'dumpedmodel.xml')
return cobramodel_2
if __name__ == "__main__": #If the module is executed as a program, run a test.
from cobra.io.sbml import create_cobra_model_from_sbml_file
cobramodel = create_cobra_model_from_sbml_file('../SBML/SCHUETZR.xml')
import loadData as load
fluxvalues = load.ExpFluxesfromXML('expdata.xml','Perrenoud','Batch','aerobe')
rmap = load.ReactionMapfromXML('reactionmaps.xml','Perrenoud','SCHUETZR')
optreq = 1.0
import QPmindist as qp
gurobimodel = qp.QPmindist(cobramodel,fluxvalues,rmap,optreq)
QPsolutiondict = qp.getgurobisolutiondict(gurobimodel)
import extractflux2 as extract
extractfluxdict = extract.extractfluxdict(QPsolutiondict,rmap)
dictdist = compdistdict(extractfluxdict)
print('dist:',dictdist)
simple1 = {"A":1,"B":2,"C":3}
simple2 = {"A":1,"B":2,"C":4}
simpledist = compdistdict(simple1, expdata = simple2)
return dist
if __name__ == "__main__": #If the module is executed as a program, run a test.
from cobra.io.sbml import read_sbml_model
cobramodel = read_sbml_model('../SBML/SCHUETZR.xml')
import loadData as load
fluxvalues = load.ExpFluxesfromXML('expdata.xml', 'Perrenoud', 'Batch',
'aerobe')
rmap = load.ReactionMapfromXML('reactionmaps.xml', 'Perrenoud', 'SCHUETZR')
optreq = 1.0
import QPmindist as qp
gurobimodel = qp.QPmindist(cobramodel, fluxvalues, rmap, optreq)
QPsolutiondict = qp.getgurobisolutiondict(gurobimodel)
import extractflux2 as extract
extractfluxdict = extract.extractfluxdict(QPsolutiondict, rmap)
dictdist = compdistdict(extractfluxdict)
print('dist:', dictdist)
simple1 = {"A": 1, "B": 2, "C": 3}
simple2 = {"A": 1, "B": 2, "C": 4}
simpledist = compdistdict(simple1, expdata=simple2)
def optreqanalysis(modeldicts,
objectives,
experiments,
steps=10,
makeplots=True,
expfluxdict="Default",
plotter="easyviz"):
'''Function for evaluating how the minimally achievable distance between a flux solution and a set of experimental fluxes
changes when varying the required relative FBA objective-optimality of the flux solution.
syntax: optreqanalysis(modeldicts,objectives,experiments,steps = 10,makeplots = True,expfluxdict)
arguments:
modeldicts: An array of python dictionaries with the following fields:
modelobject: A CobraPy model object for the model in question.
reactionmap: A reaction map array mapping between model reactions and experimental reactions for the model and experiment in question.
objectives: A list of objectives to be used in the analysis.
experiments: A list of experiments to be used in the analysis
steps: The granularity of the analysis and produced plots.
makeplots: Whether plots should be produced or not
expfluxdict: A python dictionary with experiment-ids as keys and the corresponding flux value arrays as values.
'''
for objective in objectives:
pass #Placeholder
for experiment in experiments:
pass #Placeholder
if expfluxdict == "Default": #This section should be rewritten to use an "expflux" dict.
expdata = scipy.io.loadmat('expdata.mat') #load experimentaldata
perrenoud = expdata['expdata']['perrenoud']
fluxvalarray = perrenoud[0][0][0][0][0][0][0][0][0][0][0][0][0][0]
fluxvalues = [row[0] for row in fluxvalarray
] #expdata.perrenoud.abs.batch.aerobe.fluxvalues
expfluxdict = load.ExpFluxesfromXML('expdata.xml', 'Perrenoud',
'Batch', 'aerobe')
resultlist = []
for modeldict in modeldicts:
cobramodel = modeldict['modelobject']
#print 'modeldict:',modeldict
reactionmap = modeldict['reactionmap']
#print 'cobramodel:',cobramodel
#print type(cobramodel)
optreqs = []
for i in range(steps + 1):
optreqs.append(float(i) / steps)
results = []
#print 'opreqs:',optreqs
#q = raw_input('Continue?')
for optreq in optreqs:
optimizedmodel = QPmindist(cobramodel, fluxvalues, reactionmap,
optreq)
results.append(math.sqrt(optimizedmodel.ObjVal))
resultlist.append(results)
#Using easyviz
#ev.plot(optreqs,results)
#ev.hold()
#Using prettyplotlib
#fig, ax = plt.subplots(1)
#ppl.scatter(ax, x, y)
#ppl.plot(optreqs,results)
return resultlist