def __init__(self, path2model="", medium={}, include={}, objective=None, optimizationRoutine='pFBA', koQ=True, *args, **kwargs):
self.koQ = koQ
self.optimizationRoutine = optimizationRoutine
self.objective = objective
self.lp = Metabolism(ImportCplex(path2model))
self.path2model = path2model
if objective:
self.lp.setReactionObjective(self.objective)
self.preMed = dict([(r, (-1000., 0)) for r in self.lp.getTransporters()])
self.preMed.update(include)
self.lp.modifyColumnBounds(self.preMed)
self.lp.modifyColumnBounds(dict([(r, (0., 1000.)) for r in self.lp.getReactions()]))
self.lp.modifyColumnBounds(medium)
self.lp.eraseHistory()
def __prepareProblem(self, lp):
"""Prepares the lp construct for analysis
Transposes the stoichiometric matrix S_ij -> S^T
Columns(Reactions) become rows and rows(Metabolites) become columns
Previous column, now row bounds are set to 0
Previous row, now column bounds are set to > 0
"""
oldProb = Metabolism(lp)
biomR = [r for r in oldProb.getColumnIDs() if re.search(".*.*", r, re.IGNORECASE)][0]
print biomR
oldProb.deleteReactionsFromStoich([biomR])
oldProb.eraseHistory()
newProb = Metabolism(glp_create_prob())
for elem in range(1, oldProb.getNumCols() + 1):
columnsDict = dict()
identifier = glp_get_col_name(oldProb.lp, elem)
colCoefList = oldProb.getColumnCoef(elem)
columnsDict[identifier] = (0, 0, colCoefList)
newProb.addRows(columnsDict)
for elem in range(1, oldProb.getNumRows() + 1):
rowsDict = dict()
identifier = glp_get_row_name(oldProb.lp, elem)
rowCoefList = oldProb.getRowCoef(elem)
rowsDict[identifier] = (0, 10000, rowCoefList)
newProb.addColumns(rowsDict)
newProb.eraseHistory()
return newProb.lp
def solveMedium(path2model="", medium={}, include={}, objective=None, optimizationRoutine='pFBA', koQ=True, *args, **kwargs):
"""doc"""
lp = Metabolism(ImportCplex(path2model))
if objective:
lp.setReactionObjective(objective)
preMed = dict([(r, (-1000., 0)) for r in lp.getTransporters()])
preMed.update(include)
lp.modifyColumnBounds(preMed)
lp.modifyColumnBounds(medium)
lp.modifyColumnBounds(dict([(r, (0., 1000.)) for r in lp.getReactions()]))
lp.eraseHistory()
# print lp.cplex()
f = lp.pFBA()
# simulationStorage = generateStorageObject(outputfile, lp)
knockoutEffects = dict()
wt = f[objective]
print wt
if koQ and wt > 0.:
knockoutEffects = lp.singleKoAnalysis(f.getActiveReactions())
for k in knockoutEffects:
knockoutEffects[k] = knockoutEffects[k] / wt
lp.initialize()
# print knockoutEffects
return FBAsimulationResult(f, knockoutEffects, lp.getColumnBounds(),
lp.getObjectiveFunction(),
time.time(), path2model, "Test")
class SolveMedium(object):
def __init__(self, path2model="", medium={}, include={}, objective=None, optimizationRoutine='pFBA', koQ=True, *args, **kwargs):
self.koQ = koQ
self.optimizationRoutine = optimizationRoutine
self.objective = objective
self.lp = Metabolism(ImportCplex(path2model))
self.path2model = path2model
if objective:
self.lp.setReactionObjective(self.objective)
self.preMed = dict([(r, (-1000., 0)) for r in self.lp.getTransporters()])
self.preMed.update(include)
self.lp.modifyColumnBounds(self.preMed)
self.lp.modifyColumnBounds(dict([(r, (0., 1000.)) for r in self.lp.getReactions()]))
self.lp.modifyColumnBounds(medium)
self.lp.eraseHistory()
def run(self, *args, **kwargs):
"""docstring for run"""
f = getattr(self.lp, self.optimizationRoutine)()
knockoutEffects = dict()
wt = f[self.objective]
if self.koQ and wt > 0.:
knockoutEffects = self.lp.singleKoAnalysis(f.getActiveReactions())
for k in knockoutEffects:
knockoutEffects[k] = knockoutEffects[k] / wt
self.lp.undo()
return FBAsimulationResult(f, knockoutEffects, self.lp.getColumnBounds(),
self.lp.getObjectiveFunction(),
time.time(), self.path2model, "Test")
def __del__(self):
"""docstring for __del__"""
glp_delete_prob(self.lp.lp) # FIXME this is a dirty hack
del self
Created by Nikolaus Sonnenschein on 2010-03-02.
Copyright (c) 2010 . All rights reserved.
"""
import sys
import os
if __name__ == '__main__':
from ifba.GlpkWrap.metabolism import Metabolism
from ifba.GlpkWrap.util import ImportCplex, WriteCplex
from ifba.fluxVariability.fluxVariability import Variability
import random
import pickle
lp = Metabolism(ImportCplex('../../ifba/models/iAF1260template_minimalMed_noCarb.lp'))
print len(lp.getColumnIDs())
lp.modifyColumnBounds({'R("MglcDb_Transp")':(0, 20), 'R("Mo2b_Transp")':(-20, 20)})
lp.modifyColumnBounds({'R("R_Ec_biomass_iAF1260_core_59p81M")': (1., 100.)})
lp.eraseHistory()
if os.path.exists('blocked.pcl'):
blocked = pickle.load(open('blocked.pcl'))
else:
# blocked = Variability(lp).getBlocked(['R("R_Ec_biomass_iAF1260_core_59p81M")', 'R("R_AGPR")'])
blocked = Variability(lp).getBlocked()
sys.exit()
pickle.dump(blocked, open('blocked.pcl', 'w'))
print blocked
print len(blocked)
lp.deleteReactionsFromStoich(blocked)
lp.eraseHistory()
