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
