def run_task(self, fw_spec):
print "%s: Creating/Fitting sim_data (Level %d)" % (time.ctime(),
self["fit_level"])
if self["fit_level"] == 1:
if self["cached"]:
try:
shutil.copyfile(self["cached_data"], self["output_data"])
mod_time = time.ctime(os.path.getctime(
self["cached_data"]))
print "Copied sim data from cache (modified %s)" % (
mod_time, )
return
except Exception as exc:
print(
"Warning: could not copy cached sim data due to"
" exception (%s), running fitter") % (exc, )
if self["cpus"] > 1:
print(
"Warning: running fitter in parallel with %i processes -"
" ensure there are enough cpus_per_task allocated" %
(self["cpus"], ))
with open(self["input_data"], "rb") as f:
raw_data = cPickle.load(f)
sim_data = fitSimData_1(
raw_data,
cpus=self["cpus"],
debug=self["debug"],
disable_ribosome_capacity_fitting=self[
'disable_ribosome_capacity_fitting'],
disable_rnapoly_capacity_fitting=self[
'disable_rnapoly_capacity_fitting'],
adjust_rna_and_protein_parameters=self[
'adjust_rna_and_protein_parameters'],
)
sys.setrecursionlimit(4000) #limit found manually
with open(self["output_data"], "wb") as f:
cPickle.dump(sim_data, f, protocol=cPickle.HIGHEST_PROTOCOL)
# TODO: Get rid of this if not used
if self["fit_level"] == 2:
with open(self["input_data"], "rb") as f:
sim_data = cPickle.load(f)
fitSimData_2(sim_data, self["sim_out_dir"])
with open(self["output_data"], "wb") as f:
cPickle.dump(sim_data, f, protocol=cPickle.HIGHEST_PROTOCOL)
def getExpectedComposition(doubling_time): # return np.ones(6), 100. * units.fg raw_data = KnowledgeBaseEcoli() sim_data = fitSimData_1(raw_data) # obsolete: doubling_time=doubling_time subMasses = sim_data.mass.avgCellSubMass proteinMass = subMasses['proteinMass'].asNumber(units.fg) / sim_data.mass.avgCellToInitialCellConvFactor rnaMass = subMasses['rnaMass'].asNumber(units.fg) / sim_data.mass.avgCellToInitialCellConvFactor initialMass = sim_data.mass.avgCellDryMassInit.asNumber(units.fg) return proteinMass, rnaMass, initialMass
def load_sim_data(path, raw_data):
"""
Loads sim_data from an existing file, otherwise recalculates it.
Args:
path (str): path to sim_data cPickle file to load
raw_data (KnowledgeBaseEcoli object): raw data for simulations
"""
if os.path.exists(path):
print('Loading sim_data from {}...'.format(path))
with open(path, 'rb') as f:
sim_data = cPickle.load(f)
else:
print('Calculating sim_data...')
sim_data = fitSimData_1(raw_data)
return sim_data
def getExpectedComposition(doubling_time):
if doubling_time < 24. * units.min or doubling_time > 100. * units.min:
return np.zeros(2), 0 * units.fg
raw_data = KnowledgeBaseEcoli()
sim_data = fitSimData_1(raw_data) # obsolete: doubling_time=doubling_time
subMasses = sim_data.mass.avgCellSubMass
protein = subMasses['proteinMass'].asNumber(units.fg)
tRNA = subMasses['tRnaMass'].asNumber(units.fg)
rRnaMass = (subMasses['rRna23SMass'] + subMasses['rRna5SMass'] +
subMasses['rRna16SMass']).asNumber(units.fg)
mRnaMass = subMasses['mRnaMass'].asNumber(units.fg)
dnaMass = subMasses['dnaMass'].asNumber(units.fg)
smallMolecules = sim_data.mass.fitAvgSolubleTargetMolMass.asNumber(
units.fg)
masses = np.array([protein, rRnaMass + tRNA + mRnaMass
]) / sim_data.mass.avgCellToInitialCellConvFactor
initialMass = sim_data.mass.avgCellDryMassInit
return masses, initialMass
def run_task(self, fw_spec):
print "%s: Creating/Fitting sim_data (Level %d)" % (time.ctime(),
self["fit_level"])
if self["fit_level"] == 1:
if self["cached"]:
try:
shutil.copyfile(self["cached_data"], self["output_data"])
mod_time = time.ctime(os.path.getctime(
self["cached_data"]))
print "Copied sim data from cache (modified %s)" % (
mod_time, )
return
except Exception as exc:
print(
"Warning: could not copy cached sim data due to"
" exception (%s), running fitter") % (exc, )
if self["cpus"] > 1:
print(
"Warning: running fitter in parallel with %i processes -"
" ensure there are enough cpus_per_task allocated" %
(self["cpus"], ))
with open(self["input_data"], "rb") as f:
raw_data = cPickle.load(f)
options = dict(
cpus=self["cpus"],
debug=self["debug"],
disable_ribosome_capacity_fitting=self[
'disable_ribosome_capacity_fitting'],
disable_rnapoly_capacity_fitting=self[
'disable_rnapoly_capacity_fitting'],
flat_elongation_transcription=not self[
'variable_elongation_transcription'],
flat_elongation_translation=not self[
'variable_elongation_translation'],
rnapoly_activity_fitting=self['rnapoly_activity_fitting'],
mrna_half_life_fitting=self['mrna_half_life_fitting'],
max_rnap_activity=self['max_rnap_activity'],
adjust_rna_and_protein_parameters=self[
'adjust_rna_and_protein_parameters'],
adjust_rnase_expression=self['adjust_rnase_expression'],
disable_measured_protein_deg=self[
'disable_measured_protein_deg'],
alternate_mass_fraction_protein=self[
'alternate_mass_fraction_protein'],
alternate_mass_fraction_rna=self[
'alternate_mass_fraction_rna'],
alternate_mass_fraction_mrna=self[
'alternate_mass_fraction_mrna'],
alternate_r_protein_degradation=self[
'alternate_r_protein_degradation'],
alternate_rna_seq=self['alternate_rna_seq'],
alternate_rna_half_life=self['alternate_rna_half_life'],
alternate_translation_efficiency=self[
'alternate_translation_efficiency'],
alternate_ribosome_activity=self[
'alternate_ribosome_activity'],
disable_rnap_fraction_increase=self[
'disable_rnap_fraction_increase'],
disable_ribosome_activity_fix=self[
'disable_ribosome_activity_fix'],
write_translation_efficiencies=self[
'write_translation_efficiencies'])
sim_data, cell_specs = fitSimData_1(raw_data, options)
sys.setrecursionlimit(4000) #limit found manually
with open(self["output_data"], "wb") as f:
cPickle.dump(sim_data, f, protocol=cPickle.HIGHEST_PROTOCOL)
if self["save_cell_specs"]:
with open(self["cell_specs_file"], "wb") as f:
cPickle.dump(cell_specs,
f,
protocol=cPickle.HIGHEST_PROTOCOL)
# TODO: Get rid of this if not used
if self["fit_level"] == 2:
with open(self["input_data"], "rb") as f:
sim_data = cPickle.load(f)
fitSimData_2(sim_data, self["sim_out_dir"])
with open(self["output_data"], "wb") as f:
cPickle.dump(sim_data, f, protocol=cPickle.HIGHEST_PROTOCOL)