def add_complex_to_model(me_model,
complex_id,
complex_stoichiometry,
complex_modifications=None):
"""
Adds ComplexData to the model for a given complex.
Parameters
----------
me_model : :class:`cobrame.core.model.MEModel`
complex_id : str
ID of the complex and thus the model ComplexData
complex_stoichiometry : dict
{complex_id: {protein_<locus_tag>: stoichiometry}}
complex_modifications : dict
{subreaction_id: stoichiometry}
"""
if not complex_modifications:
complex_modifications = {}
complex_data = cobrame.ComplexData(complex_id, me_model)
# must add update stoichiometry one by one since it is a defaultdict
for metabolite, value in iteritems(complex_stoichiometry):
complex_data.stoichiometry[metabolite] += value
for modification, value in iteritems(complex_modifications):
complex_data.subreactions[modification] = value
def add_lipoate_modifications(me_model):
# two different reactions can add a lipoate modification.
# We create a separate SubreactionData for each one
for mod, info in lipoate_modifications.items():
if mod in me_model.process_data:
mod_data = me_model.process_data.get_by_id(mod)
else:
mod_data = cobrame.SubreactionData(mod, me_model)
mod_data.stoichiometry = info["stoich"]
mod_data.enzyme = info["enzyme"]
# element count for lipoate modifications
mod_data._element_contribution = {'C': 8, 'H': 11, 'O': 1, 'S': 2}
lipo = me_model.process_data.get_by_id('mod_lipo_c')
alt_lipo = me_model.process_data.get_by_id('mod_lipo_c_alt')
for cplx_data in lipo.get_complex_data():
alt_cplx_data = cobrame.ComplexData(cplx_data.id + "alt", me_model)
alt_cplx_data.complex_id = cplx_data.complex_id
alt_cplx_data.stoichiometry = cplx_data.stoichiometry
alt_cplx_data.subreactions = cplx_data.subreactions.copy()
alt_cplx_data.subreactions[alt_lipo.id] = \
alt_cplx_data.subreactions.pop(lipo.id)
alt_cplx_data.create_complex_formation()
def add_HdeB_complex(me_model):
# Get the sequence
b3509_seq = SeqIO.read('data/b3509.fasta', 'fasta')
nucleo_seq = str(b3509_seq.seq).upper()
# Add transcript to model
building.create_transcribed_gene(me_model,
'b3509',
'mRNA',
nucleo_seq,
strand='-')
# Link transcription data to reaction
building.add_transcription_reaction(me_model, 'TU_b3509', {'b3509'},
nucleo_seq)
me_model.process_data.TU_b3509.RNA_polymerase = ''
me_model.process_data.TU_b3509.subreactions = {}
me_model.reactions.transcription_TU_b3509.update()
# Add translation reaction
building.add_translation_reaction(me_model, 'b3509', nucleo_seq)
me_model.process_data.b3509.add_initiation_subreactions(
start_codons=set(), start_subreactions=set())
me_model.process_data.b3509.add_elongation_subreactions(
elongation_subreactions=set())
me_model.process_data.b3509.add_termination_subreactions(
translation_terminator_dict=None)
me_model.reactions.translation_b3509.update()
#add translocation pathway
me_model.add_metabolites(
[ProcessedProtein('protein_b3509_Periplasm', 'protein_b3509')])
translocation_data = PostTranslationData('translocation_b3509', me_model,
'protein_b3509_Periplasm',
'protein_b3509')
translocation_rxn = PostTranslationReaction('translocation_b3509')
me_model.add_reaction(translocation_rxn)
translocation_rxn.posttranslation_data = translocation_data
translocation_rxn.update()
#now add the complex formation reaction, HdeB is a dimer
complex_data = cobrame.ComplexData('complex_HdeB', me_model)
complex_data.stoichiometry = {'protein_b3509_Periplasm': 2}
complex_data.subreactions = {}
# complex data has utility to link data and add reaction
complex_data.create_complex_formation()
def add_dummy_reactions(me_model, dna_seq, update=True):
"""
Add all reactions necessary to produce a dummy reaction catalyzed by
"CPLX_dummy".
Parameters
----------
me_model : :class:`cobrame.core.model.MEModel`
The MEModel object to which the content will be added
dna_seq : str
DNA sequence of dummy gene. Should be representative of the average
codon composition, amino acid composition, length of a gene in the
organism being modeled
update : bool
If True, run update functions on all transcription, translation,
complex formation, and metabolic reactions added when constructing
dummy reactions.
"""
dummy = cobrame.StoichiometricData("dummy_reaction", me_model)
dummy.lower_bound = 0
dummy.upper_bound = 1000
dummy._stoichiometry = {'CPLX_dummy': -1}
create_transcribed_gene(me_model, 'dummy', 'mRNA', dna_seq)
add_transcription_reaction(me_model, "RNA_dummy", {"dummy"}, dna_seq)
me_model.add_metabolites(cobrame.TranslatedGene("protein_" + "dummy"))
add_translation_reaction(me_model,
"dummy",
dna_sequence=dna_seq,
update=update)
try:
complex_data = cobrame.ComplexData("CPLX_dummy", me_model)
except ValueError:
warn('CPLX_dummy already in model')
complex_data = me_model.process_data.get_by_id('CPLX_dummy')
complex_data.stoichiometry = {"protein_dummy": 1}
if update:
complex_data.create_complex_formation()
sigma_to_RNAP_dict = transcription.sigma_factor_complex_to_rna_polymerase_dict
for TU_id in TU_df.index:
transcription_data = me.transcription_data.get_by_id(TU_id)
rho_dependent = TU_df.rho_dependent[TU_id]
sigma = TU_df.sigma[TU_id]
RNA_polymerase = sigma_to_RNAP_dict[sigma]
transcription_data.RNA_polymerase = RNA_polymerase
transcription_data.rho_dependent = rho_dependent
# #### Degradosome (both for RNA degradation and RNA splicing)
#
# All new data contained in **ecolime/ecoli_k12.py**
# In[ ]:
data = cobrame.ComplexData('RNA_degradosome', me)
for subunit, value in ecoli_k12.RNA_degradosome.items():
data.stoichiometry[subunit] = value
data.create_complex_formation(verbose=False)
# Used for RNA splicing
data = cobrame.ModificationData('RNA_degradation_machine', me)
data.enzyme = 'RNA_degradosome'
data = cobrame.ModificationData('RNA_degradation_atp_requirement', me)
# .25 water equivaltent for atp hydrolysis and 1 h20 equivalent for to
# restore OH group in nucleotide monophosphate during RNA hydrolysis
data.stoichiometry = {
'atp_c': -.25,
'h2o_c': -1.25,
'adp_c': .25,
def return_ME_model():
# Define Models
ijo_directory = join(flat_files.ecoli_files_dir, 'iJO1366.json')
ijo = cobra.io.load_json_model(ijo_directory)
me = cobrame.MEModel('iLE1678-ME')
# "Translational capacity" of organism
me.global_info['kt'] = 4.5 # (in h-1)scott 2010, RNA-to-protein curve fit
me.global_info['r0'] = 0.087 # scott 2010, RNA-to-protein curve fit
me.global_info['k_deg'] = 1.0 / 5. * 60.0 # 1/5 1/min 60 min/h # h-1
# Molecular mass of RNA component of ribosome
me.global_info['m_rr'] = 1700. # in kDa
# Average molecular mass of an amino acid
me.global_info['m_aa'] = 109. / 1000. # 109. / 1000. # in kDa
# Proportion of RNA that is rRNA
me.global_info['f_rRNA'] = .86
me.global_info['m_nt'] = 324. / 1000. # in kDa
me.global_info['f_mRNA'] = .02
# tRNA associated global information
me.global_info['m_tRNA'] = 25000. / 1000. # in kDA
me.global_info['f_tRNA'] = .12
me.global_info['RNA_polymerase'] = {
'CPLX0-221', 'RNAPE-CPLX', 'CPLX0-222', 'RNAP32-CPLX', 'RNAP54-CPLX',
'RNAP70-CPLX', 'RNAPS-CPLX'
}
# Translation associated global information
me.global_info[
"translation_terminators"] = translation.translation_stop_dict
me.global_info["met_start_codons"] = {"AUG", "GUG", "UUG", "AUU", "CUG"}
me.global_info["peptide_processing_subreactions"] = {
"peptide_deformylase_processing": 1,
"peptide_chain_release": 1,
"ribosome_recycler": 1
}
me.global_info["translation_elongation_subreactions"] = [
'FusA_mono_elongation', 'Tuf_gtp_regeneration'
]
me.global_info['translation_start_subreactions'] = [
'fmet_addition_at_START', 'Translation_initiation_factor_InfA',
'Translation_initiation_factor_InfC',
'Translation_gtp_initiation_factor_InfB'
]
# Used to calculate translation coupling constraints
me.global_info['translocation_multipliers'] = defaultdict(dict)
for enzyme, value in ecolime.translocation.multipliers.items():
me.global_info['translocation_multipliers'][enzyme] = value
# Folding Properties
me.global_info['temperature'] = 37
me.global_info['propensity_scaling'] = .45
# DNA Replication Parameters
me.global_info['GC_fraction'] = 0.507896997096
# Used for mass balance checks
df = pandas.read_table(join(flat_files.ecoli_files_dir,
'modification.txt'),
names=['mod', 'formula', 'na'])
df = df.drop('na', axis=1).set_index('mod').dropna(how='any')
me.global_info['modification_formulas'] = df.T.to_dict()
# ### 2) Load metabolites and build Metabolic reactions
# The below reads in:
# - Required
# * **reaction_matrix.txt** (reaction matrix w/ reactions unlumped, metabolites renamed etc.)
# * **metabolites.txt** (metabolite properties)
# * **reactions.txt** (info on reversiblity, whether enzyme catalyzed etc.)
# * **m_to_me_mets.csv** (mapping of enzymes/complexes used in M-model to their ME-model compatible ID)
# * **protein_complexes.txt** (protein subunit stoichiometry of all complexes, used to identify metabolites as such)
#
# It creates a new e coli M-model from this info then incorporates it into the ME-model using *add_m_model_content*. metabolites are added directly reactions are added as StoichiometricData
#
# Metabolite types have different properties in an ME-model so enzyme complexes need added to the model as Complexes not Metabolites. Components in the E. coli M-model that are actually Complexes are compiled in *complex_list*
# In[ ]:
# m_model = flat_files.get_m_model()
m_model = flat_files.process_m_model(
ijo,
'metabolites.txt',
'm_to_me_mets.csv',
'reactions.txt',
'reaction_matrix.txt',
'protein_complexes.txt',
defer_to_rxn_matrix=['GLUTRR', 'PAPSR2'])
m_model.reactions.EX_glc_e.id = 'EX_glc__D_e'
m_model.repair()
# some of the "metabolites" in iJO1366 "M" model are actually complexes. We pass those in
# so they get created as complexes, not metabolites.
complexes = flat_files.get_complex_subunit_stoichiometry(
'protein_complexes.txt').keys()
complex_set = set([
i.id for i in m_model.metabolites
if i.id.split('_mod_')[0] in complexes
])
building.add_m_model_content(me,
m_model,
complex_metabolite_ids=complex_set)
# flat_files.get_complex_subunit_stoichiometry('protein_complexes.txt')
# In[ ]:
# This adds exchange reactions for metabolites not contained in iJO1366
# Some of these cannot be produced by the model (C10H805) so they are added here
exchange_list = [
'LI_c',
'pqq_e',
'cs_e',
'tl_c',
'cu_c', # needed for NDH activity. TODO fix this with new reactions
'C10H8O5_c',
'C9H9O4_c', # for tRNA modifications
'NiFeCoCN2_c',
'RNase_m5',
'RNase_m16',
'RNase_m23'
] # RNAses are gaps in model
for met_id in exchange_list:
r = cobra.Reaction("EX_" + met_id)
me.add_reaction(r)
r.reaction = met_id + " <=> "
# ### 3) Add Transcription and Translation
# The below reads in:
# - Required
# * **NC_000913.2.gb** (Genbank sequence annotation)
# * **ecolime/translation.py** (codon to tRNA mapping)
# - Optional
# * **TUs_from_ecocyc.txt** (TU definitions, start/stop positions, etc.)
# * **ecolime/translation.py** (dictionary of gene to frameshift mutation)
#
# To construct the bare minimimum components of a transcription and translation reactions. For example, transcription reactions at this point include nucleotides and the synthesized RNAs.
# In[ ]:
gb_filename = join(flat_files.ecoli_files_dir, 'NC_000913.2.gb')
TU_df = pandas.read_csv(join(flat_files.ecoli_files_dir,
'TUs_from_ecocyc.txt'),
delimiter="\t",
index_col=0)
building.build_reactions_from_genbank(
me,
gb_filename,
TU_df,
verbose=False,
frameshift_dict=translation.frameshift_dict,
tRNA_to_codon=translation.tRNA_to_codon)
# ### 4) Add in complex Formation without modifications (for now)
#
# The below reads in:
# - Required
# * **protein_complexes.txt** (Metabolic complexes' protein subunit stoichiometries)
# * **protein_modification.txt** (Type and number of modifications for each protein)
# In[ ]:
# complex_stoichiometry_dict is a dict of {'complex_id': [{'bnum' : count}]}
rna_components = {
"b3123", "b0455"
} # component id should have 'RNA_ instead' of 'protein_'
# get Metabolic Complex composition from ECOLIme
complex_stoichiometry_dict = flat_files.get_complex_subunit_stoichiometry(
'protein_complexes.txt', rna_components)
# add complexes to model
complex_modification_dict = flat_files.get_complex_modifications(
'protein_modification.txt', 'protein_complexes.txt')
building.add_model_complexes(me,
complex_stoichiometry_dict,
complex_modification_dict,
verbose=False)
# remove modifications. they will be added back in later
for data in me.complex_data:
data.modifications = {}
# add formation reactions for each of the ComplexDatas
for cplx_data in me.complex_data:
formation = cplx_data.formation
if formation:
formation.update()
else:
cplx_data.create_complex_formation()
# ### 5) Add dummy reaction to model and unmodeled_protein_fraction
#
# Includes the transcription, translation, complex_formation, and metabolic reaction. Sequence based on prevelance of each codon found in *E. coli*.
# - Required
# * [**codon_usage.csv**](http://openwetware.org/wiki/Escherichia_coli/Codon_usage) (codon prevelance)
# In[ ]:
seq = "ATG"
codons = pandas.read_csv(join(flat_files.ecoli_files_dir,
"codon_usage.csv"),
index_col=0)
for codon, row in codons.iterrows():
if row.amino_acid == "Stop":
continue
seq += codon * int(row.per_1000 // 3) # want roughly 300 aa
# get the most used stop codon
seq += codons[codons.amino_acid == "Stop"].sort_values(
"per_1000").index[-1]
building.add_dummy_reactions(me, seq, update=True)
rxn = cobrame.SummaryVariable('dummy_protein_to_mass')
me.add_reaction(rxn)
me.add_metabolites([cobrame.Constraint('dummy_protein_biomass')])
mass = me.metabolites.protein_dummy.mass
rxn.add_metabolites({
'protein_biomass': -mass,
'protein_dummy': -1,
'dummy_protein_biomass': mass
})
# ### 6) Assocated Complexes and build Metabolic Reactions
# - Required
# * **enzyme_reaction_association.txt**
# * **reactions.txt** (gives reaction name, reversiblity, source and whether reaction is spontaneous)
#
# In[ ]:
# associate reaction id with the old ME complex id (including modifications)
rxn_to_cplx_dict = flat_files.get_reaction_to_complex()
rxn_info = flat_files.get_reaction_info_frame('reactions.txt')
# Required to add dummy reaction as spontaneous reaction
rxn_info = rxn_info.append(
pandas.Series(
{
'description': 'dummy reaction',
'is_reversible': 0,
'is_spontaneous': 1
},
name='dummy_reaction'))
building.add_reactions_from_stoichiometric_data(me,
rxn_to_cplx_dict,
rxn_info,
update=True)
# ### 7) Incorporate remaining biomass constituents
# There are leftover components from the *i*JO1366 biomass equation that either:
# 1. have no mechanistic function in the model (glycogen)
# 2. are cofactors that are regenerated (nad)
#
# Applies demands and coefficients from the *i*JO1366 biomass objective function
# In[ ]:
me.ngam = 16.68
me.gam = 25.40
me.unmodeled_protein_fraction = .275
biomass_components = {
"glycogen_c":
-.023 / (me.metabolites.glycogen_c.formula_weight / 1000.),
"2ohph_c": -0.000223,
"nad_c": -.001831,
"udcpdp_c": -5.5e-05,
"coa_c": -0.000576,
"ribflv_c": -0.000223,
"nadp_c": -0.000447,
"mlthf_c": -0.000223,
"thf_c": -0.000223,
"10fthf_c": -0.000223
}
rxn = cobrame.SummaryVariable('biomass_component_demand')
met = cobrame.Constraint('component_demand_biomass')
me.add_reaction(rxn)
rxn.add_metabolites(biomass_components)
component_mass = sum(
me.metabolites.get_by_id(c).formula_weight / 1000. * -v
for c, v in biomass_components.items())
rxn.lower_bound = mu
rxn.upper_bound = mu
me.reactions.biomass_component_demand.add_metabolites(
{met: component_mass})
rxn = cobrame.SummaryVariable('biomass_component_dilution')
me.add_reaction(rxn)
rxn.add_metabolites({met: -1, me._biomass: 1})
# #### Lipid components
# Metabolites and coefficients from *i*JO1366 biomass objective function
# In[ ]:
# Find biomass constituents with 3 numbers followed by a compartment in the BOF
lipid = re.compile('\d{3}_.')
lipid_demand = {}
for key, value in ijo.reactions.Ec_biomass_iJO1366_WT_53p95M.metabolites.items(
):
if lipid.search(key.id):
lipid_demand[key.id] = abs(value)
for met, requirement in lipid_demand.items():
component_mass = me.metabolites.get_by_id(met).formula_weight / 1000.
rxn = cobrame.SummaryVariable('Demand_' + met)
me.add_reaction(rxn)
rxn.add_metabolites({
met:
-1 * requirement,
'component_demand_biomass':
component_mass * requirement
})
rxn.lower_bound = mu
rxn.upper_bound = 1000.
# Kdo2lipid4
requirement = 0.01945 # in mmol/gDW
met = me.metabolites.get_by_id('kdo2lipid4_e')
component_mass = met.formula_weight / 1000.
rxn = cobrame.SummaryVariable('Demand_' + met.id)
me.add_reaction(rxn)
rxn.add_metabolites({
met.id:
-1. * requirement,
'component_demand_biomass':
component_mass * requirement
})
rxn.lower_bound = mu
rxn.upper_bound = mu
# #### DNA Demand Requirements
# Added based on growth rate dependent DNA levels as in [O'brien EJ et al 2013](https://www.ncbi.nlm.nih.gov/pubmed/24084808)
# In[ ]:
dna_demand_stoich = ecolime.DNA_replication.return_gr_dependent_dna_demand(
me.global_info['GC_fraction'])
DNA_replication = cobrame.SummaryVariable("DNA_replication")
me.add_reaction(DNA_replication)
DNA_replication.add_metabolites(dna_demand_stoich)
DNA_biomass = cobrame.Constraint("DNA_biomass")
DNA_biomass.elements = {
e: abs(v)
for e, v in DNA_replication.check_mass_balance().items()
}
dna_mw = 0
for met, value in me.reactions.DNA_replication.metabolites.items():
if met.id != 'ppi_c':
dna_mw -= value * dna_mw_no_ppi[met.id.replace('_c', '')] / 1000.
DNA_replication.add_metabolites({DNA_biomass: dna_mw})
DNA_replication.lower_bound = mu
DNA_replication.upper_bound = mu
# **Note: From this point forward, executing every codeblock should result in a solveable ME-model**
#
# ------
#
# ## Part 2: Add metastructures to solving ME-model
# This includes:
# 1. ribosome
# 2. RNA polymerase
# 3. charged_tRNAs
# Sometimes multiple entities can perform the same role. To prevent a combinatorial explosion of possibilities, we can create "generic" version, where any of those entities can fill in.
# In[ ]:
for generic, components in ecoli_k12.generic_dict.items():
cobrame.GenericData(generic, me, components).create_reactions()
# ### 1) Add ribosome
# This uses the ribosome composition definition in **ecolime/ribosome.py**
# In[ ]:
ecolime.ribosome.add_ribosome(me, verbose=False)
# ### 2) Add charged tRNA reactions
# The tRNA charging reactions were automatically added when loading the genome from the genbank file. However, the charging reactions still need to be made aware of the tRNA synthetases which are responsible.
#
# Uses **amino_acid_tRNA_synthetase.json**
# In[ ]:
with open(
join(flat_files.ecoli_files_dir,
"amino_acid_tRNA_synthetase.json"), "r") as infile:
aa_synthetase_dict = json.load(infile)
for data in me.tRNA_data:
data.synthetase = str(aa_synthetase_dict[data.amino_acid])
# Generic charged tRNAs are added to translation reactions via SubreactionData below.
#
# All new data added in this block contained in **ecolime/translation.py**
# In[ ]:
ecolime.translation.add_charged_tRNA_subreactions(me)
for data in me.translation_data:
for subreaction in data.translation_start_subreactions:
data.subreactions[subreaction] = 1
for subreaction, value in data.elongation_subreactions.items():
data.subreactions[subreaction] = value
for subreaction in data.translation_termination_subreactions:
data.subreactions[subreaction] = 1
for subreaction in data.translation_termination_subreactions:
data.subreactions[subreaction] = 1
# ### 3) Add Transcription Metacomplexes
# #### RNA Polymerase
#
# Data for RNA_polymerase composition fround in **ecolime/transcription**
#
# Uses *TU_df* from **TUs_from_ecocyc.txt**, above
# In[ ]:
for met in me.global_info['RNA_polymerase']:
RNAP_obj = cobrame.RNAP(met)
me.add_metabolites(RNAP_obj)
transcription.add_RNA_polymerase_complexes(me, verbose=False)
# associate the correct RNA_polymerase and factors to TUs
sigma_to_RNAP_dict = transcription.sigma_factor_complex_to_rna_polymerase_dict
for TU_id in TU_df.index:
transcription_data = me.transcription_data.get_by_id(TU_id)
rho_dependent = TU_df.rho_dependent[TU_id]
sigma = TU_df.sigma[TU_id]
RNA_polymerase = sigma_to_RNAP_dict[sigma]
transcription_data.RNA_polymerase = RNA_polymerase
transcription_data.rho_dependent = rho_dependent
# #### Degradosome (both for RNA degradation and RNA splicing)
#
# All new data contained in **ecolime/ecoli_k12.py**
# In[ ]:
data = cobrame.ComplexData('RNA_degradosome', me)
for subunit, value in ecoli_k12.RNA_degradosome.items():
data.stoichiometry[subunit] = value
data.create_complex_formation(verbose=False)
# Used for RNA splicing
data = cobrame.ModificationData('RNA_degradation_machine', me)
data.enzyme = 'RNA_degradosome'
data = cobrame.ModificationData('RNA_degradation_atp_requirement', me)
# .25 water equivaltent for atp hydrolysis and 1 h20 equivalent for to
# restore OH group in nucleotide monophosphate during RNA hydrolysis
data.stoichiometry = {
'atp_c': -.25,
'h2o_c': -1.25,
'adp_c': .25,
'pi_c': .25,
'h_c': 1.25
}
transcription.add_RNA_splicing(me)
# ------
# ## Part 3: Add remaining modifications
# rRNA modifications handled in *add_ribosome*
#
# ### 1) Add complex modifications
# *complex_modification_dict* from **protein_modification.text**, above
#
# The rest of the new data contained in **ecolime/modifications.py**
# In[ ]:
for complex_id, info in complex_modification_dict.items():
modifications = {}
for mod, value in info['modifications'].items():
# stoichiometry of modification determined in
# modification_data.stoichiometry
modifications['mod_' + mod] = abs(value)
me.complex_data.get_by_id(complex_id).modifications = modifications
# lipoate modifications can be accomplished using two different mechanisms
ecolime.modifications.add_lipoate_modifications(me)
# bmocogdp modifications have multiple selective chaperones that transfer the metabolite
# to the target complexes
ecolime.modifications.add_bmocogdp_modifications(me)
# add ModificationData for iron sulfur clusters
ecolime.modifications.add_iron_sulfur_modifications(me)
# add formation reactions for each of the ComplexDatas
for cplx_data in me.complex_data:
formation = cplx_data.formation
if formation:
formation.update()
else:
cplx_data.create_complex_formation()
# ### 2) Add tRNA mods and asocciate them with tRNA charging reactions
# New data from:
# 1. **ecolime/tRNA_charging.py** (read via *get_tRNA_modification_procedures()*)
# 2. **post_transcriptional_modification_of_tRNA.txt** (modification types per tRNA)
#
# In[ ]:
for mod, components in ecolime.tRNA_charging.get_tRNA_modification_procedures(
).items():
tRNA_mod = cobrame.ModificationData(mod, me)
tRNA_mod.enzyme = components['machines']
tRNA_mod.stoichiometry = components['metabolites']
tRNA_mod.keff = 65. # iOL uses 65 for all tRNA mods
if 'carriers' in components.keys():
for carrier, stoich in components['carriers'].items():
if stoich < 0:
tRNA_mod.enzyme += [carrier]
tRNA_mod.stoichiometry[carrier] = stoich
# tRNA_modifications = {tRNA_id: {modifications: count}}
tRNA_modifications = flat_files.get_tRNA_modification_targets()
for tRNA in tRNA_modifications:
for data in me.tRNA_data.query(tRNA):
data.modifications = tRNA_modifications[tRNA]
# ---
# ## Part 4: Add remaining subreactions
# ### 1) Add translation related subreactions
# All new data from **ecolime/translation.py**
# In[ ]:
# add the translation subreaction data objects to model
translation.add_translation_subreactions_to_model(me)
# add translation subreaction data to reactions
methionine_cleaved = translation.methionine_cleaved
folding_dict = translation.folding_dict
terminator_dict = translation.translation_stop_dict
for data in me.translation_data:
data.term_enzyme = terminator_dict.get(data.last_codon)
locus_id = data.id
if locus_id in methionine_cleaved:
data.subreactions['N_terminal_methionine_cleavage'] = 1
for folding_type in folding_dict:
if locus_id in folding_dict[folding_type]:
data.subreactions[folding_type] = 1
# This block was ran above, but should be ran again to
# incorporate any subreactions not added previously
for subreaction in data.translation_start_subreactions:
data.subreactions[subreaction] = 1
for subreaction, value in data.elongation_subreactions.items():
data.subreactions[subreaction] = value
for subreaction in data.translation_termination_subreactions:
data.subreactions[subreaction] = 1
# add organism specific subreactions associated with peptide processing
global_info = me.global_info
for subrxn, value in global_info[
'peptide_processing_subreactions'].items():
data.subreactions[subrxn] = value
# ### 2) Add transcription related subreactions
# All new data from **ecolime/transcription.py**
# In[ ]:
for subreaction in transcription.transcription_subreactions:
subreaction_data = cobrame.SubreactionData(subreaction, me)
enzymes = transcription.transcription_subreactions[subreaction][
'enzymes']
subreaction_data.stoichiometry = \
transcription.transcription_subreactions[subreaction]['stoich']
subreaction_data.enzyme = enzymes
for transcription_data in me.transcription_data:
if transcription_data.rho_dependent:
rho = 'dependent'
else:
rho = 'independent'
if transcription_data.codes_stable_rna:
stable = 'stable'
else:
stable = 'normal'
transcription_data.subreactions['Transcription_%s_rho_%s' %
(stable, rho)] = 1
# ----
# ## Part 5: Add in translocation
#
# New data from:
# 1. **peptide_compartment_and_pathways.txt** (Protein compartment and translocation pathway for each membrane complex)
# 2. **ecolime/translocation.py** (definitions of each translocation pathway)
# In[ ]:
# Add TranslocationData
transloc = pandas.read_csv(join(flat_files.ecoli_files_dir,
"peptide_compartment_and_pathways2.txt"),
sep='\t',
comment="#")
for pathway, info in ecolime.translocation.pathway.items():
if 'alt' not in pathway:
transloc_data = cobrame.TranslocationData(
pathway + '_translocation', me)
else:
transloc_data = cobrame.TranslocationData(
pathway.replace('_alt', '_translocation_alt'), me)
transloc_data.enzyme_dict = info['enzymes']
transloc_data.keff = info['keff']
transloc_data.length_dependent_energy = info['length_dependent_energy']
transloc_data.stoichiometry = info['stoichiometry']
# Associate data and add translocation reactions
ecolime.translocation.add_translocation_pathways(
me, transloc, membrane_constraints=False)
# Update stoichiometry of membrane complexes
# new_stoich = {complex_id: protein_w_compartment}
new_stoich = defaultdict(dict)
for cplx, row in transloc.set_index('Complex').iterrows():
protein = row.Protein.split('(')[0] + '_' + row.Protein_compartment
value = row.Protein.split('(')[1][:-1].split(':')[0]
new_stoich[cplx]['protein_' + protein] = float(value)
for cplx, stoich in new_stoich.items():
complex_data = me.complex_data.get_by_id(cplx)
for met, value in stoich.items():
complex_data.stoichiometry.pop(met[0:13])
complex_data.stoichiometry[met] = value
complex_data.formation.update()
# Complex ids in protein compartment file doesn't include mods
# Some have multiple alternative modifications so must loop through these
for complex_data in me.complex_data.query(cplx + '_mod_'):
for met, value in stoich.items():
complex_data.stoichiometry.pop(met[0:13])
complex_data.stoichiometry[met] = value
complex_data.formation.update()
# ---
# ## Part 6: Add Cell Wall Components
# All new data from **ecolime/translocation.py**
# In[ ]:
compartment_dict = {}
for prot, compartment in transloc.set_index(
'Protein').Protein_compartment.to_dict().items():
compartment_dict[prot.split('(')[0]] = compartment
# #### Add lipid modification ModificationData
# In[ ]:
lipid_modifications = ecolime.translocation.lipid_modifications
for lipid in lipid_modifications:
data = cobrame.ModificationData('mod_' + lipid, me)
data.stoichiometry = {lipid: -1, 'g3p_c': 1}
data.enzyme = ['Lgt_MONOMER', 'LspA_MONOMER']
data = cobrame.ModificationData('mod2_pg160_p', me)
data.stoichiometry = {'pg160_p': -1, '2agpg160_p': 1}
data.enzyme = 'EG10168-MONOMER'
data = cobrame.ModificationData('mod2_pe160_p', me)
data.stoichiometry = {'pe160_p': -1, '2agpe160_p': 1}
data.enzyme = 'EG10168-MONOMER'
ecolime.translocation.add_lipoprotein_formation(me,
compartment_dict,
membrane_constraints=False)
# #### Correct complex formation IDs if they contain lipoproteins
# In[ ]:
for gene in ecolime.translocation.lipoprotein_precursors.values():
compartment = compartment_dict.get(gene)
for rxn in me.metabolites.get_by_id('protein_' + gene + '_' +
compartment).reactions:
if isinstance(rxn, cobrame.ComplexFormation):
data = me.complex_data.get_by_id(rxn.complex_data_id)
value = data.stoichiometry.pop('protein_' + gene + '_' +
compartment)
data.stoichiometry['protein_' + gene + '_lipoprotein' + '_' +
compartment] = value
rxn.update()
# #### Braun's lipoprotein demand
# Metabolites and coefficients as defined in [Liu et al 2014](http://bmcsystbiol.biomedcentral.com/articles/10.1186/s12918-014-0110-6)
# In[ ]:
rxn = cobrame.SummaryVariable('core_structural_demand_brauns')
met = cobrame.Constraint('component_demand_biomass')
me.add_metabolites([met])
met1 = me.metabolites.get_by_id('murein5px4p_p')
met1_mass = met1.formula_weight / 1000.
met2 = me.metabolites.get_by_id('protein_b1677_lipoprotein_Outer_Membrane')
met2_mass = met2.formula_weight / 1000.
me.add_reaction(rxn)
rxn.add_metabolites(
{
met1:
-0.013894,
met2:
-0.003597,
'component_demand_biomass':
(0.013894 * met1_mass + 0.003597 * met2_mass)
},
combine=False)
rxn.lower_bound = mu
rxn.upper_bound = mu
# -----
# ## Part 7: Set keffs
#
# Either entirely based on SASA or using fit keffs from [Ebrahim et al 2016](https://www.ncbi.nlm.nih.gov/pubmed/27782110?dopt=Abstract)
# # Set keffs to sasa fluxes centered around 65.
# me.set_SASA_keffs(65)
# In[ ]:
keff_list = []
keffs = flat_files.get_reaction_keffs(me, verbose=True)
for reaction_id, keff in keffs.items():
if keff > 3000:
keff = 3000.
elif keff < .01:
keff = .01
keff_list.append(keff)
me.reactions.get_by_id(reaction_id).keff = keff
me.reactions.get_by_id(reaction_id).update()
# Keffs that were not set in the above block
me.subreaction_data.N_terminal_methionine_cleavage.keff = 1339.4233102860871
me.subreaction_data.peptide_deformylase_processing.keff = 1019.5963333345715
me.reactions.get_by_id(
'GLUTRR_FWD_CPLX0-3741').keff = 3000 # 3269.0108007383374
me.subreaction_data.fmet_addition_at_START.keff = 1540.4356849968603
me.subreaction_data.ribosome_recycler.keff = 1059.6910912619182
me.subreaction_data.UAG_PrfA_mono_mediated_termination.keff = 1721.7910609284945
me.subreaction_data.UGA_PrfB_mono_mediated_termination.keff = 1700.2966587695353
me.subreaction_data.UAA_generic_RF_mediated_termination.keff = 1753.4238515034572
# -----
# ## Part 8: Model updates and corrections
# # Add NDH flux split constraint
# for rxn in me.metabolites.get_by_id('NADH-DHII-MONOMER_mod_mg2_mod_cu_mod_fad').metabolic_reactions:
# rxn.stoichiometric_data.stoichiometry['ndh2_constraint'] = 1
# rxn.update()
# for rxn in me.metabolites.get_by_id('NADH-DHI-CPLX_mod_2fe2s_mod_4fe4s_mod_fmn').metabolic_reactions:
# rxn.stoichiometric_data.stoichiometry['ndh1_constraint'] = 1
# rxn.update()
# rxn = cobra.Reaction('ndh_flux_split_constraint')
# me.add_reaction(rxn)
# rxn.reaction = 'ndh1_constraint + ndh2_constraint ->'
# In[ ]:
# Add reaction subsystems from iJO to model
for rxn in ijo.reactions:
if rxn.id in me.stoichiometric_data:
data = me.stoichiometric_data.get_by_id(rxn.id)
else:
continue
for r in data.parent_reactions:
r.subsystem = rxn.subsystem
# #### Add enzymatic coupling for "carriers"
# These are enzyme complexes that act as metabolites in a metabolic reaction (i.e. are metabolites in iJO1366)
# In[ ]:
for data in me.stoichiometric_data:
if data.id == 'dummy_reaction':
continue
for met, value in data.stoichiometry.items():
if not isinstance(me.metabolites.get_by_id(met),
cobrame.Complex) or value > 0:
continue
subreaction_id = met + '_carrier_activity'
if subreaction_id not in me.subreaction_data:
sub = cobrame.SubreactionData(met + '_carrier_activity', me)
sub.enzyme = met
data.subreactions[subreaction_id] = abs(value)
# #### Corrections
# In[ ]:
# RNA_dummy, TU_b3247, TU_b3705 do not have RNAP, this is set as the most common RNAP
for data in me.transcription_data:
if len(data.RNA_polymerase) == 0:
data.RNA_polymerase = 'RNAP70-CPLX'
# Newly annotated modification gene (w/ high confidence)
mod = me.modification_data.get_by_id('mod_acetyl_c')
mod.stoichiometry = {'accoa_c': -1, 'coa_c': 1}
# If lower_bound open, model feeds G6P into EDD
me.reactions.EX_pqq_e.lower_bound = 0
me.reactions.EX_pqq_e.upper_bound = 0
# cobalamin is not essential in e. coli and there is no evidence
# of this gene requiring this modification.
me.reactions.EX_cbl1_e.lower_bound = 0
me.complex_data.QueG_mono_mod_adocbl.modifications.pop('mod_adocbl_c')
me.complex_data.QueG_mono_mod_adocbl.formation.update()
# this RNAP/sigma factor should not be used to transcribe stable rna
for rxn in me.metabolites.get_by_id('RNAP32-CPLX').reactions:
if rxn.id != 'formation_RNAP32-CPLX' and rxn.transcription_data.codes_stable_rna:
rxn.upper_bound = 0
print(rxn)
# This complex likely doesn't have ATPSYN activity
me.reactions.get_by_id('formation_ATPSYN-CPLX_EG10106-MONOMER').knock_out()
# This enyzme is involved in catalyzing this reaction
sub = cobrame.SubreactionData('EG12450-MONOMER_activity', me)
sub.enzyme = 'EG12450-MONOMER'
me.stoichiometric_data.NHFRBO.subreactions['EG12450-MONOMER_activity'] = 1
# This reaction was edited based on iJO1366 GPR
data = cobrame.ComplexData('EG11910-MONOMER_dimer_EG11911-MONOMER', me)
data.stoichiometry = {'EG11910-MONOMER_dimer': 1, 'EG11911-MONOMER': 1}
data.create_complex_formation()
me.reactions.get_by_id('PFL_FWD_EG11910-MONOMER_dimer').remove_from_model()
building.add_metabolic_reaction_to_model(
me,
'PFL',
'forward',
complex_id='EG11910-MONOMER_dimer_EG11911-MONOMER',
update=True)
# NGAM reaction is just called ATPM
me.reactions.ATPM_FWD_SPONT.remove_from_model()
# ----
# ## Part 9: Update and solve
# In[ ]:
me.reactions.dummy_reaction_FWD_SPONT.objective_coefficient = 1.
me.reactions.EX_glc__D_e.lower_bound = -1000
me.reactions.EX_o2_e.lower_bound = -16.
me.ngam = 16.68
me.gam = 25.40
me.unmodeled_protein_fraction = .275
# In[ ]:
me.update()
me.prune()
# In[ ]:
n_genes = len(me.metabolites.query(re.compile('RNA_b[0-9]')))
print("number of genes in the model %d (%.2f%%)" %
(n_genes, n_genes * 100. / (1678)))
return me
def return_me_model():
# Define Models
ijo_directory = join(flat_files.ecoli_files_dir, 'iJO1366.json')
ijo = cobra.io.load_json_model(ijo_directory)
me = cobrame.MEModel('iJL1678b-ME')
# ME-models require special OptLang interface if cobrapy version >= 0.6.0
# If cannot import SymbolicParameter, assume using cobrapy
# versions <= 0.5.11
try:
from optlang.interface import SymbolicParameter
except:
pass
else:
me.solver = me_model_interface
# "Translational capacity" of organism
me.global_info['kt'] = 4.5 # (in h-1)scott 2010, RNA-to-protein curve fit
me.global_info['r0'] = 0.087 # scott 2010, RNA-to-protein curve fit
me.global_info['k_deg'] = 1.0 / 5. * 60.0 # 1/5 1/min 60 min/h # h-1
# Molecular mass of RNA component of ribosome
me.global_info['m_rr'] = 1453. # in kDa
# Average molecular mass of an amino acid
me.global_info['m_aa'] = 109. / 1000. # in kDa
# Proportion of RNA that is rRNA
me.global_info['f_rRNA'] = .86
me.global_info['m_nt'] = 324. / 1000. # in kDa
me.global_info['f_mRNA'] = .02
# tRNA associated global information
me.global_info['m_tRNA'] = 25000. / 1000. # in kDA
me.global_info['f_tRNA'] = .12
# Folding Properties
me.global_info['temperature'] = 37
me.global_info['propensity_scaling'] = .45
# DNA Replication Parameters
me.global_info['GC_fraction'] = 0.507896997096
# Define the types of biomass that will be synthesized in the model
me.add_biomass_constraints_to_model([
"protein_biomass", "mRNA_biomass", "tRNA_biomass", "rRNA_biomass",
"ncRNA_biomass", "DNA_biomass", "lipid_biomass", "constituent_biomass",
"prosthetic_group_biomass", "peptidoglycan_biomass"
])
# Define ME-model compartments
me.compartments = {
"p": "Periplasm",
"e": "Extra-organism",
"c": "Cytosol",
"im": 'Inner Membrane',
'om': "Outer Membrane",
"mc": "ME-model Constraint",
"m": "Membrane"
}
# ### 2) Load metabolites and build Metabolic reactions
# The below reads in:
# - Required
# * **reaction_matrix.txt** (reaction matrix w/ reactions unlumped, metabolites renamed etc.)
# * **metabolites.txt** (metabolite properties)
# * **reactions.txt** (info on reversiblity, whether enzyme catalyzed etc.)
# * **m_to_me_mets.csv** (mapping of enzymes/complexes used in M-model to their ME-model compatible ID)
# * **protein_complexes.txt** (protein subunit stoichiometry of all complexes, used to identify metabolites as such)
#
# It creates a new e coli M-model from this info then incorporates it into the ME-model using *add_m_model_content*. metabolites are added directly reactions are added as StoichiometricData
#
# Metabolite types have different properties in an ME-model so enzyme complexes need added to the model as Complexes not Metabolites. Components in the E. coli M-model that are actually Complexes are compiled in *complex_list*
# In[ ]:
# m_model = flat_files.get_m_model()
m_model = flat_files.process_m_model(
ijo,
'metabolites.txt',
'm_to_me_mets.csv',
'reactions.txt',
'reaction_matrix.txt',
'protein_complexes.txt',
defer_to_rxn_matrix={'GLUTRR', 'PAPSR2'})
m_model.reactions.EX_glc_e.id = 'EX_glc__D_e'
m_model.repair()
# some of the "metabolites" in iJO1366 "M" model are actually complexes. We pass those in
# so they get created as complexes, not metabolites.
complexes = flat_files.get_complex_subunit_stoichiometry(
'protein_complexes.txt').keys()
complex_set = set([
i.id for i in m_model.metabolites
if i.id.split('_mod_')[0] in complexes
])
building.add_m_model_content(me,
m_model,
complex_metabolite_ids=complex_set)
# In[ ]:
# This adds exchange reactions for metabolites not contained in iJO1366
# Some of these cannot be produced by the model so they are added here
exchange_list = [
'LI_c', 'pqq_e', 'cs_e', 'tl_c', 'RNase_m5', 'RNase_m16', 'RNase_m23'
] # RNAses are gaps in model
for met_id in exchange_list:
r = cobrame.MEReaction("EX_" + met_id)
me.add_reaction(r)
r.reaction = met_id + " <=> "
# ### 3) Add Transcription and Translation
# The below reads in:
# - Required
# * **NC_000913.2.gb** (Genbank sequence annotation)
# * **ecolime/translation.py** (codon to tRNA mapping)
# - Optional
# * **TUs_from_ecocyc.txt** (TU definitions, start/stop positions, etc.)
# * **ecolime/translation.py** (dictionary of gene to frameshift mutation)
#
# To construct the bare minimimum components of a transcription and translation reactions. For example, transcription reactions at this point include nucleotides and the synthesized RNAs.
# In[ ]:
gb_filename = join(flat_files.ecoli_files_dir, 'NC_000913.2.gb')
tu_df = flat_files.get_tu_dataframe('TUs_from_ecocyc.txt')
building.build_reactions_from_genbank(
me,
gb_filename,
tu_df,
verbose=False,
frameshift_dict=translation.frameshift_dict,
trna_to_codon=translation.trna_to_codon)
# ### 4) Add in complex Formation without modifications (for now)
#
# The below reads in:
# - Required
# * **protein_complexes.txt** (Metabolic complexes' protein subunit stoichiometries)
# * **protein_modification.txt** (Type and number of modifications for each protein)
# In[ ]:
# complex_stoichiometry_dict is a dict of {'complex_id': [{'bnum' : count}]}
rna_components = {
"b3123", "b0455"
} # component id should have 'RNA_ instead' of 'protein_'
# get Metabolic Complex composition from ECOLIme
complex_stoichiometry_dict = flat_files.get_complex_subunit_stoichiometry(
'protein_complexes.txt', rna_components)
# add complexes to model
complex_modification_dict = flat_files.get_complex_modifications(
'protein_modification.txt', 'protein_complexes.txt')
building.add_model_complexes(me,
complex_stoichiometry_dict,
complex_modification_dict,
verbose=False)
# remove modifications. they will be added back in later
for data in me.complex_data:
data.subreactions = {}
# add formation reactions for each of the ComplexDatas
for cplx_data in me.complex_data:
formation = cplx_data.formation
if formation:
formation.update()
else:
cplx_data.create_complex_formation()
# ### 5) Add dummy reaction to model and unmodeled_protein_fraction
#
# Includes the transcription, translation, complex_formation, and metabolic reaction. Sequence based on prevelance of each codon found in *E. coli*.
# - Required
# * [**codon_usage.csv**](http://openwetware.org/wiki/Escherichia_coli/Codon_usage) (codon prevelance)
# In[ ]:
seq = "ATG"
codons = pandas.read_csv(join(flat_files.ecoli_files_dir,
"codon_usage.csv"),
index_col=0)
for codon, row in codons.iterrows():
if row.amino_acid == "Stop":
continue
seq += codon * int(row.per_1000 // 3) # want roughly 300 aa
# get the most used stop codon
seq += codons[codons.amino_acid == "Stop"].sort_values(
"per_1000").index[-1]
building.add_dummy_reactions(me, seq, update=True)
rxn = cobrame.SummaryVariable('dummy_protein_to_mass')
me.add_reactions([rxn])
mass = me.metabolites.protein_dummy.formula_weight / 1000. # in kDa
rxn.add_metabolites({
'protein_biomass': -mass,
'protein_dummy': -1,
cobrame.Constraint('unmodeled_protein_biomass'): mass
})
# ### 6) Assocated Complexes and build Metabolic Reactions
# - Required
# * **enzyme_reaction_association.txt**
# * **reactions.txt** (gives reaction name, reversiblity, source and whether reaction is spontaneous)
#
# In[ ]:
# associate reaction id with the old ME complex id (including modifications)
rxn_to_cplx_dict = flat_files.get_reaction_to_complex(m_model)
rxn_info = flat_files.get_reaction_info_frame('reactions.txt')
# Required to add dummy reaction as spontaneous reaction
rxn_info = rxn_info.append(
pandas.Series(
{
'description': 'dummy reaction',
'is_reversible': 0,
'is_spontaneous': 1
},
name='dummy_reaction'))
building.add_reactions_from_stoichiometric_data(me,
rxn_to_cplx_dict,
rxn_info,
update=True)
# ### 7) Incorporate remaining biomass constituents
# There are leftover components from the *i*JO1366 biomass equation that either:
# 1. have no mechanistic function in the model (glycogen)
# 2. are cofactors that are regenerated (nad)
#
# Applies demands and coefficients from the *i*JO1366 biomass objective function
# In[ ]:
me.ngam = 1
me.gam = 34.98
me.unmodeled_protein_fraction = .36
biomass_constituents = {
"glycogen_c":
-.023 / (me.metabolites.glycogen_c.formula_weight / 1000.),
"2ohph_c": -0.000223,
"nad_c": -.001831,
"udcpdp_c": -5.5e-05,
"coa_c": -0.000576,
"ribflv_c": -0.000223,
"nadp_c": -0.000447,
"mlthf_c": -0.000223,
"thf_c": -0.000223,
"10fthf_c": -0.000223
}
rxn = cobrame.SummaryVariable('biomass_constituent_demand')
me.add_reactions([rxn])
rxn.add_metabolites(biomass_constituents)
constituent_mass = sum(
me.metabolites.get_by_id(c).formula_weight / 1000. * -v
for c, v in biomass_constituents.items())
rxn.lower_bound = mu
rxn.upper_bound = mu
rxn.add_metabolites({me.metabolites.constituent_biomass: constituent_mass})
# #### Lipid components
# Metabolites and coefficients from *i*JO1366 biomass objective function
# In[ ]:
# Find biomass constituents with 3 numbers followed by a compartment in the BOF
lipid = re.compile('\d{3}_.')
lipid_demand = {}
for key, value in ijo.reactions.Ec_biomass_iJO1366_core_53p95M.metabolites.items(
):
if lipid.search(key.id):
lipid_demand[key.id] = abs(value)
for met, requirement in lipid_demand.items():
component_mass = me.metabolites.get_by_id(met).formula_weight / 1000.
rxn = cobrame.SummaryVariable('Demand_' + met)
me.add_reactions([rxn])
rxn.add_metabolites({
met: -1 * requirement,
'lipid_biomass': component_mass * requirement
})
rxn.lower_bound = mu
rxn.upper_bound = 1000.
# Kdo2lipid4
requirement = 0.01945 # in mmol/gDW
met = me.metabolites.get_by_id('kdo2lipid4_e')
component_mass = met.formula_weight / 1000.
rxn = cobrame.SummaryVariable('Demand_' + met.id)
me.add_reactions([rxn])
rxn.add_metabolites({
met.id: -1. * requirement,
'lipid_biomass': component_mass * requirement
})
rxn.lower_bound = mu
rxn.upper_bound = mu
# #### DNA Demand Requirements
# Added based on growth rate dependent DNA levels as in [O'brien EJ et al 2013](https://www.ncbi.nlm.nih.gov/pubmed/24084808)
# In[ ]:
dna_demand_stoich, dna_demand_bound = ecolime.dna_replication.return_gr_dependent_dna_demand(
me, me.global_info['GC_fraction'])
dna_replication = cobrame.SummaryVariable("DNA_replication")
me.add_reaction(dna_replication)
dna_replication.add_metabolites(dna_demand_stoich)
dna_biomass = cobrame.Constraint("DNA_biomass")
dna_biomass.elements = {
e: abs(v)
for e, v in dna_replication.check_mass_balance().items()
}
dna_mw = 0
dna_mw_no_ppi = ecolime.dna_replication.get_dna_mw_no_ppi_dict(me)
for met, value in me.reactions.DNA_replication.metabolites.items():
if met.id != 'ppi_c':
dna_mw -= value * dna_mw_no_ppi[met.id.replace('_c', '')] / 1000.
dna_replication.add_metabolites({dna_biomass: dna_mw})
dna_replication.lower_bound = dna_demand_bound
dna_replication.upper_bound = dna_demand_bound
# **Note**: From this point forward, executing every codeblock should result in a solveable ME-model
#
# ------
#
# ## Part 2: Add metastructures to solving ME-model
# This includes:
# 1. ribosome
# 2. RNA polymerase
# 3. charged_tRNAs
# Sometimes multiple entities can perform the same role. To prevent a combinatorial explosion of possibilities, we can create "generic" version, where any of those entities can fill in.
# In[ ]:
for generic, components in generics.generic_dict.items():
cobrame.GenericData(generic, me, components).create_reactions()
# ### 1) Add ribosome
# This uses the ribosome composition definition in **ecolime/ribosome.py**
# In[ ]:
ecolime.ribosome.add_ribosome(me, verbose=False)
# ### 2) Add charged tRNA reactions
# The tRNA charging reactions were automatically added when loading the genome from the genbank file. However, the charging reactions still need to be made aware of the tRNA synthetases which are responsible.
#
# Uses **trna_charging.py**
# In[ ]:
aa_synthetase_dict = ecolime.trna_charging.amino_acid_trna_synthetase
for data in me.tRNA_data:
data.synthetase = str(aa_synthetase_dict[data.amino_acid])
# Generic charged tRNAs are added to translation reactions via SubreactionData below.
#
# All new data added in this block contained in **ecolime/translation.py**
# In[ ]:
ecolime.translation.add_charged_trna_subreactions(me)
for data in me.translation_data:
data.add_initiation_subreactions(
start_codons=translation.translation_start_codons,
start_subreactions=set(translation.initiation_subreactions.keys()))
data.add_elongation_subreactions(elongation_subreactions=set(
translation.elongation_subreactions.keys()))
data.add_termination_subreactions(
translation_terminator_dict=translation.translation_stop_dict)
# ### 3) Add Transcription Metacomplexes
# #### RNA Polymerase
#
# Data for RNA_polymerase composition fround in **ecolime/transcription**
#
# Uses *tu_df* from **TUs_from_ecocyc.txt**, above
# In[ ]:
for met in transcription.rna_polymerases:
rnap_obj = cobrame.RNAP(met)
me.add_metabolites(rnap_obj)
transcription.add_rna_polymerase_complexes(me, verbose=False)
# associate the correct RNA_polymerase and factors to TUs
sigma_to_rnap_dict = transcription.sigma_factor_complex_to_rna_polymerase_dict
for tu_id in tu_df.index:
transcription_data = me.process_data.get_by_id(tu_id)
sigma = tu_df.sigma[tu_id]
rna_polymerase = sigma_to_rnap_dict[sigma]
transcription_data.RNA_polymerase = rna_polymerase
# #### Degradosome (both for RNA degradation and RNA splicing)
#
# All new data contained in **transcription.py**
# In[ ]:
me.add_metabolites([cobrame.Complex('RNA_degradosome')])
data = cobrame.ComplexData('RNA_degradosome', me)
for subunit, value in transcription.rna_degradosome.items():
data.stoichiometry[subunit] = value
data.create_complex_formation(verbose=False)
# Used for RNA splicing
data = cobrame.SubreactionData('RNA_degradation_machine', me)
data.enzyme = 'RNA_degradosome'
data = cobrame.SubreactionData('RNA_degradation_atp_requirement', me)
# .25 water equivaltent for atp hydrolysis per nucleotide
data.stoichiometry = {
'atp_c': -.25,
'h2o_c': -.25,
'adp_c': .25,
'pi_c': .25,
'h_c': .25
}
transcription.add_rna_splicing(me)
# ------
# ## Part 3: Add remaining modifications
# rRNA modifications handled in *add_ribosome*
#
# ### 1) Add complex modifications
# *complex_modification_dict* from **protein_modification.text**, above
#
# The rest of the new data contained in **ecolime/modifications.py**
# In[ ]:
for complex_id, info in complex_modification_dict.items():
modifications = {}
for mod, value in info['modifications'].items():
# stoichiometry of modification determined in
# subreaction_data.stoichiometry
modifications['mod_' + mod] = abs(value)
me.process_data.get_by_id(complex_id).subreactions = modifications
# Adds modification data for more complicated enzyme modifications
# (ie, iron sulfur cluster modification)
ecolime.modifications.add_modification_procedures(me)
# add formation reactions for each of the ComplexDatas
for cplx_data in me.complex_data:
formation = cplx_data.formation
if formation:
formation.update()
else:
cplx_data.create_complex_formation()
# ### 2) Add tRNA mods and asocciate them with tRNA charging reactions
# New data from:
# 1. **ecolime/trna_charging.py** (read via *add_trna_modification_procedures()*)
# 2. **post_transcriptional_modification_of_tRNA.txt** (modification types per tRNA)
#
# In[ ]:
# Add tRNA modifications to ME-model
ecolime.trna_charging.add_trna_modification_procedures(me)
# trna_modifications = {tRNA_id: {modifications: count}}
trna_modifications = flat_files.get_trna_modification_targets()
for trna in trna_modifications:
for data in me.process_data.query(trna):
data.subreactions = trna_modifications[trna]
# ---
# ## Part 4: Add remaining subreactions
# ### 1) Add translation related subreactions
# All new data from **ecolime/translation.py**
# In[ ]:
# add the translation subreaction data objects to model
translation.add_translation_subreactions_to_model(me)
# add translation subreaction data to reactions
methionine_cleaved = translation.methionine_cleaved
folding_dict = translation.folding_dict
for data in me.translation_data:
data.term_enzyme = \
translation.translation_stop_dict.get(data.last_codon)
locus_id = data.id
if locus_id in methionine_cleaved:
data.subreactions['N_terminal_methionine_cleavage'] = 1
for folding_type in folding_dict:
if locus_id in folding_dict[folding_type]:
data.subreactions[folding_type] = 1
# This block was ran above, but should be ran again to
# incorporate any subreactions not added previously
data.add_initiation_subreactions(
start_codons=translation.translation_start_codons,
start_subreactions=set(translation.initiation_subreactions.keys()))
data.add_elongation_subreactions(elongation_subreactions=set(
translation.elongation_subreactions.keys()))
data.add_termination_subreactions(
translation_terminator_dict=translation.translation_stop_dict)
# add organism specific subreactions associated with peptide processing
for subrxn in translation.peptide_processing_subreactions:
data.subreactions[subrxn] = 1
# ### 2) Add transcription related subreactions
# All new data from **ecolime/transcription.py**
# In[ ]:
for subreaction in transcription.transcription_subreactions:
subreaction_data = cobrame.SubreactionData(subreaction, me)
enzymes = transcription.transcription_subreactions[subreaction][
'enzymes']
subreaction_data.stoichiometry = \
transcription.transcription_subreactions[subreaction]['stoich']
subreaction_data.enzyme = enzymes
for transcription_data in me.transcription_data:
# Assume false if not in tu_df\n",
rho_dependent = tu_df.rho_dependent.get(transcription_data.id, False)
rho = 'dependent' if rho_dependent else 'independent'
stable = 'stable' if transcription_data.codes_stable_rna else 'normal'
transcription_data.subreactions['Transcription_%s_rho_%s' %
(stable, rho)] = 1
# ----
# ## Part 5: Add in translocation
#
# New data from:
# 1. **peptide_compartment_and_pathways.txt** (Protein compartment and translocation pathway for each membrane complex)
# 2. **ecolime/translocation.py** (definitions of each translocation pathway)
# In[ ]:
# Add TranslocationData
transloc = pandas.read_csv(join(flat_files.ecoli_files_dir,
"peptide_compartment_and_pathways2.txt"),
sep='\t',
comment="#")
for pathway, info in ecolime.translocation.pathway.items():
if 'alt' not in pathway:
transloc_data = cobrame.TranslocationData(
pathway + '_translocation', me)
else:
transloc_data = cobrame.TranslocationData(
pathway.replace('_alt', '_translocation_alt'), me)
transloc_data.enzyme_dict = info['enzymes']
transloc_data.keff = info['keff']
transloc_data.length_dependent_energy = info['length_dependent_energy']
transloc_data.stoichiometry = info['stoichiometry']
# Associate data and add translocation reactions
ecolime.translocation.add_translocation_pathways(
me, transloc, membrane_constraints=False)
# Update stoichiometry of membrane complexes
# new_stoich = {complex_id: protein_w_compartment}
new_stoich = defaultdict(dict)
for cplx, row in transloc.set_index('Complex').iterrows():
if cplx == 'EG10544-MONOMER':
continue
protein = row.Protein.split('(')[0] + '_' + row.Protein_compartment
value = me.process_data.get_by_id(cplx).stoichiometry[
'protein_' + row.Protein.split('(')[0]]
new_stoich[cplx]['protein_' + protein] = float(value)
for cplx, stoich in new_stoich.items():
complex_data = me.process_data.get_by_id(cplx)
for met, value in stoich.items():
complex_data.stoichiometry.pop(met[0:13])
complex_data.stoichiometry[met] = value
complex_data.formation.update()
# Complex ids in protein compartment file doesn't include mods
# Some have multiple alternative modifications so must loop through these
for complex_data in me.process_data.query(cplx + '_mod_'):
for met, value in stoich.items():
complex_data.stoichiometry.pop(met[0:13])
complex_data.stoichiometry[met] = value
complex_data.formation.update()
# ---
# ## Part 6: Add Cell Wall Components
# All new data from **ecolime/translocation.py**
# In[ ]:
compartment_dict = {}
for prot, compartment in transloc.set_index(
'Protein').Protein_compartment.to_dict().items():
compartment_dict[prot.split('(')[0]] = compartment
# #### Add lipid modification SubreactionData
# In[ ]:
lipid_modifications = ecolime.translocation.lipid_modifications
for lipid in lipid_modifications:
data = cobrame.SubreactionData('mod_' + lipid, me)
data.stoichiometry = {lipid: -1, 'g3p_c': 1}
data.enzyme = ['Lgt_MONOMER', 'LspA_MONOMER']
# The element contribution is based on the lipid involved in the
# modification, so calculate based on the metabolite formula
data._element_contribution = data.calculate_element_contribution()
data = cobrame.SubreactionData('mod2_pg160_p', me)
data.stoichiometry = {'pg160_p': -1, '2agpg160_p': 1}
data.enzyme = 'EG10168-MONOMER'
data._element_contribution = data.calculate_element_contribution()
data = cobrame.SubreactionData('mod2_pe160_p', me)
data.stoichiometry = {'pe160_p': -1, '2agpe160_p': 1}
data.enzyme = 'EG10168-MONOMER'
data._element_contribution = data.calculate_element_contribution()
ecolime.translocation.add_lipoprotein_formation(me,
compartment_dict,
membrane_constraints=False)
# #### Correct complex formation IDs if they contain lipoproteins
# In[ ]:
for gene in ecolime.translocation.lipoprotein_precursors.values():
compartment = compartment_dict.get(gene)
for rxn in me.metabolites.get_by_id('protein_' + gene + '_' +
compartment).reactions:
if isinstance(rxn, cobrame.ComplexFormation):
data = me.process_data.get_by_id(rxn.complex_data_id)
value = data.stoichiometry.pop('protein_' + gene + '_' +
compartment)
data.stoichiometry['protein_' + gene + '_lipoprotein' + '_' +
compartment] = value
rxn.update()
# #### Braun's lipoprotein demand
# Metabolites and coefficients as defined in [Liu et al 2014](http://bmcsystbiol.biomedcentral.com/articles/10.1186/s12918-014-0110-6)
# In[ ]:
rxn = cobrame.SummaryVariable('core_structural_demand_brauns')
met1 = me.metabolites.get_by_id('murein5px4p_p')
met1_mass = met1.formula_weight / 1000.
met2 = me.metabolites.get_by_id('protein_b1677_lipoprotein_Outer_Membrane')
me.add_reactions([rxn])
# biomass of lipoprotein accounted for in translation and lipip_modification
rxn.add_metabolites(
{
met1: -0.013894,
met2: -0.003597,
'peptidoglycan_biomass': (0.013894 * met1_mass)
},
combine=False)
rxn.lower_bound = mu
rxn.upper_bound = mu
# -----
# ## Part 7: Set keffs
#
# Either entirely based on SASA or using fit keffs from [Ebrahim et al 2016](https://www.ncbi.nlm.nih.gov/pubmed/27782110?dopt=Abstract)
# # Set keffs to sasa fluxes centered around 65.
# me.set_SASA_keffs(65)
# In[ ]:
keff_list = []
keffs = flat_files.get_reaction_keffs(me, verbose=True)
for reaction_id, keff in keffs.items():
if keff > 3000:
keff = 3000.
elif keff < .01:
keff = .01
keff_list.append(keff)
me.reactions.get_by_id(reaction_id).keff = keff
me.reactions.get_by_id(reaction_id).update()
# Keffs that were not set in the above block
me.process_data.N_terminal_methionine_cleavage.keff = 1339.4233102860871
me.process_data.peptide_deformylase_processing.keff = 1019.5963333345715
me.reactions.get_by_id(
'GLUTRR_FWD_CPLX0-3741').keff = 3000 # 3269.0108007383374
me.process_data.fmet_addition_at_START.keff = 1540.4356849968603
me.process_data.ribosome_recycler.keff = 1059.6910912619182
me.process_data.UAG_PrfA_mono_mediated_termination.keff = 1721.7910609284945
me.process_data.UGA_PrfB_mono_mediated_termination.keff = 1700.2966587695353
me.process_data.UAA_generic_RF_mediated_termination.keff = 1753.4238515034572
# -----
# ## Part 8: Model updates and corrections
# In[ ]:
# Add reaction subsystems from iJO to model
for rxn in ijo.reactions:
if rxn.id in me.process_data:
data = me.process_data.get_by_id(rxn.id)
else:
continue
for r in data.parent_reactions:
r.subsystem = rxn.subsystem
# #### Corrections and final updates
# In[ ]:
ecolime.corrections.correct_reaction_stoichiometries(
me, join(flat_files.ecoli_files_dir, 'iJL1678b_model_changes.xlsx'))
# RNA_dummy, TU_b3247, TU_b3705 do not have RNAP, this is set as the most common RNAP
for data in me.transcription_data:
if len(data.RNA_polymerase) == 0:
data.RNA_polymerase = 'RNAP70-CPLX'
# If lower_bound open, model feeds G6P into EDD
me.reactions.EX_pqq_e.lower_bound = 0
me.reactions.EX_pqq_e.upper_bound = 0
# cobalamin is not in glucose M9 media
me.reactions.EX_cbl1_e.lower_bound = 0
me.process_data.PPKr.lower_bound = 0.
me.process_data.PPKr.update_parent_reactions()
# This enyzme is involved in catalyzing this reaction
sub = cobrame.SubreactionData('EG12450-MONOMER_activity', me)
sub.enzyme = 'EG12450-MONOMER'
me.process_data.NHFRBO.subreactions['EG12450-MONOMER_activity'] = 1
# #### Add enzymatic coupling for "carriers"
# These are enzyme complexes that act as metabolites in a metabolic reaction (i.e. are metabolites in iJO1366)
# In[ ]:
for data in me.stoichiometric_data:
if data.id == 'dummy_reaction':
continue
for met, value in data.stoichiometry.items():
if not isinstance(me.metabolites.get_by_id(met),
cobrame.Complex) or value > 0:
continue
subreaction_id = met + '_carrier_activity'
if subreaction_id not in me.process_data:
sub = cobrame.SubreactionData(met + '_carrier_activity', me)
sub.enzyme = met
data.subreactions[subreaction_id] = abs(value)
# ----
# ## Part 9: Update and save
# In[ ]:
me.reactions.dummy_reaction_FWD_SPONT.objective_coefficient = 1.
me.reactions.EX_glc__D_e.lower_bound = -1000
me.reactions.EX_o2_e.lower_bound = -1000.
me.ngam = 1.
me.gam = 34.98
me.unmodeled_protein_fraction = .36
# In[ ]:
me.update()
me.prune()
# ### Add remaining complex formulas and compartments to model
# In[ ]:
# Update a second time to incorporate all of the metabolite formulas corectly
for r in me.reactions.query('formation_'):
r.update()
# Update complex formulas
df = \
pandas.read_table(join(flat_files.ecoli_files_dir, 'modification.txt'),
names=['mod', 'formula', 'na'])
df = df.drop('na', axis=1).set_index('mod').dropna(how='any')
modification_formulas = df.T.to_dict()
formulas.add_remaining_complex_formulas(me, modification_formulas)
me.metabolites.get_by_id('CPLX0-782_mod_1:2fe2s_mod_1:4fe4s'
).formula = 'C3164Fe6H5090N920O920S50'
me.metabolites.get_by_id(
'EG50003-MONOMER_mod_pan4p_mod_lipo').formula = 'C387H606N95O142PS4'
# Update reactions affected by formula update
for r in me.reactions.query('_mod_lipo'):
r.update()
for r in me.reactions.query('_mod_glycyl'):
r.update()
# add metaboolite compartments
compartments.add_compartments_to_model(me)
n_genes = len(me.metabolites.query(re.compile('RNA_b[0-9]')))
print("number of genes in the model %d (%.2f%%)" %
(n_genes, n_genes * 100. / (1678)))
return me