def do_many_prunes(network, export, reps, ins, outs):
'''
Given a string_chem_net object and whether or not to allow export,
prune the network many times and record the sizes of the pruned networks
'''
# create the COBRApy model
model = scn.make_cobra_model(
network.met_list,
network.rxn_list,
allow_export = export
)
# create a DataFrame to hold the reaction and metabolite counts of the
# pruned networks
output = pd.DataFrame(columns = ['rxn_count', 'met_count'])
# prune this network reps times
for rep in range(reps):
if rep % 10 == 0:
print(f'On prune {rep} of {reps}')
# work with a copy of the model so it remains untouched for the next
# iteration of the loop
full_model = model.copy()
# randomly choose the appropriate number of input and output mets
bm_rxn = scn.choose_bm_mets(outs, full_model)
scn.choose_inputs(ins, full_model, bm_rxn)
full_model.objective = bm_rxn
# see if there's a feasible solution on the full model
solution = full_model.optimize()
# can't just check solution.status because sometimes it's feasible but the
# flux through the biomass reaction is vanishingly small
bm_rxn_flux = solution.fluxes.get(key = bm_rxn.id)
while solution.status == 'infeasible' or bm_rxn_flux < 10e-10:
# if the solution isn't feasible, pick a different environment
in_rxns = [
# don't want to remove all boundary reactions because that would
# also remove all of the export reactions
rxn for rxn in full_model.boundary if rxn.id.startswith('->')
]
full_model.remove_reactions(in_rxns)
scn.choose_inputs(outs, full_model, bm_rxn)
solution = full_model.optimize()
bm_rxn_flux = solution.fluxes.get(key = bm_rxn.id)
# now that we know there's at least one environment that supports growth
# with this biomass reaction, we can prune the universal network
pruned_model = scn.min_flux_prune(full_model, bm_rxn)
# count reactions and metabolites
rxn_count = len(pruned_model.reactions)
# metabolites aren't automatically removed when all of their reactions
# are removed, so find out how many metabolites are left
met_count = len([
m for m in pruned_model.metabolites if len(m.reactions) > 0
])
# add to the output dataframe
some_output = pd.DataFrame(
[[rxn_count, met_count]], columns = ['rxn_count', 'met_count']
)
output = output.append(some_output, ignore_index = True)
return(output)
def prune_once(universal_model, ins, outs, flux_bins, rep):
'''
Given a universal string chemistry network, add a random biomass reaction
and random input reactions, make sure that combination can produce biomass,
prune the network, and return the degree and flux distributions of the
pruned network
'''
# work with a copy of the model so it remains untouched for the next
# iteration of the loop
full_model = universal_model.copy()
# randomly choose the appropriate number of input and output mets
bm_rxn = scn.choose_bm_mets(outs, full_model)
scn.choose_inputs(ins, full_model, bm_rxn)
full_model.objective = bm_rxn
# see if there's a feasible solution on the full model
solution = full_model.optimize()
# can't just check solution.status because sometimes it's feasible but the
# flux through the biomass reaction is vanishingly small
bm_rxn_flux = solution.fluxes.get(key=bm_rxn.id)
while solution.status == 'infeasible' or bm_rxn_flux < 10e-10:
# if the solution isn't feasible, pick a different environment
in_rxns = [
# don't want to remove all boundary reactions because that would
# also remove all of the export reactions
rxn for rxn in full_model.boundary if rxn.id.startswith('->')
]
full_model.remove_reactions(in_rxns)
scn.choose_inputs(ins, full_model, bm_rxn)
solution = full_model.optimize()
bm_rxn_flux = solution.fluxes.get(key=bm_rxn.id)
# now that we know there's at least one environment that supports growth
# with this biomass reaction, we can prune the universal network
pruned_model = scn.min_flux_prune(full_model, bm_rxn)
# get the degree and flux distributions from the pruned network
deg_dist = make_deg_dist(pruned_model)
fluxes = abs(pruned_model.optimize().fluxes)
# exclude fluxes that are approximately zero
flux_dist = pd.DataFrame(fluxes[fluxes > 10e-10])
flux_dist.columns = ['flux']
# add a column to the degree and flux distribution dataframes to indicate
# which round of pruning this data came from
deg_dist['trial'] = rep
flux_dist['trial'] = rep
return ((deg_dist, flux_dist))
def prune_model(universal_model, ins, outs):
'''
Prune the given universal model with the specified number of input and
output metabolites
'''
# work with a copy of the model so it remains untouched for the next
# iteration of the loop
full_model = universal_model.copy()
# randomly choose the appropriate number of input and output mets
bm_rxn = scn.choose_bm_mets(outs, full_model)
scn.choose_inputs(ins, full_model, bm_rxn)
full_model.objective = bm_rxn
# see if there's a feasible solution on the full model
solution = full_model.optimize()
# can't just check solution.status because sometimes it's feasible but the
# flux through the biomass reaction is vanishingly small
bm_rxn_flux = solution.fluxes.get(key=bm_rxn.id)
while solution.status == 'infeasible' or bm_rxn_flux < 10e-10:
# if the solution isn't feasible, pick a different environment
in_rxns = [
# don't want to remove all boundary reactions because that would
# also remove all of the export reactions
rxn for rxn in full_model.boundary if rxn.id.startswith('->')
]
full_model.remove_reactions(in_rxns)
scn.choose_inputs(ins, full_model, bm_rxn)
solution = full_model.optimize()
bm_rxn_flux = solution.fluxes.get(key=bm_rxn.id)
# now that we know there's at least one environment that supports growth
# with this biomass reaction, we can prune the universal network
pruned_model = scn.min_flux_prune(full_model, bm_rxn)
# metabolites aren't automatically removed when all of their reactions
# are removed, so find out how many metabolites are left
met_count = len(
[m for m in pruned_model.metabolites if len(m.reactions) > 0])
# compute the reaction-to-metabolite ratio and % of pruned reactions
ratio = len(pruned_model.reactions) / met_count
pruned_count = len(universal_model.reactions) - len(pruned_model.reactions)
pruned_pct = pruned_count / len(universal_model.reactions)
output = [ins, outs, ratio, pruned_pct]
# make everything a string so we can join it later
return ([str(x) for x in output])
# flux through the biomass reaction is vanishingly small
bm_rxn_flux = solution.fluxes.get(key=bm_rxn.id)
while solution.status == 'infeasible' or bm_rxn_flux < 10e-10:
# if the solution isn't feasible, pick a different environment
in_rxns = [
# don't want to remove all boundary reactions because that would
# also remove all of the export reactions
rxn for rxn in full_model.boundary if rxn.id.startswith('->')
]
full_model.remove_reactions(in_rxns)
scn.choose_inputs(condition[0], full_model, bm_rxn)
solution = full_model.optimize()
bm_rxn_flux = solution.fluxes.get(key=bm_rxn.id)
# now that we know there's at least one environment that supports growth
# with this biomass reaction, we can prune the universal network
pruned_model = scn.min_flux_prune(full_model, bm_rxn)
# metabolites aren't automatically removed when all of their reactions
# are removed, so find out how many metabolites are left
met_count = len(
[m for m in pruned_model.metabolites if len(m.reactions) > 0])
# compute the reaction-to-metabolite ratio and % of pruned reactions
ratio = len(pruned_model.reactions) / met_count
pruned_count = len(universal_model.reactions) - len(
pruned_model.reactions)
pruned_pct = pruned_count / len(universal_model.reactions)
output = [condition[0], condition[1], ratio, pruned_pct]
# make everything a string so we can join it later
output_data.append([str(x) for x in output])
with open(
f'data/varied_{monos}_{max_pol}_{min_ins}to{max_ins}ins_' +
def prune_many_times(arglist):
'''
Given:
- COBRApy model representing a full/complete/un-pruned string chemistry
- A number of nutrient sources
- A number of biomass precursors
- A number of different sets of nutrient sources
- A number of times to change stoichiometric coefficients in the biomass
reaction
Do:
- Create a biomass reaction with the designated number of reactants
- Create the designated number of variants on that reaction with randomized
stoichiometric coefficients (each reactant's coefficient is assigned to
a random integer between 1 and 10)
- Choose the designated number of sets of the designated number of nutrient
sources
- Prune the network once for each combination of biomass reaction and
set of nutrients
Return a Dataframe containing:
- Binary vector indicating which reactions were kept in each pruned network
- List of biomass precursors used
- List of nutrient sources used
- Maximum achievable flux through biomass reaction
'''
# probably a more elegant way to do this but I'm currently new to mp.map()
(full_model, ins, outs, envs, combos) = arglist
# add a biomass reaction but remove it from the model immediately
bm_rxn = scn.choose_bm_mets(outs, full_model)
full_model.remove_reactions([bm_rxn])
# loop over vaariants of the biomass reaction with different coefficients
# but identical reactants
for combo in range(combos):
if (combo + 1) % 10 == 0:
print(f'On coefficient set {combo+1} of {combos}')
# make a new biomass reaction
new_bm = cobra.Reaction('varied_bm_rxn')
new_bm.add_metabolites(
{m: -random.randint(1, 10)
for m in bm_rxn.metabolites})
# make a copy of the model before adding the new biomass reaction
model = full_model.copy()
model.add_reaction(new_bm)
model.objective = new_bm
# keep lists of the environments used, the reaction-inclusion vectors of
# the pruned networks and the growth rates on the pruned networks
food_mets = list()
rxn_incl_vecs = list()
pruned_growths = list()
# use a while loop and not a for loop so we can go back on occasion
i = 0
# counter for how many times it had to reselct the environment to get a
# feasible solution with the full network
j = 0
while i < envs:
i += 1
# remove existing input reactions
in_rxns = [
rxn for rxn in model.boundary if rxn.id.startswith('->')
]
model.remove_reactions(in_rxns)
# choose new input reactions
scn.choose_inputs(ins, model, new_bm)
in_rxns = [
rxn for rxn in model.boundary if rxn.id.startswith('->')
]
foods_string = ' '.join([
# getting the metabolite IDs out of a reaction is annoying
list(rxn.metabolites.keys())[0].id for rxn in in_rxns
])
# see if this choice of metabolites can produce the biomass on this network
solution = model.optimize()
bm_rxn_flux = solution.fluxes.get(key=new_bm.id)
if solution.status == 'infeasible' or bm_rxn_flux < 1e-10:
# redo this iteration of the loop
i -= 1
# increment the counter of redos
j += 1
continue
# record the metabolites that worked and prune the network
else:
if i % 100 == 0:
print(f'On environment {i}')
# reset the reselection counter
j = 0
# get the list of food source metabolites
food_mets.append('-'.join(
[met.id for rxn in in_rxns for met in rxn.metabolites]))
# prune the network
pruned_net = scn.min_flux_prune(model, new_bm)
rxn_incl = scn.make_rxn_incl(model, pruned_net)
rxn_incl_vecs.append(rxn_incl)
# get the growth rate on the pruned network
solution = pruned_net.optimize()
pruned_growth = solution.fluxes.get(key=new_bm.id)
pruned_growths.append(pruned_growth)
# make a dataframe out of the lists and add it to the larger dataframe
data = pd.DataFrame(list(zip(food_mets, rxn_incl_vecs, pruned_growths)))
data.columns = ['env', 'rxn_incl', 'growth']
# add a column with the biomass components
data['biomass'] = list(
it.repeat('-'.join([met.id for met in bm_rxn.metabolites]),
len(food_mets)))
# reorder columns
data = data[['biomass', 'env', 'rxn_incl', 'growth']]
return (data)
# flux through the biomass reaction is vanishingly small
bm_rxn_flux = solution.fluxes.get(key = bm_rxn.id)
while solution.status == 'infeasible' or bm_rxn_flux < 10e-10:
# if the solution isn't feasible, pick a different environment
in_rxns = [
# don't want to remove all boundary reactions because that would
# also remove all of the export reactions
rxn for rxn in universal_model.boundary if rxn.id.startswith('->')
]
universal_model.remove_reactions(in_rxns)
scn.choose_inputs(int(ins), universal_model, bm_rxn)
solution = universal_model.optimize()
bm_rxn_flux = solution.fluxes.get(key = bm_rxn.id)
# now that we know there's at least one environment that supports growth
# with this biomass reaction, we can prune the universal network
pruned_model = scn.min_flux_prune(universal_model, bm_rxn)
# find growth in every environment
for env in envs:
# start by removing existing input reactions
in_rxns = [
# don't want to remove all boundary reactions because that would
# also remove all of the export reactions
rxn for rxn in pruned_model.boundary if rxn.id.startswith('->')
]
pruned_model.remove_reactions(in_rxns)
# while we have the pruned network with no input reactions, make the
# reaction-inclusion vector
bitstring = scn.make_rxn_incl(universal_model, pruned_model)
# create new input reactions
for met in env:
in_rxn = cobra.Reaction(
pruned_nets = dict()
i = 0
while i < int(bms):
# pick a new biomass reaction and set it as the objective
bm_rxn = scn.choose_bm_mets(int(outs), cobra_model)
cobra_model.objective = bm_rxn
# see if these biomass precursors can be produced on this environment
# before we bother pruning
solution = cobra_model.optimize()
bm_rxn_flux = solution.fluxes.get(key=bm_rxn.id)
if solution.status != 'infeasible' and bm_rxn_flux > 10e-10:
# only increment the counter if we've chosen a usable biomass reaction
i += 1
print(f'On biomass reaction {i}')
# run the minimum flux pruner
pruned_net = scn.min_flux_prune(cobra_model, bm_rxn)
# remove the biomass reaction before making the reaction bitstring
# can't just remove the reaction because somehow in the pruning process
# the biomass reaction became different in some subtle way
pruned_bm_rxn = pruned_net.reactions.get_by_id(bm_rxn.id)
pruned_net.remove_reactions([pruned_bm_rxn])
bitstring = scn.make_rxn_incl(cobra_model, pruned_net)
pruned_nets[bitstring] = [met.id for met in bm_rxn.metabolites]
# remove this biomass reaction from the full network regardless of whether
# it worked or not
cobra_model.remove_reactions([bm_rxn])
# print a bunch of info but also write it out to a tsv
with open(
f'data/multiple_bm_min_prune_{monos}_{max_pol}_{ins}ins_' +
f'{outs}outs_{bms}_bms.tsv', 'w') as out:
# choose new input and biomass reactions
exp_bm_rxn = scn.choose_bm_mets(int(outs), export_model)
no_exp_bm_rxn = exp_bm_rxn.copy()
no_export_model.add_reaction(no_exp_bm_rxn)
export_model.objective = exp_bm_rxn
no_export_model.objective = no_exp_bm_rxn
scn.choose_inputs(int(ins), no_export_model, no_exp_bm_rxn)
export_model.add_reactions(
[rxn.copy() for rxn in no_export_model.boundary])
# see if this combination works for both networks
export_solution = export_model.optimize()
no_export_solution = no_export_model.optimize()
# do min-flux pruning and get reaction-inclusion vectors for both networks
print('Using minimum-flux pruner.')
min_pruned_export = scn.min_flux_prune(export_model, exp_bm_rxn)
min_pruned_no_export = scn.min_flux_prune(no_export_model, no_exp_bm_rxn)
min_export_count = len(min_pruned_export.reactions)
min_no_export_count = len(min_pruned_no_export.reactions)
min_export_rxn_incl = scn.make_rxn_incl(export_model, min_pruned_export)
min_no_export_rxn_incl = scn.make_rxn_incl(no_export_model,
min_pruned_no_export)
# randomly prune each network as many times as specified
print('Randomly pruning with export reactions.')
(rand_export_pruned_rxn_counts, rand_export_prune_counts,
rand_export_pruned_nets) = do_many_rand_prunes(export_model, exp_bm_rxn,
int(reps))
print('Randomly pruning without export reactions.')
(rand_no_export_pruned_rxn_counts, rand_no_export_prune_counts,
def prune_many_times(arglist):
# probably a more elegant way to do this but I'm currently new to mp.map()
full_model, ins, outs, envs = arglist
# start by making a copy of the original model so we don't have to remove
# the biomass reaction each time
model = full_model.copy()
# add a biomass reaction and set it as the objective
bm_rxn = scn.choose_bm_mets(outs, model)
model.objective = bm_rxn
# keep lists of the environments used, the reaction-inclusion vectors of
# the pruned networks and the growth rates on the pruned networks
food_mets = list()
rxn_incl_vecs = list()
pruned_growths = list()
# use a while loop and not a for loop so we can go back on occasion
i = 0
# counter for how many times it had to reselct the environment to get a
# feasible solution with the full network
j = 0
while i < envs:
i += 1
# remove existing input reactions
in_rxns = [rxn for rxn in model.boundary if rxn.id.startswith('->')]
model.remove_reactions(in_rxns)
# choose new input reactions
scn.choose_inputs(ins, model, bm_rxn)
in_rxns = [rxn for rxn in model.boundary if rxn.id.startswith('->')]
foods_string = ' '.join([
# getting the metabolite IDs out of a reaction is annoying
list(rxn.metabolites.keys())[0].id for rxn in in_rxns
])
# see if this choice of metabolites can produce the biomass on this network
solution = model.optimize()
bm_rxn_flux = solution.fluxes.get(key=bm_rxn.id)
if solution.status == 'infeasible' or bm_rxn_flux < 1e-10:
# redo this iteration of the loop
i -= 1
# increment the counter of redos
j += 1
continue
# record the metabolites that worked and prune the network
else:
if i % 100 == 0:
print(f'On environment {i}')
# reset the reselection counter
j = 0
# get the list of food source metabolites
food_mets.append('-'.join(
[met.id for rxn in in_rxns for met in rxn.metabolites]))
# prune the network
pruned_net = scn.min_flux_prune(model, bm_rxn)
rxn_incl = scn.make_rxn_incl(model, pruned_net)
rxn_incl_vecs.append(rxn_incl)
# get the growth rate on the pruned network
solution = pruned_net.optimize()
pruned_growth = solution.fluxes.get(key=bm_rxn.id)
pruned_growths.append(pruned_growth)
# make a dataframe out of the lists and add it to the larger dataframe
data = pd.DataFrame(list(zip(food_mets, rxn_incl_vecs, pruned_growths)))
data.columns = ['env', 'rxn_incl', 'growth']
# add a column with the biomass components
data['biomass'] = list(
it.repeat('-'.join([met.id for met in bm_rxn.metabolites]),
len(food_mets)))
# reorder columns
data = data[['biomass', 'env', 'rxn_incl', 'growth']]
return (data)
