def add_linear_obj(community, exchanges, weights):
"""Add a linear version of a minimal medium to the community.
Changes the optimization objective to finding the growth medium requiring
the smallest total import flux::
minimize sum w_i|r_i| for r_i in import_reactions
Arguments
---------
community : micom.Community
The community to modify.
exchanges : list of cobra.Reaction
The reactions to constrain.
weights : dict
Maps each exchange reaction to a weight that is used in the
minimization.
"""
check_modification(community)
coefs = {}
for rxn in exchanges:
export = len(
rxn.reactants) == 1 or (len(rxn.metabolites) == 2
and rxn.products[0].compartment == "m")
met = list(rxn.metabolites)[0]
if export:
coefs[rxn.reverse_variable] = weights[met]
else:
coefs[rxn.forward_variable] = weights[met]
community.objective.set_linear_coefficients(coefs)
community.objective.direction = "min"
community.modification = "minimal medium linear"
def add_linear_obj(community, exchanges):
"""Add a linear version of a minimal medium to the community.
Changes the optimization objective to finding the growth medium requiring
the smallest total import flux::
minimize sum |r_i| for r_i in import_reactions
Arguments
---------
community : micom.Community
The community to modify.
exchanges : list of cobra.Reaction
The reactions to constrain.
"""
check_modification(community)
coefs = {}
for rxn in exchanges:
export = len(rxn.reactants) == 1
if export:
coefs[rxn.reverse_variable] = 1.0
else:
coefs[rxn.forward_variable] = 1.0
community.objective.set_linear_coefficients(coefs)
community.objective.direction = "min"
community.modification = "minimal medium linear"
def cooperative_tradeoff(community, min_growth, fraction, fluxes, pfba):
"""Find the best tradeoff between community and individual growth."""
with community as com:
check_modification(community)
min_growth = _format_min_growth(min_growth, community.species)
_apply_min_growth(community, min_growth)
com.objective = 1000.0 * com.variables.community_objective
min_growth = optimize_with_retry(
com, message="could not get community growth rate.") / 1000.0
if not isinstance(fraction, Sized):
fraction = [fraction]
else:
fraction = np.sort(fraction)[::-1]
# Add needed variables etc.
regularize_l2_norm(com, 0.0)
results = []
for fr in fraction:
com.variables.community_objective.lb = fr * min_growth
com.variables.community_objective.ub = min_growth
sol = solve(community, fluxes=fluxes, pfba=pfba)
if sol.status != OPTIMAL:
sol = crossover(com, sol, fluxes=fluxes, pfba=pfba)
results.append((fr, sol))
if len(results) == 1:
return results[0][1]
return pd.DataFrame.from_records(results,
columns=["tradeoff", "solution"])
def cooperative_tradeoff(community, min_growth, fraction, fluxes, pfba):
"""Find the best tradeoff between community and individual growth."""
with community as com:
solver = interface_to_str(community.problem)
check_modification(community)
min_growth = _format_min_growth(min_growth, community.taxa)
_apply_min_growth(community, min_growth)
com.objective = com.scale * com.variables.community_objective
min_growth = (optimize_with_retry(
com, message="could not get community growth rate.") / com.scale)
if not isinstance(fraction, Sized):
fraction = [fraction]
else:
fraction = np.sort(fraction)[::-1]
# Add needed variables etc.
regularize_l2_norm(com, 0.0)
results = []
for fr in fraction:
com.variables.community_objective.lb = fr * min_growth
com.variables.community_objective.ub = min_growth
sol = solve(community, fluxes=fluxes, pfba=pfba)
# OSQP is better with QPs then LPs
# so it won't get better with the crossover
if (sol.status != OPTIMAL and solver != "osqp"):
sol = crossover(com, sol, fluxes=fluxes, pfba=pfba)
results.append((fr, sol))
if len(results) == 1:
return results[0][1]
return pd.DataFrame.from_records(results,
columns=["tradeoff", "solution"])
def add_mip_obj(community, exchanges):
"""Add a mixed-integer version of a minimal medium to the community.
Changes the optimization objective to finding the medium with the least
components::
minimize size(R) where R part of import_reactions
Arguments
---------
community : micom.Community
The community to modify.
exchanges : list of cobra.Reaction
The reactions to constrain.
"""
check_modification(community)
if len(community.variables) > 1e4:
logger.warning(
"the MIP version of minimal media is extremely slow for"
" models that large :("
)
boundary_rxns = exchanges
M = max(np.max(np.abs(r.bounds)) for r in boundary_rxns)
prob = community.problem
coefs = {}
to_add = []
for rxn in boundary_rxns:
export = len(rxn.reactants) == 1 or (
len(rxn.reactants) == 2 and rxn.products[0].compartment == "m"
)
indicator = prob.Variable("ind_" + rxn.id, lb=0, ub=1, type="binary")
if export:
vrv = rxn.reverse_variable
indicator_const = prob.Constraint(
vrv - indicator * M, ub=0, name="ind_constraint_" + rxn.id
)
else:
vfw = rxn.forward_variable
indicator_const = prob.Constraint(
vfw - indicator * M, ub=0, name="ind_constraint_" + rxn.id
)
to_add.extend([indicator, indicator_const])
coefs[indicator] = 1
community.add_cons_vars(to_add)
community.solver.update()
community.objective.set_linear_coefficients(coefs)
community.objective.direction = "min"
community.modification = "minimal medium mixed-integer"
def knockout_taxa(
community, taxa, fraction, method, progress, diag=True
):
"""Knockout a taxon from the community."""
with community as com:
check_modification(com)
min_growth = _format_min_growth(0.0, com.taxa)
_apply_min_growth(com, min_growth)
com.objective = com.scale * com.variables.community_objective
community_min_growth = (
optimize_with_retry(com, "could not get community growth rate.")
/ com.scale
)
regularize_l2_norm(com, fraction * community_min_growth)
old = com.optimize().members["growth_rate"]
results = []
iter = track(taxa, description="Knockouts") if progress else taxa
for sp in iter:
with com:
logger.info("getting growth rates for " "%s knockout." % sp)
[
r.knock_out()
for r in com.reactions.query(
lambda ri: ri.community_id == sp
)
]
sol = optimize_with_fraction(com, fraction)
new = sol.members["growth_rate"]
if "change" in method:
new = new - old
if "relative" in method:
new /= old
results.append(new)
ko = pd.DataFrame(results, index=taxa).drop("medium", 1)
ko = ko.loc[ko.index.sort_values(), ko.columns.sort_values()]
if not diag:
np.fill_diagonal(ko.values, np.NaN)
return ko
def knockout_species(community,
species,
fraction,
method,
progress,
diag=True):
"""Knockout a species from the community."""
with community as com:
check_modification(com)
min_growth = _format_min_growth(0.0, com.species)
_apply_min_growth(com, min_growth)
com.objective = 1000.0 * com.variables.community_objective
community_min_growth = (
optimize_with_retry(com, "could not get community growth rate.") /
1000.0)
regularize_l2_norm(com, fraction * community_min_growth)
old = com.optimize().members["growth_rate"]
results = []
if progress:
species = tqdm(species, unit="knockout(s)")
for sp in species:
with com:
logger.info("getting growth rates for " "%s knockout." % sp)
[
r.knock_out() for r in com.reactions.query(
lambda ri: ri.community_id == sp)
]
sol = optimize_with_fraction(com, fraction)
new = sol.members["growth_rate"]
if "change" in method:
new = new - old
if "relative" in method:
new /= old
results.append(new)
ko = pd.DataFrame(results, index=species).drop("medium", 1)
if not diag:
np.fill_diagonal(ko.values, np.NaN)
return ko
def add_moma_optcom(community, min_growth, linear=False):
"""Add a dualized MOMA version of OptCom.
Solves a MOMA (minimization of metabolic adjustment) formulation of OptCom
given by::
minimize cooperativity_cost
s.t. maximize community_objective
s.t. Sv = 0
lb >= v >= ub
where community_cost = sum (growth_rate - max_growth)**2
if linear=False or
community_cost = sum |growth_rate - max_growth|
if linear=True
Arguments
---------
community : micom.Community
The community to modify.
min_growth : positive float or array-like object.
The minimum growth rate for each individual in the community. Either
a single value applied to all individuals or one value for each.
linear : boolean
Whether to use a non-linear (sum of squares) or linear version of the
cooperativity cost. If set to False requires a QP-capable solver.
"""
logger.info("adding dual %s moma to %s" %
("linear" if linear else "quadratic", community.id))
check_modification(community)
min_growth = _format_min_growth(min_growth, community.taxa)
prob = community.solver.interface
old_obj = community.objective
coefs = old_obj.get_linear_coefficients(old_obj.variables)
# Get maximum individual growth rates
max_gcs = community.optimize_all(progress=False)
_apply_min_growth(community, min_growth)
dual_coefs = fast_dual(community)
coefs.update({v: -coef for v, coef in dual_coefs.items()})
obj_constraint = prob.Constraint(Zero,
lb=0,
ub=0,
name="optcom_suboptimality")
community.add_cons_vars([obj_constraint])
community.solver.update()
obj_constraint.set_linear_coefficients(coefs)
obj_expr = Zero
logger.info("adding expressions for %d taxa" % len(community.taxa))
for sp in community.taxa:
v = prob.Variable("gc_constant_" + sp, lb=max_gcs[sp], ub=max_gcs[sp])
community.add_cons_vars([v])
taxa_obj = community.constraints["objective_" + sp]
ex = v - taxa_obj.expression
if not linear:
ex = ex**2
obj_expr += ex.expand()
community.objective = prob.Objective(obj_expr, direction="min")
community.modification = "moma optcom"
logger.info("finished dual moma to %s" % community.id)
def add_dualized_optcom(community, min_growth):
"""Add dual Optcom variables and constraints to a community.
Uses the original formulation of OptCom and solves the following
multi-objective problem::
maximize community_growth
s.t. maximize growth_rate_i for all i
s.t. Sv_i = 0
lb_i >= v_i >= ub_i
Notes
-----
This method will only find one arbitrary solution from the Pareto front.
There may exist several other optimal solutions.
Arguments
---------
community : micom.Community
The community to modify.
min_growth : positive float or array-like object.
The minimum growth rate for each individual in the community. Either
a single value applied to all individuals or one value for each.
"""
logger.info("adding dual optcom to %s" % community.id)
check_modification(community)
min_growth = _format_min_growth(min_growth, community.taxa)
prob = community.solver.interface
# Temporarily subtitute objective with sum of individual objectives
# for correct dual variables
old_obj = community.objective
community.objective = Zero
for sp in community.taxa:
taxa_obj = community.constraints["objective_" + sp]
community.objective += taxa_obj.expression
_apply_min_growth(community, min_growth)
dual_coefs = fast_dual(community)
logger.info("adding expressions for %d taxa" % len(community.taxa))
for sp in community.taxa:
primal_const = community.constraints["objective_" + sp]
coefs = primal_const.get_linear_coefficients(primal_const.variables)
coefs.update({
dual_var: -coef
for dual_var, coef in dual_coefs.items() if sp in dual_var.name
})
obj_constraint = prob.Constraint(Zero,
lb=0,
ub=0,
name="optcom_suboptimality_" + sp)
community.add_cons_vars([obj_constraint])
community.solver.update()
obj_constraint.set_linear_coefficients(coefs)
community.objective = old_obj
community.modification = "dual optcom"
logger.info("finished adding dual optcom to %s" % community.id)
