In [1]:

    

from cobra.io import read_sbml_model


    

In [2]:

    

from optlang.symbolics import Zero, One, add, mul, Add, Mul, Real


    

In [3]:

    

one = Real(1.0)


    

In [4]:

    

model = read_sbml_model("/home/moritz/Projects/memote/Models/iJO1366.xml.gz")


    

In [5]:

    

import cobra.util.solver as sutil


    

In [6]:

    

from itertools import chain


    

In [7]:

    

model.solver = "glpk"


    

In [8]:

    

reaction_variables = ((rxn.forward_variable, rxn.reverse_variable)
                      for rxn in model.reactions)
variables = list(chain(*reaction_variables))


    

In [9]:

    

%%timeit
with model:
    model.objective = model.problem.Objective(
        Zero, direction='min', sloppy=True,
        name="_pfba_objective")
    model.objective.set_linear_coefficients({v: 1.0 for v in variables})


    

    




146 ms ± 3.94 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

In [10]:

    

%%timeit
with model:
    model.objective = model.problem.Objective(
        add([mul(one, v) for v in variables]),
        direction='min', sloppy=True, name="_pfba_objective")


    

    




149 ms ± 2.13 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

In [11]:

    

model.solver = "gurobi"


    

In [12]:

    

reaction_variables = ((rxn.forward_variable, rxn.reverse_variable)
                      for rxn in model.reactions)
variables = list(chain(*reaction_variables))


    

In [13]:

    

%%timeit
with model:
    model.objective = model.problem.Objective(
        Zero, direction='min', sloppy=True,
        name="_pfba_objective")
    model.objective.set_linear_coefficients({v: 1.0 for v in variables})


    

    




13.7 ms ± 269 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

In [14]:

    

%%timeit
with model:
    model.objective = model.problem.Objective(
        add([mul(one, v) for v in variables]),
        direction='min', sloppy=True, name="_pfba_objective")


    

    




218 ms ± 2.55 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

In [15]:

    

model.solver = "cplex"


    

In [16]:

    

reaction_variables = ((rxn.forward_variable, rxn.reverse_variable)
                      for rxn in model.reactions)
variables = list(chain(*reaction_variables))


    

In [17]:

    

%%timeit
with model:
    model.objective = model.problem.Objective(
        Zero, direction='min', sloppy=True,
        name="_pfba_objective")
    model.objective.set_linear_coefficients({v: 1.0 for v in variables})


    

    




2.04 s ± 32.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

In [18]:

    

%%timeit
with model:
    model.objective = model.problem.Objective(
        add([mul(one, v) for v in variables]),
        direction='min', sloppy=True, name="_pfba_objective")


    

    




183 ms ± 5.28 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)