@hredestig hredestig released this May 4, 2017 · 181 commits to devel since this release

Assets 2


In this release we have made major changes to pretty much all corners
of cobrapy and we hope that you will enjoy the new features as much as
we do, and that any negative impacts on existing workflows will be

The major change is the ongoing move away from cobrapy's internal
solver interfaces to those provided by
optlang which provides a
single unified interface to glpk, cplex and gurobi enhanced by the
ability to deal with symbolic expressions. This means formulating
complex constraints no longer implies defining the necessary matrix
algebra, but instead simply writing the expression and assigning that
as an objective to the model.

We feel that this, and the clarified scope and focus attained by
separating the topic of linear programming (optlang) and metabolic
flux analysis (cobrapy) to two packages is natural and makes both of
these tasks less confusing and more fun. We hope that you after
trying, feel the same and that in any case you let us know what you
think by
raising an issue or
talking directly to us on gitter or
google groups.

New features

The optlang solver interface

The main change is the addition of model.solver which is the optlang
interface to the chosen solver (cplex and glpk are currently well
supported, gurobi interface is at the time of writing mostly working
but improvements are still expected). The solver interface manages
variables, constraints and the objective of the model and the task of
turning these into a model formulation to be optimized by the
solver. From cobrapy's point-of-view, this means that all aspects
concerning generating problems, configuring solvers are handed over to
optlang and consequently the whole cobra.solver has been deprecated,
slated for removal in the next major release of cobrapy.

Importantly, configuring the solver by passing **solver_args or
solver='solver' is now instead done by assigning solver to
model.solver and then configuring via model.solver.configuration.

Creating new algorithms has been greatly facilitated as it no longer
requires formulating objectives and constraints by matrix algebra but
instead directly by expressions, e.g. see the implementation of
cobra.flux_analysis.moma.add_moma and

Instead of having only reactions as variables and metabolites as
constraints, with optlang, cobrapy now supports arbitrary constraints
and variables and these can be added/removed by model.add_cons_vars
and model.remove_cons_vars which take care of adding these to
model.problem which is the optlang's mathematical model associated
with the cobra model.

Reactions are now modeled by two variables, forward and reverse, and
these can be seen by accessing reaction.{forward,reverse}_variable
and the combined reaction.flux_expression.

Objectives can now easily be made quite advanced by simply crafting
the right expression and assigning this as usual to model.objective,
see the
contraints and objectives notebook.

Temporary changes to a model

Models are large complex objects and copying such objects is
inevitably slow. To avoid that, cobrapy has drawn on the experience
from the development of cameo to introduce the HistoryManager class
and the concept of models as contexts. Now, most changes that can be
made to a model such as changing the objective, setting reaction
bounds, adding and removing reactions, is reversed upon exit when done
inside a context, see the updated
getting started notebook.

Improved solution handling

Previously, cobra models lugged around their latest solution to enable
providing reaction.{flux,reduced_cost} (formerly
reaction.{x,y}). This was problematic because if the model had
changed since last optimization, then this would effectively give the
wrong result. On top of that, it was not easy to make a change,
optimize and get values, and then undo that change to the model
without having to copy the whole model object. To solve this, and many
similar problem, we have completely refactored cobra.Solution so
that model.optimize() now returns a solution and it is the user's
responsibility to manage this object. reaction.flux gets its values
directly from the model.problem. To sugar the new solution class,
fluxes, reduced costs, and shadow prices are now pandas series! Fluxes
and reduced costs can be returned as a data frame directlt with the
to_frame method.


Cobrapy now has flux sampling supported by
cobra.flux_analysis.sampling see
the sampling notebook.

Loopless models and solutions

Added implementations of
CycleFreeFlux and
the loopless model of
Schellenberger et al.. See
notebook on loopless
and simulating

DataFrames as return values

flux_variability_analysis, single_{gene,reaction}_deletion,
cobra.flux_analysis.sampling and
cobra.util.create_stoichiometric_matrix now return pandas data frames
instead of nested dicts as these are more convenient and fun to work
with. Pandas (and numpy) are therefore now hard requirements for
cobrapy, which should not be a problem for neither linux, windows or
mac users as there are reliable wheels for these packages now.

Model medium

model.medium is now a dict and setter for getting boundary feeding
reactions and their bounds

Knocking out genes

Addition of cobra.core.Gene.knock_out which can be used to evaluate
impact of knocking a gene (and all depending reactions).

Adding boundary reactions

The model class has new method model.add_boundary which can be used
to add sink, exchange or demand reactions with the appropriate bounds
and prefixes (DM, SK or EX).


The SMILEY and growMatch implementations were refactored to a
single new function cobra.flux_analysis.gapfilling.gapfill which
handles both use-cases.

New Output Format in YAML

Models can now be round tripped to/from YAML documents. YAML is a file format
that is even more legible than JSON. In the scope of cobrapy, YAML output is
intended for diff comparisons between models.


  • Handle multiple IDs in Matlab models
  • DictList.query behavior changed so that attribute is None if the
    search parameter is not a regex or string, to enable
    reactions.query(lambda x: x.boundary)
  • Set charge from notes if not defined elsewhere
  • Warnings are no longer issued on package import if soft requirement
    scipy, python-libsbml is not available.

Deprecated features

These features are now deprecated and slated for complete removal in
the next major cobrapy release.

  • The whole cobra.solver module is now deprecated, see New features.
  • ArrayBasedModel / Model.to_array_based_model are
    deprecated. This formulation makes little sense when handing over
    the matrix algebra to optlang, for the stoichiometry matrix (aka S),
    see cobra.util.array.create_stoichiometric_matrix.
  • Metabolite.y in favor of Metabolite.shadow_price
  • Model.add_reaction in favor of Model.add_reactions
  • Reaction.x in favor of Reaction.flux
  • Reaction.y in favor of Reaction.reduced_cost
  • Solution.{x, y, x_dict, y_dict, f} in favor of Solution.{fluxes, reduced_costs}. The setters are also deprecated.
  • phenotype_phase_plane in favor of production_envelope. The
    plotting capabilities are deprecated, to be re-implemented somewhere
  • convert_to_irreverible, revert_to_irreversible, canonical_form
    deprecated without replacement.
  • check_reaction_bounds deprecated without replacement.
  • optimize_minimal_flux was renamed to pfba

Backwards incompatible changes

  • optknock was completely removed, users are advised to use cameo for
    this functionality
  • dual_problem was removed
  • cobra.topology was removed, possibly to be reintroduced in a
    different package
  • flux_variability_analysis results must be transformed to have them
    work as the previous nested dict,
    i.e. flux_variability_analysis(model).T should give behavior as
  • In a major linting effort we renamed capitalized modules to lower-case,
    e.g. cobra.core.Model to cobra.core.model. Imports from cobra
    are unchanged though.
  • objective coefficients of reactions can now only be set once the
    reaction is attached to a model.
  • Reaction.{x,y}, Metabolite.y are defunct for legacy solvers.
  • SMILEY and growMatch algorithms are defunct in combination with
    the legacy solvers.