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Fix long-lines issue in design_algorithms.py #282

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Sep 10, 2016
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Fix long-lines issue in design_algorithms.py #282

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25 changes: 14 additions & 11 deletions cobra/design/design_algorithms.py
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Original file line number Diff line number Diff line change
Expand Up @@ -34,9 +34,10 @@ def set_up_optknock(model, chemical_objective, knockable_reactions,
n_knockouts_required=True, dual_maximum=1000, copy=True):
"""Set up the OptKnock problem described by Burgard et al., 2003:

Burgard AP, Pharkya P, Maranas CD. Optknock: a bilevel programming framework
for identifying gene knockout strategies for microbial strain optimization.
Biotechnol Bioeng. 2003;84(6):647-57. https://doi.org/10.1002/bit.10803.
Burgard AP, Pharkya P, Maranas CD. Optknock: a bilevel programming
framework for identifying gene knockout strategies for microbial strain
optimization. Biotechnol Bioeng. 2003;84(6):647-57.
https://doi.org/10.1002/bit.10803.

Arguments
---------
Expand Down Expand Up @@ -235,8 +236,9 @@ def dual_problem(model, objective_sense="maximize",
problem.

iteger_vars_to_maintain: [str]
A list of IDs for Boolean integer variables to be maintained in the dual
problem. See 'Maintaining integer variables' below for more details.
A list of IDs for Boolean integer variables to be maintained in the
dual problem. See 'Maintaining integer variables' below for more
details.

already_irreversible: bool
If True, then do not convert the model to irreversible.
Expand All @@ -251,13 +253,14 @@ def dual_problem(model, objective_sense="maximize",

**Maintaining integer variables**

The argument ``integer_vars_to_maintain`` can be used to specify certin Boolean
integer variables that will be maintained in the dual problem. This makes
it possible to join outer and inner problems in a bi-level MILP. The method
for maintaining integer variables is described by Tepper and Shlomi, 2010:
The argument ``integer_vars_to_maintain`` can be used to specify certin
Boolean integer variables that will be maintained in the dual problem. This
makes it possible to join outer and inner problems in a bi-level MILP. The
method for maintaining integer variables is described by Tepper and Shlomi,
2010:

Tepper N, Shlomi T. Predicting metabolic engineering knockout strategies for
chemical production: accounting for competing pathways. Bioinformatics.
Tepper N, Shlomi T. Predicting metabolic engineering knockout strategies
for chemical production: accounting for competing pathways. Bioinformatics.
2010;26(4):536-43. https://doi.org/10.1093/bioinformatics/btp704.

In COBRApy, this roughly translates to transforming (decision variables p,
Expand Down