GrowthCouplingPotential (#215)

* FSEOF improvements

* feat: implement GrowthCouplingPotential optimization

* fix: flake8

* test: add test for GrowthCouplingPotential

* feat: add gurobi solution pool capability

* fix: remove blocked reactions from reaction lists

* fix: flake8

* chore: bring branch up to date

* fix: avoid adding list and set

* test: update GrowthCouplingPotential tests

* fix: remove commented line

* test: assert that target is not growth coupled in wild-type

Co-authored-by: KristianJensen <krisje@nnfcb-l0275.local>
Co-authored-by: Kristian Jensen <dkkrje@chr-hansen.com>
Co-authored-by: carrascomj <carrascomurielj@gmail.com>

Author: 4 people

cameo/strain_design/deterministic/flux_variability_based.py

@@ -74,7 +74,6 @@
 
 class DifferentialFVA(StrainDesignMethod):
     r"""Differential flux variability analysis.
-
     Compares flux ranges of a reference model to a set of models that
     have been parameterized to lie on a grid of evenly spaced points in the
     # n-dimensional production envelope (n being the number of reaction bounds
@@ -89,10 +88,8 @@ class DifferentialFVA(StrainDesignMethod):
         | . . . . . . \
         o--------------*- >
                      growth
-
     Overexpression, downregulation, knockout, flux-reversal and other
     strain engineering targets can be inferred from the resulting comparison.
-
     Parameters
     ----------
     design_space_model : cobra.Model
@@ -113,7 +110,6 @@ class DifferentialFVA(StrainDesignMethod):
         will be normalized by.
     points : int, optional
         Number of points to lay on the surface of the n-dimensional production envelope (defaults to 10).
-
     Examples
     --------
     >>> from cameo import models
@@ -283,7 +279,6 @@ def _init_search_grid(self, surface_only=False, improvements_only=True):
     def run(self, surface_only=True, improvements_only=True, progress=True,
             view=None, fraction_of_optimum=1.0):
         """Run the differential flux variability analysis.
-
         Parameters
         ----------
         surface_only : bool, optional
@@ -299,7 +294,6 @@ def run(self, surface_only=True, improvements_only=True, progress=True,
             A value between zero and one that determines the width of the
             flux ranges of the reference solution. The lower the value,
             the larger the ranges.
-
         Returns
         -------
         pandas.Panel
@@ -482,25 +476,18 @@ def __init__(self, solutions, phase_plane, reference_fva, **kwargs):
     def _generate_designs(cls, solutions, reference_fva):
         """
         Generates strain designs for Differential FVA.
-
         The conversion method has three scenarios:
         #### 1. Knockout
-
             Creates a ReactionKnockoutTarget.
-
         #### 2. Flux reversal
-
             If the new flux is negative then it should be at least the upper
             bound of the interval. Otherwise it should be at least the lower
             bound of the interval.
-
         #### 3. The flux increases or decreases
-
             This table illustrates the possible combinations.
                 * Gap is the sign of the normalized gap between the intervals.
                 * Ref is the sign of the closest bound (see _closest_bound).
                 * Bound is the value to use
-
             +-------------------+
             | Gap | Ref | Bound |
             +-----+-----+-------+
@@ -509,15 +496,12 @@ def _generate_designs(cls, solutions, reference_fva):
             |  +  |  -  |   UB  |
             |  +  |  +  |   LB  |
             +-----+-----+-------+
-
-
         Parameters
         ----------
         solutions: pandas.Panel
             The DifferentialFVA panel with all the solutions. Each DataFrame is a design.
         reference_fva: pandas.DataFrame
             The FVA limits for the reference strain.
-
         Returns
         -------
         list
@@ -620,7 +604,6 @@ def __getitem__(self, item):
     def nth_panel(self, index):
         """
         Return the nth DataFrame defined by (biomass, production) pairs.
-
         When the solutions were still based on pandas.Panel this was simply
         self.solutions.iloc
         """
@@ -704,23 +687,18 @@ def plot_scale(self, palette="YlGnBu"):
         """
         Generates a color scale based on the flux distribution.
         It makes an array containing the absolute values and minus absolute values.
-
         The colors set as follows (p standsfor palette colors array):
         min   -2*std  -std      0      std      2*std   max
         |-------|-------|-------|-------|-------|-------|
         p[0] p[0] ..  p[1] .. p[2] ..  p[3] .. p[-1] p[-1]
-
-
         Parameters
         ----------
         palette: Palette, list, str
             A Palette from palettable of equivalent, a list of colors (size 5) or a palette name
-
         Returns
         -------
         tuple:
             ((-2*std, color), (-std, color) (0 color) (std, color) (2*std, color))
-
         """
         if isinstance(palette, str):
             palette = mapper.map_palette(palette, 5)
@@ -868,20 +846,17 @@ def _set_bounds(self, point):
 class FSEOF(StrainDesignMethod):
     """
     Performs a Flux Scanning based on Enforced Objective Flux (FSEOF) analysis.
-
     Parameters
     ----------
     model : cobra.Model
     enforced_reaction : Reaction
         The flux that will be enforced. Reaction object or reaction id string.
     primary_objective : Reaction
         The primary objective flux (defaults to model.objective).
-
     References
     ----------
     .. [1] H. S. Choi, S. Y. Lee, T. Y. Kim, and H. M. Woo, 'In silico identification of gene amplification targets
     for improvement of lycopene production.,' Appl Environ Microbiol, vol. 76, no. 10, pp. 3097–3105, May 2010.
-
     """
 
     def __init__(self, model, primary_objective=None, *args, **kwargs):
@@ -909,7 +884,6 @@ def run(self, target=None, max_enforced_flux=0.9, number_of_results=10, exclude=
             simulation_kwargs=None):
         """
         Performs a Flux Scanning based on Enforced Objective Flux (FSEOF) analysis.
-
         Parameters
         ----------
         target: str, Reaction, Metabolite
@@ -920,17 +894,14 @@ def run(self, target=None, max_enforced_flux=0.9, number_of_results=10, exclude=
         number_of_results : int, optional
             The number of enforced flux levels (defaults to 10).
         exclude : Iterable of reactions or reaction ids that will not be included in the output.
-
         Returns
         -------
         FseofResult
             An object containing the identified reactions and the used parameters.
-
         References
         ----------
         .. [1] H. S. Choi, S. Y. Lee, T. Y. Kim, and H. M. Woo, 'In silico identification of gene amplification targets
         for improvement of lycopene production.,' Appl Environ Microbiol, vol. 76, no. 10, pp. 3097–3105, May 2010.
-
         """
         model = self.model
         target = get_reaction_for(model, target)
@@ -1004,7 +975,6 @@ def run(self, target=None, max_enforced_flux=0.9, number_of_results=10, exclude=
 class FSEOFResult(StrainDesignMethodResult):
     """
     Object for storing a FSEOF result.
-
     Attributes:
     -----------
     reactions: list
@@ -1015,7 +985,6 @@ class FSEOFResult(StrainDesignMethodResult):
         A pandas DataFrame containing the fluxes for every reaction for each enforced flux.
     run_args: dict
         The arguments that the analysis was run with. To repeat do 'FSEOF.run(**FSEOFResult.run_args)'.
-
     """
 
     __method_name__ = "FSEOF"