diff --git a/cameo/strain_design/deterministic/__init__.py b/cameo/strain_design/deterministic/__init__.py
index 9ce3a39d4..28bfa5816 100644
--- a/cameo/strain_design/deterministic/__init__.py
+++ b/cameo/strain_design/deterministic/__init__.py
@@ -11,31 +11,104 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from itertools import izip
 
+from functools import partial
+from itertools import izip
 import logging
 from pandas import DataFrame, pandas
 from progressbar import ProgressBar
+from progressbar.widgets import ETA, Bar
 from cameo import config, flux_variability_analysis
 from cameo.parallel import SequentialView, MultiprocessingView
+from cameo.solver_based_model import Reaction
 from cameo.strain_design import StrainDesignMethod
 from cameo.flux_analysis.analysis import phenotypic_phase_plane
-
+from cameo.util import TimeMachine
 
 logging.basicConfig(level=logging.DEBUG)
 logger = logging.getLogger(__name__)
 
 
 class DifferentialFVA(StrainDesignMethod):
-    """Differential flux variability analysis.__init__.py
+    """Differential flux variability analysis.
 
-    Compare flux ranges of a reference model to and a set of models that
-    are parameterized to lie on a grid of evenly space points in the
-    production envelope of a model.
+    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
+    to be varied).
+    ::
+        production
+        ^
+        |---------.          * reference_model
+        | . . . . .\         . design_space_model
+        | . . . . . \\
+        | . . . . . .\\
+        | . . . . . . \\
+        o--------------*- >
+                     growth
 
-    ...
+    Overexpression, downregulation, knockout, flux-reversal and other
+    strain engineering targets can be inferred from the resulting comparison.
+
+    Parameters
+    ----------
+    design_space_model : SolverBasedModel
+        A model whose flux ranges will be scanned.
+    reference_model : SolverBasedModel
+        A model whose flux ranges represent the reference state and all calculates
+        flux ranges will be compared to.
+    objective : str or Reaction
+        The reaction to be maximized.
+    variables : iterable
+        A iterable of n reactions (or IDs) to be scanned.
+    exclude : iterable
+        An iterable of reactions (or IDs) to be excluded in the analysis (exchange
+        reactions will not be analyzed automatically).
+    normalize_ranges_by : str or Reaction, optional
+        A reaction ID that specifies a flux by whom all calculated flux ranges
+        will be normalized by.
+    points : int, optional
+        Number of points to lay on the surface of the n-dimensional production envelope.
     """
 
+    def __init__(self, design_space_model, reference_model, objective, variables=[],
+                 exclude=[], normalize_ranges_by=None, points=20):
+        super(DifferentialFVA, self).__init__()
+
+        self.design_space_model = design_space_model
+        self.reference_model = reference_model
+
+        if isinstance(objective, Reaction):
+            self.objective = objective.id
+        else:
+            self.objective = objective
+
+        self.variables = list()
+        for variable in variables:
+            if isinstance(variable, Reaction):
+                self.variables.append(variable.id)
+            else:
+                self.variables.append(variable)
+
+        self.exclude = list()
+        for elem in exclude:
+            if isinstance(elem, Reaction):
+                self.exclude.append(elem.id)
+            else:
+                self.exclude.append(elem)
+        self.exclude += [reaction.id for reaction in design_space_model.exchanges]
+        self.exclude += [reaction.id for reaction in reference_model.exchanges]
+        self.exclude = set(self.exclude).difference(set([self.objective] + self.variables))
+
+        self.points = points
+        self.envelope = None
+        self.grid = None
+
+        if isinstance(normalize_ranges_by, Reaction):
+            self.normalize_ranges_by = normalize_ranges_by.id
+        else:
+            self.normalize_ranges_by = normalize_ranges_by
+
     @staticmethod
     def _interval_overlap(interval1, interval2):
         return min(interval1[1] - interval2[0], interval2[1] - interval1[0])
@@ -51,21 +124,10 @@ def _interval_gap(cls, interval1, interval2):
             else:
                 return -1 * overlap
 
-    def __init__(self, design_space_model=None, reference_model=None, target=None, variables=[],
-                 normalize_fluxes_by=None, points=20):
-        super(DifferentialFVA, self).__init__()
-        self.design_space_model = design_space_model
-        self.reference_model = reference_model
-        self.target = target
-        self.variables = variables
-        self.points = points
-        self.envelope = None
-        self.grid = None
-        self.normalize_fluxes_by = normalize_fluxes_by
-
-    def _init_search_grid(self):
+    def _init_search_grid(self, surface_only=False, improvements_only=True):
+        """Initialize the grid of points to be scanned within the production envelope."""
         self.envelope = phenotypic_phase_plane(
-            self.design_space_model, self.variables, objective=self.target, points=self.points)
+            self.design_space_model, self.variables, objective=self.objective, points=self.points)
         intervals = self.envelope[['objective_lower_bound', 'objective_upper_bound']]
         max_distance = 0.
         max_interval = None
@@ -75,66 +137,112 @@ def _init_search_grid(self):
                 max_distance = distance
                 max_interval = (lb, ub)
         step_size = (max_interval[1] - max_interval[0]) / (self.points - 1)
-        print step_size
         grid = list()
+        minimal_reference_production = self.reference_flux_ranges['lower_bound'][self.objective]
         for i, row in self.envelope.iterrows():
             variables = row[self.variables]
             lb = row.objective_lower_bound
+            if improvements_only:
+                lb = max(lb, minimal_reference_production) + step_size
             ub = row.objective_upper_bound
-            coordinate = lb
-            while coordinate < ub:
-                grid.append(list(variables.values) + [coordinate])
-                coordinate += step_size
-            grid.append(list(variables.values) + [ub])
-        columns = self.variables + [self.target]
+            if not surface_only:
+                coordinate = lb
+                while coordinate < ub:
+                    grid.append(list(variables.values) + [coordinate])
+                    coordinate += step_size
+            if improvements_only and ub <= minimal_reference_production:
+                continue
+            else:
+                grid.append(list(variables.values) + [ub])
+        columns = self.variables + [self.objective]
         self.grid = DataFrame(grid, columns=columns)
 
+    def run(self, surface_only=False, improvements_only=True, view=None):
+        """Run the differential flux variability analysis.
+
+        Parameters
+        ----------
+        surface_only : bool, optional
+            If only the optimal surface should be scanned.
+        view : SequentialView or MultiprocessingView or ipython.cluster.DirectView, optional
+            A parallelization view.
+        surface_only : bool, optional
+            If only the surface of the n-dimensional production envelope should be scanned.
+        improvements_only : bool, optional
+            If only grid points should should be scanned that constitute and improvement in production
+            over the reference state.
+
+        Returns
+        -------
+        pandas.Panel
+            A pandas Panel containing a results DataFrame for every grid point scanned.
+        """
+        with TimeMachine() as tm:
+            # Make sure that the design_space_model is initialized to its original state later
+            for variable in self.variables:
+                reaction = self.design_space_model.reactions.get_by_id(variable)
+                tm(do=int, undo=partial(setattr, reaction, 'lower_bound', reaction.lower_bound))
+                tm(do=int, undo=partial(setattr, reaction, 'upper_bound', reaction.upper_bound))
+            target_reaction = self.design_space_model.reactions.get_by_id(self.objective)
+            tm(do=int, undo=partial(setattr, target_reaction, 'lower_bound', reaction.lower_bound))
+            tm(do=int, undo=partial(setattr, target_reaction, 'upper_bound', reaction.upper_bound))
+
+            if view is None:
+                view = config.default_view
+            else:
+                view = view
+
+            included_reactions = [reaction.id for reaction in self.reference_model.reactions if not reaction.id in self.exclude]
+            self.reference_flux_ranges = flux_variability_analysis(self.reference_model, reactions=included_reactions, view=view, remove_cycles=False)
+            self._init_search_grid(surface_only=surface_only, improvements_only=improvements_only)
+
+            progress = ProgressBar(len(self.grid), widgets=['Scanning grid points ', Bar(),' ', ETA()])
+            func_obj = _DifferentialFvaEvaluator(self.design_space_model, self.variables, self.objective, included_reactions)
+            iterator = progress(self.grid.iterrows())
+            results = view.map(func_obj, iterator)
+            solutions = dict((tuple(point.to_dict().items()), fva_result) for (point, fva_result) in results)
+
+            reference_intervals = self.reference_flux_ranges[['lower_bound', 'upper_bound']].values
+            for sol in solutions.itervalues():
+                intervals = sol[['lower_bound', 'upper_bound']].values
+                gaps = [self._interval_gap(interval1, interval2) for interval1, interval2 in
+                        izip(reference_intervals, intervals)]
+                sol['gaps'] = gaps
+            if self.normalize_ranges_by is not None:
+                for sol in solutions.itervalues():
+                    normalized_intervals = sol[['lower_bound', 'upper_bound']].values / sol.lower_bound[
+                        self.normalize_ranges_by]
+                    normalized_gaps = [self._interval_gap(interval1, interval2) for interval1, interval2 in
+                                       izip(reference_intervals, normalized_intervals)]
+                    sol['normalized_gaps'] = normalized_gaps
+
+            return pandas.Panel(solutions)
+
+
+class _DifferentialFvaEvaluator(object):
+    def __init__(self, model, variables, objective, included_reactions):
+        self.model = model
+        self.variables = variables
+        self.objective = objective
+        self.included_reactions = included_reactions
+
+    def __call__(self, point):
+        self._set_bounds(point[1])
+        return (point[1], flux_variability_analysis(self.model, reactions=self.included_reactions, remove_cycles=False, view=SequentialView()))
+
     def _set_bounds(self, point):
         for variable in self.variables:
-            reaction = self.design_space_model.reactions.get_by_id(variable)
+            reaction = self.model.reactions.get_by_id(variable)
             bound = point[variable]
             reaction.lower_bound, reaction.upper_bound = bound, bound
-        target_reaction = self.design_space_model.reactions.get_by_id(self.target)
-        target_bound = point[self.target]
+        target_reaction = self.model.reactions.get_by_id(self.objective)
+        target_bound = point[self.objective]
         target_reaction.lower_bound, target_reaction.upper_bound = target_bound, target_bound
 
-    def run(self, view=None):
-        if view is None:
-            view = config.default_view
-        else:
-            view = view
-        reference_flux_ranges = flux_variability_analysis(self.reference_model, view=view)
-        self.reference_flux_ranges = reference_flux_ranges
-        self._init_search_grid()
-        solutions = dict()
-        progress = ProgressBar(len(self.grid))
-        progress.start()
-        for i, point in self.grid.iterrows():
-            progress.update(i + 1)
-            # print "Processing point:", point
-            self._set_bounds(point)
-            fva_sol = flux_variability_analysis(self.design_space_model, remove_cycles=True,
-                                                view=SequentialView())  # TODO: let it remove cycles in the future
-            solutions[str(point)] = fva_sol
-        reference_intervals = reference_flux_ranges[['lower_bound', 'upper_bound']].values
-        for sol in solutions.itervalues():
-            intervals = sol[['lower_bound', 'upper_bound']].values
-            gaps = [self._interval_gap(interval1, interval2) for interval1, interval2 in
-                    izip(reference_intervals, intervals)]
-            sol['gaps'] = gaps
-        if self.normalize_fluxes_by is not None:
-            for sol in solutions.itervalues():
-                normalized_intervals = sol[['lower_bound', 'upper_bound']].values / sol.lower_bound[
-                    self.normalize_fluxes_by]
-                normalized_gaps = [self._interval_gap(interval1, interval2) for interval1, interval2 in
-                                   izip(reference_intervals, normalized_intervals)]
-                sol['normalized_gaps'] = normalized_gaps
-
-        return pandas.Panel(solutions)
-
 
 if __name__ == '__main__':
     from cameo.io import load_model
+    from cameo.util import Timer
 
     model = load_model(
         '/Users/niko/Arbejder/Dev/cameo/tests/data/EcoliCore.xml')
@@ -149,12 +257,14 @@ def run(self, view=None):
 
     diffFVA = DifferentialFVA(design_space_model=model,
                               reference_model=reference_model,
-                              target='EX_succ_LPAREN_e_RPAREN_',
+                              objective='EX_succ_LPAREN_e_RPAREN_',
                               variables=['Biomass_Ecoli_core_N_LPAREN_w_FSLASH_GAM_RPAREN__Nmet2',
                                          'EX_o2_LPAREN_e_RPAREN_'],
-                              normalize_fluxes_by='Biomass_Ecoli_core_N_LPAREN_w_FSLASH_GAM_RPAREN__Nmet2',
-                              points=2
+                              normalize_ranges_by='Biomass_Ecoli_core_N_LPAREN_w_FSLASH_GAM_RPAREN__Nmet2',
+                              points=4
     )
-    # result = diffFVA.run(view=SequentialView())
-    result = diffFVA.run(view=MultiprocessingView())
+    with Timer('Sequential'):
+        result = diffFVA.run(surface_only=True, improvements_only=True, view=SequentialView())
+    # with Timer('Multiprocessing'):
+    #     result = diffFVA.run(surface_only=True, view=MultiprocessingView())
 
diff --git a/examples/DifferentialFVA.ipynb b/examples/DifferentialFVA.ipynb
index 7327268be..bd0d6e4f2 100644
--- a/examples/DifferentialFVA.ipynb
+++ b/examples/DifferentialFVA.ipynb
@@ -1,17 +1,26 @@
 {
  "metadata": {
-  "name": ""
+  "name": "",
+  "signature": "sha256:5efc02f0a84ddcf0ea8ca8a5a381618906fb824091c867b41249ebb59c1b9d10"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
  "worksheets": [
   {
    "cells": [
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Differential flux variability analysis"
+     ]
+    },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "import copy"
+      "%matplotlib inline"
      ],
      "language": "python",
      "metadata": {},
@@ -22,9 +31,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "from cameo import load_model\n",
-      "from cameo.methods import DifferentialFVA\n",
-      "from cameo.basics import production_envelope"
+      "from matplotlib import pyplot"
      ],
      "language": "python",
      "metadata": {},
@@ -35,3867 +42,3491 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "from cameo.config import default_view"
+      "from escher import Builder"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 3
+     "prompt_number": 46
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "default_view"
+      "from cameo import load_model\n",
+      "from cameo.flux_analysis.analysis import flux_variability_analysis, phenotypic_phase_plane\n",
+      "from cameo.strain_design.deterministic import DifferentialFVA\n",
+      "from cameo.parallel import SequentialView"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 4,
-       "text": [
-        "<cameo.parallel.SequentialView at 0x111aedb90>"
-       ]
-      }
-     ],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Good IPython parallel reference\n",
-      "http://proj.badc.rl.ac.uk/cedaservices/wiki/JASMIN/IPythonPrototypeProject/Parallel"
-     ]
+     "outputs": [],
+     "prompt_number": 3
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "%pylab inline --no-import-all"
+      "try:\n",
+      "    from IPython.parallel import Client\n",
+      "    client = Client()\n",
+      "    view = client.direct_view()\n",
+      "    view.block = True\n",
+      "except:\n",
+      "    from cameo.parallel import SequentialView\n",
+      "    view = SequentialView()"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Populating the interactive namespace from numpy and matplotlib\n"
-       ]
-      }
-     ],
-     "prompt_number": 5
+     "outputs": [],
+     "prompt_number": 4
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "model = load_model('../tests/data/EcoliCore.xml')\n",
-      "# %time model = load_model('../tests/data/iJO1366.pickle')"
-     ],
-     "language": "python",
+     "cell_type": "markdown",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 6
+     "source": [
+      "Load the E. coli core model."
+     ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "envelope = production_envelope(model, 'EX_succ_LPAREN_e_RPAREN_', [model.reactions.Biomass_Ecoli_core_N_LPAREN_w_FSLASH_GAM_RPAREN__Nmet2])"
+      "model = load_model('../tests/data/EcoliCore.xml')"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 7
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "The production envelope looks like this."
+     ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "envelope['Biomass_Ecoli_core_N_LPAREN_w_FSLASH_GAM_RPAREN__Nmet2']"
+      "production_envelope = phenotypic_phase_plane(model, [model.reactions.Biomass_Ecoli_core_N_LPAREN_w_FSLASH_GAM_RPAREN__Nmet2], model.reactions.EX_succ_LPAREN_e_RPAREN_)\n",
+      "growth = production_envelope.Biomass_Ecoli_core_N_LPAREN_w_FSLASH_GAM_RPAREN__Nmet2\n",
+      "lb = production_envelope.objective_lower_bound\n",
+      "ub = production_envelope.objective_upper_bound\n",
+      "fig = pyplot.figure()\n",
+      "pyplot.plot(growth, lb, 'k', growth, ub, 'k')\n",
+      "pyplot.fill_between(growth, ub)\n",
+      "pyplot.xlabel('Growth')\n",
+      "pyplot.ylabel('Production');"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 8,
-       "text": [
-        "[0.0,\n",
-        " 0.045995868787812123,\n",
-        " 0.091991737575624247,\n",
-        " 0.13798760636343638,\n",
-        " 0.18398347515124849,\n",
-        " 0.22997934393906061,\n",
-        " 0.27597521272687275,\n",
-        " 0.32197108151468484,\n",
-        " 0.36796695030249699,\n",
-        " 0.41396281909030913,\n",
-        " 0.45995868787812122,\n",
-        " 0.50595455666593336,\n",
-        " 0.55195042545374551,\n",
-        " 0.59794629424155765,\n",
-        " 0.64394216302936969,\n",
-        " 0.68993803181718183,\n",
-        " 0.73593390060499397,\n",
-        " 0.78192976939280612,\n",
-        " 0.82792563818061826,\n",
-        " 0.8739215069684303]"
+       "output_type": "display_data",
+       "png": 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+       "text": [
+        "<matplotlib.figure.Figure at 0x118434410>"
        ]
       }
      ],
-     "prompt_number": 8
+     "prompt_number": 6
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "solution = model.optimize()\n",
-      "surrogate_experimental_fluxes = dict([(key, (val*.8, val*1.2)) for key, val in solution.x_dict.iteritems()])"
-     ],
-     "language": "python",
+     "cell_type": "markdown",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 9
+     "source": [
+      "Set up a model that represent a reference state (in this case a model with a constrained growth rate)."
+     ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "constraint_model = copy.copy(model)\n",
-      "for reaction_id, (lb, ub) in surrogate_experimental_fluxes.iteritems():\n",
-      "    reaction = constraint_model.reactions.get_by_id(reaction_id)\n",
-      "    reaction.lower_bound = lb\n",
-      "    reaction.upper_bound = ub"
+      "reference_model = model.copy()\n",
+      "biomass_rxn = reference_model.reactions.Biomass_Ecoli_core_N_LPAREN_w_FSLASH_GAM_RPAREN__Nmet2\n",
+      "biomass_rxn.lower_bound = 0.3\n",
+      "target = reference_model.reactions.EX_succ_LPAREN_e_RPAREN_\n",
+      "target.lower_bound = 2"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 10
+     "prompt_number": 7
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "diffFVA = DifferentialFVA(design_space_model=model, reference_model=constraint_model, target='EX_succ_LPAREN_e_RPAREN_', variables=[model.reactions.Biomass_Ecoli_core_N_LPAREN_w_FSLASH_GAM_RPAREN__Nmet2])"
-     ],
-     "language": "python",
+     "cell_type": "markdown",
      "metadata": {},
-     "outputs": [
-      {
-       "ename": "TypeError",
-       "evalue": "cannot concatenate 'str' and 'int' objects",
-       "output_type": "pyerr",
-       "traceback": [
-        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-        "\u001b[0;32m<ipython-input-11-00179ddc351e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdiffFVA\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDifferentialFVA\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdesign_space_model\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreference_model\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconstraint_model\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'EX_succ_LPAREN_e_RPAREN_'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvariables\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreactions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBiomass_Ecoli_core_N_LPAREN_w_FSLASH_GAM_RPAREN__Nmet2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-        "\u001b[0;32m/Users/niko/Arbejder/Dev/cameo/cameo/methods.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, design_space_model, reference_model, target, variables, points)\u001b[0m\n\u001b[1;32m     79\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menvelope\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     80\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 81\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_init\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     82\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     83\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_init\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;32m/Users/niko/Arbejder/Dev/cameo/cameo/methods.pyc\u001b[0m in \u001b[0;36m_init\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     97\u001b[0m             \u001b[0mstep\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     98\u001b[0m             \u001b[0;32mwhile\u001b[0m \u001b[0mstep\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mub\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 99\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrid\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlb\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    100\u001b[0m                 \u001b[0mstep\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mstep_size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    101\u001b[0m         \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'...'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;31mTypeError\u001b[0m: cannot concatenate 'str' and 'int' objects"
-       ]
-      }
-     ],
-     "prompt_number": 11
+     "source": [
+      "Set up the differential flux variability analysis strain design method."
+     ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "diffFVA.run()"
+      "diffFVA = DifferentialFVA(design_space_model=model,\n",
+      "                          reference_model=reference_model,\n",
+      "                          objective=reference_model.reactions.EX_succ_LPAREN_e_RPAREN_,\n",
+      "                          variables=[biomass_rxn],\n",
+      "                          normalize_ranges_by=biomass_rxn,\n",
+      "                          points=10)"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [
-      {
-       "ename": "NameError",
-       "evalue": "name 'diffFVA' is not defined",
-       "output_type": "pyerr",
-       "traceback": [
-        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-        "\u001b[0;32m<ipython-input-12-5c3d0ae5175c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdiffFVA\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-        "\u001b[0;31mNameError\u001b[0m: name 'diffFVA' is not defined"
-       ]
-      }
-     ],
-     "prompt_number": 12
+     "outputs": [],
+     "prompt_number": 8
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Run differential flux variability analysis"
+     ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "%time model.optimize().f"
+      "result = diffFVA.run(view=view)\n",
+      "x = [elem[0][1] for elem in list(result.items)]\n",
+      "y = [elem[1][1] for elem in list(result.items)]\n",
+      "pyplot.plot(growth, lb, 'k', growth, ub, 'k')\n",
+      "pyplot.plot([biomass_rxn.lower_bound], [target.lower_bound], 'go')\n",
+      "pyplot.fill_between(growth, ub, color='gray')\n",
+      "pyplot.xlabel('Growth')\n",
+      "pyplot.ylabel('Production');\n",
+      "pyplot.plot(x,y, 'ro');"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
-       "output_type": "stream",
-       "stream": "stdout",
+       "javascript": [
+        "//a09806b5-d88d-47ca-b939-649aafe3cda0\n",
+        "$(\"head\").append(\"<style> td.pb_widget {     width: auto; } td.pb_widget_fill {     width: 100%; } table.pb {     font-family: monospace;     border: 0;     margin: 0; } table.pb tr { border: 0; } table.pb td {     white-space: nowrap;     border: 0; } div.pb {     border: 1px solid #ddd;     border-radius: 3px; } div.pb_bar {     height: 1.5em; } </style>\")"
+       ],
+       "metadata": {},
+       "output_type": "display_data",
        "text": [
-        "CPU times: user 357 ms, sys: 1.16 ms, total: 358 ms\n",
-        "Wall time: 359 ms\n"
+        "<IPython.core.display.Javascript at 0x11822da10>"
        ]
       },
       {
+       "javascript": [
+        "\n",
+        "                  // a09806b5-d88d-47ca-b939-649aafe3cda0 -- used to remove this code blob in the end\n",
+        "                  IPython.OutputArea.prototype.cleanProgressBar = function(uuids) {\n",
+        "                      // filter by uuid-strings \n",
+        "                      var myfilter = function(output) { \n",
+        "                          var nuids = uuids.length;\n",
+        "                          for (var i=0; i<nuids; i++) {\n",
+        "                              if (output.hasOwnProperty('html')) {\n",
+        "                                  if (output.html.indexOf(uuids[i]) != -1) {\n",
+        "                                      return false;\n",
+        "                                  }\n",
+        "                              }\n",
+        "                              if (output.hasOwnProperty('javascript')) {\n",
+        "                                  if (output.javascript.indexOf(uuids[i]) != -1) {\n",
+        "                                      return false;\n",
+        "                                  }\n",
+        "                              }\n",
+        "                          }\n",
+        "                          // keep all others\n",
+        "                          return true;\n",
+        "                      };\n",
+        "\n",
+        "                      // Filter the ouputs\n",
+        "                      this.outputs = this.outputs.filter(myfilter);\n",
+        "                };\n",
+        "                "
+       ],
        "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 16,
+       "output_type": "display_data",
        "text": [
-        "0.9823718127270133"
+        "<IPython.core.display.Javascript at 0x11822dad0>"
        ]
-      }
-     ],
-     "prompt_number": 16
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "cameo.default_view"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
+      },
       {
+       "html": [
+        "<div class=\"pb\" id=\"a09806b5-d88d-47ca-b939-649aafe3cda0\"><table class=\"pb ui-widget\"><tr>\n",
+        "<td class=\"pb_widget\">Scanning grid points </td>\n",
+        "<td class=\"pb_widget_fill\">\n",
+        "        <div class=\"pb_bar\" id=\"688a7924-a0a3-4e7c-bb7e-526d70d9ca0b\"></div>\n",
+        "        <script type=\"text/javascript\">\n",
+        "            $(\"div#688a7924-a0a3-4e7c-bb7e-526d70d9ca0b\").progressbar({value: 0, max: 36});\n",
+        "        </script>\n",
+        "        </td>\n",
+        "<td class=\"pb_widget\"> </td>\n",
+        "<td class=\"pb_widget\"><div id=\"2fb79e94-27ab-4bd8-b717-80872b010ff6\">ETA:  --:--:--</div></td>\n",
+        "</tr></table><div>"
+       ],
        "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 32,
+       "output_type": "display_data",
        "text": [
-        "<cameo.ViewFacade at 0x115587a50>"
+        "<IPython.core.display.HTML at 0x1182287d0>"
        ]
-      }
-     ],
-     "prompt_number": 32
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "cameo.default_view = cameo.ViewFacade()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 25
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "cameo.default_view.map(lambda x: x+10, [10])"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
+      },
       {
+       "javascript": [
+        "\n",
+        "        var $myPB = $(\"div#688a7924-a0a3-4e7c-bb7e-526d70d9ca0b\")\n",
+        "        if ($myPB.hasClass('ui-progressbar')) {\n",
+        "            $myPB.progressbar('value', 1);\n",
+        "        } else {\n",
+        "            $myPB.progressbar({value: 0, max: 36});\n",
+        "        }\n",
+        "        "
+       ],
        "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 30,
+       "output_type": "display_data",
        "text": [
-        "[20]"
+        "<IPython.core.display.Javascript at 0x11822dad0>"
        ]
-      }
-     ],
-     "prompt_number": 30
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%time fva_sol = flux_variability_analysis(model)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
+      },
       {
-       "output_type": "stream",
-       "stream": "stdout",
+       "javascript": [
+        "$('div#2fb79e94-27ab-4bd8-b717-80872b010ff6').text('ETA:  0:00:03');"
+       ],
+       "metadata": {},
+       "output_type": "display_data",
        "text": [
-        "CPU times: user 44 s, sys: 251 ms, total: 44.3 s\n",
-        "Wall time: 44.4 s\n"
+        "<IPython.core.display.Javascript at 0x11822dc10>"
        ]
-      }
-     ],
-     "prompt_number": 18
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from cameo import default_view\n",
-      "default_view"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
+      },
       {
+       "javascript": [
+        "\n",
+        "        var $myPB = $(\"div#688a7924-a0a3-4e7c-bb7e-526d70d9ca0b\")\n",
+        "        if ($myPB.hasClass('ui-progressbar')) {\n",
+        "            $myPB.progressbar('value', 2);\n",
+        "        } else {\n",
+        "            $myPB.progressbar({value: 0, max: 36});\n",
+        "        }\n",
+        "        "
+       ],
        "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 22,
+       "output_type": "display_data",
        "text": [
-        "<cameo.ViewFacade at 0x1101a91d0>"
+        "<IPython.core.display.Javascript at 0x118228a90>"
        ]
-      }
-     ],
-     "prompt_number": 22
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "fva_sol_p = flux_variability_analysis_parallel(model, view=view)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 20
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%time fva_sol_p = flux_variability_analysis_parallel(model, view=view)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
+      },
       {
-       "output_type": "stream",
-       "stream": "stdout",
+       "javascript": [
+        "$('div#2fb79e94-27ab-4bd8-b717-80872b010ff6').text('ETA:  0:00:03');"
+       ],
+       "metadata": {},
+       "output_type": "display_data",
        "text": [
-        "clearing the view ...\n",
-        "CPU times: user 4.03 s, sys: 602 ms, total: 4.63 s"
+        "<IPython.core.display.Javascript at 0x118228990>"
        ]
       },
       {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
+       "javascript": [
         "\n",
-        "Wall time: 20.9 s\n"
+        "        var $myPB = $(\"div#688a7924-a0a3-4e7c-bb7e-526d70d9ca0b\")\n",
+        "        if ($myPB.hasClass('ui-progressbar')) {\n",
+        "            $myPB.progressbar('value', 3);\n",
+        "        } else {\n",
+        "            $myPB.progressbar({value: 0, max: 36});\n",
+        "        }\n",
+        "        "
+       ],
+       "metadata": {},
+       "output_type": "display_data",
+       "text": [
+        "<IPython.core.display.Javascript at 0x11822dad0>"
        ]
-      }
-     ],
-     "prompt_number": 38
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "for key, val in fva_sol.iteritems():\n",
-      "        val2 = fva_sol_p[key]\n",
-      "        # print key, val, val2\n",
-      "        print key, abs(val['minimum'] - val['minimum']), abs(val['maximum'] - val['maximum'])"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
+      },
       {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "DM_4CRSOL 0.0 0.0\n",
-        "DM_5DRIB 0.0 0.0\n",
-        "DM_AACALD 0.0 0.0\n",
-        "DM_AMOB 0.0 0.0\n",
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-      "@interactive\n",
-      "@parallel.require(flux_variability_analysis)\n",
-      "def test_parallel_fva(level, model=None):\n",
-      "    biomass_rxn = model.reactions.get_by_id('Ec_biomass_iJO1366_core_53p95M')\n",
-      "    biomass_rxn.lower_bound = level\n",
-      "    fva_result = flux_variability_analysis(model, reactions=('Ec_biomass_iJO1366_core_53p95M', 'ENO', 'PGK'))\n",
-      "    return [fva_result[key] for key in ('Ec_biomass_iJO1366_core_53p95M', 'ENO', 'PGK')]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 15
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from functools import partial\n",
-      "partial(test_parallel_fva, model=model)(.3)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
+      },
       {
+       "javascript": [
+        "$('div#2fb79e94-27ab-4bd8-b717-80872b010ff6').text('ETA:  0:00:03');"
+       ],
        "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 16,
+       "output_type": "display_data",
        "text": [
-        "[{'maximum': 0.982371812727038, 'minimum': 0.3},\n",
-        " {'maximum': 52.54565604499939, 'minimum': -67.0921997094322},\n",
-        " {'maximum': 43.16309394584123, 'minimum': -19.277896500001006}]"
+        "<IPython.core.display.Javascript at 0x118228590>"
        ]
-      }
-     ],
-     "prompt_number": 16
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from functools import partial\n",
-      "map(partial(test_parallel_fva, model=model), list(numpy.linspace(0,.8,5)))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
+      },
       {
+       "javascript": [
+        "\n",
+        "        var $myPB = $(\"div#688a7924-a0a3-4e7c-bb7e-526d70d9ca0b\")\n",
+        "        if ($myPB.hasClass('ui-progressbar')) {\n",
+        "            $myPB.progressbar('value', 6);\n",
+        "        } else {\n",
+        "            $myPB.progressbar({value: 0, max: 36});\n",
+        "        }\n",
+        "        "
+       ],
        "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 17,
-       "text": [
-        "[[{'maximum': 0.9823718127270128, 'minimum': 0.0},\n",
-        "  {'maximum': 66.24666666666832, 'minimum': -99.32830188678801},\n",
-        "  {'maximum': 66.44999999999801, 'minimum': -19.99999999999976}],\n",
-        " [{'maximum': 0.982371812726934, 'minimum': 0.2},\n",
-        "  {'maximum': 57.11265958555603, 'minimum': -77.83756710189327},\n",
-        "  {'maximum': 50.92539596389195, 'minimum': -19.518597666668335}],\n",
-        " [{'maximum': 0.9823718127270242, 'minimum': 0.4},\n",
-        "  {'maximum': 47.97865250445072, 'minimum': -56.34683231699419},\n",
-        "  {'maximum': 35.40079192777673, 'minimum': -19.037195333335006}],\n",
-        " [{'maximum': 0.9823718127269415, 'minimum': 0.6000000000000001},\n",
-        "  {'maximum': 38.84464542333376, 'minimum': -34.407831400005236},\n",
-        "  {'maximum': 19.876187891662333, 'minimum': -18.555793000000005}],\n",
-        " [{'maximum': 0.9823718127269396, 'minimum': 0.8},\n",
-        "  {'maximum': 29.71063834221903, 'minimum': -11.840744896956352},\n",
-        "  {'maximum': 4.3515838555565916, 'minimum': -18.074390666666513}]]"
+       "output_type": "display_data",
+       "text": [
+        "<IPython.core.display.Javascript at 0x1182287d0>"
        ]
-      }
-     ],
-     "prompt_number": 17
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "partial_func = partial(test_parallel_fva, model=model)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 26
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Let's try a 1-dim production envelope first ..."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from cameo.util import production_envelope2\n",
-      "ecoli_core = to_solver_based_model(read_sbml_model('../tests/data/EcoliCore.xml'))\n",
-      "envelope = production_envelope2(ecoli_core, 'EX_ac_LPAREN_e_RPAREN_', \n",
-      "                               variables=['Biomass_Ecoli_core_N_LPAREN_w_FSLASH_GAM_RPAREN__Nmet2'],\n",
-      "                               points=30\n",
-      "                               )"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
+      },
       {
-       "output_type": "stream",
-       "stream": "stdout",
+       "javascript": [
+        "$('div#2fb79e94-27ab-4bd8-b717-80872b010ff6').text('ETA:  0:00:03');"
+       ],
+       "metadata": {},
+       "output_type": "display_data",
        "text": [
-        "Populating from scratch ...\n"
+        "<IPython.core.display.Javascript at 0x118228950>"
        ]
-      }
-     ],
-     "prompt_number": 17
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print model.objective"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
+      },
       {
-       "output_type": "stream",
-       "stream": "stdout",
+       "javascript": [
+        "\n",
+        "        var $myPB = $(\"div#688a7924-a0a3-4e7c-bb7e-526d70d9ca0b\")\n",
+        "        if ($myPB.hasClass('ui-progressbar')) {\n",
+        "            $myPB.progressbar('value', 7);\n",
+        "        } else {\n",
+        "            $myPB.progressbar({value: 0, max: 36});\n",
+        "        }\n",
+        "        "
+       ],
+       "metadata": {},
+       "output_type": "display_data",
        "text": [
-        "Maximize\n",
-        "1.0*Ec_biomass_iJO1366_core_53p95M\n"
+        "<IPython.core.display.Javascript at 0x118228690>"
        ]
-      }
-     ],
-     "prompt_number": 18
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "fig = plt.figure(figsize=(8, 6))\n",
-      "ax = fig.add_subplot(111)\n",
-      "x = np.array(envelope.values()[0])\n",
-      "y = np.array(envelope.values()[1])\n",
-      "ax.fill_between(x, 0, y)\n",
-      "ax.plot(x, y, linewidth=2, c='black')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
+      },
       {
+       "javascript": [
+        "$('div#2fb79e94-27ab-4bd8-b717-80872b010ff6').text('ETA:  0:00:03');"
+       ],
        "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 19,
+       "output_type": "display_data",
        "text": [
-        "[<matplotlib.lines.Line2D at 0x10276a1d0>]"
+        "<IPython.core.display.Javascript at 0x118228a90>"
        ]
       },
       {
+       "javascript": [
+        "\n",
+        "        var $myPB = $(\"div#688a7924-a0a3-4e7c-bb7e-526d70d9ca0b\")\n",
+        "        if ($myPB.hasClass('ui-progressbar')) {\n",
+        "            $myPB.progressbar('value', 8);\n",
+        "        } else {\n",
+        "            $myPB.progressbar({value: 0, max: 36});\n",
+        "        }\n",
+        "        "
+       ],
        "metadata": {},
        "output_type": "display_data",
-       "png": 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R7733XtRFNZiBLEnKQL0JoVzIG2+8QVFREe+8807URTWIgSxJylA9CaHcnzff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\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x10c37f590>"
+        "<IPython.core.display.Javascript at 0x118228950>"
        ]
-      }
-     ],
-     "prompt_number": 19
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Now let's try a 2-dim example ..."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "envelope = production_envelope(model, 'EX_ac_LPAREN_e_RPAREN_', \n",
-      "                               variables=['Biomass_Ecoli_core_N_LPAREN_w_FSLASH_GAM_RPAREN__Nmet2',\n",
-      "                                          'EX_o2_LPAREN_e_RPAREN_'],\n",
-      "                               points=30\n",
-      "                               )"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 153
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "len(envelope)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
+      },
       {
+       "javascript": [
+        "$('div#2fb79e94-27ab-4bd8-b717-80872b010ff6').text('ETA:  0:00:03');"
+       ],
        "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 154,
+       "output_type": "display_data",
        "text": [
-        "3"
+        "<IPython.core.display.Javascript at 0x118228910>"
        ]
-      }
-     ],
-     "prompt_number": 154
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import json\n",
-      "with open('2d_production_envelop.json', 'w') as fhandle:\n",
-      "    json.dump(envelope, fhandle)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 155
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "envelope_array = [list(key) + [val] for key, val in envelope.iteritems()]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 133
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "fig = plt.figure(figsize=(8, 6))\n",
-      "ax = fig.add_subplot(111, projection='3d')\n",
-      "xs = envelope['Biomass_Ecoli_core_N_LPAREN_w_FSLASH_GAM_RPAREN__Nmet2']\n",
-      "ys = envelope['EX_o2_LPAREN_e_RPAREN_']\n",
-      "zs = envelope['EX_ac_LPAREN_e_RPAREN_']\n",
-      "# ax.scatter(xs, ys, zs, marker='^')\n",
-      "ax.plot_trisurf(xs, ys, zs, color='lightblue')\n",
-      "ax.view_init(elev=30., azim=20)\n",
-      "\n",
-      "ax.set_xlabel('Growth')\n",
-      "ax.set_ylabel('O2')\n",
-      "ax.set_zlabel('Acetate')\n",
-      "\n",
-      "plt.show()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
+      },
       {
+       "javascript": [
+        "\n",
+        "        var $myPB = $(\"div#688a7924-a0a3-4e7c-bb7e-526d70d9ca0b\")\n",
+        "        if ($myPB.hasClass('ui-progressbar')) {\n",
+        "            $myPB.progressbar('value', 9);\n",
+        "        } else {\n",
+        "            $myPB.progressbar({value: 0, max: 36});\n",
+        "        }\n",
+        "        "
+       ],
        "metadata": {},
        "output_type": "display_data",
-       "png": 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gyuOVciWsvMyrXWRXEszu9lw3iltFMPvC1QRVr9dfUuDR1UpRFlTZv1cW1O5j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\nIyMjI2MG/w85MR87fIYshgAAAABJRU5ErkJggg==\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x1124e84d0>"
+        "<IPython.core.display.Javascript at 0x118228690>"
        ]
-      }
-     ],
-     "prompt_number": 156
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "envelope = production_envelope(model, 'EX_succ_LPAREN_e_RPAREN_', \n",
-      "                               variables=['Biomass_Ecoli_core_N_LPAREN_w_FSLASH_GAM_RPAREN__Nmet2',\n",
-      "                                          'EX_o2_LPAREN_e_RPAREN_'],\n",
-      "                               points=30\n",
-      "                               )"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 157
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from matplotlib import cm\n",
-      "fig = plt.figure(figsize=(8, 6))\n",
-      "ax = fig.add_subplot(111, projection='3d')\n",
-      "xs = envelope['Biomass_Ecoli_core_N_LPAREN_w_FSLASH_GAM_RPAREN__Nmet2']\n",
-      "ys = envelope['EX_o2_LPAREN_e_RPAREN_']\n",
-      "zs = envelope['EX_succ_LPAREN_e_RPAREN_']\n",
-      "# xs, ys = np.meshgrid(xs, ys, indesing='xy')\n",
-      "# ax.scatter(xs, ys, zs, marker='^')\n",
-      "ax.plot_trisurf(xs, ys, zs, color='blue', linewidth=-0.1)\n",
-      "# ax.plot_surface(xs, ys, zs, rstride=1, cstride=1, cmap=cm.coolwarm, linewidth=1, antialiased=True)\n",
-      "ax.view_init(elev=30., azim=20)\n",
-      "\n",
-      "ax.set_xlabel('Growth')\n",
-      "ax.set_ylabel('O2')\n",
-      "ax.set_zlabel('Succinate')\n",
-      "\n",
-      "plt.show()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
+      },
       {
+       "javascript": [
+        "$('div#2fb79e94-27ab-4bd8-b717-80872b010ff6').text('ETA:  0:00:02');"
+       ],
        "metadata": {},
        "output_type": "display_data",
-       "png": 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P7hGTvmmaWJbVspSOWvet2EdtJGyrJjB/2kgro1rFZGeaJuFwuCoCE2Bvb6/h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TtTU4PrZ2/CfbQyIDRpFIvV7g6mqE+XkFs1klHjfEbX6+xsmJlc3NOpeXll4Kd3291eHGGuF2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L5Tu91OQUEB27dvZ8uWLTzwwAPHzCD+vx0lYJ6CrFq1ivj4eH7+859HHT9wHNCll17KypUr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\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x127fa5dd0>"
+        "<IPython.core.display.Javascript at 0x118228450>"
        ]
-      }
-     ],
-     "prompt_number": 158
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "Cobrapy"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Import the model"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from cobra.io import read_sbml_model\n",
-      "from cobra.flux_analysis import flux_variability_analysis\n",
-      "# from cobra.oven.aliebrahim.designAnalysis import plot_production_envelope\n",
-      "from cameo.methods import DifferentialFVA\n",
-      "from cobra.oven.phantomas1234.solver_based_model import to_solver_based_model"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "ecoli_core_model = read_sbml_model('../tests/data/EcoliCore.xml')\n",
-      "ecoli_core_model = to_solver_based_model(ecoli_core_model)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
+      },
       {
-       "output_type": "stream",
-       "stream": "stdout",
+       "javascript": [
+        "\n",
+        "        var $myPB = $(\"div#688a7924-a0a3-4e7c-bb7e-526d70d9ca0b\")\n",
+        "        if ($myPB.hasClass('ui-progressbar')) {\n",
+        "            $myPB.progressbar('value', 10);\n",
+        "        } else {\n",
+        "            $myPB.progressbar({value: 0, max: 36});\n",
+        "        }\n",
+        "        "
+       ],
+       "metadata": {},
+       "output_type": "display_data",
        "text": [
-        "Populating from scratch ...\n"
+        "<IPython.core.display.Javascript at 0x118228290>"
        ]
-      }
-     ],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print ecoli_core_model.solver.objective"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
+      },
       {
-       "output_type": "stream",
-       "stream": "stdout",
+       "javascript": [
+        "$('div#2fb79e94-27ab-4bd8-b717-80872b010ff6').text('ETA:  0:00:02');"
+       ],
+       "metadata": {},
+       "output_type": "display_data",
        "text": [
-        "Maximize\n",
-        "1.0*Biomass_Ecoli_core_N_LPAREN_w_FSLASH_GAM_RPAREN__Nmet2\n"
+        "<IPython.core.display.Javascript at 0x1182282d0>"
        ]
-      }
-     ],
-     "prompt_number": 10
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print ecoli_core_model.solver.objective"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
+      },
       {
-       "output_type": "stream",
-       "stream": "stdout",
+       "javascript": [
+        "\n",
+        "        var $myPB = $(\"div#688a7924-a0a3-4e7c-bb7e-526d70d9ca0b\")\n",
+        "        if ($myPB.hasClass('ui-progressbar')) {\n",
+        "            $myPB.progressbar('value', 11);\n",
+        "        } else {\n",
+        "            $myPB.progressbar({value: 0, max: 36});\n",
+        "        }\n",
+        "        "
+       ],
+       "metadata": {},
+       "output_type": "display_data",
        "text": [
-        "Maximize\n",
-        "1.0*Biomass_Ecoli_core_N_LPAREN_w_FSLASH_GAM_RPAREN__Nmet2\n"
+        "<IPython.core.display.Javascript at 0x118228350>"
        ]
-      }
-     ],
-     "prompt_number": 12
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "ecoli_core_model.reactions.get_by_id('EX_glc_LPAREN_e_RPAREN_').lower_bound"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
+      },
       {
+       "javascript": [
+        "$('div#2fb79e94-27ab-4bd8-b717-80872b010ff6').text('ETA:  0:00:02');"
+       ],
        "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 5,
+       "output_type": "display_data",
        "text": [
-        "-10.0"
+        "<IPython.core.display.Javascript at 0x118228a90>"
        ]
-      }
-     ],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "ecoli_core_model.optimize()\n",
-      "ecoli_core_model.solution.status\n",
-      "max_growth = ecoli_core_model.solution.f\n",
-      "max_growth"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
+      },
       {
+       "javascript": [
+        "\n",
+        "        var $myPB = $(\"div#688a7924-a0a3-4e7c-bb7e-526d70d9ca0b\")\n",
+        "        if ($myPB.hasClass('ui-progressbar')) {\n",
+        "            $myPB.progressbar('value', 12);\n",
+        "        } else {\n",
+        "            $myPB.progressbar({value: 0, max: 36});\n",
+        "        }\n",
+        "        "
+       ],
        "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 19,
+       "output_type": "display_data",
        "text": [
-        "20.0"
+        "<IPython.core.display.Javascript at 0x118228190>"
        ]
-      }
-     ],
-     "prompt_number": 19
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "ecoli_core_model._"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
+      },
       {
+       "javascript": [
+        "$('div#2fb79e94-27ab-4bd8-b717-80872b010ff6').text('ETA:  0:00:02');"
+       ],
        "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 21,
+       "output_type": "display_data",
        "text": [
-        "False"
+        "<IPython.core.display.Javascript at 0x118228210>"
        ]
-      }
-     ],
-     "prompt_number": 21
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "ecoli_core_model.optimize(new_objective='ENO')\n",
-      "ecoli_core_model.solution.status\n",
-      "ecoli_core_model.solution.f"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
+      },
       {
+       "javascript": [
+        "\n",
+        "        var $myPB = $(\"div#688a7924-a0a3-4e7c-bb7e-526d70d9ca0b\")\n",
+        "        if ($myPB.hasClass('ui-progressbar')) {\n",
+        "            $myPB.progressbar('value', 13);\n",
+        "        } else {\n",
+        "            $myPB.progressbar({value: 0, max: 36});\n",
+        "        }\n",
+        "        "
+       ],
        "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 20,
+       "output_type": "display_data",
        "text": [
-        "20.0"
+        "<IPython.core.display.Javascript at 0x118228b50>"
        ]
-      }
-     ],
-     "prompt_number": 20
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "?ecoli_core_model.optimize"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 14
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "reference_model = ecoli_core_model.copy()\n",
-      "biomass_rxn = ecoli_core_model.reactions.get_by_id('Biomass_Ecoli_core_N_LPAREN_w_FSLASH_GAM_RPAREN__Nmet2')\n",
-      "reference_model.reactions.get_by_id('Biomass_Ecoli_core_N_LPAREN_w_FSLASH_GAM_RPAREN__Nmet2').lower_bound = max_growth"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "reference_model.optimize()\n",
-      "reference_model.solution.status\n",
-      "reference_model.solution.f"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
+      },
       {
+       "javascript": [
+        "$('div#2fb79e94-27ab-4bd8-b717-80872b010ff6').text('ETA:  0:00:02');"
+       ],
        "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 8,
+       "output_type": "display_data",
        "text": [
-        "0.8739215069685605"
+        "<IPython.core.display.Javascript at 0x118228950>"
        ]
-      }
-     ],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "fva_sol = flux_variability_analysis(reference_model)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "code",
-     "collapsed": true,
-     "input": [
-      "diffFVA = DifferentialFVA(ecoli_core_model, reference_model)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 12
-    },
-    {
-     "cell_type": "code",
-     "collapsed": true,
-     "input": [
-      "diffFVA.run()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 13
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "envelope = plot_production_envelope(ecoli_core_model, 'EX_ac_LPAREN_e_RPAREN_', solver_name='gurobi', plot=False)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 47
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "envelope"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
+      },
       {
+       "javascript": [
+        "\n",
+        "        var $myPB = $(\"div#688a7924-a0a3-4e7c-bb7e-526d70d9ca0b\")\n",
+        "        if ($myPB.hasClass('ui-progressbar')) {\n",
+        "            $myPB.progressbar('value', 14);\n",
+        "        } else {\n",
+        "            $myPB.progressbar({value: 0, max: 36});\n",
+        "        }\n",
+        "        "
+       ],
        "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 48,
-       "text": [
-        "(array([ 0.87392151,  0.84979921,  0.8256769 ,  0.8015546 ,  0.7774323 ,\n",
-        "        0.75331   ,  0.7291877 ,  0.70439941,  0.67613477,  0.64787014,\n",
-        "        0.61960551,  0.5540713 ,  0.48481239,  0.41555347,  0.34629456,\n",
-        "        0.27703565,  0.20777674,  0.13851782,  0.06925891, -0.        ]),\n",
-        " array([  0.        ,   1.05263158,   2.10526316,   3.15789474,\n",
-        "         4.21052632,   5.26315789,   6.31578947,   7.36842105,\n",
-        "         8.42105263,   9.47368421,  10.52631579,  11.57894737,\n",
-        "        12.63157895,  13.68421053,  14.73684211,  15.78947368,\n",
-        "        16.84210526,  17.89473684,  18.94736842,  20.        ]))"
+       "output_type": "display_data",
+       "text": [
+        "<IPython.core.display.Javascript at 0x118228b90>"
        ]
-      }
-     ],
-     "prompt_number": 48
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "py = plotly.plotly('phantomas1234','v9yp7z92wd')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 52
-    },
-    {
-     "cell_type": "code",
-     "collapsed": true,
-     "input": [
-      "py.iplot({'x':envelope[0],'y':envelope[1],'type':'scatter'})"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
+      },
       {
-       "html": [
-        "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~phantomas1234/14/600/450\" width=\"650\"></iframe>"
+       "javascript": [
+        "$('div#2fb79e94-27ab-4bd8-b717-80872b010ff6').text('ETA:  0:00:02');"
        ],
        "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 66,
+       "output_type": "display_data",
        "text": [
-        "<IPython.core.display.HTML at 0x10f3cfa10>"
+        "<IPython.core.display.Javascript at 0x118228090>"
        ]
-      }
-     ],
-     "prompt_number": 66
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "py.plot([{'z': zd,'scl':cs,'type': 'heatmap'}])"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "?scatter"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 76
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from bokeh.plotting import line"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 80
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "?GGPlot"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 79
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "plt.axes.append('asd')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 86
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "plt = line(envelope[0], envelope[1], line_width=2, xaxis=\"asdf\")\n",
-      "plt.axes.append('asd')\n",
-      "show()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
+      },
       {
-       "html": [
-        "<div>\n",
-        "  \n",
-        "  \n",
-        "  <script type=\"text/javascript\">\n",
-        "    $(function(){      \n",
-        "    \n",
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+        "$('div#2fb79e94-27ab-4bd8-b717-80872b010ff6').text('ETA:  0:00:00');"
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+       "metadata": {},
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+        "<IPython.core.display.Javascript at 0x11922a490>"
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+       "javascript": [
+        "$('div#2fb79e94-27ab-4bd8-b717-80872b010ff6').text('ETA:  0:00:00');"
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+       "javascript": [
+        "$('div#2fb79e94-27ab-4bd8-b717-80872b010ff6').text('ETA:  0:00:00');"
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+        "<IPython.core.display.Javascript at 0x11922a890>"
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+       "javascript": [
+        "$('div#2fb79e94-27ab-4bd8-b717-80872b010ff6').text('ETA:  0:00:00');"
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+       "javascript": [
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+       "javascript": [
+        "$('div#2fb79e94-27ab-4bd8-b717-80872b010ff6').text('ETA:  0:00:00');"
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+      {
+       "javascript": [
+        "var element = document.getElementById('a09806b5-d88d-47ca-b939-649aafe3cda0');\n",
+        "                    var parent = element.parentNode\n",
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+       "javascript": [
+        "this.cleanProgressBar(['a09806b5-d88d-47ca-b939-649aafe3cda0', '688a7924-a0a3-4e7c-bb7e-526d70d9ca0b', '2fb79e94-27ab-4bd8-b717-80872b010ff6'])"
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+        "<IPython.core.display.Javascript at 0x11922aa90>"
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jBu+++y49e/Zk0aJFFBYW+jeLX/dW3c3A18ARoBTYAsywMY+IiK369OnD3Llz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\nX2ddAAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x118431ed0>"
+       ]
+      }
+     ],
+     "prompt_number": 9
     },
     {
-     "cell_type": "heading",
-     "level": 3,
+     "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Framed"
+      "Scan the optimal surface only"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "from framed.io_utils.sbml import load_sbml_model, GPR_CONSTRAINED\n",
-      "from framed.analysis.simulation import FBA, lMOMA\n",
-      "from framed.core.fixes import fix_bigg_model"
+      "result = diffFVA.run(surface_only=True,view=view)\n",
+      "\n",
+      "x = [elem[0][1] for elem in list(result.items)]\n",
+      "y = [elem[1][1] for elem in list(result.items)]\n",
+      "pyplot.plot(growth, lb, 'k', growth, ub, 'k')\n",
+      "pyplot.plot([biomass_rxn.lower_bound], [target.lower_bound], 'go')\n",
+      "pyplot.fill_between(growth, ub, color='gray')\n",
+      "pyplot.xlabel('Growth')\n",
+      "pyplot.ylabel('Production');\n",
+      "pyplot.plot(x,y, 'ro');"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "javascript": [
+        "//f6a94209-eae6-49ec-b708-aeed1ef07ca7\n",
+        "$(\"head\").append(\"<style> td.pb_widget {     width: auto; } td.pb_widget_fill {     width: 100%; } table.pb {     font-family: monospace;     border: 0;     margin: 0; } table.pb tr { border: 0; } table.pb td {     white-space: nowrap;     border: 0; } div.pb {     border: 1px solid #ddd;     border-radius: 3px; } div.pb_bar {     height: 1.5em; } </style>\")"
+       ],
+       "metadata": {},
+       "output_type": "display_data",
+       "text": [
+        "<IPython.core.display.Javascript at 0x119260d10>"
+       ]
+      },
+      {
+       "javascript": [
+        "\n",
+        "                  // f6a94209-eae6-49ec-b708-aeed1ef07ca7 -- used to remove this code blob in the end\n",
+        "                  IPython.OutputArea.prototype.cleanProgressBar = function(uuids) {\n",
+        "                      // filter by uuid-strings \n",
+        "                      var myfilter = function(output) { \n",
+        "                          var nuids = uuids.length;\n",
+        "                          for (var i=0; i<nuids; i++) {\n",
+        "                              if (output.hasOwnProperty('html')) {\n",
+        "                                  if (output.html.indexOf(uuids[i]) != -1) {\n",
+        "                                      return false;\n",
+        "                                  }\n",
+        "                              }\n",
+        "                              if (output.hasOwnProperty('javascript')) {\n",
+        "                                  if (output.javascript.indexOf(uuids[i]) != -1) {\n",
+        "                                      return false;\n",
+        "                                  }\n",
+        "                              }\n",
+        "                          }\n",
+        "                          // keep all others\n",
+        "                          return true;\n",
+        "                      };\n",
+        "\n",
+        "                      // Filter the ouputs\n",
+        "                      this.outputs = this.outputs.filter(myfilter);\n",
+        "                };\n",
+        "                "
+       ],
+       "metadata": {},
+       "output_type": "display_data",
+       "text": [
+        "<IPython.core.display.Javascript at 0x11925bdd0>"
+       ]
+      },
+      {
+       "html": [
+        "<div class=\"pb\" id=\"f6a94209-eae6-49ec-b708-aeed1ef07ca7\"><table class=\"pb ui-widget\"><tr>\n",
+        "<td class=\"pb_widget\">Scanning grid points </td>\n",
+        "<td class=\"pb_widget_fill\">\n",
+        "        <div class=\"pb_bar\" id=\"b1409d63-892c-4698-bcad-9bbd06e36dc0\"></div>\n",
+        "        <script type=\"text/javascript\">\n",
+        "            $(\"div#b1409d63-892c-4698-bcad-9bbd06e36dc0\").progressbar({value: 0, max: 8});\n",
+        "        </script>\n",
+        "        </td>\n",
+        "<td class=\"pb_widget\"> </td>\n",
+        "<td class=\"pb_widget\"><div id=\"01d9a422-c394-4759-bb05-4278d440e5ec\">ETA:  --:--:--</div></td>\n",
+        "</tr></table><div>"
+       ],
+       "metadata": {},
+       "output_type": "display_data",
+       "text": [
+        "<IPython.core.display.HTML at 0x119242290>"
+       ]
+      },
+      {
+       "javascript": [
+        "\n",
+        "        var $myPB = $(\"div#b1409d63-892c-4698-bcad-9bbd06e36dc0\")\n",
+        "        if ($myPB.hasClass('ui-progressbar')) {\n",
+        "            $myPB.progressbar('value', 1);\n",
+        "        } else {\n",
+        "            $myPB.progressbar({value: 0, max: 8});\n",
+        "        }\n",
+        "        "
+       ],
+       "metadata": {},
+       "output_type": "display_data",
+       "text": [
+        "<IPython.core.display.Javascript at 0x11922a7d0>"
+       ]
+      },
+      {
+       "javascript": [
+        "$('div#01d9a422-c394-4759-bb05-4278d440e5ec').text('ETA:  0:00:00');"
+       ],
+       "metadata": {},
+       "output_type": "display_data",
+       "text": [
+        "<IPython.core.display.Javascript at 0x11922a950>"
+       ]
+      },
+      {
+       "javascript": [
+        "\n",
+        "        var $myPB = $(\"div#b1409d63-892c-4698-bcad-9bbd06e36dc0\")\n",
+        "        if ($myPB.hasClass('ui-progressbar')) {\n",
+        "            $myPB.progressbar('value', 2);\n",
+        "        } else {\n",
+        "            $myPB.progressbar({value: 0, max: 8});\n",
+        "        }\n",
+        "        "
+       ],
+       "metadata": {},
+       "output_type": "display_data",
+       "text": [
+        "<IPython.core.display.Javascript at 0x11925bad0>"
+       ]
+      },
+      {
+       "javascript": [
+        "$('div#01d9a422-c394-4759-bb05-4278d440e5ec').text('ETA:  0:00:00');"
+       ],
+       "metadata": {},
+       "output_type": "display_data",
+       "text": [
+        "<IPython.core.display.Javascript at 0x11925b590>"
+       ]
+      },
+      {
+       "javascript": [
+        "\n",
+        "        var $myPB = $(\"div#b1409d63-892c-4698-bcad-9bbd06e36dc0\")\n",
+        "        if ($myPB.hasClass('ui-progressbar')) {\n",
+        "            $myPB.progressbar('value', 3);\n",
+        "        } else {\n",
+        "            $myPB.progressbar({value: 0, max: 8});\n",
+        "        }\n",
+        "        "
+       ],
+       "metadata": {},
+       "output_type": "display_data",
+       "text": [
+        "<IPython.core.display.Javascript at 0x11925b410>"
+       ]
+      },
+      {
+       "javascript": [
+        "$('div#01d9a422-c394-4759-bb05-4278d440e5ec').text('ETA:  0:00:00');"
+       ],
+       "metadata": {},
+       "output_type": "display_data",
+       "text": [
+        "<IPython.core.display.Javascript at 0x11925bbd0>"
+       ]
+      },
+      {
+       "javascript": [
+        "\n",
+        "        var $myPB = $(\"div#b1409d63-892c-4698-bcad-9bbd06e36dc0\")\n",
+        "        if ($myPB.hasClass('ui-progressbar')) {\n",
+        "            $myPB.progressbar('value', 4);\n",
+        "        } else {\n",
+        "            $myPB.progressbar({value: 0, max: 8});\n",
+        "        }\n",
+        "        "
+       ],
+       "metadata": {},
+       "output_type": "display_data",
+       "text": [
+        "<IPython.core.display.Javascript at 0x11925b690>"
+       ]
+      },
+      {
+       "javascript": [
+        "$('div#01d9a422-c394-4759-bb05-4278d440e5ec').text('ETA:  0:00:00');"
+       ],
+       "metadata": {},
+       "output_type": "display_data",
+       "text": [
+        "<IPython.core.display.Javascript at 0x11925b650>"
+       ]
+      },
+      {
+       "javascript": [
+        "\n",
+        "        var $myPB = $(\"div#b1409d63-892c-4698-bcad-9bbd06e36dc0\")\n",
+        "        if ($myPB.hasClass('ui-progressbar')) {\n",
+        "            $myPB.progressbar('value', 5);\n",
+        "        } else {\n",
+        "            $myPB.progressbar({value: 0, max: 8});\n",
+        "        }\n",
+        "        "
+       ],
+       "metadata": {},
+       "output_type": "display_data",
+       "text": [
+        "<IPython.core.display.Javascript at 0x11925b3d0>"
+       ]
+      },
+      {
+       "javascript": [
+        "$('div#01d9a422-c394-4759-bb05-4278d440e5ec').text('ETA:  0:00:00');"
+       ],
+       "metadata": {},
+       "output_type": "display_data",
+       "text": [
+        "<IPython.core.display.Javascript at 0x11925bad0>"
+       ]
+      },
+      {
+       "javascript": [
+        "\n",
+        "        var $myPB = $(\"div#b1409d63-892c-4698-bcad-9bbd06e36dc0\")\n",
+        "        if ($myPB.hasClass('ui-progressbar')) {\n",
+        "            $myPB.progressbar('value', 6);\n",
+        "        } else {\n",
+        "            $myPB.progressbar({value: 0, max: 8});\n",
+        "        }\n",
+        "        "
+       ],
+       "metadata": {},
+       "output_type": "display_data",
+       "text": [
+        "<IPython.core.display.Javascript at 0x11925b510>"
+       ]
+      },
+      {
+       "javascript": [
+        "$('div#01d9a422-c394-4759-bb05-4278d440e5ec').text('ETA:  0:00:00');"
+       ],
+       "metadata": {},
+       "output_type": "display_data",
+       "text": [
+        "<IPython.core.display.Javascript at 0x11925bbd0>"
+       ]
+      },
+      {
+       "javascript": [
+        "\n",
+        "        var $myPB = $(\"div#b1409d63-892c-4698-bcad-9bbd06e36dc0\")\n",
+        "        if ($myPB.hasClass('ui-progressbar')) {\n",
+        "            $myPB.progressbar('value', 7);\n",
+        "        } else {\n",
+        "            $myPB.progressbar({value: 0, max: 8});\n",
+        "        }\n",
+        "        "
+       ],
+       "metadata": {},
+       "output_type": "display_data",
+       "text": [
+        "<IPython.core.display.Javascript at 0x11925b410>"
+       ]
+      },
+      {
+       "javascript": [
+        "$('div#01d9a422-c394-4759-bb05-4278d440e5ec').text('ETA:  0:00:00');"
+       ],
+       "metadata": {},
+       "output_type": "display_data",
+       "text": [
+        "<IPython.core.display.Javascript at 0x11925bad0>"
+       ]
+      },
+      {
+       "javascript": [
+        "\n",
+        "        var $myPB = $(\"div#b1409d63-892c-4698-bcad-9bbd06e36dc0\")\n",
+        "        if ($myPB.hasClass('ui-progressbar')) {\n",
+        "            $myPB.progressbar('value', 8);\n",
+        "        } else {\n",
+        "            $myPB.progressbar({value: 0, max: 8});\n",
+        "        }\n",
+        "        "
+       ],
+       "metadata": {},
+       "output_type": "display_data",
+       "text": [
+        "<IPython.core.display.Javascript at 0x119242350>"
+       ]
+      },
+      {
+       "javascript": [
+        "$('div#01d9a422-c394-4759-bb05-4278d440e5ec').text('Time: 0:00:00');"
+       ],
+       "metadata": {},
+       "output_type": "display_data",
+       "text": [
+        "<IPython.core.display.Javascript at 0x11925bbd0>"
+       ]
+      },
+      {
+       "javascript": [
+        "var element = document.getElementById('f6a94209-eae6-49ec-b708-aeed1ef07ca7');\n",
+        "                    var parent = element.parentNode\n",
+        "                    parent.removeChild(element);\n",
+        "                    parent.parentElement.remove();"
+       ],
+       "metadata": {},
+       "output_type": "display_data",
+       "text": [
+        "<IPython.core.display.Javascript at 0x119242490>"
+       ]
+      },
+      {
+       "javascript": [
+        "this.cleanProgressBar(['f6a94209-eae6-49ec-b708-aeed1ef07ca7', 'b1409d63-892c-4698-bcad-9bbd06e36dc0', '01d9a422-c394-4759-bb05-4278d440e5ec'])"
+       ],
+       "metadata": {},
+       "output_type": "display_data",
+       "text": [
+        "<IPython.core.display.Javascript at 0x11925b510>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
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my1AiVnA4HMTHxxMfH09ZWRkul4uEhARGjx5Nbm4uKSkpVkcU8alLnVJEYCxa82uM5qGq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\niIiIiIiIiIiIiEhg+195E9esblWyPwAAAABJRU5ErkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x119242310>"
+       ]
+      }
+     ],
+     "prompt_number": 10
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "stuff = result[result.items[1]]\n",
+      "selection = stuff[abs(stuff.normalized_gaps) > .1].sort('normalized_gaps', ascending=False)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 25
+     "prompt_number": 13
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "model = load_sbml_model('data/EcoliCore.xml', GPR_CONSTRAINED)\n",
-      "fix_bigg_model(model)"
+      "Builder('iJO1366_central_metabolism', reaction_data=selection.normalized_gaps.to_dict()).display_in_notebook()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "\n",
+        "\n",
+        "\n",
+        "  <!-- These are not generally used within the notebook. -->\n",
+        "  <script src=\"\"></script>\n",
+        "  <script src=\"\"></script>\n",
+        "\n",
+        "  <link rel=\"stylesheet\" href=\"\">\n",
+        "  <link rel=\"stylesheet\" href=\"https://zakandrewking.github.io/escher/escher/css/builder.css\"/>\n",
+        "\n",
+        "\n",
+        "\n",
+        "  <div style=\"height: 500px; width: 100%;\" id=\"rwcpulewow\"></div>\n",
+        "\n",
+        "  <script>\n",
+        "   var map_data_rwcpulewow = {\"reactions\":{\"2075839\":{\"name\":\"malate dehydrogenase\",\"bigg_id\":\"MDH\",\"segments\":{\"675\":{\"to_node_id\":\"2076047\",\"b1\":null,\"b2\":null,\"from_node_id\":\"2076049\"},\"676\":{\"to_node_id\":\"2076048\",\"b1\":null,\"b2\":null,\"from_node_id\":\"2076047\"},\"677\":{\"to_node_id\":\"2076049\",\"b1\":{\"y\":3705.499283265863,\"x\":2978.858},\"b2\":{\"y\":3761.660924979759,\"x\":2978.858},\"from_node_id\":\"2075291\"},\"678\":{\"to_node_id\":\"2076049\",\"b1\":{\"y\":3746.535174805468,\"x\":2978.858},\"b2\":{\"y\":3773.9716924416402,\"x\":2978.858},\"from_node_id\":\"2075292\"},\"679\":{\"to_node_id\":\"2075293\",\"b1\":{\"y\":3996.3816703355687,\"x\":2981.219988485556},\"b2\":{\"y\":4035.2351011185624,\"x\":2982.0646282851862},\"from_node_id\":\"2076048\"},\"680\":{\"to_node_id\":\"2075294\",\"b1\":{\"y\":3992.897225740078,\"x\":2981.144239690002},\"b2\":{\"y\":4023.620285800259,\"x\":2981.812132300006},\"from_node_id\":\"2076048\"},\"681\":{\"to_node_id\":\"2075295\",\"b1\":{\"y\":4010.134540277312,\"x\":2981.518963919072},\"b2\":{\"y\":4081.0780009243736,\"x\":2983.0612130635736},\"from_node_id\":\"2076048\"}},\"reversibility\":true,\"metabolites\":{\"mal__L_c\":{\"coefficient\":-1},\"nadh_c\":{\"coefficient\":1},\"oaa_c\":{\"coefficient\":1},\"h_c\":{\"coefficient\":1},\"nad_c\":{\"coefficient\":-1}},\"label_x\":2988.858,\"label_y\":3877.7302},\"2075840\":{\"name\":\"nitrite Reductase (Ubiquinole-8, periplasm)\",\"bigg_id\":\"NTRIR3pp\",\"segments\":{\"682\":{\"to_node_id\":\"2076051\",\"b1\":null,\"b2\":null,\"from_node_id\":\"2076052\"},\"683\":{\"to_node_id\":\"2076050\",\"b1\":null,\"b2\":null,\"from_node_id\":\"2076051\"},\"684\":{\"to_node_id\":\"2076052\",\"b1\":{\"y\":513.4923755681662,\"x\":8256.857},\"b2\":{\"y\":889.5588526704498,\"x\":8256.857},\"from_node_id\":\"2075296\"},\"685\":{\"to_node_id\":\"2076052\",\"b1\":{\"y\":985.4308891307419,\"x\":8256.857},\"b2\":{\"y\":1031.1404067392225,\"x\":8256.857},\"from_node_id\":\"2075297\"},\"686\":{\"to_node_id\":\"2076052\",\"b1\":{\"y\":1016.7596244970739,\"x\":8256.857},\"b2\":{\"y\":1040.539027349122,\"x\":8256.857},\"from_node_id\":\"2075298\"},\"687\":{\"to_node_id\":\"2075299\",\"b1\":{\"y\":1167.9586648184336,\"x\":8256.857},\"b2\":{\"y\":1208.158416061445,\"x\":8256.857},\"from_node_id\":\"2076050\"},\"688\":{\"to_node_id\":\"2075300\",\"b1\":{\"y\":1379.1885212536206,\"x\":8256.857},\"b2\":{\"y\":1912.2579375120686,\"x\":8256.857},\"from_node_id\":\"2076050\"},\"689\":{\"to_node_id\":\"2075301\",\"b1\":{\"y\":1175.017233577611,\"x\":8256.857},\"b2\":{\"y\":1231.6869785920364,\"x\":8256.857},\"from_node_id\":\"2076050\"}},\"reversibility\":false,\"metabolites\":{\"h_p\":{\"coefficient\":-2},\"q8h2_c\":{\"coefficient\":-3},\"no2_p\":{\"coefficient\":-1},\"nh4_p\":{\"coefficient\":1},\"h2o_p\":{\"coefficient\":2},\"q8_c\":{\"coefficient\":3}},\"label_x\":8266.857,\"label_y\":1092.7302},\"2075841\":{\"name\":\"NADPH Quinone Reductase 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Reduction/Oxidation\",\"x\":6746.477140625,\"y\":543.5142576171875},\"2076590\":{\"text\":\"2\",\"x\":8146.858,\"y\":3584.7302},\"2076592\":{\"text\":\"2\",\"x\":4405.858,\"y\":2752.7302},\"2076593\":{\"text\":\"2\",\"x\":3220.988615234375,\"y\":655.6848508789062},\"2076594\":{\"text\":\"3\",\"x\":4886.858,\"y\":2881.7302},\"2076595\":{\"text\":\"3\",\"x\":4399.858,\"y\":2885.7302},\"2076596\":{\"text\":\"3\",\"x\":4874.858,\"y\":3207.7302},\"2076597\":{\"text\":\"2\",\"x\":680.8579,\"y\":1768.7302},\"2076599\":{\"text\":\"4\",\"x\":8116.858,\"y\":1174.7302},\"2076600\":{\"text\":\"2\",\"x\":4875.858,\"y\":2969.7302},\"2076601\":{\"text\":\"ATP Maintenance\",\"x\":4899.62899609375,\"y\":2283.036840625},\"2076602\":{\"text\":\"Oxidative Stress\",\"x\":5390.779875,\"y\":4803.884296875},\"2076603\":{\"text\":\"0.5\",\"x\":7766.858,\"y\":1454.7302},\"2076604\":{\"text\":\"2\",\"x\":4876.858,\"y\":2748.7302},\"2076606\":{\"text\":\"2\",\"x\":8148.858,\"y\":3214.7302}},\"canvas\":{\"x\":-286.3920445,\"y\":132.78023100000001,\"width\":9047.498999,\"height\":6642.899758}};var cobra_model_rwcpulewow = null;var reaction_data_rwcpulewow = {\"EX_succ_LPAREN_e_RPAREN_\": 139.55190733868156, \"MALS\": 26.897721772180294, \"GLCpts\": 92.98407726822451, \"SUCCt3\": 28.264122764219152, \"FUM\": 100.59356156340662, \"NADH16\": 123.6856188610371, \"PPC\": 19.973710991947399, \"MDH\": 33.263679219742528, \"Biomass_Ecoli_core_N_LPAREN_w_FSLASH_GAM_RPAREN__Nmet2\": 0.23047445418099433, \"PGM\": 183.06014453645096, \"PGK\": 184.10734453645097, \"PGI\": 92.840577268232792, \"RPE\": -0.17172083937173166, \"ATPM\": 78.013640828040337, \"PIt2r\": 0.84784637459500267, \"GAPD\": 184.10734453645085, \"ICL\": 26.897721772180294, \"CYTBD\": 15.592307869199701, \"TPI\": 92.287787268227262, \"ENO\": 183.06014453645048, \"ACONTa\": 27.652951772237007, \"ACONTb\": 27.652951772237007, \"PDH\": 57.174133544635836, \"CS\": 27.652951772237017, \"CO2t\": 21.93530980044833, \"H2Ot\": 87.920245316706826, \"TKT2\": -0.12898174899286352, \"O2t\": 7.796153934878852, \"FBA\": 92.287787268226026, \"PFK\": 2.1622672684155759};var metabolite_data_rwcpulewow = null;var css_string_rwcpulewow = '#mouse-node {     fill: none; } #canvas {     stroke: #ccc;     stroke-width: 7px;     fill: white; } .resize-rect {     fill: black;     opacity: 0;     stroke: none; } .label {     font-family: sans-serif;      font-style: italic;      font-weight: bold;      font-size: 8px;      fill: black;      stroke: none;     text-rendering: optimizelegibility; } .reaction-label {     fill: rgb(32, 32, 120);     text-rendering: optimizelegibility; } .text-label {     font-size: 50px; } .node-circle {     stroke-width: 2px;     fill: none;\t     stroke: rgb(50, 50, 50); } .selected .node-circle{     stroke-width: 3px;     stroke: rgb(20, 113, 199); } .selected.text-label {     fill: rgb(20, 113, 199); } .metabolite-circle {     stroke: rgb(162, 69, 16); } .selected .metabolite-circle {     stroke: rgb(5, 2, 0); } .segment {     stroke: grey;     stroke-width: 3px;     fill: none; } .membrane {     fill: none;     stroke: rgb(255, 187, 0); } .brush .extent {      fill-opacity: 0.1;     fill: black;     stroke: #fff;     shape-rendering: crispEdges; } .bezier {     fill: rgb(255,255,255);\t } .bezier1 {     stroke: red; } .bezier2 {     stroke: blue; } .connect-line{      stroke: rgb(200,200,200); } .direction-arrow {     stroke: black;     stroke-width: 1px;     fill: white;     opacity: 0.3; } .start-reaction-cursor {     cursor: pointer; } .start-reaction-target {     stroke: rgb(100,100,100);     fill: none;     opacity: 0.5; } .rotation-center-line {     stroke: red;     stroke-width: 2px; } .highlight {     fill: #106F94; } .cursor-grab {     cursor: grab;     cursor: -webkit-grab; } .cursor-grabbing {     cursor: grabbing;     cursor: -webkit-grabbing; }';\n",
+        "  </script>\n",
+        "  <script>\n",
+        "   function escher_load_lib(url, callback){\n",
+        "     var s = document.createElement('script');\n",
+        "     s.src = url;\n",
+        "     s.async = true;\n",
+        "     s.onreadystatechange = s.onload = callback;\n",
+        "     s.onerror = function() { console.warn(\"failed to load library \" + url); };\n",
+        "     document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+        "   }\n",
+        "\n",
+        "   if (typeof define === \"function\" && define.amd) {\n",
+        "     // require.js is available: use it to load d3 and escher\n",
+        "     \n",
+        "     require.config({paths: { d3: \"//cdnjs.cloudflare.com/ajax/libs/d3/3.4.8/d3.min\",\n",
+        "\t\t\t      escher: \"https://zakandrewking.github.io/escher/escher.1.0.0b2.min\" }});\n",
+        "     require([\"d3\", \"escher\"], function(d3, escher) {\n",
+        "       window.d3 = d3;\n",
+        "       escher.Builder({ selection: d3.select('#rwcpulewow'),enable_editing: false,menu: \"zoom\",enable_keys: false,scroll_behavior: \"none\",fill_screen: false,map: map_data_rwcpulewow,cobra_model: cobra_model_rwcpulewow,auto_set_data_domain: true,reaction_data: reaction_data_rwcpulewow,metabolite_data: metabolite_data_rwcpulewow,css: css_string_rwcpulewow,});\n",
+        "     });\n",
+        "     \n",
+        "   } else {\n",
+        "     if (window.escher !== undefined && window.d3 !== undefined) {\n",
+        "       // already loaded: just create the figure\n",
+        "       !function(escher, d3) {\n",
+        "\t escher.Builder({ selection: d3.select('#rwcpulewow'),enable_editing: false,menu: \"zoom\",enable_keys: false,scroll_behavior: \"none\",fill_screen: false,map: map_data_rwcpulewow,cobra_model: cobra_model_rwcpulewow,auto_set_data_domain: true,reaction_data: reaction_data_rwcpulewow,metabolite_data: metabolite_data_rwcpulewow,css: css_string_rwcpulewow,});\n",
+        "       }(window.escher, window.d3);\n",
+        "     } else {\n",
+        "       // require.js not available: dynamically load d3 & escher\n",
+        "       escher_load_lib(\"//cdnjs.cloudflare.com/ajax/libs/d3/3.4.8/d3.min.js\", function() {\n",
+        "\t escher_load_lib(\"https://zakandrewking.github.io/escher/escher.1.0.0b2.min.js\", function() {\n",
+        "\t   escher.Builder({ selection: d3.select('#rwcpulewow'),enable_editing: false,menu: \"zoom\",enable_keys: false,scroll_behavior: \"none\",fill_screen: false,map: map_data_rwcpulewow,cobra_model: cobra_model_rwcpulewow,auto_set_data_domain: true,reaction_data: reaction_data_rwcpulewow,metabolite_data: metabolite_data_rwcpulewow,css: css_string_rwcpulewow,});\n",
+        "\t })\n",
+        "       });\n",
+        "     }\n",
+        "   }\n",
+        "  </script>\n"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 56,
+       "text": [
+        "<IPython.core.display.HTML at 0x1150dcd50>"
+       ]
+      }
      ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 26
+     "prompt_number": 56
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "sol = FBA(model)"
-     ],
-     "language": "python",
+     "cell_type": "markdown",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 27
+     "source": [
+      "Per default, only solution that improve upon the reference production will be scanned. Set `improvements_only=False` if you want to scan the whole envelope."
+     ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print sol.status"
+      "result = diffFVA.run(improvements_only=False, view=view)\n",
+      "x = [elem[0][1] for elem in list(result.items)]\n",
+      "y = [elem[1][1] for elem in list(result.items)]\n",
+      "pyplot.plot(growth, lb, 'k', growth, ub, 'k')\n",
+      "pyplot.plot([biomass_rxn.lower_bound], [target.lower_bound], 'go')\n",
+      "pyplot.fill_between(growth, ub, color='gray')\n",
+      "pyplot.xlabel('Growth')\n",
+      "pyplot.ylabel('Production')\n",
+      "pyplot.plot(x,y, 'ro');"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
-       "output_type": "stream",
-       "stream": "stdout",
+       "javascript": [
+        "//2833ae73-cf02-4264-a005-29252a8f9c12\n",
+        "$(\"head\").append(\"<style> td.pb_widget {     width: auto; } td.pb_widget_fill {     width: 100%; } table.pb {     font-family: monospace;     border: 0;     margin: 0; } table.pb tr { border: 0; } table.pb td {     white-space: nowrap;     border: 0; } div.pb {     border: 1px solid #ddd;     border-radius: 3px; } div.pb_bar {     height: 1.5em; } </style>\")"
+       ],
+       "metadata": {},
+       "output_type": "display_data",
+       "text": [
+        "<IPython.core.display.Javascript at 0x1192f8f90>"
+       ]
+      },
+      {
+       "javascript": [
+        "\n",
+        "                  // 2833ae73-cf02-4264-a005-29252a8f9c12 -- used to remove this code blob in the end\n",
+        "                  IPython.OutputArea.prototype.cleanProgressBar = function(uuids) {\n",
+        "                      // filter by uuid-strings \n",
+        "                      var myfilter = function(output) { \n",
+        "                          var nuids = uuids.length;\n",
+        "                          for (var i=0; i<nuids; i++) {\n",
+        "                              if (output.hasOwnProperty('html')) {\n",
+        "                                  if (output.html.indexOf(uuids[i]) != -1) {\n",
+        "                                      return false;\n",
+        "                                  }\n",
+        "                              }\n",
+        "                              if (output.hasOwnProperty('javascript')) {\n",
+        "                                  if (output.javascript.indexOf(uuids[i]) != -1) {\n",
+        "                                      return false;\n",
+        "                                  }\n",
+        "                              }\n",
+        "                          }\n",
+        "                          // keep all others\n",
+        "                          return true;\n",
+        "                      };\n",
+        "\n",
+        "                      // Filter the ouputs\n",
+        "                      this.outputs = this.outputs.filter(myfilter);\n",
+        "                };\n",
+        "                "
+       ],
+       "metadata": {},
+       "output_type": "display_data",
+       "text": [
+        "<IPython.core.display.Javascript at 0x1192f8ad0>"
+       ]
+      },
+      {
+       "html": [
+        "<div class=\"pb\" id=\"2833ae73-cf02-4264-a005-29252a8f9c12\"><table class=\"pb ui-widget\"><tr>\n",
+        "<td class=\"pb_widget\">Scanning grid points </td>\n",
+        "<td class=\"pb_widget_fill\">\n",
+        "        <div class=\"pb_bar\" id=\"14a499b2-5a02-4671-925e-42dbd1042051\"></div>\n",
+        "        <script type=\"text/javascript\">\n",
+        "            $(\"div#14a499b2-5a02-4671-925e-42dbd1042051\").progressbar({value: 0, max: 63});\n",
+        "        </script>\n",
+        "        </td>\n",
+        "<td class=\"pb_widget\"> </td>\n",
+        "<td class=\"pb_widget\"><div id=\"0494b647-af4b-43f4-b062-73052fd9a130\">ETA:  --:--:--</div></td>\n",
+        "</tr></table><div>"
+       ],
+       "metadata": {},
+       "output_type": "display_data",
+       "text": [
+        "<IPython.core.display.HTML at 0x119300310>"
+       ]
+      },
+      {
+       "javascript": [
+        "\n",
+        "        var $myPB = $(\"div#14a499b2-5a02-4671-925e-42dbd1042051\")\n",
+        "        if ($myPB.hasClass('ui-progressbar')) {\n",
+        "            $myPB.progressbar('value', 1);\n",
+        "        } else {\n",
+        "            $myPB.progressbar({value: 0, max: 63});\n",
+        "        }\n",
+        "        "
+       ],
+       "metadata": {},
+       "output_type": "display_data",
+       "text": [
+        "<IPython.core.display.Javascript at 0x119300810>"
+       ]
+      },
+      {
+       "javascript": [
+        "$('div#0494b647-af4b-43f4-b062-73052fd9a130').text('ETA:  0:00:08');"
+       ],
+       "metadata": {},
+       "output_type": "display_data",
+       "text": [
+        "<IPython.core.display.Javascript at 0x119300890>"
+       ]
+      },
+      {
+       "javascript": [
+        "\n",
+        "        var $myPB = $(\"div#14a499b2-5a02-4671-925e-42dbd1042051\")\n",
+        "        if ($myPB.hasClass('ui-progressbar')) {\n",
+        "            $myPB.progressbar('value', 2);\n",
+        "        } else {\n",
+        "            $myPB.progressbar({value: 0, max: 63});\n",
+        "        }\n",
+        "        "
+       ],
+       "metadata": {},
+       "output_type": "display_data",
+       "text": [
+        "<IPython.core.display.Javascript at 0x1192f83d0>"
+       ]
+      },
+      {
+       "javascript": [
+        "$('div#0494b647-af4b-43f4-b062-73052fd9a130').text('ETA:  0:00:07');"
+       ],
+       "metadata": {},
+       "output_type": "display_data",
+       "text": [
+        "<IPython.core.display.Javascript at 0x1192f8350>"
+       ]
+      },
+      {
+       "javascript": [
+        "\n",
+        "        var $myPB = $(\"div#14a499b2-5a02-4671-925e-42dbd1042051\")\n",
+        "        if ($myPB.hasClass('ui-progressbar')) {\n",
+        "            $myPB.progressbar('value', 3);\n",
+        "        } else {\n",
+        "            $myPB.progressbar({value: 0, max: 63});\n",
+        "        }\n",
+        "        "
+       ],
+       "metadata": {},
+       "output_type": "display_data",
+       "text": [
+        "<IPython.core.display.Javascript at 0x1192f8210>"
+       ]
+      },
+      {
+       "javascript": [
+        "$('div#0494b647-af4b-43f4-b062-73052fd9a130').text('ETA:  0:00:07');"
+       ],
+       "metadata": {},
+       "output_type": "display_data",
+       "text": [
+        "<IPython.core.display.Javascript at 0x1192f8b50>"
+       ]
+      },
+      {
+       "javascript": [
+        "\n",
+        "        var $myPB = $(\"div#14a499b2-5a02-4671-925e-42dbd1042051\")\n",
+        "        if ($myPB.hasClass('ui-progressbar')) {\n",
+        "            $myPB.progressbar('value', 4);\n",
+        "        } else {\n",
+        "            $myPB.progressbar({value: 0, max: 63});\n",
+        "        }\n",
+        "        "
+       ],
+       "metadata": {},
+       "output_type": "display_data",
+       "text": [
+        "<IPython.core.display.Javascript at 0x1192f8150>"
+       ]
+      },
+      {
+       "javascript": [
+        "$('div#0494b647-af4b-43f4-b062-73052fd9a130').text('ETA:  0:00:07');"
+       ],
+       "metadata": {},
+       "output_type": "display_data",
+       "text": [
+        "<IPython.core.display.Javascript at 0x1192f8950>"
+       ]
+      },
+      {
+       "javascript": [
+        "\n",
+        "        var $myPB = $(\"div#14a499b2-5a02-4671-925e-42dbd1042051\")\n",
+        "        if ($myPB.hasClass('ui-progressbar')) {\n",
+        "            $myPB.progressbar('value', 5);\n",
+        "        } else {\n",
+        "            $myPB.progressbar({value: 0, max: 63});\n",
+        "        }\n",
+        "        "
+       ],
+       "metadata": {},
+       "output_type": "display_data",
+       "text": [
+        "<IPython.core.display.Javascript at 0x1192f8c90>"
+       ]
+      },
+      {
+       "javascript": [
+        "$('div#0494b647-af4b-43f4-b062-73052fd9a130').text('ETA:  0:00:06');"
+       ],
+       "metadata": {},
+       "output_type": "display_data",
+       "text": [
+        "<IPython.core.display.Javascript at 0x1192f8a10>"
+       ]
+      },
+      {
+       "javascript": [
+        "\n",
+        "        var $myPB = $(\"div#14a499b2-5a02-4671-925e-42dbd1042051\")\n",
+        "        if ($myPB.hasClass('ui-progressbar')) {\n",
+        "            $myPB.progressbar('value', 6);\n",
+        "        } else {\n",
+        "            $myPB.progressbar({value: 0, max: 63});\n",
+        "        }\n",
+        "        "
+       ],
+       "metadata": {},
+       "output_type": "display_data",
+       "text": [
+        "<IPython.core.display.Javascript at 0x1192f8210>"
+       ]
+      },
+      {
+       "javascript": [
+        "$('div#0494b647-af4b-43f4-b062-73052fd9a130').text('ETA:  0:00:07');"
+       ],
+       "metadata": {},
+       "output_type": "display_data",
+       "text": [
+        "<IPython.core.display.Javascript at 0x1192f8b10>"
+       ]
+      },
+      {
+       "javascript": [
+        "\n",
+        "        var $myPB = $(\"div#14a499b2-5a02-4671-925e-42dbd1042051\")\n",
+        "        if ($myPB.hasClass('ui-progressbar')) {\n",
+        "            $myPB.progressbar('value', 7);\n",
+        "        } else {\n",
+        "            $myPB.progressbar({value: 0, max: 63});\n",
+        "        }\n",
+        "        "
+       ],
+       "metadata": {},
+       "output_type": "display_data",
+       "text": [
+        "<IPython.core.display.Javascript at 0x1192f8c50>"
+       ]
+      },
+      {
+       "javascript": [
+        "$('div#0494b647-af4b-43f4-b062-73052fd9a130').text('ETA:  0:00:06');"
+       ],
+       "metadata": {},
+       "output_type": "display_data",
+       "text": [
+        "<IPython.core.display.Javascript at 0x1192f8e50>"
+       ]
+      },
+      {
+       "javascript": [
+        "\n",
+        "        var $myPB = $(\"div#14a499b2-5a02-4671-925e-42dbd1042051\")\n",
+        "        if ($myPB.hasClass('ui-progressbar')) {\n",
+        "            $myPB.progressbar('value', 8);\n",
+        "        } else {\n",
+        "            $myPB.progressbar({value: 0, max: 63});\n",
+        "        }\n",
+        "        "
+       ],
+       "metadata": {},
+       "output_type": "display_data",
+       "text": [
+        "<IPython.core.display.Javascript at 0x1192f8c90>"
+       ]
+      },
+      {
+       "javascript": [
+        "$('div#0494b647-af4b-43f4-b062-73052fd9a130').text('ETA:  0:00:06');"
+       ],
+       "metadata": {},
+       "output_type": "display_data",
+       "text": [
+        "<IPython.core.display.Javascript at 0x1192f8b50>"
+       ]
+      },
+      {
+       "javascript": [
+        "\n",
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+nsLCQnJzc0lISGDkyJE89dRTXHPNNXZHrJL2EEREgig2Npbx48eTkZHB8ePH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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x1193006d0>"
        ]
       }
      ],
-     "prompt_number": 29
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
+     "prompt_number": 14
     }
    ],
    "metadata": {}
diff --git a/requirements_rtd.txt b/requirements_rtd.txt
index 921f945e9..3eabb388a 100644
--- a/requirements_rtd.txt
+++ b/requirements_rtd.txt
@@ -2,3 +2,4 @@ sphinx-rtd-theme>=0.1.6
 numpydoc>=0.5
 cobra>=0.3.0b3
 mock>=1.0.1
+sphinxcontrib-napoleon>=0.2.8