diff --git a/cameo/util.py b/cameo/util.py index cd8db260f..f5b41f5bc 100644 --- a/cameo/util.py +++ b/cameo/util.py @@ -285,5 +285,6 @@ def in_ipnb(): elif 'notebook' in front_end.lower(): return True except Exception as e: + logger.debug("Cannot determine if running a notebook because of %s" % e) return False return False diff --git a/examples/DifferentialFVA.ipynb b/examples/DifferentialFVA.ipynb index a7aa9de7f..c96caabcd 100644 --- a/examples/DifferentialFVA.ipynb +++ b/examples/DifferentialFVA.ipynb @@ -1,48 +1,41 @@ { - "metadata": { - "name": "", - "signature": "sha256:8bcb057d1d21d77b1511237b62309b339c05328433bc22a618e2ae3d48e5af01" - }, - "nbformat": 3, - "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", - "level": 2, "metadata": {}, + "level": 2, "source": [ "Differential flux variability analysis" ] }, { "cell_type": "code", - "collapsed": false, + "metadata": {}, + "outputs": [], "input": [ "%matplotlib inline" ], "language": "python", - "metadata": {}, - "outputs": [], "prompt_number": 1 }, { "cell_type": "code", - "collapsed": false, + "metadata": {}, + "outputs": [], "input": [ "from IPython.html.widgets import interact, interactive, fixed\n", "from IPython.html import widgets\n", "from IPython.display import clear_output, display, HTML" ], "language": "python", - "metadata": {}, - "outputs": [], "prompt_number": 66 }, { "cell_type": "code", - "collapsed": false, + "metadata": {}, + "outputs": [], "input": [ "from matplotlib import pyplot\n", "import escher\n", @@ -50,13 +43,12 @@ "from pandas import merge" ], "language": "python", - "metadata": {}, - "outputs": [], "prompt_number": 27 }, { "cell_type": "code", - "collapsed": false, + "metadata": {}, + "outputs": [], "input": [ "from cameo import load_model\n", "from cameo.flux_analysis.analysis import flux_variability_analysis, phenotypic_phase_plane\n", @@ -64,13 +56,12 @@ "from cameo.parallel import SequentialView" ], "language": "python", - "metadata": {}, - "outputs": [], "prompt_number": 3 }, { "cell_type": "code", - "collapsed": false, + "metadata": {}, + "outputs": [], "input": [ "try:\n", " from IPython.parallel import Client\n", @@ -82,14 +73,12 @@ " view = SequentialView()" ], "language": "python", - "metadata": {}, - "outputs": [], "prompt_number": 4 }, { "cell_type": "heading", - "level": 3, "metadata": {}, + "level": 3, "source": [ "E. coli core model and succinate production" ] @@ -103,13 +92,12 @@ }, { "cell_type": "code", - "collapsed": false, + "metadata": {}, + "outputs": [], "input": [ "model = load_model('../tests/data/EcoliCore.xml')" ], "language": "python", - "metadata": {}, - "outputs": [], "prompt_number": 6 }, { @@ -121,7 +109,17 @@ }, { "cell_type": "code", - "collapsed": false, + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "png": 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p2eLN1A7zbaUP8E1IMlVagPk/1wTTpA4q0ymYJjaY/t5zMM1uP/vv48kVe/B4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\nERERERERERERERERERERSW3/D7AYlC/VQisVAAAAAElFTkSuQmCC\n", + "text": [ + "" + ], + "metadata": {} + } + ], "input": [ "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", @@ -134,17 +132,6 @@ "pyplot.ylabel('Production');" ], "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], "prompt_number": 7 }, { @@ -156,7 +143,8 @@ }, { "cell_type": "code", - "collapsed": false, + "metadata": {}, + "outputs": [], "input": [ "reference_model = model.copy()\n", "biomass_rxn = reference_model.reactions.Biomass_Ecoli_core_N_LPAREN_w_FSLASH_GAM_RPAREN__Nmet2\n", @@ -165,8 +153,6 @@ "target.lower_bound = 2" ], "language": "python", - "metadata": {}, - "outputs": [], "prompt_number": 8 }, { @@ -178,7 +164,8 @@ }, { "cell_type": "code", - "collapsed": false, + "metadata": {}, + "outputs": [], "input": [ "diffFVA = DifferentialFVA(design_space_model=model,\n", " reference_model=reference_model,\n", @@ -188,8 +175,6 @@ " points=10)" ], "language": "python", - "metadata": {}, - "outputs": [], "prompt_number": 9 }, { @@ -201,68 +186,16 @@ }, { "cell_type": "code", - "collapsed": false, - "input": [ - "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", - 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Scanning grid points
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"text": [ "" - ] + ], + "metadata": {} } ], + "input": [ + "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", "prompt_number": 13 }, { "cell_type": "code", - "collapsed": false, - "input": [ - "type(result)" - ], - "language": "python", "metadata": {}, "outputs": [ { - "metadata": {}, "output_type": "pyout", "prompt_number": 17, "text": [ "pandas.core.panel.Panel" - ] + ], + "metadata": {} } ], + "input": [ + "type(result)" + ], + "language": "python", "prompt_number": 17 }, { @@ -1317,69 +487,16 @@ }, { "cell_type": "code", - "collapsed": false, - "input": [ - "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": [ - "//7bbcb038-08b4-4977-b508-1a4e8e60a28d\n", - "$(\"head\").append(\"\")" - ], - "metadata": {}, - "output_type": "display_data", - "text": [ - "" - ] + "metadata": {} }, { - "javascript": [ - "\n", - " // 7bbcb038-08b4-4977-b508-1a4e8e60a28d -- 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" - ] + "metadata": {} }, { + "output_type": "pyout", "html": [ "
\n", "\n", @@ -1393,283 +510,105 @@ "\n", "
Scanning grid points
ETA: --:--:--
" ], - "metadata": {}, - "output_type": "display_data", "text": [ "" - ] + ], + "metadata": {} }, { - "javascript": [ - "\n", - " var $myPB = $(\"div#e59b5e6c-7ebc-4b33-ad86-7fc8f2525851\")\n", - " if ($myPB.hasClass('ui-progressbar')) {\n", - " $myPB.progressbar('value', 1);\n", - " } else {\n", - " $myPB.progressbar({value: 0, max: 8});\n", - " }\n", - " " - ], - "metadata": {}, - "output_type": "display_data", - "text": [ - "" - ] + "metadata": {} }, { - "javascript": [ - "$('div#13b226a4-cb2c-4d6a-9dad-e7575919af98').text('ETA: 0:00:00');" - ], - "metadata": {}, - "output_type": "display_data", - "text": [ - "" - ] + "metadata": {} }, { - "javascript": [ - "\n", - " var $myPB = $(\"div#e59b5e6c-7ebc-4b33-ad86-7fc8f2525851\")\n", - " if ($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": [ - "" - ] + "metadata": 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"text": [ "" - ] + ], + "metadata": {} } ], + "input": [ + "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", "prompt_number": 27 }, { "cell_type": "code", - "collapsed": false, + "metadata": {}, + "outputs": [], "input": [ "df = result[result.items[5]]" ], "language": "python", - "metadata": {}, - "outputs": [], "prompt_number": 30 }, { "cell_type": "code", - "collapsed": false, - "input": [ - "df.join(diffFVA.reference_flux_ranges, rsuffix='_ref', lsuffix='').query('suddenly_essential == True').sort('normalized_gaps', ascending=False)" - ], - "language": "python", "metadata": {}, "outputs": [ { + "output_type": "pyout", "html": [ "
\n", "\n", @@ -2340,9 +1279,6 @@ "
\n", "
" ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 41, "text": [ " lower_bound upper_bound gaps normalized_gaps KO \\\n", "TPI 8.480974 8.480974 0 7.766516 False \n", @@ -2455,21 +1391,22 @@ "NADTRHD False True 0.000000e+00 207.734735 \n", "THD2 False True 0.000000e+00 180.251040 \n", "PYK False True 0.000000e+00 98.723500 " - ] + ], + "metadata": {} } ], + "input": [ + "df.join(diffFVA.reference_flux_ranges, rsuffix='_ref', lsuffix='').query('suddenly_essential == True').sort('normalized_gaps', ascending=False)" + ], + "language": "python", "prompt_number": 41 }, { "cell_type": "code", - "collapsed": false, - "input": [ - "df.join(diffFVA.reference_flux_ranges, rsuffix='_ref', lsuffix='').query('normalized_gaps > 0.1 | normalized_gaps < -0.1').sort('normalized_gaps')" - ], - "language": "python", "metadata": {}, "outputs": [ { + "output_type": "pyout", "html": [ "
\n", "\n", @@ -2636,9 +1573,6 @@ "
\n", "
" ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 31, "text": [ " lower_bound \\\n", "Biomass_Ecoli_core_N_LPAREN_w_FSLASH_GAM_RPAREN__Nmet2 0.4855119 \n", @@ -2737,22 +1671,26 @@ "PGM -5.313260 \n", "GAPD 19.202510 \n", "PGK -5.762060 " - ] + ], + "metadata": {} } ], + "input": [ + "df.join(diffFVA.reference_flux_ranges, rsuffix='_ref', lsuffix='').query('normalized_gaps > 0.1 | normalized_gaps < -0.1').sort('normalized_gaps')" + ], + "language": "python", "prompt_number": 31 }, { "cell_type": "code", - "collapsed": false, + "metadata": {}, + "outputs": [], "input": [ "stuff = result[result.items[1]]\n", "selection = stuff[abs(stuff.normalized_gaps) > .1].sort('normalized_gaps', ascending=False)\n", "Builder('iJO1366_central_metabolism', reaction_data=selection.normalized_gaps.to_dict()).display_in_notebook()" ], "language": "python", - "metadata": {}, - "outputs": [], "prompt_number": 13 }, { @@ -2764,68 +1702,16 @@ }, { "cell_type": "code", - "collapsed": false, - "input": [ - "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": [ { - "javascript": [ - "//2833ae73-cf02-4264-a005-29252a8f9c12\n", - "$(\"head\").append(\"\")" - ], - "metadata": {}, - "output_type": "display_data", - "text": [ - "" - ] + "metadata": {} }, { - "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" - ] + "metadata": {} }, { + "output_type": "pyout", "html": [ "
\n", "\n", @@ -2839,1761 +1725,441 @@ "\n", "
Scanning grid points
ETA: --:--:--
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"text": [ "" - ] + ], + "metadata": {} } ], + "input": [ + "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", "prompt_number": 14 }, { "cell_type": "code", - "collapsed": false, + "metadata": {}, + "outputs": [], "input": [ "model = load_model('../tests/data/iJO1366.xml')" ], "language": "python", - "metadata": {}, - "outputs": [], "prompt_number": 5 }, { "cell_type": "code", - "collapsed": false, + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "png": 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+ "text": [ + "" + ], + "metadata": {} + } + ], "input": [ "production_envelope = phenotypic_phase_plane(model, [model.reactions.Ec_biomass_iJO1366_core_53p95M], model.reactions.EX_trp_DASH_L_LPAREN_e_RPAREN_)\n", "growth = production_envelope.Ec_biomass_iJO1366_core_53p95M\n", @@ -4606,22 +2172,12 @@ "pyplot.ylabel('Production');" ], "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], "prompt_number": 6 }, { "cell_type": "code", - "collapsed": false, + "metadata": {}, + "outputs": [], "input": [ "reference_model = model.copy()\n", "biomass_rxn = reference_model.reactions.Ec_biomass_iJO1366_core_53p95M\n", @@ -4630,13 +2186,12 @@ "target.lower_bound = 2" ], "language": "python", - "metadata": {}, - "outputs": [], "prompt_number": 7 }, { "cell_type": "code", - "collapsed": false, + "metadata": {}, + "outputs": [], "input": [ "diffFVA = DifferentialFVA(design_space_model=model,\n", " reference_model=reference_model,\n", @@ -4646,66 +2201,20 @@ " points=10)" ], "language": "python", - "metadata": {}, - "outputs": [], "prompt_number": 13 }, { "cell_type": "code", - "collapsed": false, - "input": [ - "result = diffFVA.run(surface_only=True,view=view)" - ], - "language": "python", "metadata": {}, "outputs": [ { - "javascript": [ - "//d89de86a-5cda-4bfd-82cf-93a66345b6ae\n", - "$(\"head\").append(\"\")" - ], - "metadata": {}, - "output_type": "display_data", - "text": [ - "" - ] + "metadata": {} }, { - "javascript": [ - "\n", - " // d89de86a-5cda-4bfd-82cf-93a66345b6ae -- 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" - ] + "metadata": {} }, { + "output_type": "pyout", "html": [ "
\n", "\n", @@ -4719,232 +2228,86 @@ "\n", "
Scanning grid points
ETA: --:--:--
" ], - "metadata": {}, - "output_type": "display_data", "text": [ "" - ] + ], + "metadata": {} }, { - "javascript": [ - "\n", - " var $myPB = $(\"div#6543bcde-bba2-4655-8a44-8a64076b880d\")\n", - " if ($myPB.hasClass('ui-progressbar')) {\n", - " $myPB.progressbar('value', 1);\n", - " } else {\n", - " $myPB.progressbar({value: 0, max: 6});\n", - " }\n", - " " - ], - "metadata": {}, - "output_type": "display_data", - "text": [ - "" - ] + "metadata": {} }, { - "javascript": [ - "$('div#bbacb647-f4d4-487e-bf3f-6a9d623a31c1').text('ETA: 0:01:29');" - ], - "metadata": {}, - "output_type": "display_data", - "text": [ - "" - ] + "metadata": {} }, { - "javascript": [ - "\n", - " var $myPB = $(\"div#6543bcde-bba2-4655-8a44-8a64076b880d\")\n", - " if ($myPB.hasClass('ui-progressbar')) {\n", - " $myPB.progressbar('value', 2);\n", - " } else {\n", - " $myPB.progressbar({value: 0, max: 6});\n", - " }\n", - " " - ], - "metadata": {}, - "output_type": "display_data", - "text": [ - "" - ] + "metadata": {} }, { - "javascript": [ - "$('div#bbacb647-f4d4-487e-bf3f-6a9d623a31c1').text('ETA: 0:01:10');" - ], - "metadata": {}, - "output_type": "display_data", - "text": [ - "" - ] + "metadata": {} }, { - "javascript": [ - "\n", - " var $myPB = $(\"div#6543bcde-bba2-4655-8a44-8a64076b880d\")\n", - " if ($myPB.hasClass('ui-progressbar')) {\n", - " $myPB.progressbar('value', 3);\n", - " } else {\n", - " $myPB.progressbar({value: 0, max: 6});\n", - " }\n", - " " - ], - "metadata": {}, - "output_type": "display_data", - "text": [ - "" - ] + "metadata": {} }, { - "javascript": [ - "$('div#bbacb647-f4d4-487e-bf3f-6a9d623a31c1').text('ETA: 0:00:51');" - ], - "metadata": {}, - "output_type": "display_data", - "text": [ - "" - ] + "metadata": {} }, { - "javascript": [ - "\n", - " var $myPB = $(\"div#6543bcde-bba2-4655-8a44-8a64076b880d\")\n", - " if ($myPB.hasClass('ui-progressbar')) {\n", - " $myPB.progressbar('value', 4);\n", - " } else {\n", - " $myPB.progressbar({value: 0, max: 6});\n", - " }\n", - " " - ], - "metadata": {}, - "output_type": "display_data", - "text": [ - "" - ] + "metadata": {} }, { - "javascript": [ - "$('div#bbacb647-f4d4-487e-bf3f-6a9d623a31c1').text('ETA: 0:00:34');" - ], - "metadata": {}, - "output_type": "display_data", - "text": [ - "" - ] + "metadata": {} }, { - "javascript": [ - "\n", - " var $myPB = $(\"div#6543bcde-bba2-4655-8a44-8a64076b880d\")\n", - " if ($myPB.hasClass('ui-progressbar')) {\n", - " $myPB.progressbar('value', 5);\n", - " } else {\n", - " $myPB.progressbar({value: 0, max: 6});\n", - " }\n", - " " - ], - "metadata": {}, - "output_type": "display_data", - "text": [ - "" - ] + "metadata": {} }, { - "javascript": [ - "$('div#bbacb647-f4d4-487e-bf3f-6a9d623a31c1').text('ETA: 0:00:17');" - ], - "metadata": {}, - "output_type": "display_data", - "text": [ - "" - ] + "metadata": {} }, { - "javascript": [ - "\n", - " var $myPB = $(\"div#6543bcde-bba2-4655-8a44-8a64076b880d\")\n", - " if ($myPB.hasClass('ui-progressbar')) {\n", - " $myPB.progressbar('value', 6);\n", - " } else {\n", - " $myPB.progressbar({value: 0, max: 6});\n", - " }\n", - " " - ], - "metadata": {}, - "output_type": "display_data", - "text": [ - "" - ] + "metadata": {} }, { - "javascript": [ - "$('div#bbacb647-f4d4-487e-bf3f-6a9d623a31c1').text('Time: 0:01:25');" - ], - "metadata": {}, - "output_type": "display_data", - "text": [ - "" - ] + "metadata": {} }, { - "javascript": [ - "var element = document.getElementById('d89de86a-5cda-4bfd-82cf-93a66345b6ae');\n", - " var parent = element.parentNode\n", - " parent.removeChild(element);\n", - " parent.parentElement.remove();" - ], - "metadata": {}, - "output_type": "display_data", - "text": [ - "" - ] + "metadata": {} }, { - "javascript": [ - "this.cleanProgressBar(['d89de86a-5cda-4bfd-82cf-93a66345b6ae', '6543bcde-bba2-4655-8a44-8a64076b880d', 'bbacb647-f4d4-487e-bf3f-6a9d623a31c1'])" - ], - "metadata": {}, - "output_type": "display_data", - "text": [ - "" - ] + "metadata": {} } ], + "input": [ + "result = diffFVA.run(surface_only=True,view=view)" + ], + "language": "python", "prompt_number": 14 }, { "cell_type": "code", - "collapsed": false, - "input": [ - "result.items[-2]" - ], - "language": "python", "metadata": {}, "outputs": [ { - "metadata": {}, "output_type": "pyout", "prompt_number": 20, "text": [ "(('Ec_biomass_iJO1366_core_53p95M', 0.43660969454532839),\n", " ('EX_trp_DASH_L_LPAREN_e_RPAREN_', 2.5696909726348878))" - ] + ], + "metadata": {} } ], + "input": [ + "result.items[-2]" + ], + "language": "python", "prompt_number": 20 }, { "cell_type": "code", - "collapsed": false, - "input": [ - "df = result[result.items[-2]]\n", - "df.join(diffFVA.reference_flux_ranges, rsuffix='_ref', lsuffix='').query('normalized_gaps > 0.1 | normalized_gaps < -0.1').sort('normalized_gaps', ascending=False)" - ], - "language": "python", "metadata": {}, "outputs": [ { + "output_type": "pyout", "html": [ "
\n", "\n", @@ -5279,9 +2642,6 @@ "
\n", "
" ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 22, "text": [ " lower_bound upper_bound gaps normalized_gaps \\\n", "PGK -12.85749 -12.85749 0 14.17059 \n", @@ -5366,21 +2726,23 @@ "GLCt2pp 0.000000 10.000000 \n", "Ec_biomass_iJO1366_core_53p95M 0.300000 0.557799 \n", "TRPAS2 -3.228922 26.963048 " - ] + ], + "metadata": {} } ], + "input": [ + "df = result[result.items[-2]]\n", + "df.join(diffFVA.reference_flux_ranges, rsuffix='_ref', lsuffix='').query('normalized_gaps > 0.1 | normalized_gaps < -0.1').sort('normalized_gaps', ascending=False)" + ], + "language": "python", "prompt_number": 22 }, { "cell_type": "code", - "collapsed": false, - "input": [ - "df.join(diffFVA.reference_flux_ranges, rsuffix='_ref', lsuffix='').query('KO == True').sort('normalized_gaps', ascending=False)" - ], - "language": "python", "metadata": {}, "outputs": [ { + "output_type": "pyout", "html": [ "
\n", "\n", @@ -5439,9 +2801,6 @@ "
\n", "
" ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 23, "text": [ " lower_bound upper_bound gaps normalized_gaps KO \\\n", "PGMT 0 0 0 0 True \n", @@ -5452,31 +2811,27 @@ "PGMT False True -6.256627e+01 9.407754e-14 \n", "G3PCabcpp False False -6.752759e-30 -6.752759e-30 \n", "FESD2s False False -9.479993e-27 -9.479993e-27 " - ] + ], + "metadata": {} } ], + "input": [ + "df.join(diffFVA.reference_flux_ranges, rsuffix='_ref', lsuffix='').query('KO == True').sort('normalized_gaps', ascending=False)" + ], + "language": "python", "prompt_number": 23 }, { "cell_type": "code", - "collapsed": false, + "metadata": {}, + "outputs": [], "input": [ "df.join(diffFVA.reference_flux_ranges, rsuffix='_ref', lsuffix='').query('KO == True').sort('normalized_gaps', ascending=False)" ], - "language": "python", - "metadata": {}, - "outputs": [] + "language": "python" }, { "cell_type": "code", - "collapsed": false, - "input": [ - "stuff = result[result.items[1]]\n", - "selection = stuff[abs(stuff.normalized_gaps) > .1].sort('normalized_gaps', ascending=False)\n", - "# Builder('iJO1366_central_metabolism', reaction_data=selection.normalized_gaps.to_dict()).display_in_notebook()\n", - "escher.Builder('iAF1260_Amino_Acid_Metabolism', reaction_data=selection.normalized_gaps.to_dict()).display_in_notebook()" - ], - "language": "python", "metadata": {}, "outputs": [ { @@ -5487,6 +2842,7 @@ ] }, { + "output_type": "pyout", "html": [ "\n", "\n", @@ -5542,48 +2898,47 @@ " }\n", " \n" ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 29, "text": [ "" - ] + ], + "metadata": {} } ], + "input": [ + "stuff = result[result.items[1]]\n", + "selection = stuff[abs(stuff.normalized_gaps) > .1].sort('normalized_gaps', ascending=False)\n", + "# Builder('iJO1366_central_metabolism', reaction_data=selection.normalized_gaps.to_dict()).display_in_notebook()\n", + "escher.Builder('iAF1260_Amino_Acid_Metabolism', reaction_data=selection.normalized_gaps.to_dict()).display_in_notebook()" + ], + "language": "python", "prompt_number": 29 }, { "cell_type": "code", - "collapsed": false, + "metadata": {}, + "outputs": [], "input": [ "import pandas" ], "language": "python", - "metadata": {}, - "outputs": [], "prompt_number": 91 }, { "cell_type": "code", - "collapsed": false, + "metadata": {}, + "outputs": [], "input": [ "?rxn_names_df.join" ], "language": "python", - "metadata": {}, - "outputs": [], "prompt_number": 108 }, { "cell_type": "code", - "collapsed": false, - "input": [ - "rxn_names_df.join(result[result.items[1]], how='inner')" - ], - "language": "python", "metadata": {}, "outputs": [ { + "output_type": "pyout", "html": [ "
\n", "\n", @@ -6274,12 +3629,9 @@ " \n", " \n", "
\n", - "

2254 rows \u00d7 8 columns

\n", + "

2254 rows × 8 columns

\n", "
" ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 109, "text": [ " reaction_name \\\n", "12DGR120tipp 1,2 diacylglycerol transport via flipping (per... \n", @@ -6471,25 +3823,30 @@ "Zn2tex False False False \n", "\n", "[2254 rows x 8 columns]" - ] + ], + "metadata": {} } ], + "input": [ + "rxn_names_df.join(result[result.items[1]], how='inner')" + ], + "language": "python", "prompt_number": 109 }, { "cell_type": "code", - "collapsed": false, + "metadata": {}, + "outputs": [], "input": [ "rxn_names_df = pandas.DataFrame({'reaction_name': [r.name for r in model.reactions]}, index=(r.id for r in model.reactions))" ], "language": "python", - "metadata": {}, - "outputs": [], "prompt_number": 96 }, { "cell_type": "code", - "collapsed": false, + "metadata": {}, + "outputs": [], "input": [ "def slideview(panel, reference_flux_ranges):\n", " def _(i=i):\n", @@ -6504,18 +3861,10 @@ " return _" ], "language": "python", - "metadata": {}, - "outputs": [], "prompt_number": 2 }, { "cell_type": "code", - "collapsed": false, - "input": [ - "func = slideview(result, diffFVA.reference_flux_ranges)\n", - "interact(func, i=widgets.IntSliderWidget(min=0, max=len(result)-1))" - ], - "language": "python", "metadata": {}, "outputs": [ { @@ -6529,26 +3878,25 @@ ] } ], + "input": [ + "func = slideview(result, diffFVA.reference_flux_ranges)\n", + "interact(func, i=widgets.IntSliderWidget(min=0, max=len(result)-1))" + ], + "language": "python", "prompt_number": 3 }, { "cell_type": "code", - "collapsed": false, + "metadata": {}, + "outputs": [], "input": [ "widgets.IntSliderWidget(min=0, max=10)" ], "language": "python", - "metadata": {}, - "outputs": [], "prompt_number": 47 }, { "cell_type": "code", - "collapsed": false, - "input": [ - "interact(func, i=widgets.IntSliderWidget(min=0, max=10))" - ], - "language": "python", "metadata": {}, "outputs": [ { @@ -6566,18 +3914,18 @@ ] } ], + "input": [ + "interact(func, i=widgets.IntSliderWidget(min=0, max=10))" + ], + "language": "python", "prompt_number": 49 }, { "cell_type": "code", - "collapsed": false, - "input": [ - "func(1)" - ], - "language": "python", "metadata": {}, "outputs": [ { + "output_type": "pyout", "html": [ "
\n", "\n", @@ -6912,9 +4260,6 @@ "
\n", "
" ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 48, "text": [ " lower_bound upper_bound gaps normalized_gaps \\\n", "PGK -12.85749 -12.85749 0 14.17059 \n", @@ -6999,21 +4344,33 @@ "GLCt2pp 0.000000 10.000000 \n", "Ec_biomass_iJO1366_core_53p95M 0.300000 0.557799 \n", "TRPAS2 -3.228922 26.963048 " - ] + ], + "metadata": {} } ], + "input": [ + "func(1)" + ], + "language": "python", "prompt_number": 48 }, { "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", "metadata": {}, - "outputs": [] + "outputs": [], + "input": [ + "" + ], + "language": "python" } - ], - "metadata": {} + ] } - ] + ], + "cells": [], + "metadata": { + "name": "", + "signature": "sha256:8bcb057d1d21d77b1511237b62309b339c05328433bc22a618e2ae3d48e5af01" + }, + "nbformat": 3, + "nbformat_minor": 0 } \ No newline at end of file diff --git a/examples/gene_ko_mo.ipynb b/examples/gene_ko_mo.ipynb index e21c54ed0..aeb5a1d4d 100644 --- a/examples/gene_ko_mo.ipynb +++ b/examples/gene_ko_mo.ipynb @@ -1,4354 +1,234 @@ { - "metadata": { - "name": "", - "signature": "sha256:49705b09b902933186c2a27ca43e97da8ef4eb8ce54b45c3840f0b72f7af881c" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### E. coli core model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import cameo\n", + "from cameo import load_model\n", + "from cameo.strain_design.heuristic import GeneKnockoutOptimization, ReactionKnockoutOptimization\n", + "from cameo.strain_design.heuristic.objective_functions import biomass_product_coupled_yield, product_yield, number_of_knockouts\n", + "from cameo.flux_analysis.simulation import fba\n", + "from cameo.parallel import SequentialView\n", + "import inspyred" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "model = load_model(\"../tests/data/EcoliCore.xml\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "objective1 = biomass_product_coupled_yield(\n", + " \"Biomass_Ecoli_core_N_LPAREN_w_FSLASH_GAM_RPAREN__Nmet2\",\n", + " \"EX_ac_LPAREN_e_RPAREN_\",\n", + " \"EX_glc_LPAREN_e_RPAREN_\")\n", + "\n", + "objective2 = number_of_knockouts()\n", + "objective = [objective1, objective2]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "rko = ReactionKnockoutOptimization(model=model,\n", + " simulation_method=fba,\n", + " objective_function=objective,\n", + " heuristic_method=inspyred.ec.emo.NSGA2,\n", + " seed=1234)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "results = rko.run(max_evaluations=3000, pop_size=10, view=SequentialView())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "mutant = model.copy()" + ] + }, { - "cells": [ - { - "cell_type": "code", - "collapsed": false, - "input": [ - "%load_ext autoreload\n", - "%autoreload 2" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], - "prompt_number": 1 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "%matplotlib inline" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 2 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### E. coli core model" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import cameo\n", - "from cameo import load_model\n", - "from cameo.strain_design.heuristic import GeneKnockoutOptimization, ReactionKnockoutOptimization\n", - "from cameo.strain_design.heuristic.objective_functions import biomass_product_coupled_yield, product_yield, number_of_knockouts\n", - "from cameo.flux_analysis.simulation import fba\n", - "from cameo.parallel import SequentialView\n", - "import inspyred" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 3 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "model = load_model(\"../tests/data/EcoliCore.xml\")" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 4 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "objective1 = biomass_product_coupled_yield(\n", - " \"Biomass_Ecoli_core_N_LPAREN_w_FSLASH_GAM_RPAREN__Nmet2\",\n", - " \"EX_ac_LPAREN_e_RPAREN_\",\n", - " \"EX_glc_LPAREN_e_RPAREN_\")\n", - "\n", - "objective2 = number_of_knockouts()\n", - "objective = [objective1, objective2]" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 5 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "rko = ReactionKnockoutOptimization(model=model,\n", - " simulation_method=fba,\n", - " objective_function=objective,\n", - " heuristic_method=inspyred.ec.emo.NSGA2,\n", - " seed=1234)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 6 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "results = rko.run(max_evaluations=3000, pop_size=10, view=SequentialView())" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "html": [ - "" - ], - "metadata": {}, - "output_type": "display_data", - "text": [ - "" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Starting optimization at Mon, 25 Aug 2014 11:05:19\n", - "found config for http://localhost:5006/" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "{}\n", - "loading it!\n", - "if you don't wish to load this config, please pass load_from_config=False\n" - ] - }, - { - "html": [ - "

Connecting notebook to document \"efea7635-2c36-11e4-a1e6-b8e856101da4\" at server http://localhost:5006/

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], - "metadata": {}, - "output_type": "display_data", - "text": [ - "" - ] - }, - { - "javascript": [ - "jQuery('#progress-bar-4cd84a83-09b5-44ab-8e14-f481225a946c').val('100')" - ], - "metadata": {}, - "output_type": "display_data", - "text": [ - "" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Finished after 01:00:23\n" - ] - } - ], - "prompt_number": 7 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "results" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "html": [ - "\n", - "

Result:

\n", - "
    \n", - "
  • model: MODELID_3473243
    • \n", - "
  • heuristic: NSGA2
    • \n", - "
  • objective function: bpcy = (Biomass_Ecoli_core_N_LPAREN_w_FSLASH_GAM_RPAREN__Nmet2 * EX_ac_LPAREN_e_RPAREN_) / EX_glc_LPAREN_e_RPAREN_| min #knockouts
    • \n", - "
  • simulation method: fba
    • \n", - "
  • type: None
    • \n", - "
      \n", - "
      \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
      FitnessKnockoutsSize
      0 (0.561319, 1.0) (CO2t) 1
      1 (0.64908, 0.5) (TKT1, SUCDi) 2
      2 (0.606279, 0.5) (ATPS4r, ATPM) 2
      3 (0.64908, 0.333333) (AKGDH, TKT1, ICL) 3
      4 (0.64908, 0.333333) (AKGDH, TKT1, SUCDi) 3
      5 (0.535608, 1.0) (ATPS4r) 1
      6 (0.617805, 0.333333) (CO2t, ATPM, ICL) 3
      7 (0.523097, 1.0) (ATPS4r) 1
      8 (0.617805, 0.333333) (CO2t, ATPM, SUCDi) 3
      9 (0.586245, 0.5) (ATPS4r, ATPM) 2
      \n", - "
      " - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 8, - "text": [ - "" - ] - } - ], - "prompt_number": 8 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "mutant = model.copy()" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 29 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "mutant.reactions.PGL.knock_out()\n", - "mutant.reactions.SUCDi.knock_out()" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 37 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "mutant_fluxes = mutant.solve().x_dict" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 38 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print mutant_fluxes['Biomass_Ecoli_core_N_LPAREN_w_FSLASH_GAM_RPAREN__Nmet2']\n", - "print mutant_fluxes['EX_ac_LPAREN_e_RPAREN_']" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "0.654744854115\n", - "10.0488603347\n" - ] - } - ], - "prompt_number": 39 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### iJO1366 model" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "model = load_model(\"../tests/data/iJO1366.xml\")" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 9 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "of1 = product_yield(\"EX_ac_LPAREN_e_RPAREN_\", \"EX_glc_LPAREN_e_RPAREN_\")\n", - "of2 = number_of_knockouts()\n", - "of3 = biomass_product_coupled_yield(\"Ec_biomass_iJO1366_core_53p95M\", \"EX_ac_LPAREN_e_RPAREN_\", \"EX_glc_LPAREN_e_RPAREN_\")" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 10 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "ko = GeneKnockoutOptimization(model=model, objective_function=[of1, of2, of3],\n", - " simulation_method=fba, heuristic_method=inspyred.ec.emo.NSGA2)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 11 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "ko.run(max_evaluations=5000, n=10, mutation_rate=0.3, popultation_size=100)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "html": [ - "" - ], - "metadata": {}, - "output_type": "display_data", - "text": [ - "" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Starting optimization at Mon, 25 Aug 2014 11:06:21\n" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "found config for http://localhost:5006/\n", - "{}\n", - "loading it!\n", - "if you don't wish to load this config, please pass load_from_config=False\n" - ] - }, - { - "html": [ - "

      Connecting notebook to document \"2687030f-2c37-11e4-b0f2-b8e856101da4\" at server http://localhost:5006/

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      Result:

      \n", - "
        \n", - "
      • model: iJO1366
        • \n", - "
      • heuristic: NSGA2
        • \n", - "
      • objective function: yield = (EX_ac_LPAREN_e_RPAREN_ / EX_glc_LPAREN_e_RPAREN_)| min #knockouts| bpcy = (Ec_biomass_iJO1366_core_53p95M * EX_ac_LPAREN_e_RPAREN_) / EX_glc_LPAREN_e_RPAREN_
        • \n", - "
      • simulation method: fba
        • \n", - "
      • type: None
        • \n", - "
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          97 (1.865577, 0.25, 0.139252) (b3503, b2913, b2779, b3732) 4
          98 (1.480139, 0.333333, 0.574833) (b2913, b3732, b2688) 3
          99 (1.655124, 0.25, 0.315983) (b2779, b3371, b3732, b1007) 4
          \n", - "

          100 rows \u00d7 3 columns

          \n", - "
          " - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 12, - "text": [ - "" - ] - } - ], - "prompt_number": 12 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [] - } - ], - "metadata": {} + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "mutant.reactions.PGL.knock_out()\n", + "mutant.reactions.SUCDi.knock_out()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "mutant_fluxes = mutant.solve().x_dict" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "print mutant_fluxes['Biomass_Ecoli_core_N_LPAREN_w_FSLASH_GAM_RPAREN__Nmet2']\n", + "print mutant_fluxes['EX_ac_LPAREN_e_RPAREN_']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### iJO1366 model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "model = load_model(\"../tests/data/iJO1366.xml\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "of1 = product_yield(\"EX_ac_LPAREN_e_RPAREN_\", \"EX_glc_LPAREN_e_RPAREN_\")\n", + "of2 = number_of_knockouts()\n", + "of3 = biomass_product_coupled_yield(\"Ec_biomass_iJO1366_core_53p95M\", \"EX_ac_LPAREN_e_RPAREN_\", \"EX_glc_LPAREN_e_RPAREN_\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "ko = GeneKnockoutOptimization(model=model, objective_function=[of1, of2, of3],\n", + " simulation_method=fba, heuristic_method=inspyred.ec.emo.NSGA2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "ko.run(max_evaluations=5000, n=10, mutation_rate=0.3, popultation_size=100)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] } - ] -} \ No newline at end of file + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +}