{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Predict gene knockout strategies\n", "\n", "In cameo we have two ways of predicting gene knockout targets: using evolutionary algorithms (OptGene) or linear programming (OptKnock)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "If you're running this notebook on [try.cameo.bio](http://try.cameo.bio), things might run very slow due to our inability to provide access to the proprietary [CPLEX](https://www-01.ibm.com/software/commerce/optimization/cplex-optimizer/) solver on a public webserver. Furthermore, Jupyter kernels might crash and restart due to memory limitations on the server.\n", "
" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from cameo import models\n", "from cameo.visualization.plotting.with_plotly import PlotlyPlotter" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "model = models.bigg.iJO1366\n", "plotter = PlotlyPlotter()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "wt_solution = model.optimize()\n", "growth = wt_solution.fluxes[\"BIOMASS_Ec_iJO1366_core_53p95M\"]\n", "acetate_production = wt_solution.fluxes[\"EX_ac_e\"]" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "from cameo import phenotypic_phase_plane" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plot.ly", "plotlyServerURL": "https://plot.ly", "showLink": false }, "data": [ { "fill": "toself", "fillcolor": "#B3E2CD", "hoverinfo": "none", "marker": { "line": { "color": "#B3E2CD" }, "opacity": 0.3 }, "mode": "lines", "name": "WT", "opacity": 0.3, "type": "scatter", "x": [ 0, 0.051703779617209705, 0.10340755923441941, 0.15511133885162912, 0.20681511846883882, 0.25851889808604855, 0.31022267770325823, 0.3619264573204679, 0.41363023693767764, 0.4653340165548874, 0.5170377961720971, 0.5687415757893067, 0.6204453554065165, 0.6721491350237262, 0.7238529146409358, 0.7755566942581456, 0.8272604738753553, 0.878964253492565, 0.9306680331097748, 0.9823718127269844, 0.9823718127269844, 0.9306680331097748, 0.878964253492565, 0.8272604738753553, 0.7755566942581456, 0.7238529146409358, 0.6721491350237262, 0.6204453554065165, 0.5687415757893067, 0.5170377961720971, 0.4653340165548874, 0.41363023693767764, 0.3619264573204679, 0.31022267770325823, 0.25851889808604855, 0.20681511846883882, 0.15511133885162912, 0.10340755923441941, 0.051703779617209705, 0, 0, 0 ], "y": [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.9529516892546894e-13, 1.9178137651819396, 3.8356275303642673, 5.753441295546358, 7.493954987518401, 9.191567907024801, 10.69046951910827, 12.166899126226138, 13.629127229343863, 15.091355332461195, 16.553604142942405, 17.95528167897003, 19.352197999711137, 20.746327201412875, 22.13751722395791, 23.52870724650301, 24.91989726904814, 26.31108729159322, 27.702277314138232, 29.093467336683382, 0, 29.093467336683382 ] }, { "marker": { "color": "green" }, "mode": "markers", "name": "Data Points", "type": "scatter", "x": [ 0.9823718127269733 ], "y": [ 0 ] } ], "layout": { "height": 432, "template": { "data": { "bar": [ { "error_x": { "color": "#2a3f5f" }, "error_y": { "color": "#2a3f5f" }, "marker": { "line": { "color": "#E5ECF6", "width": 0.5 } }, "type": "bar" } ], "barpolar": [ { "marker": { "line": { "color": "#E5ECF6", "width": 0.5 } }, "type": "barpolar" } ], "carpet": [ { "aaxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "baxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "type": "carpet" } ], "choropleth": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "choropleth" } ], "contour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "contour" } ], "contourcarpet": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "contourcarpet" } ], "heatmap": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "heatmap" } ], "heatmapgl": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "heatmapgl" } ], "histogram": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "histogram" } ], "histogram2d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "histogram2d" } ], "histogram2dcontour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "histogram2dcontour" } ], "mesh3d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "mesh3d" } ], "parcoords": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "parcoords" } ], "pie": [ { "automargin": true, "type": "pie" } ], "scatter": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatter" } ], "scatter3d": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatter3d" } ], "scattercarpet": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattercarpet" } ], "scattergeo": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergeo" } ], "scattergl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergl" } ], "scattermapbox": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattermapbox" } ], "scatterpolar": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolar" } ], "scatterpolargl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolargl" } ], "scatterternary": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterternary" } ], "surface": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "surface" } ], "table": [ { "cells": { "fill": { "color": "#EBF0F8" }, "line": { "color": "white" } }, "header": { "fill": { "color": "#C8D4E3" }, "line": { "color": "white" } }, "type": "table" } ] }, "layout": { "annotationdefaults": { "arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1 }, "autotypenumbers": "strict", "coloraxis": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "colorscale": { "diverging": [ [ 0, "#8e0152" ], [ 0.1, "#c51b7d" ], [ 0.2, "#de77ae" ], [ 0.3, "#f1b6da" ], [ 0.4, "#fde0ef" ], [ 0.5, "#f7f7f7" ], [ 0.6, "#e6f5d0" ], [ 0.7, "#b8e186" ], [ 0.8, "#7fbc41" ], [ 0.9, "#4d9221" ], [ 1, "#276419" ] ], "sequential": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "sequentialminus": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ] }, "colorway": [ "#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52" ], "font": { "color": "#2a3f5f" }, "geo": { "bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white" }, "hoverlabel": { "align": "left" }, "hovermode": "closest", "mapbox": { "style": "light" }, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": { "angularaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "radialaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "yaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "zaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "Phenotypic Phase Plane (flux)" }, "width": 700, "xaxis": { "autorange": true, "range": [ 0, 1.042614144521416 ], "title": { "text": "BIOMASS_Ec_iJO1366_core_53p95M [h^-1]" }, "type": "linear" }, "yaxis": { "autorange": true, "range": [ -2.132607894888022, 30.736944980450296 ], "title": { "text": "EX_ac_e [mmol gDW^-1 h^-1]" }, "type": "linear" } } }, "image/png": "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", "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "p = phenotypic_phase_plane(model, variables=['BIOMASS_Ec_iJO1366_core_53p95M'], objective='EX_ac_e')\n", "p.plot(plotter, points=[(growth, acetate_production)])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## OptGene\n", "\n", "OptGene is an approach to search for gene or reaction knockouts that relies on evolutionary algorithms[1]. The following image from the authors summarizes OptGene workflow.\n", "\n", "\n", "\n", "At every iteration, we keep the best 50 individuals found overall so we can generate a library of targets." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "from cameo.strain_design import OptGene" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "optgene = OptGene(model)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting optimization at Mon, 01 Mar 2021 16:38:37\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "965f6d44001d49568a66054c0d221055", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox()" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Finished after 00:45:58\n" ] } ], "source": [ "result = optgene.run(target=model.reactions.EX_ac_e, \n", " biomass=model.reactions.BIOMASS_Ec_iJO1366_core_53p95M,\n", " substrate=model.metabolites.glc__D_e,\n", " max_evaluations=5000,\n", " plot=False)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "786b00aa17454873b1ceaa3b7fe27a01", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

OptGene Result

\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", " \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", "
reactionsgenessizefva_minfva_maxtarget_fluxbiomass_fluxyieldfitness
0(ATPS4rpp,)((b3732,), (b3738,), (b3734,), (b3733,), (b373...10.014.18781913.9429320.4024781.3942930.561172
1(3HPPPNH, 3HCINNMH, ATPS4rpp)((b3735, b0347),)20.014.18781913.9429320.4024781.3942930.561172
2(ATPS4rpp,)((b3735, b3942), (b3735, b2683))20.014.18781713.2979660.4024771.3297970.535213
3(G3PD7, G3PD6, ATPS4rpp)((b3735, b2241),)20.014.1878190.2344720.4024780.0234470.009437
4(NADH16pp, NADH17pp, NADH18pp, ATPS4rpp)((b3735, b2286),)20.014.18781713.7092150.4024771.3709220.551764
5(PGCD, ATPS4rpp)((b2913, b3738),)20.014.9762968.5128950.3883640.8512890.330610
6(ATPS4rpp,)((b3735, b2095, b2486),)30.014.18781913.9429320.4024781.3942930.561172
\n", "
\n", " " ], "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "result" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plot.ly", "plotlyServerURL": "https://plot.ly", "showLink": false }, "data": [ { "fill": "toself", "fillcolor": "#B3E2CD", "hoverinfo": "none", "marker": { "line": { "color": "#B3E2CD" }, "opacity": 0.3 }, "mode": "lines", "name": "WT", "opacity": 0.3, "type": "scatter", "x": [ 0, 0.05170377961720975, 0.1034075592344195, 0.15511133885162925, 0.206815118468839, 0.2585188980860487, 0.3102226777032585, 0.36192645732046824, 0.413630236937678, 0.4653340165548877, 0.5170377961720974, 0.5687415757893072, 0.620445355406517, 0.6721491350237268, 0.7238529146409365, 0.7755566942581462, 0.827260473875356, 0.8789642534925657, 0.9306680331097754, 0.9823718127269851, 0.9823718127269851, 0.9306680331097754, 0.8789642534925657, 0.827260473875356, 0.7755566942581462, 0.7238529146409365, 0.6721491350237268, 0.620445355406517, 0.5687415757893072, 0.5170377961720974, 0.4653340165548877, 0.413630236937678, 0.36192645732046824, 0.3102226777032585, 0.2585188980860487, 0.206815118468839, 0.15511133885162925, 0.1034075592344195, 0.05170377961720975, 0, 0, 0 ], "y": [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.917813765182037, 3.8356275303641203, 5.753441295546296, 7.493954987518226, 9.191567907024663, 10.690469519108065, 12.166899126226312, 13.629127229343615, 15.091355332461298, 16.553604142942497, 17.955299118971535, 19.352213259712382, 20.746327201412534, 22.13751722395791, 23.52870724650297, 24.919897269048224, 26.31108729159316, 27.702277314138286, 29.093467336683382, 0, 29.093467336683382 ] }, { "fill": "toself", "fillcolor": "#FDCDAC", "hoverinfo": "none", "marker": { "line": { "color": "#FDCDAC" }, "opacity": 0.3 }, "mode": "lines", "name": "MT", "opacity": 0.3, "type": "scatter", "x": [ 0, 0.021183040855038617, 0.04236608171007723, 0.06354912256511586, 0.08473216342015447, 0.10591520427519308, 0.1270982451302317, 0.14828128598527032, 0.16946432684030893, 0.19064736769534754, 0.21183040855038615, 0.2330134494054248, 0.2541964902604634, 0.275379531115502, 0.29656257197054064, 0.3177456128255792, 0.33892865368061786, 0.3601116945356565, 0.3812947353906951, 0.4024777762457337, 0.4024777762457337, 0.3812947353906951, 0.3601116945356565, 0.33892865368061786, 0.3177456128255792, 0.29656257197054064, 0.275379531115502, 0.2541964902604634, 0.2330134494054248, 0.21183040855038615, 0.19064736769534754, 0.16946432684030893, 0.14828128598527032, 0.1270982451302317, 0.10591520427519308, 0.08473216342015447, 0.06354912256511586, 0.04236608171007723, 0.021183040855038617, 0, 0, 0 ], "y": [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 13.942931769394248, 15.119736901823744, 16.073270036907694, 17.026803171991606, 17.980336307075376, 18.93386944215931, 19.887402577243204, 20.84093571232699, 21.794468847410776, 22.748001982494948, 23.70153511757859, 24.388554754411455, 25.037485410109785, 25.668937772139707, 26.19551253115948, 26.713246183119583, 27.23097983507977, 27.7487134870402, 28.26644713900046, 28.784180790960498, 0, 28.784180790960498 ] } ], "layout": { "height": 432, "template": { "data": { "bar": [ { "error_x": { "color": "#2a3f5f" }, "error_y": { "color": "#2a3f5f" }, "marker": { "line": { "color": "#E5ECF6", "width": 0.5 } }, "type": "bar" } ], "barpolar": [ { "marker": { "line": { "color": "#E5ECF6", "width": 0.5 } }, "type": "barpolar" } ], "carpet": [ { "aaxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "baxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "type": "carpet" } ], "choropleth": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "choropleth" } ], "contour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "contour" } ], "contourcarpet": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "contourcarpet" } ], "heatmap": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "heatmap" } ], "heatmapgl": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "heatmapgl" } ], "histogram": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "histogram" } ], "histogram2d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "histogram2d" } ], "histogram2dcontour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "histogram2dcontour" } ], "mesh3d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "mesh3d" } ], "parcoords": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "parcoords" } ], "pie": [ { "automargin": true, "type": "pie" } ], "scatter": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatter" } ], "scatter3d": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatter3d" } ], "scattercarpet": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattercarpet" } ], "scattergeo": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergeo" } ], "scattergl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergl" } ], "scattermapbox": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattermapbox" } ], "scatterpolar": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolar" } ], "scatterpolargl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolargl" } ], "scatterternary": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterternary" } ], "surface": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "surface" } ], "table": [ { "cells": { "fill": { "color": "#EBF0F8" }, "line": { "color": "white" } }, "header": { "fill": { "color": "#C8D4E3" }, "line": { "color": "white" } }, "type": "table" } ] }, "layout": { "annotationdefaults": { "arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1 }, "autotypenumbers": "strict", "coloraxis": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "colorscale": { "diverging": [ [ 0, "#8e0152" ], [ 0.1, "#c51b7d" ], [ 0.2, "#de77ae" ], [ 0.3, "#f1b6da" ], [ 0.4, "#fde0ef" ], [ 0.5, "#f7f7f7" ], [ 0.6, "#e6f5d0" ], [ 0.7, "#b8e186" ], [ 0.8, "#7fbc41" ], [ 0.9, "#4d9221" ], [ 1, "#276419" ] ], "sequential": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "sequentialminus": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ] }, "colorway": [ "#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52" ], "font": { "color": "#2a3f5f" }, "geo": { "bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white" }, "hoverlabel": { "align": "left" }, "hovermode": "closest", "mapbox": { "style": "light" }, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": { "angularaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "radialaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "yaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "zaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "Production Envelope" }, "width": 700, "xaxis": { "autorange": true, "range": [ 0, 0.9823718127269851 ], "title": { "text": "BIOMASS_Ec_iJO1366_core_53p95M" }, "type": "linear" }, "yaxis": { "autorange": true, "range": [ -1.6163037409268548, 30.709771077610238 ], "title": { "text": "EX_ac_e" }, "type": "linear" } } }, "image/png": "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", "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "result.plot(plotter, 0)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Downloading Map from https://escher.github.io/1-0-0/6/maps/Escherichia%20coli/iJO1366.Central%20metabolism.json\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "0b4d1b99696247a7a27f4bbfd14a0490", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Builder(reaction_data={'EX_co2_e': 15.638742, 'EX_cobalt2_e': -1e-05, 'DM_4crsol_c': 9e-05, 'DM_5drib_c': 9e-0…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "result.display_on_map(0, \"iJO1366.Central metabolism\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## OptKnock\n", "\n", "OptKnock uses a bi-level mixed integer linear programming approach to identify reaction knockouts[2]:\n", "\n", "$$\n", "\\begin{matrix}\n", "maximize & \\mathit{v_{chemical}} & & (\\mathbf{OptKnock}) \\\\\n", "\\mathit{y_j} & & & \\\\\n", "subject~to & maximize & \\mathit{v_{biomass}} & (\\mathbf{Primal}) \\\\\n", "& \\mathit{v_j} & & & & \\\\\n", "\\end{matrix}\\\\\n", "\\begin{bmatrix}\n", "subject~to & \\sum_{j=1}^{M}S_{ij}v_{j} = 0,\\\\ \n", "& v_{carbon\\_uptake} = v_{carbon~target}\\\\ \n", "& v_{apt} \\ge v_{apt\\_main}\\\\ \n", "& v_{biomass} \\ge v_{target\\_biomass}\\\\ \n", "& v_{j}^{min} \\cdot y_j \\le v_j \\le v_{j}^{max} \\cdot y_j, \\forall j \\in \\boldsymbol{M} \\\\\n", "\\end{bmatrix}\\\\\n", "\\begin{align}\n", " & y_j = {0, 1}, & & \\forall j \\in \\boldsymbol{M} & \\\\\n", " & \\sum_{j \\in M} (1 - y_j) \\le K& & & \\\\\n", "\\end{align}\n", "$$\n", "\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "from cameo.strain_design import OptKnock" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "optknock = OptKnock(model, fraction_of_optimum=0.1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Running multiple knockouts with OptKnock can take a few hours or days..." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "\n", " jQuery(\"#86062113-5fa8-428c-a3d3-31005e82b7b2\").remove();\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "result = optknock.run(max_knockouts=1, target=\"EX_ac_e\", biomass=\"BIOMASS_Ec_iJO1366_core_53p95M\")" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "da94b779c9844c068feca8807f33ac68", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

OptKnock:

\n", "
    \n", "
  • Target: EX_ac_e
    • \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", "
reactionssizeEX_ac_ebiomassfva_minfva_max
0{EX_o2_e}18.2076320.2415027.6137538.662553
1{O2tex}18.2076320.2415027.6137568.662556
\n", "
" ], "text/plain": [ "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "result" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plot.ly", "plotlyServerURL": "https://plot.ly", "showLink": false }, "data": [ { "fill": "toself", "fillcolor": "#B3E2CD", "hoverinfo": "none", "marker": { "line": { "color": "#B3E2CD" }, "opacity": 0.3 }, "mode": "lines", "name": "WT", "opacity": 0.3, "type": "scatter", "x": [ 0, 0.0517037796172096, 0.1034075592344192, 0.1551113388516288, 0.2068151184688384, 0.258518898086048, 0.3102226777032576, 0.3619264573204672, 0.4136302369376768, 0.46533401655488643, 0.517037796172096, 0.5687415757893056, 0.6204453554065152, 0.6721491350237249, 0.7238529146409344, 0.775556694258144, 0.8272604738753536, 0.8789642534925632, 0.9306680331097729, 0.9823718127269825, 0.9823718127269825, 0.9306680331097729, 0.8789642534925632, 0.8272604738753536, 0.775556694258144, 0.7238529146409344, 0.6721491350237249, 0.6204453554065152, 0.5687415757893056, 0.517037796172096, 0.46533401655488643, 0.4136302369376768, 0.3619264573204672, 0.3102226777032576, 0.258518898086048, 0.2068151184688384, 0.1551113388516288, 0.1034075592344192, 0.0517037796172096, 0, 0, 0 ], "y": [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9178137651821243, 3.835627530364197, 5.753441295546472, 7.49395498751858, 9.191567907025055, 10.69046951910819, 12.166899126226456, 13.629127229343839, 15.091355332461477, 16.553604142942635, 17.955299118971677, 19.352213259712517, 20.746340227647472, 22.137528079153423, 23.52871593065942, 24.919903782165562, 26.311091633671396, 27.702279485177506, 29.09346733668343, 0, 29.09346733668343 ] }, { "fill": "toself", "fillcolor": "#FDCDAC", "hoverinfo": "none", "marker": { "line": { "color": "#FDCDAC" }, "opacity": 0.3 }, "mode": "lines", "name": "MT", "opacity": 0.3, "type": "scatter", "x": [ 0, 0.012710622738406197, 0.025421245476812394, 0.03813186821521859, 0.05084249095362479, 0.06355311369203098, 0.07626373643043718, 0.08897435916884337, 0.10168498190724957, 0.11439560464565578, 0.12710622738406196, 0.13981685012246817, 0.15252747286087437, 0.16523809559928057, 0.17794871833768675, 0.19065934107609295, 0.20336996381449915, 0.21608058655290535, 0.22879120929131155, 0.24150183202971776, 0.24150183202971776, 0.22879120929131155, 0.21608058655290535, 0.20336996381449915, 0.19065934107609295, 0.17794871833768675, 0.16523809559928057, 0.15252747286087437, 0.13981685012246817, 0.12710622738406196, 0.11439560464565578, 0.10168498190724957, 0.08897435916884337, 0.07626373643043718, 0.06355311369203098, 0.05084249095362479, 0.03813186821521859, 0.025421245476812394, 0.012710622738406197, 0, 0, 0 ], "y": [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1759408716913005, 3.3556871761719367, 5.5354334806528, 8.207629551848877, 8.207629551852053, 10.601964838596048, 12.996300125340966, 15.39063541208589, 17.4011733550536, 18.023761798050035, 18.646350241046047, 19.268938684042336, 19.8915271270389, 20.51411557003552, 21.136704013032144, 21.759292456028334, 22.38188089902484, 23.004469342020887, 23.62705778501763, 24.24964622801379, 24.872234671010485, 25.49482311400693, 26.117411557003162, 26.72307692307696, 0, 26.72307692307696 ] } ], "layout": { "height": 432, "template": { "data": { "bar": [ { "error_x": { "color": "#2a3f5f" }, "error_y": { "color": "#2a3f5f" }, "marker": { "line": { "color": "#E5ECF6", "width": 0.5 } }, "type": "bar" } ], "barpolar": [ { "marker": { "line": { "color": "#E5ECF6", "width": 0.5 } }, "type": "barpolar" } ], "carpet": [ { "aaxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "baxis": { "endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f" }, "type": "carpet" } ], "choropleth": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "choropleth" } ], "contour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "contour" } ], "contourcarpet": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "contourcarpet" } ], "heatmap": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "heatmap" } ], "heatmapgl": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "heatmapgl" } ], "histogram": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "histogram" } ], "histogram2d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "histogram2d" } ], "histogram2dcontour": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "histogram2dcontour" } ], "mesh3d": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "type": "mesh3d" } ], "parcoords": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "parcoords" } ], "pie": [ { "automargin": true, "type": "pie" } ], "scatter": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatter" } ], "scatter3d": [ { "line": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatter3d" } ], "scattercarpet": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattercarpet" } ], "scattergeo": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergeo" } ], "scattergl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattergl" } ], "scattermapbox": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scattermapbox" } ], "scatterpolar": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolar" } ], "scatterpolargl": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterpolargl" } ], "scatterternary": [ { "marker": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "type": "scatterternary" } ], "surface": [ { "colorbar": { "outlinewidth": 0, "ticks": "" }, "colorscale": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "type": "surface" } ], "table": [ { "cells": { "fill": { "color": "#EBF0F8" }, "line": { "color": "white" } }, "header": { "fill": { "color": "#C8D4E3" }, "line": { "color": "white" } }, "type": "table" } ] }, "layout": { "annotationdefaults": { "arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1 }, "autotypenumbers": "strict", "coloraxis": { "colorbar": { "outlinewidth": 0, "ticks": "" } }, "colorscale": { "diverging": [ [ 0, "#8e0152" ], [ 0.1, "#c51b7d" ], [ 0.2, "#de77ae" ], [ 0.3, "#f1b6da" ], [ 0.4, "#fde0ef" ], [ 0.5, "#f7f7f7" ], [ 0.6, "#e6f5d0" ], [ 0.7, "#b8e186" ], [ 0.8, "#7fbc41" ], [ 0.9, "#4d9221" ], [ 1, "#276419" ] ], "sequential": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ], "sequentialminus": [ [ 0, "#0d0887" ], [ 0.1111111111111111, "#46039f" ], [ 0.2222222222222222, "#7201a8" ], [ 0.3333333333333333, "#9c179e" ], [ 0.4444444444444444, "#bd3786" ], [ 0.5555555555555556, "#d8576b" ], [ 0.6666666666666666, "#ed7953" ], [ 0.7777777777777778, "#fb9f3a" ], [ 0.8888888888888888, "#fdca26" ], [ 1, "#f0f921" ] ] }, "colorway": [ "#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52" ], "font": { "color": "#2a3f5f" }, "geo": { "bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white" }, "hoverlabel": { "align": "left" }, "hovermode": "closest", "mapbox": { "style": "light" }, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": { "angularaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "radialaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "yaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "zaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "Production Envelope" }, "width": 700, "xaxis": { "autorange": true, "range": [ 0, 0.9823718127269825 ], "title": { "text": "BIOMASS_Ec_iJO1366_core_53p95M" }, "type": "linear" }, "yaxis": { "autorange": true, "range": [ -1.6163037409268572, 30.709771077610288 ], "title": { "text": "EX_ac_e" }, "type": "linear" } } }, "image/png": "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", "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "result.plot(plotter, 0)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Downloading Map from https://escher.github.io/1-0-0/6/maps/Escherichia%20coli/iJO1366.Central%20metabolism.json\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "2b3d269d41e5443b9769efc904fe3f1a", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Builder(reaction_data={'EX_co2_e': -0.088228, 'EX_cobalt2_e': -6e-06, 'DM_4crsol_c': 5.4e-05, 'DM_5drib_c': 0.…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "result.display_on_map(0, \"iJO1366.Central metabolism\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## References\n", "\n", "[1]Patil, K. R., Rocha, I., Förster, J., & Nielsen, J. (2005). Evolutionary programming as a platform for in silico metabolic engineering. BMC Bioinformatics, 6, 308. doi:10.1186/1471-2105-6-308\n", "\n", "[2]Burgard, A.P., Pharkya, P., Maranas, C.D. (2003), \"OptKnock: A Bilevel Programming Framework for Identifying Gene Knockout Strategies for Microbial Strain Optimization,\" Biotechnology and Bioengineering, 84(6), 647-657." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.2" } }, "nbformat": 4, "nbformat_minor": 4 }