{ "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": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "\n", "(function(global) {\n", " function now() {\n", " return new Date();\n", " }\n", "\n", " if (typeof (window._bokeh_onload_callbacks) === \"undefined\") {\n", " window._bokeh_onload_callbacks = [];\n", " }\n", "\n", " function run_callbacks() {\n", " window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n", " delete window._bokeh_onload_callbacks\n", " console.info(\"Bokeh: all callbacks have finished\");\n", " }\n", "\n", " function load_libs(js_urls, callback) {\n", " window._bokeh_onload_callbacks.push(callback);\n", " if (window._bokeh_is_loading > 0) {\n", " console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", " return null;\n", " }\n", " if (js_urls == null || js_urls.length === 0) {\n", " run_callbacks();\n", " return null;\n", " }\n", " console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", " window._bokeh_is_loading = js_urls.length;\n", " for (var i = 0; i < js_urls.length; i++) {\n", " var url = js_urls[i];\n", " var s = document.createElement('script');\n", " s.src = url;\n", " s.async = false;\n", " s.onreadystatechange = s.onload = function() {\n", " window._bokeh_is_loading--;\n", " if (window._bokeh_is_loading === 0) {\n", " console.log(\"Bokeh: all BokehJS libraries loaded\");\n", " run_callbacks()\n", " }\n", " };\n", " s.onerror = function() {\n", " console.warn(\"failed to load library \" + url);\n", " };\n", " console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", " document.getElementsByTagName(\"head\")[0].appendChild(s);\n", " }\n", " };\n", "\n", " var js_urls = ['https://cdn.pydata.org/bokeh/release/bokeh-0.11.1.min.js', 'https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.11.1.min.js', 'https://cdn.pydata.org/bokeh/release/bokeh-compiler-0.11.1.min.js'];\n", "\n", " var inline_js = [\n", " function(Bokeh) {\n", " Bokeh.set_log_level(\"info\");\n", " },\n", " \n", " function(Bokeh) {\n", " Bokeh.$(\"#d1978319-7775-494a-a9c5-4dd8485b7ea3\").text(\"BokehJS successfully loaded\");\n", " },\n", " function(Bokeh) {\n", " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.11.1.min.css\");\n", " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.11.1.min.css\");\n", " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.11.1.min.css\");\n", " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.11.1.min.css\");\n", " }\n", " ];\n", "\n", " function run_inline_js() {\n", " for (var i = 0; i < inline_js.length; i++) {\n", " inline_js[i](window.Bokeh);\n", " }\n", " }\n", "\n", " if (window._bokeh_is_loading === 0) {\n", " console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", " run_inline_js();\n", " } else {\n", " load_libs(js_urls, function() {\n", " console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n", " run_inline_js();\n", " });\n", " }\n", "}(this));" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from cameo import models" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "model = models.bigg.e_coli_core.copy()\n", "model.solver = \"cplex\"" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from cameo import phenotypic_phase_plane" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "
\n", "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ppp = phenotypic_phase_plane(model, variables=[model.reactions.BIOMASS_Ecoli_core_w_GAM], objective=model.reactions.EX_ac_e)\n", "ppp.plot()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "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 authors summarizes the OptGene workflow.\n", "\n", "\n", "\n", "Every iteration we keep the best 50 individuals so we can generate a library of targets." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from cameo.strain_design.heuristic.evolutionary_based import OptGene" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [], "source": [ "optgene = OptGene(model)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting optimization at Fri, 10 Jun 2016 06:38:55\n" ] }, { "data": { "text/html": [ "\n", "\n", "
\n", "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/envs/python3.4/lib/python3.4/site-packages/ipywidgets/widgets/widget_string.py:55: UserWarning:\n", "\n", "The Latex widget is deprecated. Use Label instead\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Finished after 00:00:11\n" ] } ], "source": [ "result = optgene.run(target=\"EX_ac_e\", \n", " biomass=model.reactions.BIOMASS_Ecoli_core_w_GAM,\n", " substrate=model.metabolites.glc__D_e,\n", " max_evaluations=5000,\n", " plot=False)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/envs/python3.4/lib/python3.4/site-packages/ipywidgets/widgets/widget_string.py:55: UserWarning:\n", "\n", "The Latex widget is deprecated. Use Label instead\n", "\n" ] }, { "data": { "text/html": [ "\n", "

OptGene Result

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reactionsgenessizefva_minfva_maxtarget_fluxbiomass_fluxyieldfitness
0(SUCDi, GND)((b4090, b2296, b0723, b2029, b0724),)5.09.08353710.71573710.6219570.6170371.0621960.655414
1(SUCDi, GND, GLUt2r)((b0721, b3962, b4077, b2029, b2133),)5.09.08353710.71573710.6219570.6170371.0621960.655414
2(FRD7, SUCDi, PGL, ADK1)((b0474, b0767, b0118, b4154, b0721),)5.09.08353710.71573710.6219570.6170371.0621960.655414
3(SUCDi, PGL, ADK1)((b0767, b2492, b0903, b2464, b0474, b0721),)6.09.08353710.71573710.6219570.6170371.0621960.655414
4(FRD7, SUCDi, GND)((b0903, b4152, b4232, b0724, b0723, b2029),)6.09.08353710.71573710.6219570.6170371.0621960.655414
5(SUCDi, GND, PPS)((b1702, b2464, b0902, b0733, b0721, b2029), (...6.09.08353710.71573710.6219570.6170371.0621960.655414
6(SUCDi, GND, ADK1)((b2464, b0474, b4395, b0721, b2935, b2029),)6.09.08353710.71573710.6219570.6170371.0621960.655414
7(FRD7, SUCDi, SUCOAS, GND)((b0729, b4152, b1621, b1241, b0723, b2029),)6.09.62760410.71573710.6219570.6170371.0621960.655414
8(SUCDi, GND, ME1)((b1479, b1812, b0724, b1297, b0723, b2029),)6.09.08353710.71573710.6219570.6170371.0621960.655414
9(GLUSy, SUCDi, GND)((b3952, b3212, b3925, b2464, b0721, b2029),)6.09.08353710.71573710.6219570.6170371.0621960.655414
10(GLUt2r, SUCDi, GND)((b3386, b2464, b2133, b0721, b4077, b2029), (...6.09.08353710.71573710.6219570.6170371.0621960.655414
11(SUCDi, PGL, GND)((b0767, b0722, b2464, b0118, b2935, b2029),)6.09.08353710.71573710.6219570.6170371.0621960.655414
12(SUCDi, PGL)((b0767, b0734, b0903, b2464, b0118, b0721, b4...7.09.08353710.71573710.6219570.6170371.0621960.655414
13(SUCDi, GND, PPS)((b1702, b3603, b2458, b0721, b0979, b0875, b2...7.09.08353710.71573710.6219570.6170371.0621960.655414
14(SUCDi, GND, PGL, ADK1)((b0767, b0903, b2464, b0118, b0474, b0721, b2...7.09.08353710.71573710.6219570.6170371.0621960.655414
15(SUCDi, GND, FRUpts2)((b1818, b0733, b4395, b0721, b0723, b2935, b2...7.09.08353710.71573710.6219570.6170371.0621960.655414
16(SUCDi, GND, AKGDH, ME2)((b0727, b2463, b0724, b0721, b4090, b0723, b2...7.09.62760410.71573710.6219570.6170371.0621960.655414
17(SUCDi, PGL, ADK1)((b0767, b0903, b2464, b0118, b0474, b0721, b3...7.09.08353710.71573710.6219570.6170371.0621960.655414
18(SUCDi, GND, AKGDH)((b0727, b0451, b0724, b0721, b4090, b0723, b2...7.09.62760410.71573710.6219570.6170371.0621960.655414
19(AKGDH, G6PDH2r, SUCDi, GND, PPS)((b1702, b0727, b1852, b0721, b0875, b2029, b0...7.09.62760410.71573710.6219570.6170371.0621960.655414
20(SUCDi, GND, ADK1)((b3952, b0903, b2464, b0118, b0474, b0721, b2...7.09.08353710.71573710.6219570.6170371.0621960.655414
21(PGL, SUCDi)((b0767, b0903, b2464, b0118, b1241, b0721, b4...7.09.08353710.71573710.6219570.6170371.0621960.655414
22(ME1, SUCDi, GND, FRUpts2)((b3925, b1818, b1479, b4395, b0723, b3115, b2...7.09.08353710.71573710.6219570.6170371.0621960.655414
23(GLUSy, ME1, SUCDi, GND)((b3212, b1479, b0721, b1297, b0723, b3115, b2...7.09.08353710.71573710.6219570.6170371.0621960.655414
24(FRD7, SUCDi, GND, ME1)((b0903, b4152, b1479, b0724, b0721, b0723, b2...7.09.08353710.71573710.6219570.6170371.0621960.655414
25(FRD7, SUCDi, GND, AKGDH)((b4152, b0727, b0724, b0721, b4090, b0723, b2...7.09.62760410.71573710.6219570.6170371.0621960.655414
26(FRD7, SUCDi, GND)((b3603, b2133, b0451, b0721, b2417, b2029, b4...7.09.08353710.71573710.6219570.6170371.0621960.655414
27(FRD7, ME1, SUCDi, GND)((b4152, b1479, b1241, b0724, b0721, b0723, b2...7.09.08353710.71573710.6219570.6170371.0621960.655414
28(FRD7, GLUSy, ME1, SUCDi, GND)((b3212, b4152, b1479, b0721, b1297, b0723, b2...7.09.08353710.71573710.6219570.6170371.0621960.655414
29(AKGDH, SUCDi, GND, PPS)((b1702, b3603, b0727, b0721, b0875, b2029, b0...7.09.62760410.71573710.6219570.6170371.0621960.655414
30(SUCDi, PGL, GND, ADK1)((b0767, b2464, b0118, b0474, b0721, b2935, b2...7.09.08353710.71573710.6219570.6170371.0621960.655414
31(SUCDi, PGL, GND)((b0351, b0767, b0903, b2464, b0118, b0721, b2...8.09.08353710.71573710.6219570.6170371.0621960.655414
32(FRD7, AKGDH, SUCDi, GND, ADK1)((b0903, b4152, b2464, b0727, b0118, b0474, b0...8.09.62760410.71573710.6219570.6170371.0621960.655414
33(SUCDi, GND, ADK1)((b0903, b2464, b0474, b0978, b0721, b4090, b3...8.09.08353710.71573710.6219570.6170371.0621960.655414
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\n", " " ], "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "result" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "
\n", "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "result.plot(0)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", "\n", "\n", "\n", "
\n", "\n", " \n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "result.display_on_map(0, \"e_coli_core.Core 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": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from cameo.strain_design.deterministic.linear_programming import OptKnock" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Warning: File contains basis. Basis is loaded.\n" ] } ], "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": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "\n", " jQuery(\"#5c512b9e-3400-4b8c-9417-13a937191376\").remove();\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "result = optknock.run(max_knockouts=1, target=\"EX_ac_e\", biomass=\"BIOMASS_Ecoli_core_w_GAM\")" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/envs/python3.4/lib/python3.4/site-packages/ipywidgets/widgets/widget_string.py:55: UserWarning:\n", "\n", "The Latex widget is deprecated. Use Label instead\n", "\n" ] }, { "data": { "text/html": [ "\n", "

OptKnock:

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  • Target: EX_ac_e
    • \n", "
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reactionssizeEX_ac_ebiomassfva_minfva_max
0{ATPS4r}1.014.3122670.374230.014.369145
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" ], "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "result" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "
\n", "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "result.plot(0)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", "\n", "\n", "\n", "
\n", "\n", " \n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "result.display_on_map(0, \"e_coli_core.Core 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." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercises" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Use OptGene or OptKnock to find a growth coupled design for a product of your choice!" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3.4", "language": "python", "name": "python3.4" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.4" }, "widgets": { "state": { "075e53251c414266b84f8f2b24eee1e8": { "views": [] }, "08a43129692e4478bd06b62439929a5a": { "views": [] }, 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