{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "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": { "deletable": true, "editable": true }, "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": { "collapsed": false, "deletable": true, "editable": true }, "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", " var force = \"1\";\n", "\n", " if (typeof (window._bokeh_onload_callbacks) === \"undefined\" || force !== \"\") {\n", " window._bokeh_onload_callbacks = [];\n", " window._bokeh_is_loading = undefined;\n", " }\n", "\n", "\n", " \n", " if (typeof (window._bokeh_timeout) === \"undefined\" || force !== \"\") {\n", " window._bokeh_timeout = Date.now() + 5000;\n", " window._bokeh_failed_load = false;\n", " }\n", "\n", " var NB_LOAD_WARNING = {'data': {'text/html':\n", " \"
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\n", "
\n", "
\n", "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "p = phenotypic_phase_plane(model, variables=['BIOMASS_Ec_iJO1366_core_53p95M'], objective='EX_ac_e')\n", "p.plot(points=[(growth, acetate_production)])" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false, "deletable": true, "editable": true }, "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": 6, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "from cameo.strain_design.heuristic.evolutionary_based import OptGene" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "deletable": true, "editable": true, "scrolled": false }, "outputs": [], "source": [ "optgene = OptGene(model)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "deletable": true, "editable": true, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting optimization at Wed, 29 Mar 2017 20:41:05\n" ] }, { "data": { "text/html": [ "\n", "\n", "
\n", "
\n", "
\n", "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "2da3c06fd70e4cac91cb084c964393f2" } }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Finished after 00:15:48\n" ] } ], "source": [ "result = optgene.run(target=\"EX_ac_e\", \n", " biomass=\"BIOMASS_Ec_iJO1366_core_53p95M\",\n", " substrate=\"glc__D_e\",\n", " max_evaluations=5000,\n", " plot=False)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "deletable": true, "editable": true, "scrolled": true }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "d162aa1d32754f95b7db5f62305cc963" } }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

OptGene Result

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reactionsgenessizefva_minfva_maxtarget_fluxbiomass_fluxyieldfitness
0(ATPS4rpp,)((b3738,), (b3736,))1.00.00000014.187819-0.0000000.402478-0.000000-0.000000
1(ACNAMt2pp, ATPS4rpp)((b3731, b3224),)2.00.00000014.18781912.5406130.4024781.2540610.504732
2(SUCptspp, ATPS4rpp, ACMUMptspp)((b2429, b3731),)2.00.00000014.18781913.9209560.4024781.3920960.560288
3(SUCOAS, ATPS4rpp)((b0728, b3731),)2.012.22446214.18781913.9429320.4024781.3942930.561172
..............................
29(ADMDC, ATPS4rpp)((b0120, b3731),)2.00.00000014.18781913.9429320.4024781.3942930.561172
30(TDSR1, ATPS4rpp, DSBCGT)((b2893, b3731),)2.00.00000014.18781912.7298810.4024781.2729880.512350
31(ATPS4rpp,)((b0997, b2489, b3731),)3.00.00000014.18781913.9429320.4024781.3942930.561172
32(CYTBO3_4pp, ATPS4rpp, ZNabcpp)((b1858, b3731, b0432),)3.00.00000014.18781913.9429320.4024781.3942930.561172
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33 rows × 9 columns

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\n", " " ], "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "result" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "
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\n", "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "result.plot(0)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "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, \"iJO1366.Central metabolism\")" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "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": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "from cameo.strain_design.deterministic.linear_programming import OptKnock" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2583\n", "1705\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/niso/Dev/cameo/cameo/flux_analysis/structural.py:166: ClusterWarning:\n", "\n", "scipy.cluster: The symmetric non-negative hollow observation matrix looks suspiciously like an uncondensed distance matrix\n", "\n" ] } ], "source": [ "optknock = OptKnock(model, fraction_of_optimum=0.1)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Running multiple knockouts with OptKnock can take a few hours or days..." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "\n", " jQuery(\"#807b55b6-609b-4276-8414-b05f7458eb4c\").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": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "542d603c02c745059dcf16ca011a9360" } }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "

OptKnock:

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  • Target: EX_ac_e
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reactionssizeEX_ac_ebiomassfva_minfva_max
0{ATPS4rpp}1.013.9429320.4024770.014.187819
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\n", "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "result.plot(0)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "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, \"iJO1366.Central metabolism\")" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "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": "3.5.0" } }, "nbformat": 4, "nbformat_minor": 0 }