{ "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" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "model = models.bigg.iJO1366" ] }, { "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": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "" ], "text/vnd.plotly.v1+html": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.plotly.v1+json": { "data": [ { "fill": "toself", "fillcolor": "#B3E2CD", "hoverinfo": "none", "marker": { "line": { "color": "#B3E2CD" }, "opacity": 0.3 }, "mode": "line", "name": "WT", "opacity": 0.3, "type": "scatter", "x": [ 0, 0.05170377961720957, 0.10340755923441915, 0.15511133885162873, 0.2068151184688383, 0.2585188980860479, 0.31022267770325745, 0.361926457320467, 0.4136302369376766, 0.46533401655488615, 0.5170377961720958, 0.5687415757893053, 0.6204453554065149, 0.6721491350237244, 0.723852914640934, 0.7755566942581436, 0.8272604738753532, 0.8789642534925628, 0.9306680331097723, 0.9823718127269819, 0.9823718127269819, 0.9306680331097723, 0.8789642534925628, 0.8272604738753532, 0.7755566942581436, 0.723852914640934, 0.6721491350237244, 0.6204453554065149, 0.5687415757893053, 0.5170377961720958, 0.46533401655488615, 0.4136302369376766, 0.361926457320467, 0.31022267770325745, 0.2585188980860479, 0.2068151184688383, 0.15511133885162873, 0.10340755923441915, 0.05170377961720957, 0, 0, 0 ], "y": [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.443045765687657e-13, 1.9178137651822735, 3.8356275303643903, 5.753441295546489, 7.49395498751835, 9.191567907024954, 10.69046951910808, 12.166899126226081, 13.629127229343755, 15.091355332461378, 16.553604142942426, 17.955299118971524, 19.352213259712343, 20.746340227647277, 22.137517223957918, 23.52870724650301, 24.919897269047976, 26.31108729159315, 27.7022773141384, 29.09346733668346, 0, 29.09346733668346 ] }, { "marker": { "color": "green" }, "mode": "markers", "name": "Data Points", "type": "scatter", "x": [ 0.9823718127269799 ], "y": [ 0 ] } ], "layout": { "height": 432, "title": "Phenotypic Phase Plane (flux)", "width": 700, "xaxis": { "title": "BIOMASS_Ec_iJO1366_core_53p95M [h^-1]" }, "yaxis": { "title": "EX_ac_e [mmol gDW^-1 h^-1]" } } }, "text/html": [ "
" ], "text/vnd.plotly.v1+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(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": { "scrolled": false }, "outputs": [], "source": [ "optgene = OptGene(model)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting optimization at Mon, 22 Jan 2018 13:41:01\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "e7aa5a7a3eb4410e9dcdc72383c01c85", "version_major": 2, "version_minor": 0 }, "text/html": [ "

Failed to display Jupyter Widget of type HBox.

\n", "

\n", " If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n", " that the widgets JavaScript is still loading. If this message persists, it\n", " likely means that the widgets JavaScript library is either not installed or\n", " not enabled. See the Jupyter\n", " Widgets Documentation for setup instructions.\n", "

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\n" ], "text/plain": [ "HBox()" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Finished after 05:13:14\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": { "scrolled": false }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "2164424c8b9344d0be7a09f2e2e61fc3", "version_major": 2, "version_minor": 0 }, "text/html": [ "

Failed to display Jupyter Widget of type HBox.

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OptGene Result

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reactionsgenessizefva_minfva_maxtarget_fluxbiomass_fluxyieldfitness
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3(PSP_L, ATPS4rpp)((b3733, b4388),)20.014.97629614.8013910.3883651.4801390.574834
4(PHEMEabcpp, ATPS4rpp)((b3733, b2199), (b3733, b2198))20.014.18781913.7316880.4024781.3731690.552670
5(EDTXS4, EDTXS2, ATPS4rpp)((b1855, b3733),)20.014.1878190.2344720.4024780.0234470.009437
6(SULFACabcpp, ETHSO3abcpp, ATPS4rpp, MSO3abcpp)((b0933, b3733),)20.014.18781913.7316880.4024781.3731690.552670
7(CITL, ATPS4rpp)((b0614, b3733),)20.014.18781913.9429320.4024781.3942930.561172
8(ATPS4rpp, NMNPtpp)((b3733, b0751),)20.014.18781913.7316880.4024781.3731690.552670
9(ATPS4rpp, GHMT2r, THFAT)((b3733, b2551),)20.014.64467414.2608530.3933621.4260850.560968
10(ATPS4rpp, G6PDA)((b3733, b0678),)20.014.18781913.7316880.4024781.3731690.552670
11(ATPS4rpp,)((b3386, b3733), (b0186, b3733))20.014.18781912.7518570.4024781.2751860.513234
12(COLIPAKpp, ATPS4rpp)((b2174, b3733),)20.014.18781913.9429320.4024781.3942930.561172
13(SUCTARTtpp, ATPS4rpp, TARTt2_3pp)((b4123, b3733, b4219),)30.014.18781912.7518570.4024781.2751860.513234
14(PPCSCT, ATPS4rpp)((b2920, b3733, b4219),)30.014.18781912.7518570.4024781.2751860.513234
15(PSP_L, ATPS4rpp)((b3733, b4388, b4219),)30.014.97629612.8021750.3883651.2802180.497192
16(FEENTERabcpp, ATPS4rpp, FE3DHBZSabcpp)((b0592, b2224, b3733),)30.014.187819-0.0000000.402478-0.000000-0.000000
17(NMNPtpp, ATPS4rpp, MTRPOX, SARCOX)((b2291, b0751, b3733, b1059),)40.014.18781913.9429320.4024781.3942930.561172
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" ], "text/vnd.plotly.v1+html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "result.plot(0)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "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": {}, "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(\"#22086c8a-5ba6-405c-b460-0b6e358a0d76\").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": "ace89bf5283b4ec6b6ee817ab5936531", "version_major": 2, "version_minor": 0 }, "text/html": [ "

Failed to display Jupyter Widget of type HBox.

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OptKnock:

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  • Target: EX_ac_e
    • \n", "
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reactionssizeEX_ac_ebiomassfva_minfva_max
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