{ "cells": [ { "cell_type": "markdown", "id": "49bcb5b0-f19d-4b96-a5f1-e0ae30f66d8f", "metadata": {}, "source": [ "### `A` up-regulates `B` , by being *the limiting reagent* in the reaction: \n", "### `A + X <-> 2B` (mostly forward), where `X` is plentiful\n", "1st-order kinetics. \n", "If [A] is low, [B] remains low, too. Then, if [A] goes high, then so does [B]. However, at that point, A can no longer bring B down to any substantial extent.\n", "\n", "**Single-bin reaction**\n", "\n", "Based on experiment \"reactions_single_compartment/up_regulate_1\"\n", "\n", "LAST REVISED: June 4, 2023" ] }, { "cell_type": "code", "execution_count": 1, "id": "d9efa3fd-e95d-4e1c-878a-81ae932b2709", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Added 'D:\\Docs\\- MY CODE\\BioSimulations\\life123-Win7' to sys.path\n" ] } ], "source": [ "import set_path # Importing this module will add the project's home directory to sys.path" ] }, { "cell_type": "code", "execution_count": 2, "id": "01bae555-3dcf-42c1-bddc-9477a37f49f8", "metadata": { "tags": [] }, "outputs": [], "source": [ "from experiments.get_notebook_info import get_notebook_basename\n", "\n", "from src.modules.reactions.reaction_data import ChemData\n", "from src.modules.reactions.reaction_dynamics import ReactionDynamics\n", "from src.life_1D.bio_sim_1d import BioSim1D\n", "\n", "import plotly.express as px\n", "from src.modules.html_log.html_log import HtmlLog as log\n", "from src.modules.visualization.graphic_log import GraphicLog" ] }, { "cell_type": "code", "execution_count": 3, "id": "cc53849f-351d-49e0-bfa8-22f8d8e22f8e", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-> Output will be LOGGED into the file 'up_regulation_1.log.htm'\n" ] } ], "source": [ "# Initialize the HTML logging\n", "log_file = get_notebook_basename() + \".log.htm\" # Use the notebook base filename for the log file\n", "\n", "# Set up the use of some specified graphic (Vue) components\n", "GraphicLog.config(filename=log_file,\n", " components=[\"vue_cytoscape_1\"],\n", " extra_js=\"https://cdnjs.cloudflare.com/ajax/libs/cytoscape/3.21.2/cytoscape.umd.js\")" ] }, { "cell_type": "code", "execution_count": 4, "id": "23c15e66-52e4-495b-aa3d-ecddd8d16942", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of reactions: 1 (at temp. 25 C)\n", "0: A + X <-> 2 B (kF = 8 / kR = 2 / Delta_G = -3,436.56 / K = 4) | 1st order in all reactants & products\n", "[GRAPHIC ELEMENT SENT TO LOG FILE `up_regulation_1.log.htm`]\n" ] } ], "source": [ "# Initialize the system\n", "chem_data = ChemData(names=[\"A\", \"X\", \"B\"]) # NOTE: Diffusion not applicable (just 1 bin)\n", "\n", "# Reaction A + X <-> 2B , with 1st-order kinetics for all species\n", "chem_data.add_reaction(reactants=[(\"A\") , (\"X\")], products=[(2, \"B\")],\n", " forward_rate=8., reverse_rate=2.)\n", "\n", "chem_data.describe_reactions()\n", "\n", "# Send the plot of the reaction network to the HTML log file\n", "graph_data = chem_data.prepare_graph_network()\n", "GraphicLog.export_plot(graph_data, \"vue_cytoscape_1\")" ] }, { "cell_type": "code", "execution_count": 5, "id": "be6fabbe-bded-4ff6-b220-5610e73b401f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SYSTEM STATE at Time t = 0:\n", "1 bins and 3 species:\n", " Species 0 (A). Diff rate: None. Conc: [5.]\n", " Species 1 (X). Diff rate: None. Conc: [100.]\n", " Species 2 (B). Diff rate: None. Conc: [0.]\n" ] } ], "source": [ "bio = BioSim1D(n_bins=1, chem_data=chem_data)\n", "\n", "bio.set_uniform_concentration(species_name=\"A\", conc=5.) # Scarce\n", "bio.set_uniform_concentration(species_name=\"X\", conc=100.) # Plentiful\n", "# Initially, no \"B\" is present\n", "\n", "bio.describe_state()" ] }, { "cell_type": "code", "execution_count": 6, "id": "5562fea2-834e-40a9-9b1d-5ea28a0100bf", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
SYSTEM TIMEAXBcaption
005.0100.00.0
\n", "
" ], "text/plain": [ " SYSTEM TIME A X B caption\n", "0 0 5.0 100.0 0.0 " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Save the state of the concentrations of all species at bin 0 (the only bin in this system)\n", "bio.add_snapshot(bio.bin_snapshot(bin_address = 0))\n", "bio.get_history()" ] }, { "cell_type": "markdown", "id": "0b46b395-3f68-4dbd-b0c5-d67a0e623726", "metadata": { "tags": [] }, "source": [ "### Take the initial system to equilibrium" ] }, { "cell_type": "code", "execution_count": 7, "id": "bcf652b8-e0dc-438e-bdbe-02216c1d52a0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SYSTEM STATE at Time t = 0.015:\n", "1 bins and 3 species:\n", " Species 0 (A). Diff rate: None. Conc: [0.02617327]\n", " Species 1 (X). Diff rate: None. Conc: [95.02617327]\n", " Species 2 (B). Diff rate: None. Conc: [9.94765346]\n" ] }, { "data": { "text/html": [ "
\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", " \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", "
SYSTEM TIMEAXBcaption
00.0005.000000100.0000000.000000
10.0011.82800096.8280006.344000
20.0020.70100295.7010028.597996
30.0030.28191695.2819169.436168
40.0040.12351995.1235199.752963
50.0050.06328795.0632879.873425
60.0060.04033195.0403319.919337
70.0070.03157595.0315759.936851
80.0080.02823395.0282339.943534
90.0090.02695895.0269589.946084
100.0100.02647195.0264719.947058
110.0110.02628595.0262859.947429
120.0120.02621595.0262159.947571
130.0130.02618895.0261889.947625
140.0140.02617795.0261779.947646
150.0150.02617395.0261739.947653
\n", "
" ], "text/plain": [ " SYSTEM TIME A X B caption\n", "0 0.000 5.000000 100.000000 0.000000 \n", "1 0.001 1.828000 96.828000 6.344000 \n", "2 0.002 0.701002 95.701002 8.597996 \n", "3 0.003 0.281916 95.281916 9.436168 \n", "4 0.004 0.123519 95.123519 9.752963 \n", "5 0.005 0.063287 95.063287 9.873425 \n", "6 0.006 0.040331 95.040331 9.919337 \n", "7 0.007 0.031575 95.031575 9.936851 \n", "8 0.008 0.028233 95.028233 9.943534 \n", "9 0.009 0.026958 95.026958 9.946084 \n", "10 0.010 0.026471 95.026471 9.947058 \n", "11 0.011 0.026285 95.026285 9.947429 \n", "12 0.012 0.026215 95.026215 9.947571 \n", "13 0.013 0.026188 95.026188 9.947625 \n", "14 0.014 0.026177 95.026177 9.947646 \n", "15 0.015 0.026173 95.026173 9.947653 " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bio.react(time_step=0.0005, n_steps=30, snapshots={\"frequency\": 2, \"sample_bin\": 0}) # At every other step, take a snapshot \n", " # of all species at bin 0\n", "bio.describe_state()\n", "bio.get_history()" ] }, { "cell_type": "markdown", "id": "7dc56592-179d-4e4c-b75a-8eb81dcafe71", "metadata": {}, "source": [ "A, as the scarse limiting reagent, stops the reaction. \n", "When A is low, B is also low." ] }, { "cell_type": "markdown", "id": "962acf15-3b50-40e4-9daa-3dcca7d3291a", "metadata": {}, "source": [ "### Equilibrium" ] }, { "cell_type": "markdown", "id": "809b4afa-fb2f-4ac3-92c9-083fc487c81b", "metadata": {}, "source": [ "Consistent with the 4/1 ratio of forward/reverse rates (and the 1st order reactions),\n", "the systems settles in the following equilibrium:" ] }, { "cell_type": "code", "execution_count": 8, "id": "064d9592-d9f6-4d12-9f54-67f75bdf72ed", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "A + X <-> 2 B\n", "Final concentrations: [B] = 9.948 ; [A] = 0.02617 ; [X] = 95.03\n", "1. Ratio of reactant/product concentrations, adjusted for reaction orders: 3.99963\n", " Formula used: [B] / ([A][X])\n", "2. Ratio of forward/reverse reaction rates: 4.0\n", "Discrepancy between the two values: 0.009347 %\n", "Reaction IS in equilibrium (within 1% tolerance)\n", "\n" ] }, { "data": { "text/plain": [ "True" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Verify that the reaction has reached equilibrium\n", "bio.reaction_dynamics.is_in_equilibrium(rxn_index=0, conc=bio.bin_snapshot(bin_address = 0))" ] }, { "cell_type": "markdown", "id": "cbf6c9c7-8cec-400f-9e70-49ff1a9f485c", "metadata": { "tags": [] }, "source": [ "# Plots of changes of concentration with time" ] }, { "cell_type": "code", "execution_count": 9, "id": "665dfff9-e943-44e1-b76d-af363d94c9f8", "metadata": {}, "outputs": [ { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "Chemical=A
SYSTEM TIME=%{x}
concentration=%{y}", "legendgroup": "A", "line": { "color": "red", "dash": "solid" }, "marker": { "symbol": "circle" }, "mode": "lines", "name": "A", "orientation": "v", "showlegend": true, "type": "scatter", "x": [ 0, 0.001, 0.002, 0.003, 0.004, 0.005000000000000001, 0.006000000000000002, 0.007000000000000003, 0.008000000000000004, 0.009000000000000005, 0.010000000000000005, 0.011000000000000006, 0.012000000000000007, 0.013000000000000008, 0.014000000000000009, 0.01500000000000001 ], "xaxis": "x", "y": [ 5, 1.828, 0.7010021302186202, 0.28191596761389115, 0.12351872192313587, 0.06328727686095315, 0.04033138739837671, 0.031574626645931, 0.02823316007394518, 0.026957938077751462, 0.02647124466112761, 0.026285492804494114, 0.02621459808083849, 0.026187540072133496, 0.02617721297656817, 0.026173271483838915 ], "yaxis": "y" }, { "hovertemplate": "Chemical=X
SYSTEM TIME=%{x}
concentration=%{y}", "legendgroup": "X", "line": { "color": "darkorange", "dash": "solid" }, "marker": { "symbol": "circle" }, "mode": "lines", "name": "X", "orientation": "v", "showlegend": true, "type": "scatter", "x": [ 0, 0.001, 0.002, 0.003, 0.004, 0.005000000000000001, 0.006000000000000002, 0.007000000000000003, 0.008000000000000004, 0.009000000000000005, 0.010000000000000005, 0.011000000000000006, 0.012000000000000007, 0.013000000000000008, 0.014000000000000009, 0.01500000000000001 ], "xaxis": "x", "y": [ 100, 96.828, 95.70100213021863, 95.2819159676139, 95.12351872192315, 95.06328727686096, 95.04033138739838, 95.03157462664595, 95.02823316007397, 95.02695793807777, 95.02647124466115, 95.02628549280452, 95.02621459808086, 95.02618754007214, 95.02617721297658, 95.02617327148384 ], "yaxis": "y" }, { "hovertemplate": "Chemical=B
SYSTEM TIME=%{x}
concentration=%{y}", "legendgroup": "B", "line": { "color": "green", "dash": "solid" }, "marker": { "symbol": "circle" }, "mode": "lines", "name": "B", "orientation": "v", "showlegend": true, "type": "scatter", "x": [ 0, 0.001, 0.002, 0.003, 0.004, 0.005000000000000001, 0.006000000000000002, 0.007000000000000003, 0.008000000000000004, 0.009000000000000005, 0.010000000000000005, 0.011000000000000006, 0.012000000000000007, 0.013000000000000008, 0.014000000000000009, 0.01500000000000001 ], "xaxis": "x", "y": [ 0, 6.343999999999999, 8.59799573956276, 9.43616806477222, 9.752962556153731, 9.873425446278096, 9.919337225203249, 9.93685074670814, 9.943533679852111, 9.946084123844498, 9.947057510677745, 9.947429014391012, 9.947570803838325, 9.947624919855734, 9.947645574046865, 9.947653457032322 ], "yaxis": "y" } ], "layout": { "autosize": true, "legend": { "title": { "text": "Chemical" }, "tracegroupgap": 0 }, "template": { "data": { "bar": [ { "error_x": { "color": "#2a3f5f" }, "error_y": { "color": "#2a3f5f" }, "marker": { "line": { "color": "#E5ECF6", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "bar" } ], "barpolar": [ { "marker": { "line": { "color": "#E5ECF6", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "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": { "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "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": [ { "fillpattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 }, "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": "Changes in concentrations (reaction A + X <-> 2B)" }, "xaxis": { "anchor": "y", "autorange": true, "domain": [ 0, 1 ], "range": [ 0, 0.01500000000000001 ], "title": { "text": "SYSTEM TIME" }, "type": "linear" }, "yaxis": { "anchor": "x", "autorange": true, "domain": [ 0, 1 ], "range": [ -5.555555555555555, 105.55555555555556 ], "title": { "text": "concentration" }, "type": "linear" } } }, "image/png": "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", "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = px.line(data_frame=bio.get_history(), x=\"SYSTEM TIME\", y=[\"A\", \"X\", \"B\"], \n", " title=\"Changes in concentrations (reaction A + X <-> 2B)\",\n", " color_discrete_sequence = ['red', 'darkorange', 'green'],\n", " labels={\"value\":\"concentration\", \"variable\":\"Chemical\"})\n", "fig.show()" ] }, { "cell_type": "markdown", "id": "448ec7fa-6529-438b-84ba-47888c2cd080", "metadata": { "tags": [] }, "source": [ "# Now, let's suddenly increase [A]" ] }, { "cell_type": "code", "execution_count": 10, "id": "7245be7a-c9db-45f5-b033-d6c521237a9c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SYSTEM STATE at Time t = 0.015:\n", "1 bins and 3 species:\n", " Species 0 (A). Diff rate: None. Conc: [50.]\n", " Species 1 (X). Diff rate: None. Conc: [95.02617327]\n", " Species 2 (B). Diff rate: None. Conc: [9.94765346]\n" ] } ], "source": [ "bio.set_bin_conc(bin_address=0, species_index=0, conc=50.)\n", "bio.describe_state()" ] }, { "cell_type": "code", "execution_count": 11, "id": "007161ef-f4d0-4623-92c5-0fe3d2bda98a", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\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", " \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", "
SYSTEM TIMEAXBcaption
00.0005.000000100.0000000.000000
10.0011.82800096.8280006.344000
20.0020.70100295.7010028.597996
30.0030.28191695.2819169.436168
40.0040.12351995.1235199.752963
50.0050.06328795.0632879.873425
60.0060.04033195.0403319.919337
70.0070.03157595.0315759.936851
80.0080.02823395.0282339.943534
90.0090.02695895.0269589.946084
100.0100.02647195.0264719.947058
110.0110.02628595.0262859.947429
120.0120.02621595.0262159.947571
130.0130.02618895.0261889.947625
140.0140.02617795.0261779.947646
150.0150.02617395.0261739.947653
160.01550.00000095.0261739.947653
\n", "
" ], "text/plain": [ " SYSTEM TIME A X B caption\n", "0 0.000 5.000000 100.000000 0.000000 \n", "1 0.001 1.828000 96.828000 6.344000 \n", "2 0.002 0.701002 95.701002 8.597996 \n", "3 0.003 0.281916 95.281916 9.436168 \n", "4 0.004 0.123519 95.123519 9.752963 \n", "5 0.005 0.063287 95.063287 9.873425 \n", "6 0.006 0.040331 95.040331 9.919337 \n", "7 0.007 0.031575 95.031575 9.936851 \n", "8 0.008 0.028233 95.028233 9.943534 \n", "9 0.009 0.026958 95.026958 9.946084 \n", "10 0.010 0.026471 95.026471 9.947058 \n", "11 0.011 0.026285 95.026285 9.947429 \n", "12 0.012 0.026215 95.026215 9.947571 \n", "13 0.013 0.026188 95.026188 9.947625 \n", "14 0.014 0.026177 95.026177 9.947646 \n", "15 0.015 0.026173 95.026173 9.947653 \n", "16 0.015 50.000000 95.026173 9.947653 " ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Save the state of the concentrations of all species at bin 0 (the only bin in this system)\n", "bio.add_snapshot(bio.bin_snapshot(bin_address = 0))\n", "bio.get_history()" ] }, { "cell_type": "markdown", "id": "24455d58-a0ea-43fa-b6ad-95c42a8b34b2", "metadata": {}, "source": [ "### Again, take the system to equilibrium" ] }, { "cell_type": "code", "execution_count": 12, "id": "c06fd8d8-d550-4e35-a239-7b91bee32be9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SYSTEM STATE at Time t = 0.035:\n", "1 bins and 3 species:\n", " Species 0 (A). Diff rate: None. Conc: [0.60107953]\n", " Species 1 (X). Diff rate: None. Conc: [45.6272528]\n", " Species 2 (B). Diff rate: None. Conc: [108.74549439]\n" ] }, { "data": { "text/html": [ "
\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", " \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", " \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", "
SYSTEM TIMEAXBcaption
00.0005.000000100.0000000.000000
10.0011.82800096.8280006.344000
20.0020.70100295.7010028.597996
30.0030.28191695.2819169.436168
40.0040.12351995.1235199.752963
50.0050.06328795.0632879.873425
60.0060.04033195.0403319.919337
70.0070.03157595.0315759.936851
80.0080.02823395.0282339.943534
90.0090.02695895.0269589.946084
100.0100.02647195.0264719.947058
110.0110.02628595.0262859.947429
120.0120.02621595.0262159.947571
130.0130.02618895.0261889.947625
140.0140.02617795.0261779.947646
150.0150.02617395.0261739.947653
160.01550.00000095.0261739.947653
170.01621.62338866.64956166.700877
180.01712.12073757.14691085.706180
190.0187.47130152.49747595.005051
200.0194.87132449.897498100.205005
210.0203.31694948.343122103.313756
220.0212.35187347.378046105.243908
230.0221.73888646.765059106.469882
240.0231.34397146.370144107.259712
250.0241.08723846.113411107.773177
260.0250.91936145.945534108.108931
270.0260.80916945.835342108.329315
280.0270.73666145.762834108.474332
290.0280.68887145.715044108.569911
300.0290.65734045.683513108.632974
310.0300.63652045.662694108.674613
320.0310.62276845.648941108.702118
330.0320.61368045.639853108.720293
340.0330.60767445.633847108.732305
350.0340.60370445.629877108.740246
360.0350.60108045.627253108.745494
\n", "
" ], "text/plain": [ " SYSTEM TIME A X B caption\n", "0 0.000 5.000000 100.000000 0.000000 \n", "1 0.001 1.828000 96.828000 6.344000 \n", "2 0.002 0.701002 95.701002 8.597996 \n", "3 0.003 0.281916 95.281916 9.436168 \n", "4 0.004 0.123519 95.123519 9.752963 \n", "5 0.005 0.063287 95.063287 9.873425 \n", "6 0.006 0.040331 95.040331 9.919337 \n", "7 0.007 0.031575 95.031575 9.936851 \n", "8 0.008 0.028233 95.028233 9.943534 \n", "9 0.009 0.026958 95.026958 9.946084 \n", "10 0.010 0.026471 95.026471 9.947058 \n", "11 0.011 0.026285 95.026285 9.947429 \n", "12 0.012 0.026215 95.026215 9.947571 \n", "13 0.013 0.026188 95.026188 9.947625 \n", "14 0.014 0.026177 95.026177 9.947646 \n", "15 0.015 0.026173 95.026173 9.947653 \n", "16 0.015 50.000000 95.026173 9.947653 \n", "17 0.016 21.623388 66.649561 66.700877 \n", "18 0.017 12.120737 57.146910 85.706180 \n", "19 0.018 7.471301 52.497475 95.005051 \n", "20 0.019 4.871324 49.897498 100.205005 \n", "21 0.020 3.316949 48.343122 103.313756 \n", "22 0.021 2.351873 47.378046 105.243908 \n", "23 0.022 1.738886 46.765059 106.469882 \n", "24 0.023 1.343971 46.370144 107.259712 \n", "25 0.024 1.087238 46.113411 107.773177 \n", "26 0.025 0.919361 45.945534 108.108931 \n", "27 0.026 0.809169 45.835342 108.329315 \n", "28 0.027 0.736661 45.762834 108.474332 \n", "29 0.028 0.688871 45.715044 108.569911 \n", "30 0.029 0.657340 45.683513 108.632974 \n", "31 0.030 0.636520 45.662694 108.674613 \n", "32 0.031 0.622768 45.648941 108.702118 \n", "33 0.032 0.613680 45.639853 108.720293 \n", "34 0.033 0.607674 45.633847 108.732305 \n", "35 0.034 0.603704 45.629877 108.740246 \n", "36 0.035 0.601080 45.627253 108.745494 " ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bio.react(time_step=0.0005, n_steps=40, snapshots={\"frequency\": 2, \"sample_bin\": 0}) # At every other step, take a snapshot \n", " # of all species at bin 0\n", "bio.describe_state()\n", "bio.get_history()" ] }, { "cell_type": "markdown", "id": "158e3787-f2d5-4a01-aaa9-6066e93e584c", "metadata": {}, "source": [ "A, still the limiting reagent, is again stopping the reaction. \n", "The (transiently) high value of [A] led to a high value of [B]" ] }, { "cell_type": "code", "execution_count": 13, "id": "a571736a-98f4-4626-bfb8-3d23f7f99840", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ratio of equilibrium concentrations (B_eq / (A_eq * X_eq)): 3.9651079073726363\n", "Ratio of forward/reverse rates: 4.0\n" ] } ], "source": [ "# Verify the equilibrium\n", "A_eq = bio.bin_concentration(0, 0)\n", "X_eq = bio.bin_concentration(0, 1)\n", "B_eq = bio.bin_concentration(0, 2)\n", "print(\"Ratio of equilibrium concentrations (B_eq / (A_eq * X_eq)): \", (B_eq / (A_eq * X_eq)))\n", "print(\"Ratio of forward/reverse rates: \", chem_data.get_forward_rate(0) / chem_data.get_reverse_rate(0))" ] }, { "cell_type": "code", "execution_count": 14, "id": "58f4f09c-8af6-46b7-bd85-2f6ca194c42a", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "Chemical=A
SYSTEM TIME=%{x}
concentration=%{y}", "legendgroup": "A", "line": { "color": "red", "dash": "solid" }, "marker": { "symbol": "circle" }, "mode": "lines", "name": "A", "orientation": "v", "showlegend": true, "type": "scatter", "x": [ 0, 0.001, 0.002, 0.003, 0.004, 0.005000000000000001, 0.006000000000000002, 0.007000000000000003, 0.008000000000000004, 0.009000000000000005, 0.010000000000000005, 0.011000000000000006, 0.012000000000000007, 0.013000000000000008, 0.014000000000000009, 0.01500000000000001, 0.01500000000000001, 0.01600000000000001, 0.01700000000000001, 0.018000000000000013, 0.019000000000000013, 0.020000000000000014, 0.021000000000000015, 0.022000000000000016, 0.023000000000000017, 0.024000000000000018, 0.02500000000000002, 0.02600000000000002, 0.02700000000000002, 0.02800000000000002, 0.029000000000000022, 0.030000000000000023, 0.031000000000000024, 0.03200000000000002, 0.03300000000000002, 0.03400000000000002, 0.035000000000000024 ], "xaxis": "x", "y": [ 5, 1.828, 0.7010021302186202, 0.28191596761389115, 0.12351872192313587, 0.06328727686095315, 0.04033138739837671, 0.031574626645931, 0.02823316007394518, 0.026957938077751462, 0.02647124466112761, 0.026285492804494114, 0.02621459808083849, 0.026187540072133496, 0.02617721297656817, 0.026173271483838915, 50, 21.623387995046514, 12.120736870506565, 7.4713012668016265, 4.871324304129126, 3.316948682572402, 2.3518726285178757, 1.738885783882158, 1.343970798859013, 1.0872382086134893, 0.9193611478351968, 0.8091691103602572, 0.7366607965738188, 0.6888711519440374, 0.6573395464390022, 0.6365202639649677, 0.6227675543081823, 0.6136800478198229, 0.6076739876581376, 0.6037039640125998, 0.6010795330423921 ], "yaxis": "y" }, { "hovertemplate": "Chemical=X
SYSTEM TIME=%{x}
concentration=%{y}", "legendgroup": "X", "line": { "color": "darkorange", "dash": "solid" }, "marker": { "symbol": "circle" }, "mode": "lines", "name": "X", "orientation": "v", "showlegend": true, "type": "scatter", "x": [ 0, 0.001, 0.002, 0.003, 0.004, 0.005000000000000001, 0.006000000000000002, 0.007000000000000003, 0.008000000000000004, 0.009000000000000005, 0.010000000000000005, 0.011000000000000006, 0.012000000000000007, 0.013000000000000008, 0.014000000000000009, 0.01500000000000001, 0.01500000000000001, 0.01600000000000001, 0.01700000000000001, 0.018000000000000013, 0.019000000000000013, 0.020000000000000014, 0.021000000000000015, 0.022000000000000016, 0.023000000000000017, 0.024000000000000018, 0.02500000000000002, 0.02600000000000002, 0.02700000000000002, 0.02800000000000002, 0.029000000000000022, 0.030000000000000023, 0.031000000000000024, 0.03200000000000002, 0.03300000000000002, 0.03400000000000002, 0.035000000000000024 ], "xaxis": "x", "y": [ 100, 96.828, 95.70100213021863, 95.2819159676139, 95.12351872192315, 95.06328727686096, 95.04033138739838, 95.03157462664595, 95.02823316007397, 95.02695793807777, 95.02647124466115, 95.02628549280452, 95.02621459808086, 95.02618754007214, 95.02617721297658, 95.02617327148384, 95.02617327148384, 66.64956126653036, 57.14691014199041, 52.49747453828547, 49.89749757561297, 48.34312195405624, 47.37804590000171, 46.765059055365995, 46.370144070342846, 46.11341148009732, 45.94553441931903, 45.83534238184409, 45.76283406805765, 45.71504442342787, 45.68351281792283, 45.662693535448796, 45.64894082579201, 45.639853319303654, 45.633847259141966, 45.62987723549642, 45.627252804526215 ], "yaxis": "y" }, { "hovertemplate": "Chemical=B
SYSTEM TIME=%{x}
concentration=%{y}", "legendgroup": "B", "line": { "color": "green", "dash": "solid" }, "marker": { "symbol": "circle" }, "mode": "lines", "name": "B", "orientation": "v", "showlegend": true, "type": "scatter", "x": [ 0, 0.001, 0.002, 0.003, 0.004, 0.005000000000000001, 0.006000000000000002, 0.007000000000000003, 0.008000000000000004, 0.009000000000000005, 0.010000000000000005, 0.011000000000000006, 0.012000000000000007, 0.013000000000000008, 0.014000000000000009, 0.01500000000000001, 0.01500000000000001, 0.01600000000000001, 0.01700000000000001, 0.018000000000000013, 0.019000000000000013, 0.020000000000000014, 0.021000000000000015, 0.022000000000000016, 0.023000000000000017, 0.024000000000000018, 0.02500000000000002, 0.02600000000000002, 0.02700000000000002, 0.02800000000000002, 0.029000000000000022, 0.030000000000000023, 0.031000000000000024, 0.03200000000000002, 0.03300000000000002, 0.03400000000000002, 0.035000000000000024 ], "xaxis": "x", "y": [ 0, 6.343999999999999, 8.59799573956276, 9.43616806477222, 9.752962556153731, 9.873425446278096, 9.919337225203249, 9.93685074670814, 9.943533679852111, 9.946084123844498, 9.947057510677745, 9.947429014391012, 9.947570803838325, 9.947624919855734, 9.947645574046865, 9.947653457032322, 9.947653457032322, 66.70087746693929, 85.70617971601918, 95.00505092342905, 100.20500484877407, 103.31375609188753, 105.24390819999658, 106.46988188926801, 107.25971185931431, 107.77317703980536, 108.10893116136194, 108.32931523631181, 108.4743318638847, 108.56991115314426, 108.63297436415434, 108.67461292910241, 108.70211834841598, 108.72029336139269, 108.73230548171607, 108.74024552900715, 108.74549439094757 ], "yaxis": "y" } ], "layout": { "autosize": true, "legend": { "title": { "text": "Chemical" }, "tracegroupgap": 0 }, "template": { "data": { "bar": [ { "error_x": { "color": "#2a3f5f" }, "error_y": { "color": "#2a3f5f" }, "marker": { "line": { "color": "#E5ECF6", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "bar" } ], "barpolar": [ { "marker": { "line": { "color": "#E5ECF6", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "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": { "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "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": [ { "fillpattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 }, "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": "Changes in concentrations (reaction A + X <-> 2B)" }, "xaxis": { "anchor": "y", "autorange": true, "domain": [ 0, 1 ], "range": [ 0, 0.035000000000000024 ], "title": { "text": "SYSTEM TIME" }, "type": "linear" }, "yaxis": { "anchor": "x", "autorange": true, "domain": [ 0, 1 ], "range": [ -6.041416355052642, 114.78691074600022 ], "title": { "text": "concentration" }, "type": "linear" } } }, "image/png": "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", "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = px.line(data_frame=bio.get_history(), x=\"SYSTEM TIME\", y=[\"A\", \"X\", \"B\"], \n", " title=\"Changes in concentrations (reaction A + X <-> 2B)\",\n", " color_discrete_sequence = ['red', 'darkorange', 'green'],\n", " labels={\"value\":\"concentration\", \"variable\":\"Chemical\"})\n", "fig.show()" ] }, { "cell_type": "markdown", "id": "bc4c0dd9-609a-40ba-93e7-910dd2550ba6", "metadata": {}, "source": [ "`A`, still the limiting reagent, is again stopping the reaction. \n", "The (transiently) high value of [A] led to a high value of [B]" ] }, { "cell_type": "markdown", "id": "f6619731-c5ea-484c-af3e-cea50d685361", "metadata": { "tags": [] }, "source": [ "# Let's again suddenly increase [A]" ] }, { "cell_type": "code", "execution_count": 15, "id": "d3618eba-a673-4ff5-85d0-08f5ea592361", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SYSTEM STATE at Time t = 0.035:\n", "1 bins and 3 species:\n", " Species 0 (A). Diff rate: None. Conc: [30.]\n", " Species 1 (X). Diff rate: None. Conc: [45.6272528]\n", " Species 2 (B). Diff rate: None. Conc: [108.74549439]\n" ] } ], "source": [ "bio.set_bin_conc(bin_address=0, species_index=0, conc=30.)\n", "bio.describe_state()" ] }, { "cell_type": "code", "execution_count": 16, "id": "359b153e-9b63-45a3-851a-6c49259909b7", "metadata": {}, "outputs": [], "source": [ "# Save the state of the concentrations of all species at bin 0 (the only bin in this system)\n", "bio.add_snapshot(bio.bin_snapshot(bin_address = 0))" ] }, { "cell_type": "markdown", "id": "0974480d-ca45-46fe-addd-c8d394780fdb", "metadata": {}, "source": [ "### Yet again, take the system to equilibrium" ] }, { "cell_type": "code", "execution_count": 17, "id": "8fe20f9c-05c4-45a4-b485-a51005440200", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SYSTEM STATE at Time t = 0.07:\n", "1 bins and 3 species:\n", " Species 0 (A). Diff rate: None. Conc: [2.31631253]\n", " Species 1 (X). Diff rate: None. Conc: [17.94356534]\n", " Species 2 (B). Diff rate: None. Conc: [164.11286933]\n" ] }, { "data": { "text/html": [ "
\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", "
SYSTEM TIMEAXBcaption
00.0005.000000100.0000000.000000
10.0011.82800096.8280006.344000
20.0020.70100295.7010028.597996
30.0030.28191695.2819169.436168
40.0040.12351995.1235199.752963
..................
680.0662.34209717.969350164.061300
690.0672.33388817.961141164.077718
700.0682.32698917.954242164.091517
710.0692.32118917.948442164.103117
720.0702.31631317.943565164.112869
\n", "

73 rows × 5 columns

\n", "
" ], "text/plain": [ " SYSTEM TIME A X B caption\n", "0 0.000 5.000000 100.000000 0.000000 \n", "1 0.001 1.828000 96.828000 6.344000 \n", "2 0.002 0.701002 95.701002 8.597996 \n", "3 0.003 0.281916 95.281916 9.436168 \n", "4 0.004 0.123519 95.123519 9.752963 \n", ".. ... ... ... ... ...\n", "68 0.066 2.342097 17.969350 164.061300 \n", "69 0.067 2.333888 17.961141 164.077718 \n", "70 0.068 2.326989 17.954242 164.091517 \n", "71 0.069 2.321189 17.948442 164.103117 \n", "72 0.070 2.316313 17.943565 164.112869 \n", "\n", "[73 rows x 5 columns]" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bio.react(time_step=0.0005, n_steps=70, snapshots={\"frequency\": 2, \"sample_bin\": 0}) # At every other step, take a snapshot \n", " # of all species at bin 0\n", "bio.describe_state()\n", "bio.get_history()" ] }, { "cell_type": "markdown", "id": "81a8be4a-f374-494e-b647-184e35707295", "metadata": {}, "source": [ "A, again the scarse limiting reagent, stops the reaction yet again" ] }, { "cell_type": "code", "execution_count": 18, "id": "5d7ded33-8a16-4fdb-b099-a4ea11f7583c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ratio of equilibrium concentrations (B_eq / (A_eq * X_eq)): 3.9485418139406785\n", "Ratio of forward/reverse rates: 4.0\n" ] } ], "source": [ "# Verify the equilibrium\n", "A_eq = bio.bin_concentration(0, 0)\n", "X_eq = bio.bin_concentration(0, 1)\n", "B_eq = bio.bin_concentration(0, 2)\n", "print(\"Ratio of equilibrium concentrations (B_eq / (A_eq * X_eq)): \", (B_eq / (A_eq * X_eq)))\n", "print(\"Ratio of forward/reverse rates: \", chem_data.get_forward_rate(0) / chem_data.get_reverse_rate(0))" ] }, { "cell_type": "code", "execution_count": 19, "id": "4229e039-b484-4849-a446-59409885deb4", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "Chemical=A
SYSTEM TIME=%{x}
concentration=%{y}", "legendgroup": "A", "line": { "color": "red", "dash": "solid" }, "marker": { "symbol": "circle" }, "mode": "lines", "name": "A", "orientation": "v", "showlegend": true, "type": "scatter", "x": [ 0, 0.001, 0.002, 0.003, 0.004, 0.005000000000000001, 0.006000000000000002, 0.007000000000000003, 0.008000000000000004, 0.009000000000000005, 0.010000000000000005, 0.011000000000000006, 0.012000000000000007, 0.013000000000000008, 0.014000000000000009, 0.01500000000000001, 0.01500000000000001, 0.01600000000000001, 0.01700000000000001, 0.018000000000000013, 0.019000000000000013, 0.020000000000000014, 0.021000000000000015, 0.022000000000000016, 0.023000000000000017, 0.024000000000000018, 0.02500000000000002, 0.02600000000000002, 0.02700000000000002, 0.02800000000000002, 0.029000000000000022, 0.030000000000000023, 0.031000000000000024, 0.03200000000000002, 0.03300000000000002, 0.03400000000000002, 0.035000000000000024, 0.035000000000000024, 0.036000000000000025, 0.037000000000000026, 0.03800000000000003, 0.03900000000000003, 0.04000000000000003, 0.04100000000000003, 0.04200000000000003, 0.04300000000000003, 0.04400000000000003, 0.04500000000000003, 0.046000000000000034, 0.047000000000000035, 0.048000000000000036, 0.04900000000000004, 0.05000000000000004, 0.05100000000000004, 0.05200000000000004, 0.05300000000000004, 0.05400000000000004, 0.05500000000000004, 0.05600000000000004, 0.057000000000000044, 0.058000000000000045, 0.059000000000000045, 0.060000000000000046, 0.06100000000000005, 0.06200000000000005, 0.06300000000000004, 0.06400000000000004, 0.06500000000000004, 0.06600000000000004, 0.06700000000000005, 0.06800000000000005, 0.06900000000000005, 0.07000000000000005 ], "xaxis": "x", "y": [ 5, 1.828, 0.7010021302186202, 0.28191596761389115, 0.12351872192313587, 0.06328727686095315, 0.04033138739837671, 0.031574626645931, 0.02823316007394518, 0.026957938077751462, 0.02647124466112761, 0.026285492804494114, 0.02621459808083849, 0.026187540072133496, 0.02617721297656817, 0.026173271483838915, 50, 21.623387995046514, 12.120736870506565, 7.4713012668016265, 4.871324304129126, 3.316948682572402, 2.3518726285178757, 1.738885783882158, 1.343970798859013, 1.0872382086134893, 0.9193611478351968, 0.8091691103602572, 0.7366607965738188, 0.6888711519440374, 0.6573395464390022, 0.6365202639649677, 0.6227675543081823, 0.6136800478198229, 0.6076739876581376, 0.6037039640125998, 0.6010795330423921, 30, 20.78590713353102, 15.620951544522198, 12.321668962573067, 10.049916181784992, 8.407693929463546, 7.179695039949368, 6.238536049026302, 5.503733429098575, 4.921808117315241, 4.455781425530507, 4.0792490236048256, 3.7728540703729303, 3.522093325113542, 3.3159005866267086, 3.1457023187471025, 3.0047703250561706, 2.887767075012364, 2.7904193729304776, 2.709279596876919, 2.641547986288441, 2.5849383266855717, 2.5375750363217384, 2.497913347105048, 2.464676724435737, 2.4368073314881324, 2.413426487445248, 2.3938028695914815, 2.3773267774749263, 2.3634891865109093, 2.351864616889117, 2.342097064151129, 2.333888402655969, 2.3269887978016692, 2.321188758142156, 2.3163125320956275 ], "yaxis": "y" }, { "hovertemplate": "Chemical=X
SYSTEM TIME=%{x}
concentration=%{y}", "legendgroup": "X", "line": { "color": "darkorange", "dash": "solid" }, "marker": { "symbol": "circle" }, "mode": "lines", "name": "X", "orientation": "v", "showlegend": true, "type": "scatter", "x": [ 0, 0.001, 0.002, 0.003, 0.004, 0.005000000000000001, 0.006000000000000002, 0.007000000000000003, 0.008000000000000004, 0.009000000000000005, 0.010000000000000005, 0.011000000000000006, 0.012000000000000007, 0.013000000000000008, 0.014000000000000009, 0.01500000000000001, 0.01500000000000001, 0.01600000000000001, 0.01700000000000001, 0.018000000000000013, 0.019000000000000013, 0.020000000000000014, 0.021000000000000015, 0.022000000000000016, 0.023000000000000017, 0.024000000000000018, 0.02500000000000002, 0.02600000000000002, 0.02700000000000002, 0.02800000000000002, 0.029000000000000022, 0.030000000000000023, 0.031000000000000024, 0.03200000000000002, 0.03300000000000002, 0.03400000000000002, 0.035000000000000024, 0.035000000000000024, 0.036000000000000025, 0.037000000000000026, 0.03800000000000003, 0.03900000000000003, 0.04000000000000003, 0.04100000000000003, 0.04200000000000003, 0.04300000000000003, 0.04400000000000003, 0.04500000000000003, 0.046000000000000034, 0.047000000000000035, 0.048000000000000036, 0.04900000000000004, 0.05000000000000004, 0.05100000000000004, 0.05200000000000004, 0.05300000000000004, 0.05400000000000004, 0.05500000000000004, 0.05600000000000004, 0.057000000000000044, 0.058000000000000045, 0.059000000000000045, 0.060000000000000046, 0.06100000000000005, 0.06200000000000005, 0.06300000000000004, 0.06400000000000004, 0.06500000000000004, 0.06600000000000004, 0.06700000000000005, 0.06800000000000005, 0.06900000000000005, 0.07000000000000005 ], "xaxis": "x", "y": [ 100, 96.828, 95.70100213021863, 95.2819159676139, 95.12351872192315, 95.06328727686096, 95.04033138739838, 95.03157462664595, 95.02823316007397, 95.02695793807777, 95.02647124466115, 95.02628549280452, 95.02621459808086, 95.02618754007214, 95.02617721297658, 95.02617327148384, 95.02617327148384, 66.64956126653036, 57.14691014199041, 52.49747453828547, 49.89749757561297, 48.34312195405624, 47.37804590000171, 46.765059055365995, 46.370144070342846, 46.11341148009732, 45.94553441931903, 45.83534238184409, 45.76283406805765, 45.71504442342787, 45.68351281792283, 45.662693535448796, 45.64894082579201, 45.639853319303654, 45.633847259141966, 45.62987723549642, 45.627252804526215, 45.627252804526215, 36.413159938057234, 31.248204349048414, 27.948921767099282, 25.677168986311205, 24.03494673398976, 22.806947844475584, 21.865788853552516, 21.13098623362479, 20.549060921841455, 20.083034230056725, 19.706501828131046, 19.40010687489915, 19.149346129639763, 18.94315339115293, 18.772955123273324, 18.632023129582393, 18.51501987953859, 18.4176721774567, 18.336532401403144, 18.268800790814666, 18.212191131211796, 18.164827840847963, 18.125166151631273, 18.091929528961963, 18.06406013601436, 18.040679291971475, 18.02105567411771, 18.004579582001156, 17.99074199103714, 17.979117421415346, 17.969349868677355, 17.961141207182195, 17.954241602327894, 17.948441562668382, 17.94356533662185 ], "yaxis": "y" }, { "hovertemplate": "Chemical=B
SYSTEM TIME=%{x}
concentration=%{y}", "legendgroup": "B", "line": { "color": "green", "dash": "solid" }, "marker": { "symbol": "circle" }, "mode": "lines", "name": "B", "orientation": "v", "showlegend": true, "type": "scatter", "x": [ 0, 0.001, 0.002, 0.003, 0.004, 0.005000000000000001, 0.006000000000000002, 0.007000000000000003, 0.008000000000000004, 0.009000000000000005, 0.010000000000000005, 0.011000000000000006, 0.012000000000000007, 0.013000000000000008, 0.014000000000000009, 0.01500000000000001, 0.01500000000000001, 0.01600000000000001, 0.01700000000000001, 0.018000000000000013, 0.019000000000000013, 0.020000000000000014, 0.021000000000000015, 0.022000000000000016, 0.023000000000000017, 0.024000000000000018, 0.02500000000000002, 0.02600000000000002, 0.02700000000000002, 0.02800000000000002, 0.029000000000000022, 0.030000000000000023, 0.031000000000000024, 0.03200000000000002, 0.03300000000000002, 0.03400000000000002, 0.035000000000000024, 0.035000000000000024, 0.036000000000000025, 0.037000000000000026, 0.03800000000000003, 0.03900000000000003, 0.04000000000000003, 0.04100000000000003, 0.04200000000000003, 0.04300000000000003, 0.04400000000000003, 0.04500000000000003, 0.046000000000000034, 0.047000000000000035, 0.048000000000000036, 0.04900000000000004, 0.05000000000000004, 0.05100000000000004, 0.05200000000000004, 0.05300000000000004, 0.05400000000000004, 0.05500000000000004, 0.05600000000000004, 0.057000000000000044, 0.058000000000000045, 0.059000000000000045, 0.060000000000000046, 0.06100000000000005, 0.06200000000000005, 0.06300000000000004, 0.06400000000000004, 0.06500000000000004, 0.06600000000000004, 0.06700000000000005, 0.06800000000000005, 0.06900000000000005, 0.07000000000000005 ], "xaxis": "x", "y": [ 0, 6.343999999999999, 8.59799573956276, 9.43616806477222, 9.752962556153731, 9.873425446278096, 9.919337225203249, 9.93685074670814, 9.943533679852111, 9.946084123844498, 9.947057510677745, 9.947429014391012, 9.947570803838325, 9.947624919855734, 9.947645574046865, 9.947653457032322, 9.947653457032322, 66.70087746693929, 85.70617971601918, 95.00505092342905, 100.20500484877407, 103.31375609188753, 105.24390819999658, 106.46988188926801, 107.25971185931431, 107.77317703980536, 108.10893116136194, 108.32931523631181, 108.4743318638847, 108.56991115314426, 108.63297436415434, 108.67461292910241, 108.70211834841598, 108.72029336139269, 108.73230548171607, 108.74024552900715, 108.74549439094757, 108.74549439094757, 127.17368012388553, 137.50359130190319, 144.10215646580144, 148.6456620273776, 151.9301065320205, 154.38610431104883, 156.26842229289497, 157.7380275327504, 158.90187815631708, 159.83393153988655, 160.5869963437379, 161.19978625020167, 161.70130774072044, 162.1136932176941, 162.45408975345333, 162.7359537408352, 162.96996024092283, 163.1646556450866, 163.32693519719373, 163.4623984183707, 163.57561773757644, 163.6703443183041, 163.74966769673748, 163.8161409420761, 163.87187972797128, 163.91864141605706, 163.9578886517646, 163.9908408359977, 164.01851601792572, 164.04176515716932, 164.0613002626453, 164.0777175856356, 164.0915167953442, 164.10311687466324, 164.11286932675628 ], "yaxis": "y" } ], "layout": { "autosize": true, "legend": { "title": { "text": "Chemical" }, "tracegroupgap": 0 }, "template": { "data": { "bar": [ { "error_x": { "color": "#2a3f5f" }, "error_y": { "color": "#2a3f5f" }, "marker": { "line": { "color": "#E5ECF6", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "type": "bar" } ], "barpolar": [ { "marker": { "line": { "color": "#E5ECF6", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "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": { "pattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 } }, "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": [ { "fillpattern": { "fillmode": "overlay", "size": 10, "solidity": 0.2 }, "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": "Changes in concentrations (reaction A + X <-> 2B)" }, "xaxis": { "anchor": "y", "autorange": true, "domain": [ 0, 1 ], "range": [ 0, 0.07000000000000005 ], "title": { "text": "SYSTEM TIME" }, "type": "linear" }, "yaxis": { "anchor": "x", "autorange": true, "domain": [ 0, 1 ], "range": [ -9.117381629264237, 173.2302509560205 ], "title": { "text": "concentration" }, "type": "linear" } } }, "image/png": "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", "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = px.line(data_frame=bio.get_history(), x=\"SYSTEM TIME\", y=[\"A\", \"X\", \"B\"], \n", " title=\"Changes in concentrations (reaction A + X <-> 2B)\",\n", " color_discrete_sequence = ['red', 'darkorange', 'green'],\n", " labels={\"value\":\"concentration\", \"variable\":\"Chemical\"})\n", "fig.show()" ] }, { "cell_type": "markdown", "id": "e86e8319-2d19-4a59-91f1-9e45cf2dd333", "metadata": {}, "source": [ "`A`, again the scarse limiting reagent, stops the reaction yet again. \n", "And, again, the (transiently) high value of [A] up-regulated [B] \n", "\n", "Note: `A` can up-regulate `B`, but it cannot bring it down. \n", "`X` will soon need to be replenished, if `A` is to continue being the limiting reagent." ] }, { "cell_type": "markdown", "id": "837435d0-d2ea-4b98-85c4-6a6d44371ae8", "metadata": {}, "source": [ "# For additional exploration, see the experiment \"reactions_single_compartment/up_regulate_1\"" ] }, { "cell_type": "code", "execution_count": null, "id": "c1ee6c54-9795-4fca-8972-e5ed4cb84019", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "jupytext": { "formats": "ipynb,py:percent" }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 5 }