{ "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: Dec. 6, 2023" ] }, { "cell_type": "code", "execution_count": 1, "id": "c1ee6c54-9795-4fca-8972-e5ed4cb84019", "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": "1dc1a2e7", "metadata": { "tags": [] }, "outputs": [], "source": [ "from experiments.get_notebook_info import get_notebook_basename\n", "\n", "from src.modules.chemicals.chem_data import ChemData\n", "from src.life_1D.bio_sim_1d import BioSim1D\n", "\n", "import plotly.express as px\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 = -3,436.6 / K = 4) | 1st order in all reactants & products\n", "Set of chemicals involved in the above reactions: {'X', 'B', 'A'}\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\", 1)],\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: [A] = 0.02617 ; [X] = 95.03 ; [B] = 9.948\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": "116d06a6", "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 }