{ "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: May 6, 2024" ] }, { "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_2\"],\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", "chem_data.plot_reaction_network(\"vue_cytoscape_2\")" ] }, { "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", "
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" ], "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": [ "
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" ], "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
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" ] }, "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": [ "
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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
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" ], "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": [ "
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" ] }, "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": [ "
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" ], "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
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" ] }, "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 }