{ "cells": [ { "cell_type": "markdown", "id": "49bcb5b0-f19d-4b96-a5f1-e0ae30f66d8f", "metadata": {}, "source": [ "### `A` down-regulates `B` , by being the *limiting reagent* in reaction `A + 2 B <-> Y` (mostly forward)\n", "1st-order kinetics. \n", "If [A] is low and [B] is high, then [B] remains high. If [A] goes high, [B] goes low. However, at that point, A can no longer bring B up to any substantial extent.\n", "\n", "Single-bin reaction\n", "\n", "Based on experiment `reactions_single_compartment/down_regulate_2`\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": "bea6d4a4", "metadata": { "tags": [] }, "outputs": [], "source": [ "from experiments.get_notebook_info import get_notebook_basename\n", "\n", "from src.modules.chemicals.chem_data import ChemData as chem\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 'down_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 + 2 B <-> Y (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: {'B', 'A', 'Y'}\n", "[GRAPHIC ELEMENT SENT TO LOG FILE `down_regulation_1.log.htm`]\n" ] } ], "source": [ "# Initialize the system\n", "chem_data = chem(names=[\"A\", \"B\", \"Y\"]) # NOTE: Diffusion not applicable (just 1 bin)\n", "\n", "# Reaction A + 2 B <-> Y , with 1st-order kinetics for all species\n", "chem_data.add_reaction(reactants=[(\"A\") , (2, \"B\", 1)], products=[(\"Y\")],\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 (B). Diff rate: None. Conc: [100.]\n", " Species 2 (Y). 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=\"B\", conc=100.) # Plentiful\n", "# Initially, no \"Y\" 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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005.0100.00.0
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" ], "text/plain": [ " SYSTEM TIME A B Y 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: [0.01385228]\n", " Species 1 (B). Diff rate: None. Conc: [90.02770457]\n", " Species 2 (Y). Diff rate: None. Conc: [4.98614772]\n" ] }, { "data": { "text/html": [ "
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" ], "text/plain": [ " SYSTEM TIME A B Y caption\n", "0 0.000 5.000000 100.000000 0.000000 \n", "1 0.001 1.850000 93.700000 3.150000 \n", "2 0.002 0.735332 91.470665 4.264668 \n", "3 0.003 0.303911 90.607822 4.696089 \n", "4 0.004 0.131501 90.263002 4.868499 \n", "5 0.005 0.061738 90.123476 4.938262 \n", "6 0.006 0.033369 90.066737 4.966631 \n", "7 0.007 0.021809 90.043618 4.978191 \n", "8 0.008 0.017095 90.034189 4.982905 \n", "9 0.009 0.015172 90.030343 4.984828 \n", "10 0.010 0.014387 90.028774 4.985613 \n", "11 0.011 0.014067 90.028134 4.985933 \n", "12 0.012 0.013936 90.027872 4.986064 \n", "13 0.013 0.013883 90.027766 4.986117 \n", "14 0.014 0.013861 90.027722 4.986139 \n", "15 0.015 0.013852 90.027705 4.986148 " ] }, "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": "code", "execution_count": 8, "id": "c3afbcc8-bdae-4938-a3f1-ce00d62816f2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0: A + 2 B <-> Y\n", "Final concentrations: [A] = 0.01385 ; [B] = 90.03 ; [Y] = 4.986\n", "1. Ratio of reactant/product concentrations, adjusted for reaction orders: 3.99823\n", " Formula used: [Y] / ([A][B])\n", "2. Ratio of forward/reverse reaction rates: 4.0\n", "Discrepancy between the two values: 0.0443 %\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(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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}, "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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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = px.line(data_frame=bio.get_history(), x=\"SYSTEM TIME\", y=[\"A\", \"B\", \"Y\"], \n", " title=\"Changes in concentrations (reaction A + 2 B <-> Y)\",\n", " color_discrete_sequence = ['red', 'blue', '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: [40.]\n", " Species 1 (B). Diff rate: None. Conc: [90.02770457]\n", " Species 2 (Y). Diff rate: None. Conc: [4.98614772]\n" ] } ], "source": [ "bio.set_bin_conc(bin_address=0, species_index=0, conc=40.)\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 TIMEABYcaption
00.0005.000000100.0000000.000000
10.0011.85000093.7000003.150000
20.0020.73533291.4706654.264668
30.0030.30391190.6078224.696089
40.0040.13150190.2630024.868499
50.0050.06173890.1234764.938262
60.0060.03336990.0667374.966631
70.0070.02180990.0436184.978191
80.0080.01709590.0341894.982905
90.0090.01517290.0303434.984828
100.0100.01438790.0287744.985613
110.0110.01406790.0281344.985933
120.0120.01393690.0278724.986064
130.0130.01388390.0277664.986117
140.0140.01386190.0277224.986139
150.0150.01385290.0277054.986148
160.01540.00000090.0277054.986148
\n", "
" ], "text/plain": [ " SYSTEM TIME A B Y caption\n", "0 0.000 5.000000 100.000000 0.000000 \n", "1 0.001 1.850000 93.700000 3.150000 \n", "2 0.002 0.735332 91.470665 4.264668 \n", "3 0.003 0.303911 90.607822 4.696089 \n", "4 0.004 0.131501 90.263002 4.868499 \n", "5 0.005 0.061738 90.123476 4.938262 \n", "6 0.006 0.033369 90.066737 4.966631 \n", "7 0.007 0.021809 90.043618 4.978191 \n", "8 0.008 0.017095 90.034189 4.982905 \n", "9 0.009 0.015172 90.030343 4.984828 \n", "10 0.010 0.014387 90.028774 4.985613 \n", "11 0.011 0.014067 90.028134 4.985933 \n", "12 0.012 0.013936 90.027872 4.986064 \n", "13 0.013 0.013883 90.027766 4.986117 \n", "14 0.014 0.013861 90.027722 4.986139 \n", "15 0.015 0.013852 90.027705 4.986148 \n", "16 0.015 40.000000 90.027705 4.986148 " ] }, "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 = 0.055:\n", "1 bins and 3 species:\n", " Species 0 (A). Diff rate: None. Conc: [0.97997411]\n", " Species 1 (B). Diff rate: None. Conc: [11.98765279]\n", " Species 2 (Y). Diff rate: None. Conc: [44.00617361]\n" ] }, { "data": { "text/html": [ "
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SYSTEM TIMEABYcaption
00.0005.000000100.0000000.000000
10.0011.85000093.7000003.150000
20.0020.73533291.4706654.264668
30.0030.30391190.6078224.696089
40.0040.13150190.2630024.868499
50.0050.06173890.1234764.938262
60.0060.03336990.0667374.966631
70.0070.02180990.0436184.978191
80.0080.01709590.0341894.982905
90.0090.01517290.0303434.984828
100.0100.01438790.0287744.985613
110.0110.01406790.0281344.985933
120.0120.01393690.0278724.986064
130.0130.01388390.0277664.986117
140.0140.01386190.0277224.986139
150.0150.01385290.0277054.986148
160.01540.00000090.0277054.986148
170.01619.34997348.72765125.636175
180.01713.05968136.14706731.926466
190.0189.78362229.59494935.202526
200.0197.74952825.52676037.236620
210.0206.36138522.75047538.624763
220.0215.35570420.73911339.630443
230.0224.59638719.22047840.389761
240.0234.00551018.03872440.980638
250.0243.53503817.09778041.451110
260.0253.15365316.33501141.832494
270.0262.84002115.70774742.146127
280.0272.57907915.18586242.407069
290.0282.35987614.74745642.626272
300.0292.17425414.37621242.811894
310.0302.01600414.05971242.970144
320.0311.88031413.78833343.105833
330.0321.76340013.55450443.222748
340.0331.66223913.35218243.323909
350.0341.57439213.17648843.411756
360.0351.49786613.02343743.488281
370.0361.43102212.88974943.555125
380.0371.37249612.77269643.613652
390.0381.32114612.66999643.665002
400.0391.27601012.57972443.710138
410.0401.23627312.50025143.749875
420.0411.20124012.43018543.784907
430.0421.17031712.36833843.815831
440.0431.14299112.31368643.843157
450.0441.11882012.26534543.867328
460.0451.09742312.22255043.888725
470.0461.07846612.18463643.907682
480.0471.06166012.15102443.924488
490.0481.04675212.12120943.939395
500.0491.03352212.09474843.952626
510.0501.02177412.07125243.964374
520.0511.01133812.05038143.974809
530.0521.00206512.03183543.984083
540.0530.99382212.01534943.992326
550.0540.98649312.00069043.999655
560.0550.97997411.98765344.006174
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" ], "text/plain": [ " SYSTEM TIME A B Y caption\n", "0 0.000 5.000000 100.000000 0.000000 \n", "1 0.001 1.850000 93.700000 3.150000 \n", "2 0.002 0.735332 91.470665 4.264668 \n", "3 0.003 0.303911 90.607822 4.696089 \n", "4 0.004 0.131501 90.263002 4.868499 \n", "5 0.005 0.061738 90.123476 4.938262 \n", "6 0.006 0.033369 90.066737 4.966631 \n", "7 0.007 0.021809 90.043618 4.978191 \n", "8 0.008 0.017095 90.034189 4.982905 \n", "9 0.009 0.015172 90.030343 4.984828 \n", "10 0.010 0.014387 90.028774 4.985613 \n", "11 0.011 0.014067 90.028134 4.985933 \n", "12 0.012 0.013936 90.027872 4.986064 \n", "13 0.013 0.013883 90.027766 4.986117 \n", "14 0.014 0.013861 90.027722 4.986139 \n", "15 0.015 0.013852 90.027705 4.986148 \n", "16 0.015 40.000000 90.027705 4.986148 \n", "17 0.016 19.349973 48.727651 25.636175 \n", "18 0.017 13.059681 36.147067 31.926466 \n", "19 0.018 9.783622 29.594949 35.202526 \n", "20 0.019 7.749528 25.526760 37.236620 \n", "21 0.020 6.361385 22.750475 38.624763 \n", "22 0.021 5.355704 20.739113 39.630443 \n", "23 0.022 4.596387 19.220478 40.389761 \n", "24 0.023 4.005510 18.038724 40.980638 \n", "25 0.024 3.535038 17.097780 41.451110 \n", "26 0.025 3.153653 16.335011 41.832494 \n", "27 0.026 2.840021 15.707747 42.146127 \n", "28 0.027 2.579079 15.185862 42.407069 \n", "29 0.028 2.359876 14.747456 42.626272 \n", "30 0.029 2.174254 14.376212 42.811894 \n", "31 0.030 2.016004 14.059712 42.970144 \n", "32 0.031 1.880314 13.788333 43.105833 \n", "33 0.032 1.763400 13.554504 43.222748 \n", "34 0.033 1.662239 13.352182 43.323909 \n", "35 0.034 1.574392 13.176488 43.411756 \n", "36 0.035 1.497866 13.023437 43.488281 \n", "37 0.036 1.431022 12.889749 43.555125 \n", "38 0.037 1.372496 12.772696 43.613652 \n", "39 0.038 1.321146 12.669996 43.665002 \n", "40 0.039 1.276010 12.579724 43.710138 \n", "41 0.040 1.236273 12.500251 43.749875 \n", "42 0.041 1.201240 12.430185 43.784907 \n", "43 0.042 1.170317 12.368338 43.815831 \n", "44 0.043 1.142991 12.313686 43.843157 \n", "45 0.044 1.118820 12.265345 43.867328 \n", "46 0.045 1.097423 12.222550 43.888725 \n", "47 0.046 1.078466 12.184636 43.907682 \n", "48 0.047 1.061660 12.151024 43.924488 \n", "49 0.048 1.046752 12.121209 43.939395 \n", "50 0.049 1.033522 12.094748 43.952626 \n", "51 0.050 1.021774 12.071252 43.964374 \n", "52 0.051 1.011338 12.050381 43.974809 \n", "53 0.052 1.002065 12.031835 43.984083 \n", "54 0.053 0.993822 12.015349 43.992326 \n", "55 0.054 0.986493 12.000690 43.999655 \n", "56 0.055 0.979974 11.987653 44.006174 " ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bio.react(time_step=0.0005, n_steps=80, 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": "code", "execution_count": 13, "id": "2783a665-fca0-44e5-8d42-af2a96eae392", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0: A + 2 B <-> Y\n", "Final concentrations: [A] = 0.98 ; [B] = 11.99 ; [Y] = 44.01\n", "1. Ratio of reactant/product concentrations, adjusted for reaction orders: 3.74597\n", " Formula used: [Y] / ([A][B])\n", "2. Ratio of forward/reverse reaction rates: 4.0\n", "Discrepancy between the two values: 6.351 %\n", "Reaction IS in equilibrium (within 7% tolerance)\n", "\n" ] }, { "data": { "text/plain": [ "True" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Verify that the reaction has reached equilibrium\n", "bio.reaction_dynamics.is_in_equilibrium(conc=bio.bin_snapshot(bin_address = 0), tolerance=7)" ] }, { "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
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = px.line(data_frame=bio.get_history(), x=\"SYSTEM TIME\", y=[\"A\", \"B\", \"Y\"], \n", " title=\"Changes in concentrations (reaction A + 2 B <-> Y)\",\n", " color_discrete_sequence = ['red', 'blue', 'green'],\n", " labels={\"value\":\"concentration\", \"variable\":\"Chemical\"})\n", "fig.show()" ] }, { "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": "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 = 0.055:\n", "1 bins and 3 species:\n", " Species 0 (A). Diff rate: None. Conc: [30.]\n", " Species 1 (B). Diff rate: None. Conc: [11.98765279]\n", " Species 2 (Y). Diff rate: None. Conc: [44.00617361]\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 = 0.09:\n", "1 bins and 3 species:\n", " Species 0 (A). Diff rate: None. Conc: [24.26245387]\n", " Species 1 (B). Diff rate: None. Conc: [0.51256052]\n", " Species 2 (Y). Diff rate: None. Conc: [49.74371974]\n" ] }, { "data": { "text/html": [ "
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SYSTEM TIMEABYcaption
00.0005.000000100.0000000.000000
10.0011.85000093.7000003.150000
20.0020.73533291.4706654.264668
30.0030.30391190.6078224.696089
40.0040.13150190.2630024.868499
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880.08624.2624580.51256949.743715
890.08724.2624560.51256649.743717
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" ], "text/plain": [ " SYSTEM TIME A B Y caption\n", "0 0.000 5.000000 100.000000 0.000000 \n", "1 0.001 1.850000 93.700000 3.150000 \n", "2 0.002 0.735332 91.470665 4.264668 \n", "3 0.003 0.303911 90.607822 4.696089 \n", "4 0.004 0.131501 90.263002 4.868499 \n", ".. ... ... ... ... ...\n", "88 0.086 24.262458 0.512569 49.743715 \n", "89 0.087 24.262456 0.512566 49.743717 \n", "90 0.088 24.262455 0.512563 49.743718 \n", "91 0.089 24.262454 0.512562 49.743719 \n", "92 0.090 24.262454 0.512561 49.743720 \n", "\n", "[93 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": "code", "execution_count": 18, "id": "aff608b1-5c78-4070-845a-118afe7c2108", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0: A + 2 B <-> Y\n", "Final concentrations: [A] = 24.26 ; [B] = 0.5126 ; [Y] = 49.74\n", "1. Ratio of reactant/product concentrations, adjusted for reaction orders: 3.99999\n", " Formula used: [Y] / ([A][B])\n", "2. Ratio of forward/reverse reaction rates: 4.0\n", "Discrepancy between the two values: 0.0003695 %\n", "Reaction IS in equilibrium (within 1% tolerance)\n", "\n" ] }, { "data": { "text/plain": [ "True" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Verify that the reaction has reached equilibrium\n", "bio.reaction_dynamics.is_in_equilibrium(conc=bio.bin_snapshot(bin_address = 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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"title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "Changes in concentrations (reaction A + 2 B <-> Y)" }, "xaxis": { "anchor": "y", "autorange": true, "domain": [ 0, 1 ], "range": [ 0, 0.09000000000000007 ], "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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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = px.line(data_frame=bio.get_history(), x=\"SYSTEM TIME\", y=[\"A\", \"B\", \"Y\"], \n", " title=\"Changes in concentrations (reaction A + 2 B <-> Y)\",\n", " color_discrete_sequence = ['red', 'blue', 'green'],\n", " labels={\"value\":\"concentration\", \"variable\":\"Chemical\"})\n", "fig.show()" ] }, { "cell_type": "markdown", "id": "81a8be4a-f374-494e-b647-184e35707295", "metadata": {}, "source": [ "**A**, again the scarse limiting reagent, stops the reaction yet again \n", "\n", "Note: A can down-regulate B, but it cannot bring it up." ] }, { "cell_type": "markdown", "id": "0f4b82d6-f617-4af9-b4b6-67e03ae891ac", "metadata": {}, "source": [ "# For additional exploration, see the experiment \"reactions_single_compartment/down_regulate_2\"" ] }, { "cell_type": "code", "execution_count": null, "id": "c2a858c2", "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 }