{ "cells": [ { "cell_type": "markdown", "id": "49bcb5b0-f19d-4b96-a5f1-e0ae30f66d8f", "metadata": {}, "source": [ "## `A` up-regulates `B` , \n", "### by being *the limiting reagent* in the reaction `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", "See also the experiment \"1D/reactions/up_regulation_1\"\n", "\n", "LAST REVISED: Dec. 3, 2023" ] }, { "cell_type": "code", "execution_count": 1, "id": "53fed9be-020d-4500-a68b-1638f9159fca", "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": "ad48644a", "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.modules.reactions.reaction_dynamics import ReactionDynamics\n", "\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_regulate_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": "markdown", "id": "d6d3ca49-589d-49b7-8424-37c7b01bcacf", "metadata": {}, "source": [ "### Initialize the system" ] }, { "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: {'B', 'X', 'A'}\n", "[GRAPHIC ELEMENT SENT TO LOG FILE `up_regulate_1.log.htm`]\n" ] } ], "source": [ "# Initialize the system\n", "chem_data = chem(names=[\"A\", \"X\", \"B\"])\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": "markdown", "id": "d1d0eabb-b5b1-4e15-846d-5e483a5a24a7", "metadata": {}, "source": [ "### Set the initial concentrations of all the chemicals" ] }, { "cell_type": "code", "execution_count": 5, "id": "e80645d6-eb5b-4c78-8b46-ae126d2cb2cf", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SYSTEM STATE at Time t = 0:\n", "3 species:\n", " Species 0 (A). Conc: 5.0\n", " Species 1 (X). Conc: 100.0\n", " Species 2 (B). Conc: 0.0\n", "Set of chemicals involved in reactions: {'B', 'X', 'A'}\n" ] } ], "source": [ "dynamics = ReactionDynamics(chem_data=chem_data)\n", "dynamics.set_conc(conc={\"A\": 5., \"X\": 100., \"B\": 0.},\n", " snapshot=True) # A is scarce, X is plentiful, B is absent\n", "dynamics.describe_state()" ] }, { "cell_type": "markdown", "id": "0b46b395-3f68-4dbd-b0c5-d67a0e623726", "metadata": { "tags": [] }, "source": [ "### Take the initial system to equilibrium" ] }, { "cell_type": "code", "execution_count": 6, "id": "dde62826-d170-4b39-b027-c0d56fb21387", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "* INFO: the tentative time step (0.0005) leads to a least one norm value > its ABORT threshold:\n", " -> will backtrack, and re-do step with a SMALLER delta time, multiplied by 0.5 (set to 0.00025) [Step started at t=0, and will rewind there]\n", "Some steps were backtracked and re-done, to prevent negative concentrations or excessively large concentration changes\n", "55 total step(s) taken\n" ] } ], "source": [ "dynamics.set_diagnostics() # To save diagnostic information about the call to single_compartment_react()\n", "\n", "# All of these settings are currently close to the default values... but subject to change; set for repeatability\n", "dynamics.set_thresholds(norm=\"norm_A\", low=0.5, high=0.8, abort=1.44)\n", "dynamics.set_thresholds(norm=\"norm_B\", low=0.08, high=0.5, abort=1.5)\n", "dynamics.set_step_factors(upshift=1.5, downshift=0.5, abort=0.5)\n", "dynamics.set_error_step_factor(0.5)\n", "\n", "dynamics.single_compartment_react(initial_step=0.0005, reaction_duration=0.015,\n", " variable_steps=True, explain_variable_steps=False)" ] }, { "cell_type": "code", "execution_count": 7, "id": "db4e74d0-3f9d-49dc-9553-bf3cdfe785f2", "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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Changes in concentrations with time" }, "xaxis": { "anchor": "y", "autorange": true, "domain": [ 0, 1 ], "range": [ 0, 0.016626464843750004 ], "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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", 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SYSTEM TIMEAXBcaption
00.0000005.000000100.0000000.000000Initial state
10.0002504.00000099.0000002.000000
20.0005003.20900098.2090003.582000
30.0006252.89474397.8947434.210514
40.0007502.61241597.6124154.775169
50.0008752.35860597.3586055.282790
60.0010002.13029597.1302955.739410
70.0011251.92481496.9248146.150372
80.0012501.73978996.7397896.520422
90.0013751.57311296.5731126.853775
100.0015001.42290696.4229067.154189
110.0016251.28749396.2874937.425013
120.0017501.16538096.1653807.669240
130.0018751.05522896.0552287.889544
140.0020000.95584095.9558408.088319
150.0021250.86614495.8661448.267712
160.0022500.78517795.7851778.429646
170.0023750.71207695.7120768.575848
180.0025000.64606695.6460668.707868
190.0026250.58644995.5864498.827102
200.0027500.53259995.5325998.934801
210.0028750.48395295.4839529.032095
220.0030000.44000195.4400019.119998
230.0031250.40028795.4002879.199426
240.0032500.36439995.3643999.271201
250.0033750.33196795.3319679.336067
260.0035000.30265495.3026549.394693
270.0036250.27615895.2761589.447683
280.0037500.25220995.2522099.495582
290.0038750.23056095.2305609.538881
300.0040000.21098895.2109889.578024
310.0041250.19329495.1932949.613412
320.0042500.17729795.1772979.645406
330.0043750.16283495.1628349.674332
340.0045000.14975795.1497579.700487
350.0046250.13793295.1379329.724135
360.0048130.12189595.1218959.756210
370.0050000.10816195.1081619.783677
380.0051880.09640095.0964009.807201
390.0053750.08632695.0863269.827347
400.0055630.07769995.0776999.844602
410.0057500.07031095.0703109.859381
420.0059380.06398095.0639809.872039
430.0061250.05855995.0585599.882882
440.0063130.05391595.0539159.892169
450.0065940.04794995.0479499.904103
460.0068750.04326695.0432669.913469
470.0071560.03959095.0395909.920821
480.0074380.03670495.0367049.926591
490.0078590.03330795.0333079.933386
500.0082810.03100595.0310059.937989
510.0089140.02866795.0286679.942667
520.0098630.02685695.0268569.946289
530.0112870.02611095.0261109.947779
540.0134230.02620995.0262099.947582
550.0166260.02611595.0261159.947769
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" ], "text/plain": [ " SYSTEM TIME A X B caption\n", "0 0.000000 5.000000 100.000000 0.000000 Initial state\n", "1 0.000250 4.000000 99.000000 2.000000 \n", "2 0.000500 3.209000 98.209000 3.582000 \n", "3 0.000625 2.894743 97.894743 4.210514 \n", "4 0.000750 2.612415 97.612415 4.775169 \n", "5 0.000875 2.358605 97.358605 5.282790 \n", "6 0.001000 2.130295 97.130295 5.739410 \n", "7 0.001125 1.924814 96.924814 6.150372 \n", "8 0.001250 1.739789 96.739789 6.520422 \n", "9 0.001375 1.573112 96.573112 6.853775 \n", "10 0.001500 1.422906 96.422906 7.154189 \n", "11 0.001625 1.287493 96.287493 7.425013 \n", "12 0.001750 1.165380 96.165380 7.669240 \n", "13 0.001875 1.055228 96.055228 7.889544 \n", "14 0.002000 0.955840 95.955840 8.088319 \n", "15 0.002125 0.866144 95.866144 8.267712 \n", "16 0.002250 0.785177 95.785177 8.429646 \n", "17 0.002375 0.712076 95.712076 8.575848 \n", "18 0.002500 0.646066 95.646066 8.707868 \n", "19 0.002625 0.586449 95.586449 8.827102 \n", "20 0.002750 0.532599 95.532599 8.934801 \n", "21 0.002875 0.483952 95.483952 9.032095 \n", "22 0.003000 0.440001 95.440001 9.119998 \n", "23 0.003125 0.400287 95.400287 9.199426 \n", "24 0.003250 0.364399 95.364399 9.271201 \n", "25 0.003375 0.331967 95.331967 9.336067 \n", "26 0.003500 0.302654 95.302654 9.394693 \n", "27 0.003625 0.276158 95.276158 9.447683 \n", "28 0.003750 0.252209 95.252209 9.495582 \n", "29 0.003875 0.230560 95.230560 9.538881 \n", "30 0.004000 0.210988 95.210988 9.578024 \n", "31 0.004125 0.193294 95.193294 9.613412 \n", "32 0.004250 0.177297 95.177297 9.645406 \n", "33 0.004375 0.162834 95.162834 9.674332 \n", "34 0.004500 0.149757 95.149757 9.700487 \n", "35 0.004625 0.137932 95.137932 9.724135 \n", "36 0.004813 0.121895 95.121895 9.756210 \n", "37 0.005000 0.108161 95.108161 9.783677 \n", "38 0.005188 0.096400 95.096400 9.807201 \n", "39 0.005375 0.086326 95.086326 9.827347 \n", "40 0.005563 0.077699 95.077699 9.844602 \n", "41 0.005750 0.070310 95.070310 9.859381 \n", "42 0.005938 0.063980 95.063980 9.872039 \n", "43 0.006125 0.058559 95.058559 9.882882 \n", "44 0.006313 0.053915 95.053915 9.892169 \n", "45 0.006594 0.047949 95.047949 9.904103 \n", "46 0.006875 0.043266 95.043266 9.913469 \n", "47 0.007156 0.039590 95.039590 9.920821 \n", "48 0.007438 0.036704 95.036704 9.926591 \n", "49 0.007859 0.033307 95.033307 9.933386 \n", "50 0.008281 0.031005 95.031005 9.937989 \n", "51 0.008914 0.028667 95.028667 9.942667 \n", "52 0.009863 0.026856 95.026856 9.946289 \n", "53 0.011287 0.026110 95.026110 9.947779 \n", "54 0.013423 0.026209 95.026209 9.947582 \n", "55 0.016626 0.026115 95.026115 9.947769 " ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dynamics.get_history()" ] }, { "cell_type": "code", "execution_count": 9, "id": "b56d1612-a68c-4da3-be37-a7245b6c1a80", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "From time 0 to 0.0005, in 2 steps of 0.00025\n", "From time 0.0005 to 0.004625, in 33 steps of 0.000125\n", "From time 0.004625 to 0.006313, in 9 steps of 0.000188\n", "From time 0.006313 to 0.007438, in 4 steps of 0.000281\n", "From time 0.007438 to 0.008281, in 2 steps of 0.000422\n", "From time 0.008281 to 0.008914, in 1 step of 0.000633\n", "From time 0.008914 to 0.009863, in 1 step of 0.000949\n", "From time 0.009863 to 0.01129, in 1 step of 0.00142\n", "From time 0.01129 to 0.01342, in 1 step of 0.00214\n", "From time 0.01342 to 0.01663, in 1 step of 0.0032\n", "(55 steps total)\n" ] } ], "source": [ "dynamics.explain_time_advance()" ] }, { "cell_type": "markdown", "id": "962acf15-3b50-40e4-9daa-3dcca7d3291a", "metadata": {}, "source": [ "### Equilibrium" ] }, { "cell_type": "code", "execution_count": 10, "id": "2783a665-fca0-44e5-8d42-af2a96eae392", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0: A + X <-> 2 B\n", "Final concentrations: [A] = 0.02612 ; [X] = 95.03 ; [B] = 9.948\n", "1. Ratio of reactant/product concentrations, adjusted for reaction orders: 4.00855\n", " Formula used: [B] / ([A][X])\n", "2. Ratio of forward/reverse reaction rates: 4.0\n", "Discrepancy between the two values: 0.2137 %\n", "Reaction IS in equilibrium (within 1% tolerance)\n", "\n" ] }, { "data": { "text/plain": [ "True" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Verify that the reaction has reached equilibrium\n", "dynamics.is_in_equilibrium()" ] }, { "cell_type": "code", "execution_count": null, "id": "15355aeb-f702-4d10-9d13-8365f6a76772", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "448ec7fa-6529-438b-84ba-47888c2cd080", "metadata": { "tags": [] }, "source": [ "# Now, let's suddenly increase [A]" ] }, { "cell_type": "code", "execution_count": 11, "id": "7245be7a-c9db-45f5-b033-d6c521237a9c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SYSTEM STATE at Time t = 0.016626465:\n", "3 species:\n", " Species 0 (A). Conc: 50.0\n", " Species 1 (X). Conc: 95.02611534596562\n", " Species 2 (B). Conc: 9.947769308068802\n", "Set of chemicals involved in reactions: {'B', 'X', 'A'}\n" ] } ], "source": [ "dynamics.set_single_conc(species_name=\"A\", conc=50., snapshot=True)\n", "dynamics.describe_state()" ] }, { "cell_type": "code", "execution_count": 12, "id": "61eead55-fcef-41cd-b29e-f2d5ad5c6078", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SYSTEM TIMEAXBcaption
520.0098630.02685695.0268569.946289
530.0112870.02611095.0261109.947779
540.0134230.02620995.0262099.947582
550.0166260.02611595.0261159.947769
560.01662650.00000095.0261159.947769Set concentration of `A`
\n", "
" ], "text/plain": [ " SYSTEM TIME A X B caption\n", "52 0.009863 0.026856 95.026856 9.946289 \n", "53 0.011287 0.026110 95.026110 9.947779 \n", "54 0.013423 0.026209 95.026209 9.947582 \n", "55 0.016626 0.026115 95.026115 9.947769 \n", "56 0.016626 50.000000 95.026115 9.947769 Set concentration of `A`" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dynamics.get_history(tail=5)" ] }, { "cell_type": "markdown", "id": "24455d58-a0ea-43fa-b6ad-95c42a8b34b2", "metadata": {}, "source": [ "### Again, take the system to equilibrium" ] }, { "cell_type": "code", "execution_count": 13, "id": "c06fd8d8-d550-4e35-a239-7b91bee32be9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "* INFO: the tentative time step (0.0005) leads to a least one norm value > its ABORT threshold:\n", " -> will backtrack, and re-do step with a SMALLER delta time, multiplied by 0.5 (set to 0.00025) [Step started at t=0.016626, and will rewind there]\n", "* INFO: the tentative time step (0.00025) leads to a least one norm value > its ABORT threshold:\n", " -> will backtrack, and re-do step with a SMALLER delta time, multiplied by 0.5 (set to 0.000125) [Step started at t=0.016626, and will rewind there]\n", "* INFO: the tentative time step (0.000125) leads to a least one norm value > its ABORT threshold:\n", " -> will backtrack, and re-do step with a SMALLER delta time, multiplied by 0.5 (set to 6.25e-05) [Step started at t=0.016626, and will rewind there]\n", "* INFO: the tentative time step (6.25e-05) leads to a least one norm value > its ABORT threshold:\n", " -> will backtrack, and re-do step with a SMALLER delta time, multiplied by 0.5 (set to 3.125e-05) [Step started at t=0.016626, and will rewind there]\n", "Some steps were backtracked and re-done, to prevent negative concentrations or excessively large concentration changes\n", "78 total step(s) taken\n" ] } ], "source": [ "dynamics.single_compartment_react(initial_step=0.0005, target_end_time=0.035,\n", " variable_steps=True, explain_variable_steps=False)" ] }, { "cell_type": "code", "execution_count": 14, "id": "ea3bc6ce-e7c3-4ba4-873a-0104286a2fe3", "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": [ "dynamics.plot_history(colors=['red', 'darkorange', 'green'])" ] }, { "cell_type": "markdown", "id": "44beb909-5071-47e5-9499-482cf37f9ce3", "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": 15, "id": "35850ec7-e78e-4b57-976c-bc0ad6c824d5", "metadata": {}, "outputs": [], "source": [ "#dynamics.get_history()\n", "\n", "#dynamics.explain_time_advance()" ] }, { "cell_type": "code", "execution_count": 16, "id": "aff608b1-5c78-4070-845a-118afe7c2108", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0: A + X <-> 2 B\n", "Final concentrations: [A] = 0.5945 ; [X] = 45.62 ; [B] = 108.8\n", "1. Ratio of reactant/product concentrations, adjusted for reaction orders: 4.00995\n", " Formula used: [B] / ([A][X])\n", "2. Ratio of forward/reverse reaction rates: 4.0\n", "Discrepancy between the two values: 0.2488 %\n", "Reaction IS in equilibrium (within 1% tolerance)\n", "\n" ] }, { "data": { "text/plain": [ "True" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Verify that the reaction has reached equilibrium\n", "dynamics.is_in_equilibrium()" ] }, { "cell_type": "code", "execution_count": null, "id": "7ddbe0ec-53c3-4d25-825a-cbe3bdf8e50a", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "f6619731-c5ea-484c-af3e-cea50d685361", "metadata": { "tags": [] }, "source": [ "# Let's again suddenly increase [A]" ] }, { "cell_type": "code", "execution_count": 17, "id": "d3618eba-a673-4ff5-85d0-08f5ea592361", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SYSTEM STATE at Time t = 0.03752232:\n", "3 species:\n", " Species 0 (A). Conc: 30.0\n", " Species 1 (X). Conc: 45.62063150861952\n", " Species 2 (B). Conc: 108.75873698276109\n", "Set of chemicals involved in reactions: {'B', 'X', 'A'}\n" ] } ], "source": [ "dynamics.set_single_conc(species_name=\"A\", conc=30., snapshot=True)\n", "dynamics.describe_state()" ] }, { "cell_type": "code", "execution_count": 18, "id": "e5ce5d59", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SYSTEM TIMEAXBcaption
1310.0305730.67329545.699410108.601180
1320.0320360.63094145.657056108.685887
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1350.03752230.00000045.620632108.758737Set concentration of `A`
\n", "
" ], "text/plain": [ " SYSTEM TIME A X B caption\n", "131 0.030573 0.673295 45.699410 108.601180 \n", "132 0.032036 0.630941 45.657056 108.685887 \n", "133 0.034231 0.602230 45.628346 108.743309 \n", "134 0.037522 0.594516 45.620632 108.758737 \n", "135 0.037522 30.000000 45.620632 108.758737 Set concentration of `A`" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dynamics.get_history(tail=5)" ] }, { "cell_type": "markdown", "id": "0974480d-ca45-46fe-addd-c8d394780fdb", "metadata": {}, "source": [ "### Yet again, take the system to equilibrium" ] }, { "cell_type": "code", "execution_count": 19, "id": "8fe20f9c-05c4-45a4-b485-a51005440200", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "* INFO: the tentative time step (0.0005) leads to a least one norm value > its ABORT threshold:\n", " -> will backtrack, and re-do step with a SMALLER delta time, multiplied by 0.5 (set to 0.00025) [Step started at t=0.037522, and will rewind there]\n", "* INFO: the tentative time step (0.00025) leads to a least one norm value > its ABORT threshold:\n", " -> will backtrack, and re-do step with a SMALLER delta time, multiplied by 0.5 (set to 0.000125) [Step started at t=0.037522, and will rewind there]\n", "41 total step(s) taken\n" ] } ], "source": [ "dynamics.single_compartment_react(initial_step=0.0005, target_end_time=0.070,\n", " variable_steps=True, explain_variable_steps=False)" ] }, { "cell_type": "code", "execution_count": 20, "id": "c388dae7-c4a6-4644-a390-958e3862d102", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "Chemical=A
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"text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dynamics.plot_history(colors=['red', 'darkorange', 'green'])" ] }, { "cell_type": "markdown", "id": "4cba88b2-96af-415b-85e9-efa48f862f3c", "metadata": {}, "source": [ "`A`, again the scarce limiting reagent, stops the reaction yet again. \n", "And, again, the (transiently) high value of [A] up-regulated [B]\n", "\n", "Notes: \n", "`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": "code", "execution_count": 21, "id": "9556b84d-b977-4a4b-9250-bc97634d8356", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Min abs distance found at data row: 2\n" ] }, { "data": { "text/plain": [ "(0.0004607037505267594, 3.333333333333333)" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Look up the some of the intersections of the [A] and [B] curves\n", "dynamics.curve_intersection(\"A\", \"B\", t_start=0, t_end=0.01)" ] }, { "cell_type": "code", "execution_count": 22, "id": "f044d268-7262-4154-bb29-f02b0f702242", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Min abs distance found at data row: 73\n" ] }, { "data": { "text/plain": [ "(0.017062701624030972, 36.64925643602293)" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dynamics.curve_intersection(\"A\", \"B\", t_start=0.0151, t_end=0.02)" ] }, { "cell_type": "markdown", "id": "af3637e5-8495-4db0-b43c-194c7bdc4f67", "metadata": {}, "source": [ "Note: the _curve_intersection()_ function currently cannot location the intersection at t=0.015 (the vertical rise in the red line); this issue will get addressed in future versions..." ] }, { "cell_type": "code", "execution_count": 23, "id": "6de58fe9-ff1e-40dd-9ac7-83eee458f818", "metadata": {}, "outputs": [], "source": [ "#dynamics.get_history()\n", "\n", "#dynamics.explain_time_advance()" ] }, { "cell_type": "code", "execution_count": 24, "id": "c3afbcc8-bdae-4938-a3f1-ce00d62816f2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0: A + X <-> 2 B\n", "Final concentrations: [A] = 2.29 ; [X] = 17.91 ; [B] = 164.2\n", "1. Ratio of reactant/product concentrations, adjusted for reaction orders: 4.00332\n", " Formula used: [B] / ([A][X])\n", "2. Ratio of forward/reverse reaction rates: 4.0\n", "Discrepancy between the two values: 0.08288 %\n", "Reaction IS in equilibrium (within 1% tolerance)\n", "\n" ] }, { "data": { "text/plain": [ "True" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Verify that the reaction has reached equilibrium\n", "dynamics.is_in_equilibrium()" ] }, { "cell_type": "code", "execution_count": null, "id": "3e5baa56", "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 }