{
"cells": [
{
"cell_type": "markdown",
"id": "49bcb5b0-f19d-4b96-a5f1-e0ae30f66d8f",
"metadata": {},
"source": [
"## `U` (\"Up-regulator\") up-regulates `X` , by sharing an upstream reagent `S` (\"Source\") across 2 separate reactions: \n",
"### `2 S <-> U` and `S <-> X` (both mostly forward)\n",
"\n",
"1st-order kinetics throughout. \n",
"\n",
"Invoking [Le Chatelier's principle](https://www.chemguide.co.uk/physical/equilibria/lechatelier.html), it can be seen that, starting from equilibrium, when [U] goes up, so does [S]; and when [S] goes up, so does [X]. \n",
"Conversely, when [U] goes down, so does [S]; and when [S] goes down, so does [X]. \n",
"\n",
"This experiment is a counterpart of experiment `up_regulate_2`, with \"upstream\" rather than \"downstream\" reactions.\n",
"\n",
"Note: numerical errors in the same reactions (with the same initial conditions) is explored in the experiment \"large_time_steps_2\"\n",
"\n",
"LAST REVISED: Feb. 19, 2023"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "d9efa3fd-e95d-4e1c-878a-81ae932b2709",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Added 'D:\\Docs\\- MY CODE\\BioSimulations\\life123-Win7' to sys.path\n"
]
}
],
"source": [
"# Extend the sys.path variable, to contain the project's root directory\n",
"import set_path\n",
"set_path.add_ancestor_dir_to_syspath(2) # The number of levels to go up \n",
" # to reach the project's home, from the folder containing this notebook"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "01bae555-3dcf-42c1-bddc-9477a37f49f8",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from experiments.get_notebook_info import get_notebook_basename\n",
"\n",
"from src.modules.reactions.reaction_data import ReactionData as chem\n",
"from src.modules.reactions.reaction_dynamics import ReactionDynamics\n",
"\n",
"import numpy as np\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_regulate_3.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: 2 (at temp. 25 C)\n",
"0: 2 S <-> U (kF = 8 / kR = 2 / Delta_G = -3,436.56 / K = 4) | 1st order in all reactants & products\n",
"1: S <-> X (kF = 6 / kR = 3 / Delta_G = -1,718.28 / K = 2) | 1st order in all reactants & products\n",
"[GRAPHIC ELEMENT SENT TO LOG FILE `up_regulate_3.log.htm`]\n"
]
}
],
"source": [
"# Initialize the system\n",
"chem_data = chem(names=[\"U\", \"X\", \"S\"])\n",
"\n",
"# Reaction 2 S <-> U , with 1st-order kinetics for all species (mostly forward)\n",
"chem_data.add_reaction(reactants=[(2, \"S\")], products=\"U\",\n",
" forward_rate=8., reverse_rate=2.)\n",
"\n",
"# Reaction S <-> X , with 1st-order kinetics for all species (mostly forward)\n",
"chem_data.add_reaction(reactants=\"S\", products=\"X\",\n",
" forward_rate=6., reverse_rate=3.)\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 (U). Conc: 50.0\n",
" Species 1 (X). Conc: 100.0\n",
" Species 2 (S). Conc: 0.0\n"
]
}
],
"source": [
"dynamics = ReactionDynamics(reaction_data=chem_data)\n",
"dynamics.set_conc(conc={\"U\": 50., \"X\": 100., \"S\": 0.})\n",
"dynamics.describe_state()"
]
},
{
"cell_type": "markdown",
"id": "0b46b395-3f68-4dbd-b0c5-d67a0e623726",
"metadata": {
"tags": []
},
"source": [
"# 1. Take the initial system to equilibrium"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "bcf652b8-e0dc-438e-bdbe-02216c1d52a0",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"single_compartment_react(): setting abs_fast_threshold to 50.0\n",
"150 total step(s) taken\n",
"From time 0 to 0.44, in 176 substeps of 0.0025 (each 1/4 of full step)\n",
"From time 0.44 to 1.5, in 106 FULL steps of 0.01\n",
"(for a grand total of the equivalent of 150 FULL steps)\n"
]
}
],
"source": [
"dynamics = ReactionDynamics(reaction_data=chem_data)\n",
"dynamics.set_conc(conc={\"U\": 50., \"X\": 100., \"S\": 0.})\n",
"#dynamics.describe_state()\n",
"\n",
"dynamics.set_diagnostics() # To save diagnostic information about the call to single_compartment_react()\n",
"\n",
"dynamics.single_compartment_react(time_step=0.01, stop_time=1.5,\n",
" dynamic_substeps=4, rel_fast_threshold=50)\n",
"\n",
"df = dynamics.get_history()\n",
"#df\n",
"dynamics.explain_time_advance()"
]
},
{
"cell_type": "markdown",
"id": "cbf6c9c7-8cec-400f-9e70-49ff1a9f485c",
"metadata": {
"tags": []
},
"source": [
"## Plots of changes of concentration with time"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "c388dae7-c4a6-4644-a390-958e3862d102",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
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"output_type": "display_data"
},
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"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 concentration for `2 S <-> U` and `S <-> X`"
},
"xaxis": {
"anchor": "y",
"autorange": true,
"domain": [
0,
1
],
"range": [
0,
1.500000000000001
],
"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": [
"dynamics.plot_curves(colors=['green', 'orange', 'blue'])"
]
},
{
"cell_type": "markdown",
"id": "53406956-8084-43fe-9540-1212e9cc2258",
"metadata": {},
"source": [
"### Note that [S] is initially 0, and that it builds up thru _reverse_ reactions"
]
},
{
"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": [
"2 S <-> U\n",
"Final concentrations: [U] = 72.24 ; [S] = 18.23\n",
"1. Ratio of reactant/product concentrations, adjusted for reaction orders: 3.9635\n",
" Formula used: [U] / [S]\n",
"2. Ratio of forward/reverse reaction rates: 4.0\n",
"Discrepancy between the two values: 0.9125 %\n",
"Reaction IS in equilibrium (within 3% tolerance)\n",
"\n",
"S <-> X\n",
"Final concentrations: [X] = 37.3 ; [S] = 18.23\n",
"1. Ratio of reactant/product concentrations, adjusted for reaction orders: 2.04648\n",
" Formula used: [X] / [S]\n",
"2. Ratio of forward/reverse reaction rates: 2.0\n",
"Discrepancy between the two values: 2.324 %\n",
"Reaction IS in equilibrium (within 3% tolerance)\n",
"\n"
]
},
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Verify that the reaction has reached equilibrium\n",
"dynamics.is_in_equilibrium(tolerance=3)"
]
},
{
"cell_type": "markdown",
"id": "448ec7fa-6529-438b-84ba-47888c2cd080",
"metadata": {
"tags": []
},
"source": [
"# 2. Now, let's suddenly increase [U]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "e7ced06d-f506-41ee-80d6-d4b2a8ed28bc",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SYSTEM STATE at Time t = 1.5:\n",
"3 species:\n",
" Species 0 (U). Conc: 72.23775751588529\n",
" Species 1 (X). Conc: 37.298728471715336\n",
" Species 2 (S). Conc: 18.225756496514013\n"
]
}
],
"source": [
"dynamics.describe_state()"
]
},
{
"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 = 1.5:\n",
"3 species:\n",
" Species 0 (U). Conc: 100.0\n",
" Species 1 (X). Conc: 37.298728471715336\n",
" Species 2 (S). Conc: 18.225756496514013\n"
]
}
],
"source": [
"dynamics.set_chem_conc(species_name=\"U\", conc=100.)\n",
"dynamics.describe_state()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "e5ce5d59",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" SYSTEM TIME | \n",
" U | \n",
" X | \n",
" S | \n",
" caption | \n",
"
\n",
" \n",
" \n",
" \n",
" | 281 | \n",
" 1.49 | \n",
" 72.224080 | \n",
" 37.324855 | \n",
" 18.226984 | \n",
" | \n",
"
\n",
" \n",
" | 282 | \n",
" 1.50 | \n",
" 72.237758 | \n",
" 37.298728 | \n",
" 18.225756 | \n",
" | \n",
"
\n",
" \n",
" | 283 | \n",
" 1.50 | \n",
" 100.000000 | \n",
" 37.298728 | \n",
" 18.225756 | \n",
" Set concentration of `U` | \n",
"
\n",
" \n",
"
\n",
"
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""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"dynamics.plot_curves(colors=['green', 'orange', 'blue'])"
]
},
{
"cell_type": "markdown",
"id": "158e3787-f2d5-4a01-aaa9-6066e93e584c",
"metadata": {},
"source": [
"### The (transiently) high value of [U] led to an increase in [X]"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "2783a665-fca0-44e5-8d42-af2a96eae392",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2 S <-> U\n",
"Final concentrations: [U] = 93 ; [S] = 23.22\n",
"1. Ratio of reactant/product concentrations, adjusted for reaction orders: 4.00501\n",
" Formula used: [U] / [S]\n",
"2. Ratio of forward/reverse reaction rates: 4.0\n",
"Discrepancy between the two values: 0.1252 %\n",
"Reaction IS in equilibrium (within 1% tolerance)\n",
"\n",
"S <-> X\n",
"Final concentrations: [X] = 46.3 ; [S] = 23.22\n",
"1. Ratio of reactant/product concentrations, adjusted for reaction orders: 1.99362\n",
" Formula used: [X] / [S]\n",
"2. Ratio of forward/reverse reaction rates: 2.0\n",
"Discrepancy between the two values: 0.3189 %\n",
"Reaction IS in equilibrium (within 1% tolerance)\n",
"\n"
]
},
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Verify that the reaction has reached equilibrium\n",
"dynamics.is_in_equilibrium()"
]
},
{
"cell_type": "markdown",
"id": "f6619731-c5ea-484c-af3e-cea50d685361",
"metadata": {
"tags": []
},
"source": [
"# 3. Let's again suddenly increase [U]"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "e82c46d1-482a-41fb-873e-11a42561603d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SYSTEM STATE at Time t = 3.01:\n",
"3 species:\n",
" Species 0 (U). Conc: 93.00357364838469\n",
" Species 1 (X). Conc: 46.29552051953213\n",
" Species 2 (S). Conc: 23.221817151927773\n"
]
}
],
"source": [
"dynamics.describe_state()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "d3618eba-a673-4ff5-85d0-08f5ea592361",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SYSTEM STATE at Time t = 3.01:\n",
"3 species:\n",
" Species 0 (U). Conc: 150.0\n",
" Species 1 (X). Conc: 46.29552051953213\n",
" Species 2 (S). Conc: 23.221817151927773\n"
]
}
],
"source": [
"dynamics.set_chem_conc(species_name=\"U\", conc=150.)\n",
"dynamics.describe_state()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "61eead55-fcef-41cd-b29e-f2d5ad5c6078",
"metadata": {},
"outputs": [
{
"data": {
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\n",
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" | \n",
" SYSTEM TIME | \n",
" U | \n",
" X | \n",
" S | \n",
" caption | \n",
"
\n",
" \n",
" \n",
" \n",
" | 448 | \n",
" 3.00 | \n",
" 93.005965 | \n",
" 46.290953 | \n",
" 23.221603 | \n",
" | \n",
"
\n",
" \n",
" | 449 | \n",
" 3.01 | \n",
" 93.003574 | \n",
" 46.295521 | \n",
" 23.221817 | \n",
" | \n",
"
\n",
" \n",
" | 450 | \n",
" 3.01 | \n",
" 150.000000 | \n",
" 46.295521 | \n",
" 23.221817 | \n",
" Set concentration of `U` | \n",
"
\n",
" \n",
"
\n",
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",
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"dynamics.plot_curves(colors=['green', 'orange', 'blue'])"
]
},
{
"cell_type": "markdown",
"id": "ffbf3294-7a8d-4679-9c4b-5b9a975bf8fc",
"metadata": {},
"source": [
"### The (transiently) high value of [U] again led to an increase in [X]"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "aff608b1-5c78-4070-845a-118afe7c2108",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Verify that the reaction has reached equilibrium\n",
"dynamics.is_in_equilibrium(explain=False)"
]
},
{
"cell_type": "markdown",
"id": "64ebc51b-0dc7-4cff-b231-4c35843a7113",
"metadata": {
"tags": []
},
"source": [
"# 4. Now, instead, let's DECREASE [U]"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "32de9623-5221-420b-a6f2-5414df281d44",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SYSTEM STATE at Time t = 4.51:\n",
"3 species:\n",
" Species 0 (U). Conc: 134.56867943830346\n",
" Species 1 (X). Conc: 66.80533208508905\n",
" Species 2 (S). Conc: 33.57464670976427\n"
]
}
],
"source": [
"dynamics.describe_state()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "52f4843c-0671-4cd9-9c51-74a44feb4fe4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SYSTEM STATE at Time t = 4.51:\n",
"3 species:\n",
" Species 0 (U). Conc: 80.0\n",
" Species 1 (X). Conc: 66.80533208508905\n",
" Species 2 (S). Conc: 33.57464670976427\n"
]
}
],
"source": [
"dynamics.set_chem_conc(species_name=\"U\", conc=80.)\n",
"dynamics.describe_state()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "e8fe3554-d5ab-4306-b890-4e36289b5b4b",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" SYSTEM TIME | \n",
" U | \n",
" X | \n",
" S | \n",
" caption | \n",
"
\n",
" \n",
" \n",
" \n",
" | 644 | \n",
" 4.50 | \n",
" 134.574232 | \n",
" 66.794725 | \n",
" 33.574148 | \n",
" | \n",
"
\n",
" \n",
" | 645 | \n",
" 4.51 | \n",
" 134.568679 | \n",
" 66.805332 | \n",
" 33.574647 | \n",
" | \n",
"
\n",
" \n",
" | 646 | \n",
" 4.51 | \n",
" 80.000000 | \n",
" 66.805332 | \n",
" 33.574647 | \n",
" Set concentration of `U` | \n",
"
\n",
" \n",
"
\n",
"
SYSTEM TIME=%{x}
concentration=%{y}",
"legendgroup": "U",
"line": {
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"symbol": "circle"
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"mode": "lines",
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"type": "scattergl",
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",
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"dynamics.plot_curves(colors=['green', 'orange', 'blue'])"
]
},
{
"cell_type": "markdown",
"id": "a1629b91-2753-4df7-b6a0-dedf86ac3dc1",
"metadata": {},
"source": [
"### The (transiently) LOW value of [U] led to an DECREASE in [X]"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "31c9c18f-3a7f-4690-8e2f-70fdb02ef5c7",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Verify that the reaction has reached equilibrium\n",
"dynamics.is_in_equilibrium(explain=False)"
]
},
{
"cell_type": "markdown",
"id": "777ebec9-ed31-4cf3-adfc-b44db383bd39",
"metadata": {},
"source": [
"**IDEAS TO EXPLORE**: \n",
"\n",
"* Effect of the stoichiometry and the Delta_G on the \"amplification\" of the signal (from [U] to [X]) \n",
"\n",
"* Effect of a continuously-varying (maybe oscillating [U]), and its being affected by the reactions' kinetics\n",
"\n",
"* Combining this experiment and `up_regulate_2` in a \"bifan motif\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6fd8ca29-4a92-4381-8fcb-0acf5e4a1f16",
"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
}