{
"cells": [
{
"cell_type": "markdown",
"id": "f43cd6af-d772-43f7-a4da-30bcc41c7e50",
"metadata": {},
"source": [
"## A transient concentration inside an organelle can \"escape\" from it by passive transport across the membranes, only it if diffuses quicky - before a reaction transforms it into a chemical unable to cross membranes.\n",
"### The \"window of opportunity to escape\" closes fast!\n",
"\n",
"#### SCENARIO 1 : `A`, the chemical injected into the organelle, diffuses slowly - and gets converted into `C` (which cannot cross the membranes) by the reaction `A + B <-> C` , before it has a chance to leave the organelle. Trapped! \n",
"\n",
"#### SCENARIO 2 : `A` diffuses fast enough to \"escape\" out of the organelle before getting completely trapped there\n",
"\n",
"Note: `B` is plentiful everywhere\n",
"\n",
"**Recommended background:** \n",
"\n",
"* experiment `1D/diffusion/membrane_gradient_diffusion_1`\n",
"* experiment `1D/reaction_diffusion/transient_getting_mopped_up`"
]
},
{
"cell_type": "markdown",
"id": "6398f935-72f9-458e-90ab-f02a05132302",
"metadata": {},
"source": [
"### TAGS : \"reactions 1D\", \"diffusion 1D\", \"membranes 1D\""
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "2b6f0de7-d66f-4f4e-8850-8a092fca20b8",
"metadata": {},
"outputs": [],
"source": [
"LAST_REVISED = \"Aug. 28, 2025\"\n",
"LIFE123_VERSION = \"1.0.0rc6\" # Library version this experiment is based on"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "806647d0-4c0b-4abf-8d86-53460378d93e",
"metadata": {},
"outputs": [],
"source": [
"#import set_path # Using MyBinder? Uncomment this before running the next cell!"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "f6e0f263",
"metadata": {},
"outputs": [],
"source": [
"#import sys\n",
"#sys.path.append(\"C:/some_path/my_env_or_install\") # CHANGE to the folder containing your venv or libraries installation!\n",
"# NOTE: If any of the imports below can't find a module, uncomment the lines above, or try: import set_path \n",
"\n",
"from life123 import BioSim1D, ChemData, ReactionRegistry, PlotlyHelper, check_version"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "fe26e828-4428-4754-af9b-b54739b3a109",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"OK\n"
]
}
],
"source": [
"check_version(LIFE123_VERSION)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "79045ff7-ed3f-4c3a-be79-9f8a4476f3de",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "e9c62aaf-525f-46ef-b923-ec3083f8a4c0",
"metadata": {},
"source": [
"## Initialize the Chemical Data and the Reactions. They will be re-used in both scenarios"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "aeb04519-513f-4509-b7e0-5d42f1196613",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of reactions: 1\n",
"0: A + B <-> C (Elementary Synthesis reaction) (kF = 0.1 / kR = 0.02 / K = 5)\n",
"Chemicals involved in the above reactions: {\"A\" (red), \"B\" (turquoise), \"C\" (green)}\n"
]
}
],
"source": [
"# Initialize the chemical data\n",
"chem_data = ChemData(names=[\"A\", \"B\", \"C\"], \n",
" diffusion_rates=[100., 800., 500.], # The diffusion rate of `A` will later be increased in scenario 2\n",
" plot_colors=[\"red\", \"turquoise\", \"green\"]) \n",
"\n",
"rxns = ReactionRegistry(chem_data=chem_data)\n",
"\n",
"# Reaction A + B <-> C , with 1st-order kinetics for each species; note that it's mostly in the forward direction\n",
"# The reaction is mostly in the forward direction\n",
"rxns.add_reaction(reactants=[\"A\", \"B\"], products=\"C\", kF=0.1, kR=0.02)\n",
"rxns.describe_reactions()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "409e1d41-2a65-4668-8b44-f5c0c36f1a35",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "a8832e6c-cf7f-4d88-a303-3c80d9218e6e",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "8221d2c0-4b6c-49b5-b24d-97c153e2a892",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "1369e74e-3ef3-4adf-b2f7-65ecce2d3183",
"metadata": {
"tags": []
},
"source": [
"# SCENARIO 1 - `A` diffuses slowly, relatively to the reaction `A + B <-> C`"
]
},
{
"cell_type": "markdown",
"id": "d233bc62-72ce-400c-9d66-0c91900ea6ab",
"metadata": {},
"source": [
"### Initialize the 1D System, including Membranes"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "5afedbcc-a438-4c84-9b3c-22460c3a1904",
"metadata": {},
"outputs": [],
"source": [
"bio = BioSim1D(n_bins=30, chem_data=chem_data, reactions=rxns)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "e17e8404-cc7e-45d2-a644-9e33c43f7e7c",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"[(2, 18)]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bio.membranes().set_membranes(membranes=[ (2, 18) ])\n",
"bio.membranes().membrane_list"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "eba727ae-f919-490a-ace7-133871c85e66",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# We'll use 1/2 of the diffusion rate of `A` and `B` \n",
"# as their respective membrane permeability (by passive transport)\n",
"# `C`, by constrast, keeps the default 0 permeability (i.e., can't cross membranes)\n",
"bio.membranes().change_permeability(\"A\", 50.)\n",
"bio.membranes().change_permeability(\"B\", 400.)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "afff430b-e1b0-40c9-aab9-c2d039af4a9e",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "9c980cca-f504-4be4-aa91-2814e646eea1",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "57fd3bb1-8d35-438c-8caa-d2ce5fe4f4e1",
"metadata": {},
"source": [
"### Initialize the initial concentrations"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "c984f746-d373-4a8a-89a4-9c3f0a3fca69",
"metadata": {},
"outputs": [],
"source": [
"# Set up the initial bell-shape concentration of `A`, with a narrow peak close to one end of the system,\n",
"# centered at 20% of the width of the system, i.e. at bin 6\n",
"bio.inject_bell_curve(chem_label=\"A\", center=0.2, sd=0.05, max_amplitude=200., bias=0., clip=(2,17))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "cd5b5823-d0d0-47be-a4d3-0f636def4d6b",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# Chemical `B`, by contrast, is uniformly distributed\n",
"bio.set_uniform_concentration(chem_label=\"B\", conc=80.)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "98c74ccd-f9b9-4c5b-8697-8f9d07e0f6bc",
"metadata": {
"tags": []
},
"outputs": [
{
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| \n", " | Species | \n", "Diff rate | \n", "Bin 0 | \n", "Bin 1 | \n", "Bin 2 | \n", "Bin 3 | \n", "Bin 4 | \n", "Bin 5 | \n", "Bin 6 | \n", "Bin 7 | \n", "... | \n", "Bin 20 | \n", "Bin 21 | \n", "Bin 22 | \n", "Bin 23 | \n", "Bin 24 | \n", "Bin 25 | \n", "Bin 26 | \n", "Bin 27 | \n", "Bin 28 | \n", "Bin 29 | \n", "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", "A | \n", "100.0 | \n", "0.0 | \n", "0.0 | \n", "6.451484 | \n", "30.996376 | \n", "92.555278 | \n", "171.763341 | \n", "198.106523 | \n", "142.0058 | \n", "... | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "
| 1 | \n", "B | \n", "800.0 | \n", "80.0 | \n", "80.0 | \n", "80.000000 | \n", "80.000000 | \n", "80.000000 | \n", "80.000000 | \n", "80.000000 | \n", "80.0000 | \n", "... | \n", "80.0 | \n", "80.0 | \n", "80.0 | \n", "80.0 | \n", "80.0 | \n", "80.0 | \n", "80.0 | \n", "80.0 | \n", "80.0 | \n", "80.0 | \n", "
| 2 | \n", "C | \n", "500.0 | \n", "0.0 | \n", "0.0 | \n", "0.000000 | \n", "0.000000 | \n", "0.000000 | \n", "0.000000 | \n", "0.000000 | \n", "0.0000 | \n", "... | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "
3 rows × 32 columns
\n", "| \n", " | Bin 0 | \n", "Bin 1 | \n", "Bin 2 | \n", "Bin 3 | \n", "Bin 4 | \n", "Bin 5 | \n", "Bin 6 | \n", "Bin 7 | \n", "Bin 8 | \n", "Bin 9 | \n", "Bin 10 | \n", "Bin 11 | \n", "Bin 12 | \n", "Bin 13 | \n", "Bin 14 | \n", "Bin 15 | \n", "Bin 16 | \n", "Bin 17 | \n", "Bin 18 | \n", "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", "0.0 | \n", "0.0 | \n", "6.451484 | \n", "30.996376 | \n", "92.555278 | \n", "171.763341 | \n", "198.106523 | \n", "142.0058 | \n", "63.263381 | \n", "17.516112 | \n", "3.014131 | \n", "0.322348 | \n", "0.021425 | \n", "0.000885 | \n", "0.000023 | \n", "3.625508e-07 | \n", "3.595235e-09 | \n", "2.215770e-11 | \n", "0.0 | \n", "
| 1 | \n", "80.0 | \n", "80.0 | \n", "80.000000 | \n", "80.000000 | \n", "80.000000 | \n", "80.000000 | \n", "80.000000 | \n", "80.0000 | \n", "80.000000 | \n", "80.000000 | \n", "80.000000 | \n", "80.000000 | \n", "80.000000 | \n", "80.000000 | \n", "80.000000 | \n", "8.000000e+01 | \n", "8.000000e+01 | \n", "8.000000e+01 | \n", "80.0 | \n", "
| 2 | \n", "0.0 | \n", "0.0 | \n", "0.000000 | \n", "0.000000 | \n", "0.000000 | \n", "0.000000 | \n", "0.000000 | \n", "0.0000 | \n", "0.000000 | \n", "0.000000 | \n", "0.000000 | \n", "0.000000 | \n", "0.000000 | \n", "0.000000 | \n", "0.000000 | \n", "0.000000e+00 | \n", "0.000000e+00 | \n", "0.000000e+00 | \n", "0.0 | \n", "