{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Homodimerization and Annihilation\n", "\n", "This is for an integrated test of E-Cell4. Here, we test homodimerization and annihilation." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "from ecell4.prelude import *" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Parameters are given as follows. `D`, `radius`, `N_A`, and `ka_factor` mean a diffusion constant, a radius of molecules, an initial number of molecules of `A`, and a ratio between an intrinsic association rate and collision rate defined as `ka` and`kD` below, respectively. Dimensions of length and time are assumed to be micro-meter and second." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "D = 1\n", "radius = 0.005\n", "N_A = 60\n", "ka_factor = 0.1 # 0.1 is for reaction-limited" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "N = 30 # a number of samples" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calculating optimal reaction rates. `ka` is intrinsic, `kon` is effective reaction rates. Be careful about the calculation of a effective rate for homo-dimerization. An intrinsic must be halved in the formula. This kind of parameter modification is not automatically done." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "import numpy\n", "kD = 4 * numpy.pi * (radius * 2) * (D * 2)\n", "ka = kD * ka_factor\n", "kon = ka * kD / (ka + kD)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Start with `A` molecules, and simulate 3 seconds." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "y0 = {'A': N_A}\n", "duration = 3\n", "opt_kwargs = {'xlim': (0, duration), 'ylim': (0, N_A)}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Make a model with an effective rate. This model is for macroscopic simulation algorithms." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "with species_attributes():\n", " A | {'radius': radius, 'D': D}\n", "\n", "with reaction_rules():\n", " A + A > ~A2 | kon * 0.5\n", "\n", "m = get_model()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Save a result with `ode` as `obs`, and plot it:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ret1 = run_simulation(duration, y0=y0, model=m)\n", "ret1.plot(**opt_kwargs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Simulating with `gillespie`:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ret2 = ensemble_simulations(duration, ndiv=20, y0=y0, model=m, solver='gillespie', repeat=N)\n", "ret2.plot('o', ret1, '-', **opt_kwargs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Simulating with `meso`:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ret2 = ensemble_simulations(duration, ndiv=20, y0=y0, model=m, solver=('meso', Integer3(4, 4, 4)), repeat=N)\n", "ret2.plot('o', ret1, '-', **opt_kwargs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Make a model with an intrinsic rate. This model is for microscopic (particle) simulation algorithms." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "with species_attributes():\n", " A | {'radius': radius, 'D': D}\n", "\n", "with reaction_rules():\n", " A + A > ~A2 | ka\n", "\n", "m = get_model()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Simulating with `spatiocyte`:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ret2 = ensemble_simulations(duration, ndiv=20, y0=y0, model=m, solver=('spatiocyte', radius), repeat=N)\n", "ret2.plot('o', ret1, '-', **opt_kwargs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Simulating with `egfrd`:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ret2 = ensemble_simulations(duration, ndiv=20, y0=y0, model=m, solver=('egfrd', Integer3(4, 4, 4)), repeat=N)\n", "ret2.plot('o', ret1, '-', **opt_kwargs)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 1 }