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January 15, 2016 13:32
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "# 9. Spatial Gillespie Method\n", | |
| "\n", | |
| "## 9.1. Spaces in E-Cell4\n", | |
| "\n", | |
| "What the space in E-Cell4 looks like?" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 1, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "from ecell4 import *\n", | |
| "\n", | |
| "w1 = ode.ODEWorld(Real3(1, 1, 1))\n", | |
| "w2 = gillespie.GillespieWorld(Real3(1, 1, 1))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "We created a cube size, `1`, on a side for `ODEWorld` and `GillespieWorld`. In this case the volume only matters, that is" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "w3 = ode.ODEWorld(Real3(2, 0.5, 1)) # is almost equivalent to 'w1'\n", | |
| "w4 = gillespie.GillespieWorld(Real3(2, 2, 0.25)) # is almost equivalent to 'w2'" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "source": [ | |
| "This returns the same results. Because the volume is same as `1`.\n", | |
| "\n", | |
| "This seems reasonable in homogeneous system, but the cell is NOT homogeneous. So we need to consider a space for molecular localization.\n", | |
| "\n", | |
| "You can use several types of space and simulation methods in E-Cell4. We show an example with spatial Gillespie method here." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## 9.2. Spatial Gillespie Method\n", | |
| "\n", | |
| "In E-Cell4, the Spatial Gillespie method is included in `meso` module. Let's start with `run_simulation` like `ode`." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 3, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
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++YfIN9+A228PyGUSn36G96e/yIFnZtK8/jnM6dmQJ6M6UXL6Rzk7UUICp8+p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\n2HvgKNVH1+Gb23/kmovquh1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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x5af2d50>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "%matplotlib inline\n", | |
| "import numpy\n", | |
| "from ecell4 import *\n", | |
| "\n", | |
| "with reaction_rules():\n", | |
| " A + B == C | (0.01, 0.3)\n", | |
| "\n", | |
| "y = run_simulation(numpy.linspace(0, 10, 100), {'C': 60}, solver='meso')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "source": [ | |
| "At the steady state, the number of `C` is given as follows:\n", | |
| "\n", | |
| "$$\n", | |
| "\\frac{d\\mathrm{C}}{dt}=0.01{\\cdot}\\frac{\\mathrm{A}}{V}{\\cdot}\\frac{\\mathrm{B}}{V}-0.3{\\cdot}\\frac{\\mathrm{C}}{V}=0\\\\\n", | |
| "0.01\\left(60-\\mathrm{C}\\right)^2=0.3\\mathrm{C}\\times V\\\\\n", | |
| "\\mathrm{C}=30.\n", | |
| "$$" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "You will obtain almost the same result with `ode` or `gillespie` (may take longer time than `ode` or `gillespie`).\n", | |
| "This is not surprising because `meso` module is almost same with Gillespie unless you give additional spatial parameter.\n", | |
| "\n", | |
| "Next we will decompose `run_simulation`." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 4, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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UfwMqePdXBH7L5DUh/HXlU5995oySST0qaPRov0ocaI8e6drxY+M76q1j3gwo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\nP07+QKcmlgyMMSZUIj4Z7N5/lOPFfuO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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x5b6f810>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "from ecell4 import *\n", | |
| "\n", | |
| "with reaction_rules():\n", | |
| " A + B == C | (0.01, 0.3)\n", | |
| "\n", | |
| "m = get_model()\n", | |
| "\n", | |
| "w = meso.MesoscopicWorld(Real3(1, 1, 1), Integer3(1, 1, 1)) # XXX: Point2\n", | |
| "w.bind_to(m) # XXX: Point1\n", | |
| "w.add_molecules(Species('C'), 60)\n", | |
| "\n", | |
| "sim = meso.MesoscopicSimulator(w) # XXX: Point1\n", | |
| "obs = FixedIntervalNumberObserver(0.1, ('A', 'B', 'C'))\n", | |
| "sim.run(10, obs)\n", | |
| "\n", | |
| "viz.plot_number_observer(obs)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "This is nothing out of the ordinary one except for `MesoscopicWorld` and `MesoscopicSimulator`, but you can see some new elements.\n", | |
| "\n", | |
| "First in `w.bind_to(m)` we asscociated a `Model` to the `World`.\n", | |
| "In the basic exercises before, we did NOT do this.\n", | |
| "In spatial methods, `Species` attributes are necessary. Do not forget to call this.\n", | |
| "After that, only the `World` is required to create a `MesoscopicSimulator`.\n", | |
| "\n", | |
| "Next, the important difference is the second argument for MesoscopicWorld, i.e. `Integer3(1, 1, 1)`.\n", | |
| "`ODEWorld` and `GillespieWorld` do NOT have this second argument.\n", | |
| "Before we explain this, let's change this argument and run the simulation again." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 5, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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XkjHGmGBLdz2DhHULzhORQiSloVitqsFbfscYY0zAZTiaSERaAGuAd4H3gL9E\npHmgAzPGmPymZs2aFCtWjFKlSlG+fHmioqLYsmVLUK7tz9DSQUArVW2hqs2B1sBgf04uIoVFZIGI\nLBaRZSLSN2F7WRGZISKrRWS6iNi6ysaYfE9E+O677zhw4ADbtm0jIiKCJ554IijX9qcwCFfV1YlP\nVPUvINyfk6vqMeByVb0AOB+4WkQuAXoCs1S1LjAHeDbTkRtjTB6UmDupUKFCtG/fnj///DMo1/Wn\nMFgoIh+LSGTC4yPcKmh+UdXDCb8WxvVRKHAdkJiTdSRwfSZiNsaYPO/w4cOMHz+eJkFaLjTdDuQE\njwKdgCcTnv+E6zvwi4iEAYuAOsC7qvqbiFRS1R3gOqlFJCJzYRtjTGDIC9lf91L7Zj0z6vXXX0/B\nggU5ePAgERERTJ8+Pdvx+CPDwiChqWdQwiPTVNUHXCAipYBvROQcTkmHncrzE/r163fi98jISCIj\nI7MShjHG+CU7X+Q5YdKkSVx++eWoKhMnTqR58+asXLmSiIi075m3b99+0ndlVqS5nkEgiMjzwGHg\nASBSVXckzGX4XlXPTmV/W8/AGJNjQn09g1q1ajF8+HBatkxaZj4iIoIPPviAG2+8McX+ObmeQUAT\n1YlIhcSRQiJSFLgKWAl8C3RM2O0e3AI6xhhjkpk0aRL79u3j7LNT3CvnuHSbiUSkAPC6qj6dxfNX\nAUYm9BuEAeNVdYqI/AJMEJH7gA3ALVk8vzHG5ClRUVEUKFAAEaFGjRqMGjXK+8JAVeNFpFlWT66q\ny4CGqWzfA1yZ1fMaY0xetG7dOs+u7c9oosUi8i3wBXAocaOqfh2wqIwxxgSVP4VBEWA30DLZNgWs\nMDDGmDzCn6Gl92a0jzHGmNzNn0R1Z4nIbBFZnvC8gYj0DnxoxhhjgsWfoaUf4XIHxQGo6lLgtkAG\nZYwxJrj8KQyKqeqvp2w7HohgjDHGeMOfwmCXiNQhIWWEiLQHtgU0KmOMMUHlz2iiTsAw4H8isgVY\nB9wR0KiMMcYElT+jif4BrhSR4kCYqv4X+LCMMcYEkz+jicqLyFu41NUxIjJURMoHPjRjjMl/xowZ\nw8UXX0zJkiWpWrUq11xzDfPmzQv4df3pMxgH/AvcBLRP+H18IIMyxpj8aNCgQXTt2pXevXuzc+dO\nNm7cSKdOnYiOjg74tTNMYS0iy1X13FO2LVPV+gGNDEthbYzJWaGcwvrAgQNUrVqVkSNHppquOjXB\nTmE9Q0RYyGnGAAAgAElEQVRuE5GwhMctQHCW3jHGmHxi/vz5HDt2jOuv92YV4DQ7kEXkP9xwUgG6\nAJ8nvBQGHASymtbaGGPMKXbv3k2FChUICwvoMjNpSrMwUNWSwQzEGGNCgmR/DWSy0BRVvnx5du3a\nhc/n86RA8GeeASLSAKiZfH9LYW2MyZM86lNo0qQJhQsXZuLEiX73GeSkDAsDEfkEaACsAHwJmy2F\ntTHG5KBSpUrxwgsv0KlTJwoUKECrVq0IDw9n1qxZxMTE8NprrwX0+v7UDBqrar2ARmGMydOmL/yL\n68bcxOz7J9P0nBpehxOyunbtSpUqVejfvz933nknJUuW5MILL6RXr14Bv7Y/hcF8Eamnqn8GPBpj\nTJ40ZPo3xMsRWg5vw/Kuczmzms1bTUuHDh3o0KFD0K/rTy/FKFyBsFpElorIMhFZGujAjDF5x7x/\no+l78TucVzSKhm9GsWv/Ya9DMqfwp2YwHLgLWEZSn4Exxvhl9aZd/FdsGY+3i6Tnza2o26Mj5/S7\nlaebJU2Santhfc6pGZHuebbu/o/RMadm03cKFSxIp3aXUbCAN8My8wJ/CoN/VfXbgEdijMmTBn47\nhSpHWlKmRBEAlvUfzkV9nuDVn14BIJ44npu/hUWPzaNB7cqpnmPX/sPU7d8KH8cprKVSvH4gfA2L\nN/Tl0yfvD9w/JI/zpzBYLCJjgGjgWOJGG1pqjPHHlLXRtK4VdeJ5sSLh/Dngg5P2ueLFl2j8dlv+\nei6GahVP/rI/Gnucc/rdSkSBs1gz4FPCwlLOA3jsg9F8s3oCYIVBVvlTpyqKKwRaAVEJj3aBDMoY\nkzccPBLLlsIz6RZ1Tbr7zezdm1qFGtHg5Rs5eCT2xHafT2nQ62GUeJb1/zjVggDgqXZt2F70e/Yc\nOJKj8ecn/qxncG8wAjGhbc+BI3T55HM+eeJ+a5c1aXrsg9F0vqY1dU+vAMB73/1I8aP/49xaldI9\nLixMWNz/HWr1uIUaz15DneIXALDtyHr26nr+6fM9xYqEp3n8mdXKU+rw+bw9+Xv63t425/5B+Yg/\n6xmMEJFPTn0EIzgTOh7/eCSf7X2Exn26ex2KCVEzF63h/a0duXBQO3buPQTAmEXRNCkXlcGRTqHw\nAqx4cTSXV72WCsUqUKFYBc6v2IhFXacQUbZ4hsc3i4hiwpLAp3rOq/zpM5ic7PciwA3A1sCEY0LR\n8XgfX24ZTM8GXzF4SS+iXq1C9LPdvA7LhJinJgymaeEebCuwifov3sqG1yeyIi6asZET/T5HmRJF\n+PKZJ7J0/U5XRnHtF1fh872XZnNSjRo1kJzIPRQiKlVKv8aVGf40E32V/LmIjAXm5lgEJuT1HTOZ\ncF8pXr7rOq6/5EKaftKUxz6ozHuP2FLYxlmzeTd/ho3jj45/csZp5anx7LVU634NGn6cG5sGfOkT\nANpcVJew8UUY/+MfdIi8INV91q9fD8CQIUOIiEh/KGt+41eiulOcCfj1LopINdyktUq4OQofqepb\nIlIWt1paDWA9cIuq7s9CLCYI3l00kPvrdSMsTGh09ul8dcNUbpjUksti6qX5R2fyl0c/eZ8zjt9w\nYmjosue/4IyXWnJOgevTvEvPaWFhQv3CUXz0Q3SGn8uKFSuyc+fOoMQVCN8vX8nRuCO0Of8CRISK\nFStm+5z+rHSWuK5Bou3As6fWGNI4tjJQWVX/EJESwCLgOuBeYLeqDhCRHkBZVe2ZyvG20pnHRs78\njftntOfgy2spUijp3uG61waxeOevbBw0zsPoTCjYd/Ao5V+qydfXzea6S885sf3AoWMcj/dRrlTR\noMUy8Os59PmpB4cG/xa0awbbQ++O4pP1z1MwvhSXl7+Tqb16pNgnICudqWpJVS2V7HGWPwVBwrHb\nVfWPhN8PAiuBargCYWTCbiMBb5b2MRl6fupArinf+aSCAODd+x9gc6GZzFuxwaPITKjo8sloysVe\ncFJBAFCqeOGgFgQAj7a9jCNF/uaPtduCet1g6T9uGsM3dmdi+2nMfWQaM/e+z4Pvjsz4QD+kt9JZ\n9fQOVNWNmbmQiNQEzgd+ASqp6o6E82wXEWu8C4DDR+PSHY6XkXkrNrC50Ex+vn9YiteqVSxFwwL3\n8uTooSx6ZVCWzr9u2172HnTjwsPChAa1KgetScHkDJ9PGbd+EC81e8vrUAA3oa3asdb0+2IcfW66\nFYAKpYtTPaK0x5Fl38iZv9Hnj7t4v8Uk2jU6G4CJN0/luq8vp+yIktzWrHG2zp9mM5GILCNp2ctE\nClQEIlS1gN8XcU1EMcBLqjpJRPaoarlkr+9W1RRpDEVE+/bte+J5ZGQkkZGR/l42X2v78gBm7v2Q\n/15ZneKu3h+Hj8ZR89nrOLPkBcx78eVU95n/50aafnY+67r8Q41KZTJ1/uc/i6b/qtsJi3ML6vnC\n9/NEjeG89dBtmY7VeOeaV97gh3+/4MDABSFTkL86YQa9F95LYuu2r8Bhfrt3BRedVdXbwLJh5qI1\ntBnfgp71P+Dlu6496bVhU+fz6Kc34dv8X9LGnw9mupkIVfXrgVvp7H1gDfBEJo4rCEwDOifbthJX\nOwCoDKxM41g1mffAO59qgW41tFiXC/WJD8dm+vj4eJ+e0a2jVuzSVg8diU133xpdb9e2Lw/I1Pk/\n+G6eSveK+umMX09su+7VQXpu98cyHavxzsPvjtICT1fXX1dt8jqUdDXo8YQ27t3T6zCybMnabVqw\nW229e8hHfh+T8N3p9/e7qmZcGOBGD32a8AX+ABCeqQu40USDTtn2OtAj4fcewGtpHJv5dy6fe2HM\nFA3rXkmjf/lTnxs1SYt1uVDj432ZOselzz+nxbs00h17Dma47+ezF2mBp6tlWGgkiv7lT5XuEfrS\n2Kknbf942i9atMv5mYrTeKf/uGkq3SN00vwVXoeSoe//WKvSo7xu2/2f16Fk2qad+7Vol/O15Qsv\nZuq4rBQG6TUTnQv0As4BBgBjVTU+M7UOEWkK/IhLf60Jj+eAX4EJwOnABtzQ0n2pHK9pxWdSGjVr\nIR1nXc0HLb7loaubcDzeR7HuZ/NG82F0vq7Fif1ilvzDqB9/SPUcK3asYvGRiSzrMu9ESoGMlO1y\nOXWKX0iDyq4DsVrZirx4Z8r0VQv/2kLjjy7lvpovMazT3Se9dvBILCX7l2VL1+2cVr6kv/9k44HP\nZy/i7hlX836LiTzc9lKvw/FLta7taVS5OV91f9LrUPx2+Ggc1XpezWmFz2Lpq+9mqhkuK6OJ0isM\n4oFNwHdAikJAVQP+rlphkDmlujTltjMfPumL9vZBHzBn0xS2D3ZZyOcuX0+Lkc043deccCmc4hwF\nw8L56O7naHZuTb+vOzZmMX0mJ3UgrtefaFPh0ZNmKW/YsY+6r19Gy/J3MaVX6iktSnVpRt/mL9Lt\nxpZ+X9sEX+kuzbn5jPv4+PGOXofit2FT59Np9h0cenUNhcL97u70VP9x03h1QW/2DliQ6ZhzujC4\nJ70DVTVnxjOlwwoD/7kP++3898qakzqMd+0/TMQrtfiu/Y/UrlyeBkOaEVW5U5an/PtjwcpNNP2k\nKQ/VeZX3HrmDfQePUqN3K+oUvZCFLw9K8w7n4l7PULpwaWb16R2w2Ez2jJjxKw/OuIWDr/ydpYEJ\nXirVpSkP1n+Kgfe39zoUv9Tv0YlqpaqnOo8gI1kpDNL83wzGl73JOS/OHMi1lZ9K8QdaoXQxLiv6\nCI+Pf5kd8X/RsMQNAS0IgJNmKZ82rjzv//oRpcNO49f+A9Ot6rY881JGLPkooLGZ7Ok7bSBRlbrk\nuoIA4JHzuvH+0jcYSOgXBj6fstIXzctXTAvaNS0XcR4Qs+QfthaK4d0H7kv19fc6duKfohOoVKAu\n8154JSgxXXfpObxz2Vc8v+wmjvj2s/ylkRmmvr6jeRN2FfmF4/G2uqoXnvnkK177Ymaar89dvp7N\nhWbz7gO5cwGZ/ndex9Gwf/l42i9eh5KhL+cuRbQg7S45O2jXzH3Fu0mh89ghXFLwASqXK5Hq6+fU\njGDclb8Q1eicoI4Ff6xdMyqV+YWm9WpRqnjK/olTNahdmQJxZZi2cPWJSTUmOA4cOsagVY+jYXGU\nLf5tqh3DT4wewkUF7su1HfyFwgvQrNRdvP/jBB5ok70JWoH20Q+TOSc8Kqh/r1YzyOXWbt3DMvmc\nd+5Ov+nn1hbnZ2s2clbd1Kx+moVUaqrppXy1YH4AIzKp6Tx8NGXjzqPfeZ/x2A83MnnBypNe37Bj\nH0sYxbt3557ROKl54LIolh2LxucL7b7I+Xuiuf1C/9aByCn+LG5zlojMFpHlCc8biIj18IWIxz75\nkFpx7XL17MrkGp12KT9v+tnrMPIVn08Zu34QT1/ajT4drub+6gO4/ss2LPxry4l9Hv14GDVi23Jx\n3WoeRpp9HSIvID7sCNMX/eV1KGlavm4Hh4us5rFrmgf1uv40E30EPAN8CKCqS0VkDNA/kIGZ1CXO\nJdCwhHViJZ7x187zNqgcdN2FTfjm23e8DiNfeWXCdEQL0P2mKwEY1uluNr+8nYtHnQG+Qm4nOc64\nPPA5CwsT6ko73p0VzdUX1/U6nFQNjP6OqseuokTRQkG9rj+FQTFV/fWU1YGOBygek4G35oylSaGH\nGPu4G6tfrHA4FUoX8ziqnHPDpfW5fcZm1m3bS60qZb0OJ18Y/MtA7jyz60nt01N6dWfr7kdPdObn\npc/ZLedFMeS3AcDTXoeSqunrorn2zBuDfl1/+gx2iUgdErI+iUh7IG/mh80Flh6L5rHIm6geUZrq\nEaXzzB9ooiKFClLm8EV8FhP6Iz7yggk/LmFfoT8ZfF+HFK+dVr5knvycPRnVkv3FFrN26x6vQ0lh\n38GjbCs6h6eirg76tf2pGXQChgH/E5EtwDrgzoBGZVI1feFfxBc4lOdXFzun1KXMWjWfPgT/DyIr\nfl+zld/XJmV0v+K8uhnWavYcOMLXPy858fys0yrRvEGtTF13zebdVClfMlvNCc9+O5CryjwR9CYJ\nL5UrVZRKRyIZ+O3UkFu69Z3JMZQ8XN/vVDA5yZ81kP8BrhSR4kCYqv6X0TEmMN6ZGc1ZtAuZVMGB\n0vHSdjw850bm/HEPLc+v43U46fL5lEuGNabw8UoIBYgN203t2ZGseiPtyXP7Dh6ldp82HA3bTUF1\nI60OFf2TTVU2U61iKb+v22BgS8pQk3Wvf5WlSWBbd//HP4UmMuO+IZk+NrdrVSOK79ZMBkKnMNi4\ncz8vL+jJTTUf8+T6/owmKiwitwOdgadEpI+I9Al8aOZUP+2I5uYGwR1u5oUH2jTm1sp9aP1Za5av\n2+F1OOkaG7OYMF9h/hv4KwcH/8KIa8azPj7t0VCxcfGc0+cOSoVV4eAbSzk4+BcODv6F8oebMDQ6\n7Qlfpxrw1SxU4onTo1zQ+7EsDZUc8u1Myh1uTJ3TymW8cx7TNaodmwpN5/DROK9DAdw8j/NeuYEz\nCjVl1JMPehKDP30Gk3DLVB4HDiV7mCBat20v+4v9Tpdrr/A6lKAY0/URLi15B43euoatu0O3Mjr8\np8nUL5w0OeimZg04VnQj67btTbGvz6dc2PtJjvj2pZiRfXnVKL75M9rv677580Bur9WNpc9/yYa4\nRbR88YVMx/7Nimgur5r3by5Sc36dKhQ9WocPps71OhSOx/s49/m7KRZWlkX93/Ks5u9P3bKaqrYJ\neCQmXYO+nUbEkRZBX1PWS9/36cc5PbfR4KX2bB84NcN0Fl5YsC+al5oPOPG8SKGClDl0EaN/WEDv\n207+s2n36husjf2Zv3r9kGJGdpe2UVw26kVi4+IzzFD5zbzl7A1fypD7JlGqeGF+fXIKF7zTlHN6\n7KBi0UoAlCxckrFd0u4LiI2LZ22B7xhxdd9UX88PGpWJ4tMFX9P1hss9jaNJnx4c8G1nff/pnmZU\n9eev62cRqR/wSEy6ov+K5qoa+esuLixMWNz/PQ7LTvqPm+p1OCn8vmYrR4qs5ZGrm520vV6pS5mx\n8uSmoti4eKb9N4ApHb9KtV+g6Tk1KBxXmREzF2R43e7fDKRlyU4nCpRza1Xi+3tnEVGs8ol95u2Y\nwrm9700zz9Ons36lUFylTKUqz2teveVeVvi+osen33gWw/Y9B1nIByzo9hVlShTxLA5IpzAQkeUi\nshRoBvwuIqtFZKmILEvYboLk8NE4NhaaRtdrUi4Yk9cVKVSQjnWfZuhvb3odSgpDvvuO02Nbp0jz\nccVZTVi+/+TC4OPp8yl8rBqR59VO83wNS0Qxcv7kdK/5x9ptrA2fxHv3PnLS9mbn1uT7vn2J6deP\nmH79+PvFyeyJX0/jPqmvHTFy/mQaFs9fNxenanT26YxsHc0bqx7ineifPIlhaPQsyh5q5MnooVOl\nVzOoCkQBVwNnAK0SnrdL+GmCZNi0eRQ9WpuGZ57mdSieeLPjLfwX/jej5/zudSgnmbEhmnZnpvxT\nuKNFY/YW+5XYuKQ1oUb+Es1FpdL/s7n30ih+P5R+v0Gnke9wrt7OmdXKp7tfuVJFWdw9muVHphL1\n6sAUr/9+MJp7muS/m4tT3XnFhbxy4RienNueb+YtD/r1v1oeTeRpofF1ml5hsE5VN6T1CFqE+dDR\n2ONU69qeEk81psRTjXl63j00KnOt12F5pliRcNqU60yv71J+qXllz4Ej7CgaQ9drU3an1T29AoWO\nncbE+UlfLksOT+a+pun/0d99xcXEhu9k7vL1qb7+2+rNzI8dxuBbu/gVY53TyvHDg9OYumcoj30w\n+sT2eSs2cCx8O/de1civ8+R1PW++ik61h3DzxLbM/3NjxgfkkOPxPtaGfcfjrUKjUE6vAzlCRLqm\n9aKqDgpAPAboMfIr9usWhrROGv99Z8uLPIzIe+/d/yA1B9VmwcpNNDr7dK/D4a3oOZQ+fEGawzKr\nSxMmLpzPLc3PI2bJP8SF7+auK9L/PywUXoA68W0ZMjWaZueenIV23ba9XPZhG9pW7MEVF5zhd5zJ\nFxo6fUJFnr2lFUOmRFM7vm2uWf4xGN5+uAObXttO5Mdt+PPpuUEZbjty1m8UjCsXMnNp0qsZFABK\nACXTeJgA8PmU4X8O5IkLevJAm8YnHrlxZamcVD2iNBeE3cPjn7+V8c5BMGFJNM0qpX2n36TapSzY\n6voN3poeTR3fNX6NhrrhnChitpzcb7DnwBHOf/1azi3SmsnPZj6fTuJCQ70W3cnnsxfx/dbJ3FAv\nNJomQsnEnk/RoOg1XDAgil37Dwf8eiPmRXN+sdD5f0hvDeTfVbVhkOM5NYY8twbyvoNHOXD4GABh\nIilGlrz97Y90++EBDg9YFZJDKb00b8UGLvusIRu7rUt1RM7xeJ9f79mBQ8fYd+hohvsVCS9IRNni\nKbb7fEp4j9OZfMvsNDNffjNvObd8fT1xA/+mfJereOiCTrx6z/UZXnP7noNUefM0Ft27igqlixMf\n76PpgPsoFFaMvwd8lq3PxLMjJzJgxWP4Ch5kS7ctuXaRmkA6Hu/jrO73cMR3IMszu/1V9KnzGXjF\nOzzWrlnGO2dSVtZATq8wWKyqnibByWuFwfJ1O2gw7GxUEjoWw+I4J64jS19998REkypPXUdktTaM\n7faoh5GGrjpP302hsCKseO3DkybnjJjxK/fPuZqe9T7hlbuvS/P4CT8u4bYpV6Bhfsw8DYvlnoh3\n+PTJk5d5vGfox4zfMJSjg5aleejxeB/hvcsx65aFXPlFQ3Y8sy3VgiU1Zz1zH2vCvzrxvPKRSNa8\n8kWO5A+6Z+jH/LxpHmveHJHtc+VVB4/EUvPZKCqEV+fP14cFZBLY/D830nTUhRx9aXtAmuuyUhig\nqqk+gHJpvRashwsv72jWp7fW6/7Iieebdu7Xol0u0Mv7vaCqqtN+W63SvaL+u++QVyGGvC27DmjR\nLg01sl+/E9um/bZaw56prK1felWle0V9b/LcVI/9adk6DXu6qnb5aLxf15ry6yoN615Je4/69sS2\n5z+L1rBnKuu031ZneHz5Lq21Vtc7tWKXq/26ngkd23b/p8W6XKTN+/YJyPlvffNdrd3troCcW1U1\n4bszc9+3mT0gmI+8VBj8u++QSveKKb5ElqzdpgW71dY7Bn+o9bo/os369PYowtxj2T/btWDXOnrH\n4A8T3r9aes/Qj1VVtf+4aSrdI3TS/BUnHbNq479aqGtdvfH1oZm61sfTflHpUUE/nPKzfjR1vkqP\nCvrxtF/8OjayXz+lr+itb76bqWua0LB83Q4N73qGdhj4fo6fu0KXNn7flGRFVgqDNJuJQkFubSaa\nvfhv6laLOKldu8PA94nZPJ1tgyem2H/mojW0Gd8cX8HDLHvwL86tVSmY4eZKsxf/TauxzQmLL0aL\n0h2Z1SdpJdZH3vuMj9f15uE6r1KwgKuCD/9zMPVLXM78l17N9LVeGDOFF5bciyD0Oe8T+t7e1q/j\nXvtiJs/+2Yqfb95Ak3rVM31d4705f6z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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x5bc11f0>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "from ecell4 import *\n", | |
| "\n", | |
| "with reaction_rules():\n", | |
| " A + B == C | (0.01, 0.3)\n", | |
| "\n", | |
| "m = get_model()\n", | |
| "\n", | |
| "w = meso.MesoscopicWorld(Real3(1, 1, 1), Integer3(4, 4, 4)) # XXX: Point2\n", | |
| "w.bind_to(m) # XXX: Point1\n", | |
| "w.add_molecules(Species('C'), 60)\n", | |
| "\n", | |
| "sim = meso.MesoscopicSimulator(w) # XXX: Point1\n", | |
| "obs = FixedIntervalNumberObserver(0.1, ('A', 'B', 'C'))\n", | |
| "sim.run(10, obs)\n", | |
| "\n", | |
| "viz.plot_number_observer(obs)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "You must have the different plot. If you increase value in the `Integer3`, you will have more different one. \n", | |
| "Actually this second argument means the number of spatical partitions. `meso` is almost same with `gillespie`, but `meso` divides the space into cuboids (we call these cuboids subvolumes) and each subvolume has different molecular concentration by contrast `gillespie` has only one uniform closed space. So in the preceding example, we divided `1` cube with sides `1` into `64` (4x4x4) cubes with sides `0.25`. We threw 60 `C` molecules into the `World`. Thus, each `subvolume` has 1 species at most." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "source": [ | |
| "## 9.3. Defining Molecular Diffusion Coefficient" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Where the difference is coming from? This is because we do NOT consider molecular diffusion coefficient, although we got a space with `meso`. To setup diffusion coefficient, use `Species` attribute `'D'` in the way described before ([2. How to Build a Model](2. How to Build a Model.ipynb)).\n", | |
| "As shown in [1. Brief Tour of E-Cell4 Simulations](1. Brief Tour of E-Cell4 Simulations.ipynb), we use E-Cell4 special notation here." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 6, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<ecell4.core.NetworkModel at 0x5bd3ee0>" | |
| ] | |
| }, | |
| "execution_count": 6, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "with species_attributes():\n", | |
| " A | {'D': '1'}\n", | |
| " B | {'D': '1'}\n", | |
| " C | {'D': '1'}\n", | |
| "\n", | |
| " # A | B | C | {'D': '1'} # means the same as above\n", | |
| "\n", | |
| "get_model()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "You can setup diffusion coefficient with `with species_attributes():` statement. Here we set all the diffusion coefficient as `1`. Let's simulate this model again. Now you must have the almost same result with `gillespie` even with large `Integer3` value (the simulation will takes much longer than `gillespie`).\n", | |
| "\n", | |
| "How did the molecular diffusion work for the problem? Think about free diffusion (the diffusion coefficient of a Species is $D$) in 3D space. The unit of diffusion coefficient is the square of length divided by time like \n", | |
| "$\\mathrm{\\mu m}^2/s$ or $\\mathrm{nm}^2/\\mu s$.\n", | |
| "\n", | |
| "It is known that the average of the square of point distance from time $0$ to $t$ is equal to $6Dt$.\n", | |
| "Conversely the average of the time scale in a space with length scale $l$ is about $l^2/6D$. \n", | |
| "\n", | |
| "In the above case, the size of each subvolume is 0.25 and the diffusion coefficient is 1. Thus the time scale is about 0.01 sec.\n", | |
| "If the molecules of the `Species` `A` and `B` are in the same subvolume, it takes about 1.5 sec to react, so in most cases the diffusion is faster than the reaction and the molecules move to other subvolume even dissociated in the same subvolume.\n", | |
| "The smaller $l$, the smaller subvolume's volume $l^3$, so the reaction rate after dissociation is faster, and the time of the diffusion and the transition between the subvolume gets smaller too." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## 9.4. Molecular localization\n", | |
| "\n", | |
| "We have used `add_molecules` function to add molecules to `World` in the same manner as `ode` or `gillespie`.\n", | |
| "Meanwhile in `MesoscopicWorld`, you can put in molecules according to the spatial presentation." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 7, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "from ecell4 import *\n", | |
| "\n", | |
| "w = meso.MesoscopicWorld(Real3(1, 1, 1), Integer3(3, 3, 3))\n", | |
| "w.add_molecules(Species('A'), 120)\n", | |
| "w.add_molecules(Species('B'), 120, Integer3(1, 1, 1))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "In `MesoscopicWorld`, you can set the subvolume and the molecule locations by giving the third argument `Integer3` to `add_molecules`.\n", | |
| "In the above example, the molecule type `A` spreads all over the space, but the molecule type `B` only locates in a subvolume at the center of the volume.\n", | |
| "To check this, use `num_molecules` function with a coordinate." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 8, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "120\n", | |
| "0\n", | |
| "120\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "print(w.num_molecules(Species('B'))) # must print 120\n", | |
| "print(w.num_molecules(Species('B'), Integer3(0, 0, 0))) # must print 0\n", | |
| "print(w.num_molecules(Species('B'), Integer3(1, 1, 1))) # must print 120" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Furthermore, if you have IPython Notebook environment, you can visualize the molecular localization with `ecell4.viz` module." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 9, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
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H2hAms9kcMUtO/Z5lWT7rRDifjIlEz72pU6eyd+9eDh48SGVlJS+++CJ//OMf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\n9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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x5c0c090>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "# viz.plot_world(w, radius=0.01)\n", | |
| "viz.plot_world(w, interactive=False)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "`viz.plot_world` function visualize the location of the molecules in IPython Notebook cell by giving the `World`. You can set the molecule size with `radius`.\n", | |
| "Now you can set the molecular localization to the `World`, next let's simulate this.\n", | |
| "In the above example, we set the diffusion coefficient 1 and the World side 1, so 10 seconds is enough to stir this.\n", | |
| "After the simulation, check the result with calling `viz.plot_world` again." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## 9.5. Molecular initial location and the reaction\n", | |
| "\n", | |
| "This is an extreme example to check how the molecular localization affects the reaction." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 10, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "%matplotlib inline\n", | |
| "from ecell4 import *\n", | |
| "\n", | |
| "with species_attributes():\n", | |
| " A | B | C | {'D': '1'}\n", | |
| "\n", | |
| "with reaction_rules():\n", | |
| " A + B > C | 0.01\n", | |
| "\n", | |
| "m = get_model()\n", | |
| "w = meso.MesoscopicWorld(Real3(10, 1, 1), Integer3(10, 1, 1))\n", | |
| "w.bind_to(m)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "This model consists only of a simple binding reaction. The `World` is a long x axis cuboid, and molecules are located off-center." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 11, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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CtrYQNm2KY/9+Dvk8h0hEweef85gzJ4VIJHJQZEVjLhmjejiOQyQSKTuFt5xZ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\nz4jAyrIcmE0z7fDQFStW4Ne//jWWLFkS+Om5GzduxKRJk7B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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x46f2e30>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "w.add_molecules(Species('A'), 1200, Integer3(2, 0, 0))\n", | |
| "w.add_molecules(Species('B'), 1200, Integer3(7, 0, 0))\n", | |
| "# viz.plot_world(w, radius=0.025)\n", | |
| "viz.plot_world(w, interactive=False)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "On a different note, there is a reason not to set `Integer3(0, 0, 0)` or `Integer3(9, 0, 0)`.\n", | |
| "In E-Cell4, basically we adopt periodic boundary condition for everything.\n", | |
| "So the forementioned two subvolumes are actually adjoining.\n", | |
| "\n", | |
| "After realizing the location expected, simulate it with `MesoscopicSimulator`." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 12, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x61d0930>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "sim = meso.MesoscopicSimulator(w)\n", | |
| "obs1 = NumberObserver(('A', 'B', 'C')) # XXX: saves the numbers after every steps\n", | |
| "sim.run(5, obs1)\n", | |
| "viz.plot_number_observer(obs1)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 13, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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FES0tLZanB1eyOay3Eiav8TN3Tl47KOVp+Xw+8F66QAOJrtN0ghsbacZ1+ZVOsAPZ1OM4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\niCKSoSuEEEX0DySScwLB86txAAAAAElFTkSuQmCC\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x5bf62b0>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "# viz.plot_world(w, radius=0.025)\n", | |
| "viz.plot_world(w, interactive=False)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "To check the effect of initial coordinates, we recommend that you locate the molecules homogeneously with `meso` or simulate with `gillespie`." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 14, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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lwAwgL77hpIinn4brroPevWM3giJELbM0T0NEoksYK9397bhHkkqeey7sCMrE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\njkJEJGWkbsKYP19rR4mIxFD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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x60cfd70>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "w = meso.MesoscopicWorld(Real3(10, 1, 1), Integer3(10, 1, 1))\n", | |
| "w.bind_to(m)\n", | |
| "w.add_molecules(Species('A'), 1200)\n", | |
| "w.add_molecules(Species('B'), 1200)\n", | |
| "\n", | |
| "sim = meso.MesoscopicSimulator(w)\n", | |
| "obs2 = NumberObserver(('A', 'B', 'C')) # XXX: saves the numbers after every steps\n", | |
| "sim.run(5, obs2)\n", | |
| "viz.plot_number_observer(obs1, \"-\", obs2, \"--\")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "The solid line is biased case, and the dash line is non-biased.\n", | |
| "The biased reaction is obviously slow.\n", | |
| "And you may notice that the shape of time-series is also different between the solid and dash lines.\n", | |
| "This is because it takes some time for the molecule `A` and `B` to collide due to the initial separation.\n", | |
| "Actually it takes $4^2/2(D_\\mathrm{A}+D_\\mathrm{B})=4$ seconds to move the initial distance between `A` and `B` (about 4)." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [] | |
| } | |
| ], | |
| "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": "3.5.1" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 0 | |
| } |
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