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{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TD1: Lotka-Volterra's prey-predator enzymatic model\n",
    "- reaction network\n",
    "- differential, stochastic and boolean semantics\n",
    "- addition of immigration/emigration reactions\n",
    "\n",
    "F. Fages, 18 Jan. 2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "clear_model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "present(A,a). present(B,b)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "k1*A*B for A+B => 2*B."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "k2*A for A => 2*A."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "k3*B for B => _."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameter(a=1, b=1, k1=2, k2=2, k3=1)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "k1*A*B for A=[B]=>B.\r\n",
       "k2*A for _=[A]=>A.\r\n",
       "k3*B for B=>_.\r\n",
       "present(A,a).\r\n",
       "present(B,b).\r\n",
       "parameter(\r\n",
       "  a = 1,\r\n",
       "  b = 1,\r\n",
       "  k1 = 2,\r\n",
       "  k2 = 2,\r\n",
       "  k3 = 1\r\n",
       ").\r\n"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_reactions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_influences."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Continuous semantics\n",
    "* One observes sustained oscillations and limit cycle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "\\begin{align*}\n",
       "B_0 &= 1\\\\\n",
       "A_0 &= 1\\\\\n",
       "a &= 1\\\\\n",
       "b &= 1\\\\\n",
       "k1 &= 2\\\\\n",
       "k2 &= 2\\\\\n",
       "k3 &= 1\\\\\n",
       "\\frac{dB}{dt} &= k1*A*B-k3*B\\\\\n",
       "\\frac{dA}{dt} &= k2*A-k1*A*B\\\\\n",
       "\\end{align*}\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "list_ode."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No complex invariant found\r\n"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search_conservations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "option(time:40)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "\n",
       "  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "  var JS_MIME_TYPE = 'application/javascript';\n",
       "  var HTML_MIME_TYPE = 'text/html';\n",
       "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
       "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
       "\n",
       "  /**\n",
       "   * Render data to the DOM node\n",
       "   */\n",
       "  function render(props, node) {\n",
       "    var script = document.createElement(\"script\");\n",
       "    node.appendChild(script);\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when an output is cleared or removed\n",
       "   */\n",
       "  function handleClearOutput(event, handle) {\n",
       "    var cell = handle.cell;\n",
       "\n",
       "    var id = cell.output_area._bokeh_element_id;\n",
       "    var server_id = cell.output_area._bokeh_server_id;\n",
       "    // Clean up Bokeh references\n",
       "    if (id !== undefined) {\n",
       "      Bokeh.index[id].model.document.clear();\n",
       "      delete Bokeh.index[id];\n",
       "    }\n",
       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
       "      cell.notebook.kernel.execute(cmd, {\n",
       "        iopub: {\n",
       "          output: function(msg) {\n",
       "            var element_id = msg.content.text.trim();\n",
       "            Bokeh.index[element_id].model.document.clear();\n",
       "            delete Bokeh.index[element_id];\n",
       "          }\n",
       "        }\n",
       "      });\n",
       "      // Destroy server and session\n",
       "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
       "      cell.notebook.kernel.execute(cmd);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when a new output is added\n",
       "   */\n",
       "  function handleAddOutput(event, handle) {\n",
       "    var output_area = handle.output_area;\n",
       "    var output = handle.output;\n",
       "\n",
       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
       "    if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
       "      return\n",
       "    }\n",
       "\n",
       "    var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
       "\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
       "      toinsert[0].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
       "      // store reference to embed id on output_area\n",
       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
       "    }\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
       "      var bk_div = document.createElement(\"div\");\n",
       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "      var script_attrs = bk_div.children[0].attributes;\n",
       "      for (var i = 0; i < script_attrs.length; i++) {\n",
       "        toinsert[0].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
       "      }\n",
       "      // store reference to server id on output_area\n",
       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function register_renderer(events, OutputArea) {\n",
       "\n",
       "    function append_mime(data, metadata, element) {\n",
       "      // create a DOM node to render to\n",
       "      var toinsert = this.create_output_subarea(\n",
       "        metadata,\n",
       "        CLASS_NAME,\n",
       "        EXEC_MIME_TYPE\n",
       "      );\n",
       "      this.keyboard_manager.register_events(toinsert);\n",
       "      // Render to node\n",
       "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "      render(props, toinsert[0]);\n",
       "      element.append(toinsert);\n",
       "      return toinsert\n",
       "    }\n",
       "\n",
       "    /* Handle when an output is cleared or removed */\n",
       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
       "    events.on('delete.Cell', handleClearOutput);\n",
       "\n",
       "    /* Handle when a new output is added */\n",
       "    events.on('output_added.OutputArea', handleAddOutput);\n",
       "\n",
       "    /**\n",
       "     * Register the mime type and append_mime function with output_area\n",
       "     */\n",
       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "      /* Is output safe? */\n",
       "      safe: true,\n",
       "      /* Index of renderer in `output_area.display_order` */\n",
       "      index: 0\n",
       "    });\n",
       "  }\n",
       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
       "  if (root.Jupyter !== undefined) {\n",
       "    var events = require('base/js/events');\n",
       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
       "\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  \n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    var el = document.getElementById(null);\n",
       "    if (el != null) {\n",
       "      el.textContent = \"BokehJS is loading...\";\n",
       "    }\n",
       "    if (root.Bokeh !== undefined) {\n",
       "      if (el != null) {\n",
       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
       "      }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "    }\n",
       "    finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.info(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(js_urls, callback) {\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = js_urls.length;\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var s = document.createElement('script');\n",
       "      s.src = url;\n",
       "      s.async = false;\n",
       "      s.onreadystatechange = s.onload = function() {\n",
       "        root._bokeh_is_loading--;\n",
       "        if (root._bokeh_is_loading === 0) {\n",
       "          console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "          run_callbacks()\n",
       "        }\n",
       "      };\n",
       "      s.onerror = function() {\n",
       "        console.warn(\"failed to load library \" + url);\n",
       "      };\n",
       "      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "    }\n",
       "  };\n",
       "\n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.14.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.14.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.14.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.14.min.js\"];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "    },\n",
       "    function(Bokeh) {\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.14.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.14.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.14.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.14.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.14.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.14.min.css\");\n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if ((root.Bokeh !== undefined) || (force === true)) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i].call(root, root.Bokeh);\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(null)).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(js_urls, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(null);\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n    }\n    finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.info(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(js_urls, callback) {\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = js_urls.length;\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var s = document.createElement('script');\n      s.src = url;\n      s.async = false;\n      s.onreadystatechange = s.onload = function() {\n        root._bokeh_is_loading--;\n        if (root._bokeh_is_loading === 0) {\n          console.log(\"Bokeh: all BokehJS libraries loaded\");\n          run_callbacks()\n        }\n      };\n      s.onerror = function() {\n        console.warn(\"failed to load library \" + url);\n      };\n      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.getElementsByTagName(\"head\")[0].appendChild(s);\n    }\n  };\n\n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.14.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.14.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.14.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.14.min.js\"];\n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    \n    function(Bokeh) {\n      \n    },\n    function(Bokeh) {\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.14.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.14.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.14.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.14.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.14.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.14.min.css\");\n    }\n  ];\n\n  function run_inline_js() {\n    \n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(null)).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(js_urls, function() {\n      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<div class=\"bk-root\">\n",
       "    <div class=\"bk-plotdiv\" id=\"b650765e-15f8-41bc-8d74-5f85305a7595\"></div>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "  function embed_document(root) {\n",
       "    \n",
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       "  var render_items = [{\"docid\":\"100358a9-b81d-4b97-b0cd-d2f6a8bfc698\",\"elementid\":\"b650765e-15f8-41bc-8d74-5f85305a7595\",\"modelid\":\"a05d1e48-d42c-4a0e-89d0-4150f469d9ae\",\"notebook_comms_target\":\"bb46eac5-4d8f-43a4-bd6e-10e0f5222f06\"}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\")\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "a05d1e48-d42c-4a0e-89d0-4150f469d9ae"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "numerical_simulation. plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div class=\"bk-root\">\n",
       "    <div class=\"bk-plotdiv\" id=\"9d7ec4e2-f205-4ca1-9818-df3fb043f9a9\"></div>\n",
       "</div>"
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     "metadata": {},
     "output_type": "display_data"
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       "  function embed_document(root) {\n",
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       "  var render_items = [{\"docid\":\"596225e2-1c4b-4a0d-96c1-eaaac193b3af\",\"elementid\":\"9d7ec4e2-f205-4ca1-9818-df3fb043f9a9\",\"modelid\":\"444fc2c7-bf97-4db0-872e-6f26c5551b7d\",\"notebook_comms_target\":\"b44c5b6f-ac7e-423b-adb3-e2b0a2e279cd\"}];\n",
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   "source": [
    "plot(show:B, against:A)."
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   "source": [
    "## Stochastic semantics\n",
    "* With this parameter set, the prey and predator extend almost surely\n",
    "* The intuitive explanation is that low concentrations become almost surely 0 at some time point in the stochastic simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
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       "  var render_items = [{\"docid\":\"d63d110a-18dd-47d2-b46e-b84e8f6bd730\",\"elementid\":\"0cb4b08e-5346-4fca-b8ce-7aff85542de5\",\"modelid\":\"6ea2a627-86bd-4984-8354-2d76541a017b\",\"notebook_comms_target\":\"cbca4ada-b0a3-4fec-a12e-4e3eb9f6bcc8\"}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\")\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "6ea2a627-86bd-4984-8354-2d76541a017b"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "seed(0). numerical_simulation(method:ssa). plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div class=\"bk-root\">\n",
       "    <div class=\"bk-plotdiv\" id=\"4e3e4552-397a-4a42-91cf-d14290953e58\"></div>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
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       "  var render_items = [{\"docid\":\"c1981bfd-2484-4fd2-9f0a-91a7b5b8ccb5\",\"elementid\":\"4e3e4552-397a-4a42-91cf-d14290953e58\",\"modelid\":\"d698ce22-1b0c-4848-8ff0-950e2a7142dd\",\"notebook_comms_target\":\"1a660ca9-4914-4b75-b472-742b0311f658\"}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
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       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
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       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\")\n",
       "        clearInterval(timer);\n",
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       "    }, 10, root)\n",
       "  }\n",
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      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(show:B, against:A)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Boolean semantics\n",
    "* reason just on the presence or absence of molecular species\n",
    "* asynchronous Boolean transition system ignoring reaction rates\n",
    "* SAT algorithm for enumerating all stable states, here: \n",
    " * either both A, B absent is a stable state\n",
    " * or B absent A present\n",
    " * yet both A and B present is not stable since A and B may disappear in this case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[A-0,B-0]\r\n",
       "[A-1,B-0]\r\n"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_stable_states."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Computation Tree Logic (CTL) symbolic model-checking algorithm for enumerating possible qualitative behaviors of interest, here:\n",
    " * both A and B may extinguish\n",
    " * species A may survive with no possibility of disappearing (reachability of stable state A present)\n",
    " * B may survive but with always the possibility of disappearing (reachability of non stable steady state B present)\n",
    " * no Boolean oscillation possible (0 is stable)\n",
    " * checkpoints are possibly non causal phenomenological properties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "reachable(stable('A'))\r\n",
       "reachable(stable(not'A'))\r\n",
       "reachable(stable(not'B'))\r\n",
       "reachable(steady('B'))\r\n",
       "checkpoint2('B',not'A')\r\n",
       "checkpoint2('A',not'B')\r\n"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "generate_ctl_not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AG(f)\r\n"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expand_ctl(stable(f))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EG(f)\r\n"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expand_ctl(steady(f))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EF(EG(s))\r\n"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expand_ctl(reachable(steady(s)))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "not EU(not x,y)\r\n"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expand_ctl(checkpoint(x,y))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EF(y)/\\not EU(not x,y)\r\n"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expand_ctl(checkpoint2(x,y))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "reachable(checkpoint2(not A,not B)) is true\r\n"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "check_ctl(query: reachable(checkpoint2(not A, not B)))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "reachable(checkpoint2(not B,not A)) is false\r\n"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "check_ctl(query: reachable(checkpoint2(not B, not A)))."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Questions on the continuous semantics\n",
    "\n",
    "\n",
    "### 1) play with the sliders below to change the parameter values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%slider k1 k2 k3 a b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### 2) enumerate the possible qualitative behaviors that you could obtain in the continuous semantics\n",
    "\n",
    "*write your answer here*\n",
    "\n",
    "...\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3)  add immigration and emigration reactions for the prey (with parameters k4 k5)\n",
    "\n",
    "*write your commands in the cells below*\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%slider k4 k5 k1 k2 k3 a b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4) enumerate the qualitative behaviors that can now be obtained in the continuous semantics\n",
    "\n",
    "*write your answer here*\n",
    "\n",
    "\n",
    "...\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5) can you make a general conjecture about the existence of sustained oscillations in the stochastic semantics in presence of synthesis and degradation reactions ?\n",
    "\n",
    "*write your answer here* (to my knowledge there is no known theorem ensuring that property)\n",
    "\n",
    " \n",
    " \n",
    " ...\n",
    " "
   ]
  }
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