{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9. Blinking bacteria: The repressilator enables self-sustaining oscillations\n",
    "\n",
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Design principle**\n",
    "\n",
    "- Ultrasensitive negative feedback can generate periodic oscillations in cells.\n",
    "- Oscillator phase can be used as a growth timer.\n",
    "\n",
    "**Techniques**\n",
    "\n",
    "- Composition of functions\n",
    "- Linear stability analysis\n",
    "- Linear stability diagrams\n",
    "- Numerical calculation of a scalar fixed point\n",
    "- Synthetic biology\n",
    "\n",
    "<hr>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-05T00:52:06.213619Z",
     "start_time": "2019-06-05T00:52:06.190940Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
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       "\n",
       "const JS_MIME_TYPE = 'application/javascript';\n",
       "  const HTML_MIME_TYPE = 'text/html';\n",
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       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
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       "\n",
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       "      register_renderer(events, OutputArea);\n",
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       "\n",
       "  const 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",
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       "      delete root._bokeh_onload_callbacks\n",
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       "\n",
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       "      return null;\n",
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       "    if (js_urls == null || js_urls.length === 0) {\n",
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       "      return null;\n",
       "    }\n",
       "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
       "\n",
       "    function on_load() {\n",
       "      root._bokeh_is_loading--;\n",
       "      if (root._bokeh_is_loading === 0) {\n",
       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
       "        run_callbacks()\n",
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       "\n",
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       "      const element = document.createElement(\"link\");\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error.bind(null, url);\n",
       "      element.rel = \"stylesheet\";\n",
       "      element.type = \"text/css\";\n",
       "      element.href = url;\n",
       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
       "      document.body.appendChild(element);\n",
       "    }\n",
       "\n",
       "    for (let i = 0; i < js_urls.length; i++) {\n",
       "      const url = js_urls[i];\n",
       "      const element = document.createElement('script');\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error.bind(null, url);\n",
       "      element.async = false;\n",
       "      element.src = url;\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "  };\n",
       "\n",
       "  function inject_raw_css(css) {\n",
       "    const element = document.createElement(\"style\");\n",
       "    element.appendChild(document.createTextNode(css));\n",
       "    document.body.appendChild(element);\n",
       "  }\n",
       "\n",
       "  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.1.0.min.js\"];\n",
       "  const css_urls = [];\n",
       "\n",
       "  const inline_js = [    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "function(Bokeh) {\n",
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       "\n",
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       "      inline_js[i].call(root, root.Bokeh);\n",
       "    }\n",
       "if (force === true) {\n",
       "        display_loaded();\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",
       "      const cell = $(document.getElementById(\"p1001\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(css_urls, js_urls, function() {\n",
       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const 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  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const 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    const el = document.getElementById(\"p1001\");\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  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"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.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.1.0.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\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      const cell = $(document.getElementById(\"p1001\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Colab setup ------------------\n",
    "import os, sys, subprocess\n",
    "if \"google.colab\" in sys.modules:\n",
    "    cmd = \"pip install --upgrade biocircuits colorcet watermark\"\n",
    "    process = subprocess.Popen(cmd.split(), stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n",
    "    stdout, stderr = process.communicate()\n",
    "# ------------------------------\n",
    "\n",
    "import numpy as np\n",
    "import scipy.integrate\n",
    "import scipy.optimize\n",
    "\n",
    "import biocircuits\n",
    "\n",
    "import bokeh.plotting\n",
    "import bokeh.io\n",
    "\n",
    "import colorcet\n",
    "\n",
    "# We will use Matplotlib to make a 3D plot\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = \"retina\"\n",
    "\n",
    "# Set to True to have fully interactive plots with Python;\n",
    "# Set to False to use pre-built JavaScript-based plots\n",
    "interactive_python_plots = False\n",
    "notebook_url = \"localhost:8888\"\n",
    "\n",
    "bokeh.io.output_notebook()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How to design a biological clock\n",
    "\n",
    "In this chapter we will explore ways to build a clock in a living cell. More specifically, we will discuss a **synthetic genetic clock circuit** called the **Repressilator** ([Elowitz and Leibler, 2000](https://doi.org/10.1038/35002125)). We first discuss the  roles of oscillators and clocks in natural biological systems, and then ask how one might go about designing a synthetic clock circuit that can operate in a cell. To do this, we will use **linear stability analysis**, a broadly useful approach for analyzing diverse systems. We will also introduce the concept of a **limit cycle** attractor. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clocks are necessary for life\n",
    "\n",
    "Our lives depend on accurate time-keeping. This is enabled by generations of improvement in clocks. A major turning point in global navigation was the development of portable clocks, which enabled sailors to measure longitude. [John Harrison](https://en.wikipedia.org/wiki/John_Harrison) engineered a series of increasingly precise, and beautiful, clocks, or \"marine chronometers.\" A big challenge was making them function accurately despite variations in temperature, humidity, and other conditions. To do this, he invented internal compensation mechanisms that made critical parameters robust to these conditions. Even today, navigation remains dependent on clocks. The Global Positioning System (GPS), which we use to navigate our cities, relies on precise atomic clocks installed in orbiting satellites. \n",
    "\n",
    "Time-keeping is as fundamental to our inner lives as it is to our outer lives. The cell division cycle drives the cell through a series of states, including periods of growth, chromosome replication, separation of daughter cells, and so on. When cells are rapidly proliferating, this circuit acts as a kind of clock. Organisms also have **circadian clocks** that allow them to track and anticipate the daily environmental changes that occur during the 24 hour day. Plants have mechanisms to keep track of time on longer timescales, allowing them to anticipate seasonal changes. Hormones can cycle over timescales of weeks or months. On shorter timescales, some factors in the cell oscillate periodically in and out of the nucleus over timescales of minutes to hours. Countless processes in biology involve periodic cycles rather than steady states.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Circadian clocks function in single cells\n",
    "\n",
    "**Circadian clocks** control our 24 hour cycles of sleep, hunger, and activity. Humans confined to environments with constant light and temperature still maintain circadian activity cycles with nearly 24-hour periods ([Czeisler, et al., 1999](https://doi.org/10.1126/science.284.5423.2177)). This experiment, along with the more familiar experience of jet lag, shows that our body has an internal  clock that enforces temporal organization on our bodies and minds. The **cell cycle** represents another kind of oscillator that takes cells through repetitive cycles of growth and division. **Hormones** cycle on a range of timescales from hours to weeks. Plants contain circadian and **seasonal clocks** that control their movement and flowering, in response to time as well as light, temperature and other inputs. \n",
    "\n",
    "How do biological clocks work? In 1971, Ronald Konopka and Seymour Benzer identified mutations that altered the circadian behavioral rhythms of fruit flies ([Konompa and Benzer, 1971](https://doi.org/10.1073/pnas.68.9.2112)). Over the next decades, biologists discovered key molecular components that enable these clocks to function, including transcription factors, light sensors, and other components, and worked out many aspects of the molecular mechanism of circadian oscillations. This work led to the [2017 nobel prize](https://www.nobelprize.org/prizes/medicine/2017/summary/) for Jeffrey Hall, Michael Rosbash, and Michael Young  \"for their discoveries about how internal clocks and biological rhythms govern human life.\" \n",
    "\n",
    "In multicellular organisms, you might wonder whether the clock emerges from interactions among cells, or is controlled from within each cell. The answer seems to be both. Clocks synchronize between cells and organs. However, clocks are not solely a multicellular phenomenon: Individual mammalian fibroblasts exhibit free-running circadian oscillations ([Welsh, et al., 1995](https://doi.org/10.1016/0896-6273%2895%2990214-7)), as one can see in [this movie](figs/independent_circadian_neurons.mov) ([Welsh, et al., 2004](https://doi.org/10.1016/j.cub.2004.11.057)). Circadian behavior here is revealed through expression of a luminescent reporter gene knocked into the mPer2 circadian clock gene. \n",
    "\n",
    "<div style=\"margin: auto; width: 350px;\">\n",
    "\n",
    "<video src=\"figs/independent_circadian_neurons.mov\" style=\"width: 350px;\" controls>Your browser does not support display of this video.</video>\n",
    "\n",
    "</div>\n",
    "\n",
    "Time traces of individual cells from this movie show periodic 24 hour oscillations:\n",
    "\n",
    "<div style=\"width: 500px; margin: auto;\">\n",
    "\n",
    "![Welsh_circadian_single_cells_plotsonly_cropped](figs/Welsh_circadian_single_cells_plotsonly_cropped.png)\n",
    "\n",
    "</div>\n",
    "\n",
    "This work revealed a complex clock containing many interacting protein components. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### In cyanobacteria, a three-protein phosphorylation circuit generates oscillations.\n",
    "\n",
    "But clocks need not be complex. Even single cell cyanobacteria have precise, cell-autonomous circadian clocks ([Mihalcescu, et al., 2004](https://doi.org/10.1038/nature02533)). To learn how they work, Kondo and colleagues genetically identified a single locus containing three genes required for the cyanobacterial clock. In 2005 they showed, amazingly, that these three proteins, plus ATP, were sufficient to reconstitute 24 hour oscillations of phosphorylation in a test tube ([Nakajima, et al., 2005](https://doi.org/10.1126/science.1108451)). \n",
    "\n",
    "The mechanism for these oscillations was subsequently explained by Rust, et al. in terms of ordered phosphorylation and feedback ([Rust, et al., 2007](https://doi.org/10.1126/science.1148596)). One protein, KaiC, has two phosphorylation sites. During each cycle, one site is phosphorylated, then the second is phosphorylated, then the first is dephosphorylated, and finally the second is dephosphorylated. The use of two phosphorylation sites ensures the dynamics are at least two dimensional. A one dimensional dynamical system cannot oscillate (see [Strogatz, 2015](https://doi.org/10.1201/9780429492563)).\n",
    "\n",
    "<!-- I would like to add a little more on the cyanobacterial clock circuit in the future -- but where should we insert it? --> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Limit cycles are ideal dynamical behaviors for clocks\n",
    "\n",
    "So far in this course, we have discussed only one kind of \"attractor\"—the stable fixed point. When a system sits at a stable fixed point, it does not change over time. (While true for a continuous system, biological systems are made of discrete molecules whose dynamics are subject to stochastic fluctuations, or noise, and therefore they cannot really remain precisely at a single state. We will learn more about noise soon.) By contrast, in a functioning clock circuit, the state of the system constantly changes in a periodic fashion, progressing through a cyclic sequence of \"phases\" that returns it to its starting point. This cycling occurs on its own, without any  external input. Furthermore, if such an ideal clock circuit is perturbed in some way—whether by environmental fluctuations or internal noise—it returns to its oscillatory trajectory. It \"has to\" oscillate. \n",
    "\n",
    "The behavior we are describing is more formally known as a [limit cycle](https://en.wikipedia.org/wiki/Limit_cycle). Stable limit cycles are defined by [Strogatz](https://doi.org/10.1201/9780429492563) as \"isolated closed orbits\", meaning that the system goes around the limit cycle, and that neighboring points ultimately feed into the limit cycle. As a result, if the system is perturbed a little bit away from the limit cycle, it will tend to return back to it, as shown here:\n",
    "\n",
    "<div style=\"width: 500px; margin: auto;\">\n",
    "\n",
    "![limit cycles](figs/limit_cycles.png)\n",
    "\n",
    "</div>\n",
    "\n",
    "Note that one can also have unstable limit cycles. \n",
    "\n",
    "As Strogatz notes, linear systems such as a frictionless pendulum can produce a family of orbits but not a limit cycle, because multiplying any solution of\n",
    "\n",
    "\\begin{align}\n",
    "\\frac{\\mathrm{d}\\mathbf{x}}{\\mathrm{d}t} =\\mathsf{A} \\cdot \\mathbf{x}\n",
    "\\end{align}\n",
    "\n",
    "by a constant produces another solution. Limit cycle oscillators are thus inherently non-linear systems.\n",
    "\n",
    "For our purposes, the key point is that limit cycle dynamics are ideal for natural or synthetic clock circuits because they ensure periodic oscillations that do not damp out over time and can resume even if temporarily perturbed. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build to understand: Designing a synthetic clock circuit\n",
    "\n",
    "In biology, clocks have long been metaphorically identified with the perplexing mystery of how the undirected process of evolution could generate precise behaviors from seemingly \"messy\" molecular components. Dawkins's book, _The Blind Watchmaker_ ([Dawkins, 1986](https://en.wikipedia.org/wiki/The_Blind_Watchmaker)), personifies evolution as a watchmaker, who creates devices of astonishing precision (tissues and organisms) without being able to \"see,\" much less plan, what she is doing. How \"hard\" is it for evolution—or anyone, really—to make a biological clock? \n",
    "\n",
    "We will now discuss the design of a synthetic clock circuit in bacteria. By seeing whether we can design a clock out of biological components, we can find out \"how hard\" it is to make a clock, what circuits are sufficient to produce self-sustaining **limit cycle** oscillations, and  whether the sorts of models we write down can describe biological circuits operating in the more complex, and largely uncharacterized, intracellular milieu. Beyond their value in teaching us about circuit design, synthetic clock circuits can also provide useful functions, as we will see below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clock components should ideally be composable, orthogonal, and well-characterized \n",
    "\n",
    "The first question we must ask is what components we can build a clock from. Among the best characterized regulators in biology are prokaryotic (bacterial) transcriptional repressors and their cognate target promoters. These components are  **modular**, **orthogonal**, and **composable**. By modular, we mean that they can be taken out of their natural context and used to generate a new regulatory circuit. By orthogonal, we mean that a variety of variants exist that operate similarly, but independently. Thus, different repressors bind to distinct DNA binding sites. Composability is a stronger form of modularity in which a set of components can regulate each other in the  same way. LEGOs are a familiar example of composability: each of the standard bricks can be stuck onto a similar type of brick from below and above. Transcription factors are composable because any one can be engineered to regulate any other simply by combining corresponding target promoter sequences with open reading frames for the transcription factors. \n",
    "\n",
    "(Transcriptional activators, which we have already encountered, are also excellent components for synthetic design, and [have been used to build oscillators](https://doi.org/10.1038/nature07389). At the time this work was done, there were generally fewer examples that were as well-understood as repressors. Therefore, we will focus below on a circuit design built exclusively from repressors)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Design strategy\n",
    "\n",
    "Based on the considerations above, we will try to design a biological circuit that generates **limit cycle oscillations** across a **broad range of biochemical parameter values** using a set of well-characterized, composable, and orthogonal **transcriptional repressors**. \n",
    "\n",
    "One of the first designs one can imagine building with repressors is a  \"rock-scissors-paper\" feedback loop composed of three repressors, each of which represses the next one, in a cycle:\n",
    "\n",
    "<div style=\"width: 200px; margin: auto;\">\n",
    "\n",
    "![repressilator diagram](figs/simple_repressilator_diagram.png)\n",
    "\n",
    "</div>\n",
    "\n",
    "\n",
    "This diagram refers to three specific repressors, TetR, λ cI, and LacI. We will discuss the rationale for choosing these below, after we work out the design. For now, the names of the repressors are unimportant, so we will refer to them as repressors 1, 2, and 3. Repressor 1 represses production of repressor 2, which in turn represses production of repressor 3.  Finally, repressor 3 represses production or repressor 1, completing the loop.\n",
    "\n",
    "<div style=\"width: 150px; margin: auto;\">\n",
    "\n",
    "![simple repressilator numbers](figs/simple_repressilator_numbers.png)\n",
    "\n",
    "</div>\n",
    "\n",
    "\n",
    "This design is a three-component negative feedback loop (analogous to a \"three ring oscillator\" in electronics). If one were to turn up the level of the first protein in this system, it would lead to a decrease in the second, which would cause an increase in the third, and finally a decrease in the first. Thus, one can see, intuitively, that this system produces a negative feedback that tends to push back in the opposite direction to any perturbation, after the delay required to propagate the perturbation around the loop. \n",
    "\n",
    "We can try to work out the dynamics of this system by intuitive reasoning. We might achieve a limit cycle oscillation: Say that repressor 1 is present at a high protein concentration, while repressors 2 and 3 are low. The high concentration of repressor 1 will limit expression of repressor 2, keeping its level low. This means that repressor 3 is free to be expressed.  As its concentration grows, it will start to repress repressor 1.  As repressor 1 goes down, repressor 2 is expressed in higher numbers.  The increased repressor 2 concentration leads to less repressor 3.  Then, repressor 1 comes back up again.  So, we see a cycle, where repressor 1 is high, then repressor 3, and finally repressor 2. \n",
    "\n",
    "However, this behavior is by no means guaranteed. We might equally well just get a stable steady state, where all three repressors evolve to intermediate values, each sufficient to keep its target repressor at the appropriate level to maintain its target at its steady-state level. This behavior would be much more boring. \n",
    "\n",
    "In fact, both behaviors are possible.\n",
    "\n",
    "So, our questions are now: \n",
    "\n",
    "1. What kinds of behaviors would this circuit be expected to produce?\n",
    "2. How can we engineer the circuit to favor, or better yet, guarantee, limit cycle oscillations?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dynamical equations for the repressilator\n",
    "\n",
    "To analyze the repressilator, we will, as usual, write down a set of differential equations for each of the proteins. For simplicity, we will assume perfect symmetry among the species, with each one having identical biochemical properties (this will not be true in the real system). While we initially consider only protein dynamics here, later on, we will ask how including mRNA modifies the conclusions.\n",
    "\n",
    "\\begin{align}\n",
    "\\frac{\\mathrm{d}x_1}{\\mathrm{d}t} &= \\frac{\\beta}{1 + (x_3/k)^n} - \\gamma x_1, \\\\[1em]\n",
    "\\frac{\\mathrm{d}x_2}{\\mathrm{d}t} &= \\frac{\\beta}{1 + (x_1/k)^n} - \\gamma x_2, \\\\[1em]\n",
    "\\frac{\\mathrm{d}x_3}{\\mathrm{d}t} &= \\frac{\\beta}{1 + (x_2/k)^n} - \\gamma x_3. \n",
    "\\end{align}\n",
    "\n",
    "In dimensionless units, we measure protein concentrations in units of $k$, the relevant protein concentration scale, and time in units of $\\gamma^{-1}$, the only timescale in the system. With this, the equations become:\n",
    "\n",
    "\\begin{align}\n",
    "\\frac{\\mathrm{d}x_i}{\\mathrm{d}t} &= \\frac{\\beta}{1 + x_j^n} - x_i, \\quad \\text{ with } (i,j) \\text{ pairs } (1,3), (2,1), (3,2).\n",
    "\\end{align}\n",
    "\n",
    "Note that $\\beta$ has been redefined as $\\beta \\leftarrow \\beta/k\\gamma$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Temporal dynamics\n",
    "\n",
    "We can integrate the dynamical equations to follow the concentrations of three proteins over time. Interactive plotting further allows us to see how these dynamics depend on the parameters $\\beta$ and $n$. Oscillations are clear when the curves go up and down periodically in time.\n",
    "\n",
    "Alternatively, it is also instructive to plot the trajectory of the system as a projection in the $x_2$-$x_1$ plane (or in either of the other two planes this three-dimensional system can be projected onto). When the fixed point is stable, the trajectory in the $x_2$-$x_1$ plane spirals into the fixed point. When it is unstable, the trajectory spirals away from it, eventually cycling around the fixed point to join a limit cycle, corresponding to oscillations.\n",
    "\n",
    "The layout below shows both plots. For sufficiently large $\\beta$ and $n$, we see beautiful 3-phase oscillations. A few things to notice: \n",
    "\n",
    "* If $n<2$, oscillations diminish over time.\n",
    "* If $n>2$, sustained oscillations occur, but only for large enough $\\beta$\n",
    "* If $n=2$, see what happens..."
   ]
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\"},\"shape\":[1000],\"dtype\":\"float64\",\"order\":\"little\"}],[\"x2\",{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"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\"},\"shape\":[1000],\"dtype\":\"float64\",\"order\":\"little\"}],[\"x3\",{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"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\"},\"shape\":[1000],\"dtype\":\"float64\",\"order\":\"little\"}]]}}}],[\"xRange\",{\"id\":\"p1155\"}],[\"betaSlider\",{\"type\":\"object\",\"name\":\"Slider\",\"id\":\"p1144\",\"attributes\":{\"js_property_callbacks\":{\"type\":\"map\",\"entries\":[[\"change:value\",[{\"id\":\"p1281\"}]]]},\"title\":\"\\u03b2\",\"start\":0.01,\"end\":100,\"value\":10.0,\"step\":0.01}}],[\"nSlider\",{\"type\":\"object\",\"name\":\"Slider\",\"id\":\"p1145\",\"attributes\":{\"js_property_callbacks\":{\"type\":\"map\",\"entries\":[[\"change:value\",[{\"id\":\"p1281\"}]]]},\"title\":\"n\",\"start\":1,\"end\":5,\"value\":3,\"step\":0.1}}]]},\"code\":\"\\nfunction rep_hill(x, n) {\\n\\treturn 1.0 / (1.0 + Math.pow(x, n));\\n}\\n\\n\\nfunction act_hill(x, n) {\\n\\treturn 1.0 - 1.0 / (1.0 + Math.pow(x, n));\\n}\\n\\n\\nfunction aa_and(x, y, nx, ny) {\\n\\tvar xnx = Math.pow(x, nx);\\n\\tvar yny = Math.pow(y, ny);\\n\\treturn xnx * yny / (1.0 + xnx) / (1.0 + yny);\\n}\\n\\n\\nfunction aa_or(x, y, nx, ny) {\\n\\tvar denom = (1.0 + Math.pow(x, nx)) * (1.0 + Math.pow(y, ny));\\n\\treturn (denom - 1.0) / denom;\\n}\\n\\n\\nfunction aa_or_single(x, y, nx, ny) {\\n\\tvar num = Math.pow(x, nx) + Math.pow(y, ny);\\n\\treturn num / (1.0 + num);\\n}\\n\\n\\nfunction rr_and(x, y, nx, ny) {\\n\\treturn 1.0 / (1.0 + Math.pow(x, nx)) / (1.0 + Math.pow(y, ny));\\n}\\n\\n\\nfunction rr_and_single(x, y, nx, ny) {\\n\\treturn 1.0 / (1.0 + Math.pow(x, nx) + Math.pow(y, ny));\\n}\\n\\n\\nfunction rr_or(x, y, nx, ny) {\\n\\tvar xnx = Math.pow(x, nx);\\n\\tvar yny = Math.pow(y, ny);\\n\\n\\treturn (1.0 + xnx + yny) / (1.0 + xnx) / (1.0 + yny);\\n}\\n\\n\\nfunction ar_and(x, y, nx, ny) {\\n\\txnx = Math.pow(x, nx);\\n\\treturn xnx / (1.0 + xnx) / (1.0 + Math.pow(y, ny));\\n}\\n\\n\\nfunction ar_or(x, y, nx, ny) {\\n\\tvar nxn = Math.pow(x, nx);\\n\\tvar yny = Math.pow(y, ny);\\n\\n\\treturn (1.0 + xnx * (1.0 + yny)) / (1.0 + xnx) / (1.0 + yny);\\n}\\n\\n\\nfunction ar_and_single(x, y, nx, ny) {\\n\\txnx = Math.pow(x, nx);\\n\\n\\treturn xnx / (1.0 + xnx + Math.pow(y, ny));\\n}\\n\\n\\nfunction ar_or_single(x, y, nx, ny) {\\n\\txnx = Math.pow(x, nx);\\n\\n\\treturn (1.0 + xnx) / (1.0 + xnx + Math.pow(y, ny));\\n}\\n\\n\\nfunction dActHill(x, n) {\\n\\txn = Math.pow(x, n);\\n\\n\\treturn n * Math.Pow(x, n - 1.0) / Math.pow((1 + Math.pow(x, n)), 2);\\n}\\n\\n\\nfunction dRepHill(x, n) {\\n\\txn = Math.pow(x, n);\\n\\n\\treturn -n * Math.Pow(x, n - 1.0) / Math.pow((1 + Math.pow(x, n)), 2);\\n}\\n\\n\\n// module.exports = {\\n//   rep_hill,\\n//   act_hill\\n// };\\n\\nfunction rkf45(\\n    f,\\n    initialCondition,\\n    timePoints,\\n    args,\\n    dt,\\n    tol,\\n    relStepTol,\\n    maxDeadSteps,\\n    sBounds,\\n    hMin,\\n    enforceNonnegative,\\n    debugMode,\\n) {\\n    // Set up return variables\\n    let tSol = [timePoints[0]];\\n    let t = timePoints[0];\\n    let iMax = timePoints.length;\\n    let y = [initialCondition];\\n    let y0 = initialCondition;\\n    let i = 1;\\n    let nDeadSteps = 0;\\n    let deadStep = false;\\n\\n    // DEBUG\\n    let nSteps = 0;\\n    // END EDEBUG\\n\\n    // Default parameters\\n    let h;\\n    if (dt === undefined) h = timePoints[1] - timePoints[0];\\n    else h = dt;\\n\\n    if (tol === undefined) tol = 1e-7;\\n    if (relStepTol === undefined) relStepTol = 0.0;\\n    if (sBounds === undefined) sBounds = [0.1, 10.0];\\n    if (hMin === undefined) hMin = 0.0;\\n    if (enforceNonnegative === undefined) enforceNonnegative = true;\\n    if (maxDeadSteps === undefined) maxDeadSteps = 10;\\n    if (debugMode === undefined) debugMode = false;\\n\\n    while (i < iMax && nDeadSteps < maxDeadSteps) {\\n        nDeadSteps = 0;\\n        while (t < timePoints[i] && nDeadSteps < maxDeadSteps) {\\n            [y0, t, h, deadStep] = rkf45Step(\\n                f,\\n                y0,\\n                t,\\n                args,\\n                h,\\n                tol,\\n                relStepTol,\\n                sBounds,\\n                hMin\\n            );\\n            nDeadSteps = deadStep ? nDeadSteps + 1 : 0;\\n            if (enforceNonnegative) {\\n                y0 = y0.map(function (x) {\\n                    if (x < 0.0) return 0.0;\\n                    else return x;\\n                });\\n            }\\n            // DEBUG\\n            nSteps += 1;\\n            // END DEBUG\\n        }\\n        if (t > tSol[tSol.length - 1]) {\\n            y.push(y0);\\n            tSol.push(t);\\n        }\\n        i += 1;\\n    }\\n\\n    // DEBUG\\n    if (debugMode) console.log(nSteps);\\n    // END DEBUG\\n\\n    let yInterp;\\n    if (nDeadSteps == maxDeadSteps) {\\n    \\tyInterp = nanArray(initialCondition.length, iMax);\\n    }\\n    else yInterp = interpolateSolution(timePoints, tSol, transpose(y));\\n\\n    return yInterp;\\n}\\n\\n\\nfunction rkf45Step(f, y, t, args, h, tol, relStepTol, sBounds, hMin) {\\n    let k1 = svMult(h, f(y, t, ...args));\\n\\n    let y2 = svMultAdd([0.25, 1.0], [k1, y]);\\n    let k2 = svMult(h, f(y2, t + 0.25 * h, ...args));\\n\\n    let y3 = svMultAdd([0.09375, 0.28125, 1.0], [k1, k2, y]);\\n    let k3 = svMult(h, f(y3, t + 0.375 * h, ...args));\\n\\n    let y4 = svMultAdd(\\n        [1932.0 / 2197.0, -7200.0 / 2197.0, 7296.0 / 2197.0, 1.0],\\n        [k1, k2, k3, y]\\n    );\\n    let k4 = svMult(h, f(y4, t + (12.0 * h) / 13.0, ...args));\\n\\n    let y5 = svMultAdd(\\n        [\\n            8341.0 / 4104.0,\\n            -32832.0 / 4104.0,\\n            29440.0 / 4104.0,\\n            -845.0 / 4104.0,\\n            1.0,\\n        ],\\n        [k1, k2, k3, k4, y]\\n    );\\n    let k5 = svMult(h, f(y5, t + h, ...args));\\n\\n    let y6 = svMultAdd(\\n        [\\n            -6080.0 / 20520.0,\\n            41040.0 / 20520.0,\\n            -28352.0 / 20520.0,\\n            9295.0 / 20520.0,\\n            -5643.0 / 20520.0,\\n            1.0,\\n        ],\\n        [k1, k2, k3, k4, k5, y]\\n    );\\n    let k6 = svMult(h, f(y6, t + h / 2.0, ...args));\\n\\n    // Calculate new step\\n    let yNew = svMultAdd(\\n        [\\n            2375.0 / 20520.0,\\n            11264.0 / 20520.0,\\n            10985.0 / 20520.0,\\n            -4104.0 / 20520.0,\\n            1.0,\\n        ],\\n        [k1, k3, k4, k5, y]\\n    );\\n\\n    // Relative difference between steps\\n\\tlet relChangeStep = norm(vectorAdd(yNew, svMult(-1.0, y))) / norm(yNew);\\n\\n    // Calculate error (note that k2's contribution to the error is zero)\\n    let errorVector = svMultAdd(\\n        [\\n            209.0 / 75240.0,\\n            -2252.8 / 75240.0,\\n            -2197.0 / 75240.0,\\n            1504.8 / 75240.0,\\n            2736.0 / 75240.0,\\n        ],\\n        [k1, k3, k4, k5, k6]\\n    );\\n    let error = Math.max(...absVector(errorVector));\\n\\n    // Either don't take a step or use the RK4 step\\n    let deadStep;\\n    if (error < tol || relChangeStep < relStepTol || h <= hMin) {\\n        t += h;\\n        deadStep = false;\\n    } else {\\n        yNew = y;\\n        deadStep = true;\\n    }\\n\\n    // Compute scaling for new step size\\n    let s;\\n    if (error === 0.0) {\\n        s = sBounds[1];\\n    } else {\\n        s = Math.pow((tol * h) / 2.0 / error, 0.25);\\n    }\\n    if (s < sBounds[0]) {\\n        s = sBounds[0];\\n    } else if (s > sBounds[1]) {\\n        s = sBounds[1];\\n    }\\n\\n    // Return new y-values, new time, and updated step size h\\n    return [yNew, t, Math.max(s * h, hMin), deadStep];\\n}\\n\\n\\nfunction dydtIMEX(y, t, f, cfun, Afun, fArgs, cfunArgs, AfunArgs, diagonalA) {\\n    /*\\n     * Right hand side of ODEs for initializing IMEX method with RKF.\\n     */\\n\\n    n = y.length;\\n    let rhs = zeros(n);\\n\\n    let A = Afun(t, ...AfunArgs);\\n    let c = cfun(t, ...cfunArgs);\\n\\n    // Linear part\\n    let nonConstantLinear = diagonalA\\n        ? elementwiseVectorMult(A, y)\\n        : mvMult(A, y, diagonalA);\\n    let linearPart = vectorAdd(nonConstantLinear, c);\\n\\n    // Nonlinear part\\n    let nonlinearPart = f(y, t, ...fArgs);\\n\\n    return vectorAdd(nonlinearPart, linearPart);\\n}\\n\\n\\nfunction cnab2Step(u, c, A, f1, f0, g1, omega, k, diagonalA) {\\n    /*\\n     * Take a CNAB2 step.\\n     *\\n     * - u is the current value of the solution.\\n     * - c is the constant term.\\n     * - A is the matrix for the linear function.\\n     * - f1 is the nonlinear function evaluated at the current value of y.\\n     * - f0 is the nonlinear function evaluated at the previous value of y.\\n     * - g1 is the linear function evaluated at the current value of y.\\n     * - omega is the ratio of the most recent step size to the one before that.\\n     * - k is the current step size.\\n     * - diagonalA is true if A is diagonal. This leads to a *much* faster time step.\\n     *   If diagonalA is true, then A is provided only as the diagonal.\\n     */\\n\\n    let invk = 1.0 / k;\\n    let b = vectorAdd(\\n        svMult(0.5, c),\\n        svMult(invk, u),\\n        svMult(1.0 + omega / 2.0, f1),\\n        svMult(-omega / 2.0, f0),\\n        svMult(0.5, g1)\\n    );\\n\\n    if (diagonalA) {\\n        let Aaug = svAdd(invk, svMult(-0.5, A));\\n        let result = elementwiseVectorDivide(b, Aaug);\\n    } else {\\n        let n = A.length;\\n        let Aaug = smMult(-0.5, A);\\n        for (i = 0; i < n; i++) {\\n            Aaug[i][i] += invk;\\n        }\\n        let result = solve(Aaug, b);\\n    }\\n\\n    return result;\\n}\\n\\n\\nfunction vsimexAdjustStepSizePID(\\n    k,\\n    relChange,\\n    relChangeStep,\\n    tol,\\n    kP,\\n    kI,\\n    kD,\\n    kBounds,\\n    sBounds\\n) {\\n    /*\\n     * Adjust step size using a PID controller.\\n     */\\n    let mult =\\n        Math.pow(relChange[1] / relChangeStep, kP) *\\n        Math.pow(tol / relChangeStep, kI) *\\n        Math.pow(Math.pow(relChange[0], 2) / relChange[1] / relChangeStep, kD);\\n    if (mult > sBounds[1]) mult = sBounds[1];\\n    else if (mult < sBounds[0]) mult = sBounds[0];\\n\\n    let newk = mult * k;\\n\\n    if (newk > kBounds[1]) newk = kBounds[1];\\n    else if (newk < kBounds[0]) newk = kBounds[0];\\n\\n    return newk;\\n}\\n\\n\\nfunction vsimexAdjustStepSizeRejectedStep(\\n    k,\\n    relChangeStep,\\n    tol,\\n    kBounds,\\n    sBounds\\n) {\\n    /*\\n     * Adjust step for rejected step\\n     */\\n\\n    let mult = tol / relChangeStep;\\n    if (mult < sBounds[0]) mult = sBounds[0];\\n\\n    let newk = mult * k;\\n    if (newk < kBounds[0]) newk = kBounds[0];\\n\\n    return newk;\\n}\\n\\n\\nfunction vsimexAdjustStepSizeFailedSolve(k, failedSolveS) {\\n    /*\\n     * Adjust step for failed solve. Bringing step size down will\\n     * eventually make matrix for linear solve positive definite.\\n     */\\n\\n    return k * failedSolveS;\\n}\\n\\n\\nfunction vsimex(\\n    f,\\n    cfun,\\n    Afun,\\n    initialCondition,\\n    timePoints,\\n    fArgs,\\n    cfunArgs,\\n    AfunArgs,\\n    diagonalA,\\n    k0,\\n    kBounds,\\n    tol,\\n    tolBuffer,\\n    kP,\\n    kI,\\n    kD,\\n    sBounds,\\n    failedSolveS,\\n    enforceNonnegative,\\n    maxDeadSteps\\n) {\\n    /*\\n     *\\n     */\\n\\n    // Defaults\\n    if (k0 === undefined) k0 = 1.0e-5;\\n    if (kBounds === undefined) kBounds = [1.0e-6, 100.0];\\n    if (tol === undefined) tol = 0.001;\\n    if (tolBuffer === undefined) tolBuffer = 0.01;\\n    if (kP === undefined) kP = 0.075;\\n    if (kI === undefined) kI = 0.175;\\n    if (kD === undefined) kD = 0.01;\\n    if (sBounds === undefined) sBounds = [0.1, 10.0];\\n    if (failedSolveS === undefined) failedSolveS = 0.1;\\n    if (enforceNonnegative == undefined) enforceNonnegative = true;\\n    if (maxDeadSteps === undefined) maxDeadSteps = 10;\\n\\n    // Do RKF to get the first few time points\\n    let rkf45TimePoints = [\\n        timePoints[0],\\n        timePoints[0] + k0,\\n        timePoints[0] + 2.0 * k0,\\n    ];\\n\\n    let args = [f, cfun, Afun, fArgs, cfunArgs, AfunArgs, diagonalA];\\n    let yRKF = rkf45(\\n        dydtIMEX,\\n        initialCondition,\\n        rkf45TimePoints,\\n        args,\\n        k0 / 10.0,\\n        tol,\\n        sBounds,\\n        0.0,\\n        enforceNonnegative,\\n        maxDeadSteps\\n    );\\n\\n    yRKF = transpose(yRKF);\\n\\n    // Set up variables for running CNAB2 VSIMEX\\n    let tSol = [timePoints[0]];\\n    let iMax = timePoints.length;\\n    let y = [initialCondition];\\n    let k = 2.0 * k0;\\n    let newk;\\n    let t = rkf45TimePoints[2];\\n    let y0 = yRKF[2];\\n    let i = 1;\\n    let nDeadSteps = 0;\\n    let deadStep = false;\\n    let c = cfun(t, ...cfunArgs);\\n    let A = Afun(t, ...AfunArgs);\\n    let f0 = f(initialCondition, timePoints[0], ...fArgs);\\n    let f1 = f(y0, t, ...fArgs);\\n    let g1 = vectorAdd(c, mvMult(A, y0, diagonalA));\\n    let omega = 1.0;\\n    let yStep;\\n    let relChangeStep;\\n    let relTol = tol * (1.0 + tolBuffer);\\n    let relChange = [\\n        norm(vectorAdd(y0, svMult(-1.0, yRKF[1]))) / norm(y0),\\n        norm(vectorAdd(yRKF[1], svMult(-1.0, initialCondition))) /\\n            norm(yRKF[1]),\\n    ];\\n\\n    // DEBUG\\n    let nSteps = 3;\\n    // END EDEBUG\\n\\n    while (i < iMax && nDeadSteps < maxDeadSteps) {\\n        nDeadSteps = 0;\\n        while (t < timePoints[i] && nDeadSteps < maxDeadSteps) {\\n            // Take CNAB2 step\\n            yStep = cnab2Step(y0, c, A, f1, f0, g1, omega, k, diagonalA);\\n\\n            // Reject the step if failed to solve\\n            if (yStep === null) {\\n                newk = vsimexAdjustStepSizeFailedSolve(k, failedSolveS);\\n                omega *= newk / k;\\n                k = newk;\\n                nDeadSteps += 1;\\n                console.log(\\\"null yStep\\\");\\n            } else {\\n                // Relative change\\n                relChangeStep =\\n                    norm(vectorAdd(yStep, svMult(-1.0, y0))) / norm(yStep);\\n\\n                // Take step if below tolerance\\n                if (relChangeStep <= relTol) {\\n                    f0 = f(y0, t, ...fArgs);\\n                    t += k;\\n                    y0 = yStep;\\n                    f1 = f(y0, t, ...fArgs);\\n                    c = cfun(t, ...cfunArgs);\\n                    A = Afun(t, ...AfunArgs);\\n                    g1 = vectorAdd(c, mvMult(A, y0, diagonalA));\\n                    newk = vsimexAdjustStepSizePID(\\n                        k,\\n                        relChange,\\n                        relChangeStep,\\n                        tol,\\n                        kP,\\n                        kI,\\n                        kD,\\n                        kBounds,\\n                        sBounds\\n                    );\\n                    relChange = [relChange[1], relChangeStep];\\n                    omega = newk / k;\\n                    k = newk;\\n                    nDeadSteps = 0;\\n                }\\n                // Reject the step is not within tolerance\\n                else {\\n                    newk = vsimexAdjustStepSizeRejectedStep(\\n                        k,\\n                        relChangeStep,\\n                        tol,\\n                        kBounds,\\n                        sBounds\\n                    );\\n                    omega *= newk / k;\\n                    k = newk;\\n                    nDeadSteps += 1;\\n                }\\n            }\\n            if (enforceNonnegative) {\\n                y0 = y0.map(function (x) {\\n                    if (x < 0.0) return 0.0;\\n                    else return x;\\n                });\\n            }\\n\\n            // DEBUG\\n\\t\\t    nSteps += 1;\\n\\t\\t    // END EDEBUG\\n        }\\n        if (t > tSol[tSol.length - 1]) {\\n            y.push(y0);\\n            tSol.push(t);\\n        }\\n        i += 1;\\n    }\\n\\n    // DEBUG\\n    console.log(nSteps);\\n    // END DEBUG\\n\\n    if (nDeadSteps == maxDeadSteps) {\\n        return nanArray(initialCondition, iMax);\\n    }\\n    let yInterp = interpolateSolution(timePoints, tSol, transpose(y));\\n\\n    return yInterp;\\n}\\n\\n\\nfunction interpolate1d(x, xs, ys) {\\n    let y2s = naturalSplineSecondDerivs(xs, ys);\\n\\n    let yInterp = x.map(function (xVal) {\\n        return splineEvaluate(xVal, xs, ys, y2s);\\n    });\\n\\n    return yInterp;\\n}\\n\\n\\nfunction interpolateSolution(timePoints, t, y) {\\n    // Interpolate each row of y\\n    let yInterp = y.map(function (yi) {\\n        return interpolate1d(timePoints, t, yi);\\n    });\\n\\n    return yInterp;\\n}\\n\\n\\nfunction naturalSplineSecondDerivs(xs, ys) {\\n    /*\\n     * Compute the second derivatives for a cubic spline data\\n     * measured at positions xs, ys.\\n     *\\n     * The second derivatives are then used to evaluate the spline.\\n     */\\n\\n    let n = xs.length;\\n\\n    // Storage used in tridiagonal solve\\n    let u = zeros(n);\\n\\n    // Return value\\n    let y2s = zeros(n);\\n\\n    // Solve trigiadonal matrix by decomposition\\n    for (let i = 1; i < n - 1; i++) {\\n        let fracInterval = (xs[i] - xs[i - 1]) / (xs[i + 1] - xs[i - 1]);\\n        let p = fracInterval * y2s[i - 1] + 2.0;\\n        y2s[i] = (fracInterval - 1.0) / p;\\n        u[i] =\\n            (ys[i + 1] - ys[i]) / (xs[i + 1] - xs[i]) -\\n            (ys[i] - ys[i - 1]) / (xs[i] - xs[i - 1]);\\n        u[i] =\\n            ((6.0 * u[i]) / (xs[i + 1] - xs[i - 1]) - fracInterval * u[i - 1]) /\\n            p;\\n    }\\n\\n    // Tridiagonal solve back substitution\\n    for (let k = n - 2; k >= 0; k--) {\\n        y2s[k] = y2s[k] * y2s[k + 1] + u[k];\\n    }\\n\\n    return y2s;\\n}\\n\\n\\nfunction splineEvaluate(x, xs, ys, y2s) {\\n    /*\\n     * Evaluate a spline computed from points xs, ys, with second derivatives\\n     * y2s, as compute by naturalSplineSecondDerivs().\\n     *\\n     * Assumes that x and xs are sorted.\\n     */\\n    let n = xs.length;\\n\\n    // Indices bracketing where x is\\n    let lowInd = 0;\\n    let highInd = n - 1;\\n\\n    // Perform bisection search to find index of x\\n    while (highInd - lowInd > 1) {\\n        let i = (highInd + lowInd) >> 1;\\n        if (xs[i] > x) {\\n            highInd = i;\\n        } else {\\n            lowInd = i;\\n        }\\n    }\\n    let h = xs[highInd] - xs[lowInd];\\n    let a = (xs[highInd] - x) / h;\\n    let b = (x - xs[lowInd]) / h;\\n\\n    let y = a * ys[lowInd] + b * ys[highInd];\\n    y +=\\n        (((Math.pow(a, 3) - a) * y2s[lowInd] + (Math.pow(b, 3) - b) * y2s[highInd]) * Math.pow(h, 2)) /\\n        6.0;\\n\\n    return y;\\n}\\n\\n\\n// module.exports = {\\n//   vsimex,\\n//   rkf45,\\n//   zeros, \\n//   linspace\\n// };\\n\\n// vsimex(lotkaVolterra, [1.0, 3.0], linspace(0.0, 20.0, 200), [1.0, 2.0, 3.0, 4.0], 0.01, 1e-7, [0.1, 10.0], 0.0)\\n// let lv = lotkaVolterraIMEX(1.0, 2.0, 3.0, 4.0);\\n// let sol = vsimex(lv.f, lv.cfun, lv.Afun, [1.0, 3.0], linspace(0.0, 20.0, 200), [], [], [], lv.diagonalA)\\n\\n\\nfunction lotkaVolterra(xy, t, alpha, beta, gamma, delta) {\\n\\t// Unpack\\n\\tvar [x, y] = xy;\\n\\n\\tvar dxdt = alpha * x - beta * x * y;\\n\\tvar dydt = delta * x * y - gamma * y;\\n\\n\\treturn [dxdt, dydt];\\n}\\n\\n\\nfunction lotkaVolterraIMEX(alpha, beta, gamma, delta) {\\n\\tf = function(xy) {\\n\\t\\tvar [x, y] = xy;\\n\\t\\treturn [-beta * x * y, delta * x * y];\\n\\t}\\n\\n\\tcfun = (x) => [0.0, 0.0];\\n\\n\\tAfun = (x) => [alpha, -gamma];\\n\\n\\tdiagonalA = true;\\n\\n\\treturn {f: f, cfun: cfun, Afun: Afun, diagonalA: true};\\n}\\n\\n\\nfunction cascade(yz, t, beta, gamma, n_x, n_y, x_fun, x_args) {\\n\\t// Unpack\\n\\tvar [y, z] = yz;\\n\\n\\tvar x = x_fun(t, ...x_args);\\n\\n\\tvar dy_dt = beta * act_hill(x, n_x) - y;\\n\\tvar dz_dy = gamma * (act_hill(y, n_y) - z)\\n\\n\\treturn [dy_dt, dz_dt];\\n}\\n\\n\\nfunction repressilator(x, t, beta, n) {\\n\\t// Unpack\\n\\tvar [x1, x2, x3] = x;\\n\\n\\treturn [\\n\\t\\tbeta * rep_hill(x3, n) - x1,\\n\\t\\tbeta * rep_hill(x1, n) - x2,\\n\\t\\tbeta * rep_hill(x2, n) - x3\\n\\t]\\n}\\n\\n\\n// module.exports = {\\n//   repressilator,\\n//   lotkaVolterra,\\n//   lotkaVolterraIMEX\\n// };\\n\\nfunction ij(i, j, n) {\\n    /*\\n     * Lexicographic indexing of 2D array represented as 1D.\\n     */\\n\\n    return i * n + j;\\n}\\n\\n\\nfunction twoDto1D(A) {\\n    /*\\n     * Convert a 2D matrix to a 1D representation with row-based (C)\\n     * lexicographic ordering.\\n     */\\n\\n    var m = A.length;\\n    var n = A[0].length;\\n\\n    var A1d = [];\\n    for (var i = 0; i < m; i++) {\\n        for (var j = 0; j < n; j++) {\\n            A1d.push(A[i][j]);\\n        }\\n    }\\n\\n    return A1d;\\n}\\n\\n\\nfunction linspace(start, stop, n) {\\n    var x = [];\\n    var currValue = start;\\n    var step = (stop - start) / (n - 1);\\n    for (var i = 0; i < n; i++) {\\n        x.push(currValue);\\n        currValue += step;\\n    }\\n    return x;\\n}\\n\\n\\nfunction zeros(n) {\\n    var x = [];\\n    for (var i = 0; i < n; i++) x.push(0.0);\\n    return x;\\n}\\n\\n\\nfunction shallowCopyMatrix(A) {\\n    /*\\n     * Make a shallow copy of a matrix.\\n     */\\n\\n    var Ac = [];\\n    var n = A.length;\\n    for (i = 0; i < n; i++) {\\n        Ac.push([...A[i]]);\\n    }\\n\\n    return Ac;\\n}\\n\\n\\nfunction nanArray() {\\n    /*\\n     * Return a NaN array of shape given by arguments.\\n     */\\n    if (arguments.length == 1) {\\n        var x = [];\\n        for (var i = 0; i < arguments[0]; i++) x.push(NaN);\\n    }\\n    else if (arguments.length == 2) {\\n        var x = [];\\n        for (var i = 0; i < arguments[0]; i++) {\\n            var xRow = [];\\n            for (var j = 0; j < arguments[1]; j++) xRow.push(NaN);\\n            x.push(xRow);\\n        }\\n    }\\n    else {\\n        throw 'Must only have one or two arguments to nanArray().'\\n    }\\n\\n    return x;\\n}\\n\\nfunction transpose(A) {\\n    var m = A.length;\\n    var n = A[0].length;\\n    var AT = [];\\n\\n    for (var j = 0; j < n; j++) {\\n        var ATj = [];\\n        for (var i = 0; i < m; i++) {\\n            ATj.push(A[i][j]);\\n        }\\n        AT.push(ATj);\\n    }\\n\\n    return AT;\\n}\\n\\n\\nfunction dot(v1, v2) {\\n    /*\\n     * Compute dot product v1 . v2.\\n     */\\n\\n    var n = v1.length;\\n    var result = 0.0;\\n    for (var i = 0; i < n; i++) result += v1[i] * v2[i];\\n\\n    return result;\\n}\\n\\n\\nfunction norm(v) {\\n    /*\\n     * 2-norm of a vector\\n     */\\n\\n    return Math.sqrt(dot(v, v));\\n}\\n\\n\\nfunction mvMult(A, v, diagonalA) {\\n    /*\\n     * Compute dot product A . v, where A is a matrix.\\n     * If diagonalA is true, then A must be a 1-D array.\\n     */\\n\\n    if (diagonalA) return elementwiseVectorMult(A, v);\\n    else {\\n        return A.map(function (Arow) {\\n            return dot(Arow, v);\\n        });\\n    }\\n}\\n\\n\\nfunction svMult(a, v) {\\n    /*\\n     * Multiply vector v by scalar a.\\n     */\\n\\n    return v.map(function (x) {\\n        return a * x;\\n    });\\n}\\n\\n\\nfunction smMult(a, A) {\\n    /*\\n     * Multiply matrix A by scalar a.\\n     */\\n\\n    return A.map(function (Arow) {\\n        return svMult(a, Arow);\\n    });\\n}\\n\\n\\nfunction svAdd(a, v) {\\n    /*\\n     * Add a scalar a to every element of vector v.\\n     */\\n\\n    return v.map(function (x) {\\n        return a + x;\\n    });\\n}\\n\\n\\nfunction vectorAdd() {\\n    var m = arguments[0].length;\\n    var n = arguments.length;\\n\\n    var result = [];\\n    for (var i = 0; i < m; i++) {\\n        var element = 0.0;\\n        for (var j = 0; j < n; j++) {\\n            element += arguments[j][i];\\n        }\\n        result.push(element);\\n    }\\n\\n    return result;\\n}\\n\\n\\nfunction elementwiseVectorDivide(v1, v2) {\\n    /*\\n     * Compute v1 / v2 elementwise.\\n     */\\n\\n    var result = [];\\n    n = v1.length;\\n\\n    for (var i = 0; i < n; i++) {\\n        result.push(v1[i] / v2[i]);\\n    }\\n\\n    return result;\\n}\\n\\n\\nfunction elementwiseVectorMult(v1, v2) {\\n    /*\\n     * Compute v1 * v2 elementwise.\\n     */\\n\\n    var result = [];\\n    n = v1.length;\\n\\n    for (var i = 0; i < n; i++) {\\n        result.push(v1[i] * v2[i]);\\n    }\\n\\n    return result;\\n}\\n\\n\\nfunction svMultAdd(scalars, vectors) {\\n    /*\\n     * Add a set of vectors together, each multiplied by a scalar.\\n     */\\n\\n    var m = vectors[0].length;\\n    var n = scalars.length;\\n\\n    if (vectors.length != n) {\\n        console.warn(\\\"svMultAdd: Difference number of scalars and vectors.\\\");\\n        return null;\\n    }\\n\\n    var result = [];\\n    for (var i = 0; i < m; i++) {\\n        var element = 0.0;\\n        for (var j = 0; j < n; j++) {\\n            element += scalars[j] * vectors[j][i];\\n        }\\n        result.push(element);\\n    }\\n\\n    return result;\\n}\\n\\n\\nfunction absVector(v) {\\n    var result = [];\\n    for (var i = 0; i < v.length; i++) {\\n        result[i] = Math.abs(v[i]);\\n    }\\n\\n    return result;\\n}\\n\\n\\nfunction LUPDecompose(A, eps) {\\n    /*\\n     * LUP decomposition.\\n     */\\n\\n    var i, j, k, imax;\\n    var maxA, absA;\\n    var Arow;\\n    var p = [];\\n    var n = A.length;\\n    var LU = shallowCopyMatrix(A);\\n\\n    // Permutation matrix\\n    for (i = 0; i <= n; i++) p.push(i);\\n\\n    for (i = 0; i < n; i++) {\\n        maxA = 0.0;\\n        imax = i;\\n\\n        for (k = i; k < n; k++) {\\n            absA = Math.abs(LU[k][i]);\\n            if (absA > maxA) {\\n                maxA = absA;\\n                imax = k;\\n            }\\n        }\\n\\n        // Failure; singular matrix\\n        if (maxA < eps) return [null, null];\\n\\n        if (imax != i) {\\n            // Pivot\\n            j = p[i];\\n            p[i] = p[imax];\\n            p[imax] = j;\\n\\n            // Pivot rows of A\\n            Arow = LU[i];\\n            LU[i] = LU[imax];\\n            LU[imax] = Arow;\\n\\n            // Count pivots\\n            p[n]++;\\n        }\\n\\n        for (j = i + 1; j < n; j++) {\\n            LU[j][i] /= LU[i][i];\\n\\n            for (k = i + 1; k < n; k++) LU[j][k] -= LU[j][i] * LU[i][k];\\n        }\\n    }\\n\\n    return [LU, p];\\n}\\n\\nfunction LUPSolve(LU, p, b) {\\n    /*\\n     * Solve a linear system where LU and p are stored as the\\n     * output of LUPDecompose().\\n     */\\n\\n    var n = b.length;\\n    var x = [];\\n\\n    for (var i = 0; i < n; i++) {\\n        x.push(b[p[i]]);\\n        for (var k = 0; k < i; k++) x[i] -= LU[i][k] * x[k];\\n    }\\n\\n    for (i = n - 1; i >= 0; i--) {\\n        for (k = i + 1; k < n; k++) x[i] -= LU[i][k] * x[k];\\n\\n        x[i] /= LU[i][i];\\n    }\\n\\n    return x;\\n}\\n\\nfunction solve(A, b) {\\n    /*\\n     * Solve a linear system using LUP decomposition.\\n     *\\n     * Returns null if singular.\\n     */\\n\\n    var eps = 1.0e-14;\\n    var LU, p;\\n\\n    [LU, p] = LUPDecompose(A, eps);\\n\\n    // Return null if singular\\n    if (LU === null) return null;\\n\\n    return LUPSolve(LU, p, b);\\n}\\n\\n\\nfunction proteinRepressilator(x, t, beta, n) {\\n\\t// Unpack\\n\\tvar [x1, x2, x3] = x;\\n\\n\\treturn [\\n\\t\\tbeta * rep_hill(x3, n) - x1,\\n\\t\\tbeta * rep_hill(x1, n) - x2,\\n\\t\\tbeta * rep_hill(x2, n) - x3\\n\\t];\\n}\\n\\n\\nfunction callback() {\\n\\tlet xRangeMax = xRange.end;\\n\\tlet dt = 0.01;\\n\\tlet x0 = [1.0, 1.0, 1.2];\\n\\tlet beta = betaSlider.value;\\n\\tlet n = nSlider.value;\\n\\n\\tlet t = linspace(0.0, xRangeMax, 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       "  const render_items = [{\"docid\":\"2e02051b-3b10-4981-9c53-69b7cc0a2a66\",\"roots\":{\"p1286\":\"f4b68035-5696-4c39-ac41-2c1c26206642\"},\"root_ids\":[\"p1286\"]}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    let attempts = 0;\n",
       "    const timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "p1286"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def protein_repressilator_rhs(x, t, beta, n):\n",
    "    \"\"\"\n",
    "    Returns 3-array of (dx_1/dt, dx_2/dt, dx_3/dt)\n",
    "    \"\"\"\n",
    "    x_1, x_2, x_3 = x\n",
    "\n",
    "    return np.array(\n",
    "        [\n",
    "            beta / (1 + x_3 ** n) - x_1,\n",
    "            beta / (1 + x_1 ** n) - x_2,\n",
    "            beta / (1 + x_2 ** n) - x_3,\n",
    "        ]\n",
    "    )\n",
    "\n",
    "\n",
    "# Initial condiations\n",
    "x0 = np.array([1, 1, 1.2])\n",
    "\n",
    "# Number of points to use in plots\n",
    "n_points = 1000\n",
    "\n",
    "# Widgets for controlling parameters\n",
    "beta_slider_protein = bokeh.models.Slider(title=\"β\", start=0, end=100, step=0.1, value=10)\n",
    "n_slider_protein = bokeh.models.Slider(title=\"n\", start=1, end=5, step=0.1, value=3)\n",
    "\n",
    "# Solve for species concentrations\n",
    "def _solve_protein_repressilator(beta, n, t_max):\n",
    "    t = np.linspace(0, t_max, n_points)\n",
    "    x = scipy.integrate.odeint(protein_repressilator_rhs, x0, t, args=(beta, n))\n",
    "\n",
    "    return t, x.transpose()\n",
    "\n",
    "\n",
    "# Obtain solution for plot\n",
    "t, x = _solve_protein_repressilator(beta_slider_protein.value, n_slider_protein.value, 40.0)\n",
    "\n",
    "# Build the plot\n",
    "colors = colorcet.b_glasbey_category10[:3]\n",
    "\n",
    "p_rep = bokeh.plotting.figure(\n",
    "    frame_width=550, frame_height=200, x_axis_label=\"t\", x_range=[0, 40.0]\n",
    ")\n",
    "\n",
    "cds = bokeh.models.ColumnDataSource(data=dict(t=t, x1=x[0], x2=x[1], x3=x[2]))\n",
    "labels = dict(x1=\"x₁\", x2=\"x₂\", x3=\"x₃\")\n",
    "for color, x_val in zip(colors, labels):\n",
    "    p_rep.line(\n",
    "        source=cds,\n",
    "        x=\"t\",\n",
    "        y=x_val,\n",
    "        color=color,\n",
    "        legend_label=labels[x_val],\n",
    "        line_width=2,\n",
    "    )\n",
    "\n",
    "p_rep.legend.location = \"top_left\"\n",
    "\n",
    "\n",
    "# Set up plot\n",
    "p_phase = bokeh.plotting.figure(\n",
    "    frame_width=200, frame_height=200, x_axis_label=\"x₁\", y_axis_label=\"x₂\",\n",
    ")\n",
    "\n",
    "p_phase.line(source=cds, x=\"x1\", y=\"x2\", line_width=2)\n",
    "\n",
    "# Set up callbacks\n",
    "def _callback(attr, old, new):\n",
    "    t, x = _solve_protein_repressilator(beta_slider_protein.value, n_slider_protein.value, p_rep.x_range.end)\n",
    "    cds.data = dict(t=t, x1=x[0], x2=x[1], x3=x[2])\n",
    "\n",
    "    \n",
    "beta_slider_protein.on_change(\"value\", _callback)\n",
    "n_slider_protein.on_change(\"value\", _callback)\n",
    "p_rep.x_range.on_change(\"end\", _callback)\n",
    "\n",
    "# Build layout\n",
    "protein_repressilator_layout = bokeh.layouts.column(\n",
    "    p_rep,\n",
    "    bokeh.layouts.Spacer(height=10),\n",
    "    bokeh.layouts.row(\n",
    "        p_phase,\n",
    "        bokeh.layouts.Spacer(width=70),\n",
    "        bokeh.layouts.column(beta_slider_protein, n_slider_protein,width=150),\n",
    "    ),\n",
    ")\n",
    "\n",
    "# Build the app\n",
    "def protein_repressilator_app(doc):\n",
    "    doc.add_root(protein_repressilator_layout)\n",
    "\n",
    "\n",
    "# Display\n",
    "if interactive_python_plots:\n",
    "    bokeh.io.show(protein_repressilator_app, notebook_url=notebook_url)\n",
    "else:\n",
    "    bokeh.io.show(biocircuits.jsplots.protein_repressilator())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, here is a simple three-dimensional plot of the limit cycle in the space of the three protein concentrations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-05T00:52:06.824005Z",
     "start_time": "2019-06-05T00:52:06.423504Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 393,
       "width": 405
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can do with with Bokeh a viz.js, but for now, we use Matplotlib\n",
    "\n",
    "# Resolve problem for β = 20 and n = 3\n",
    "t = np.linspace(0, 50, 1000)\n",
    "x = scipy.integrate.odeint(protein_repressilator_rhs, x0, t, args=(20, 3))\n",
    "\n",
    "# Generate the plot\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection=\"3d\")\n",
    "ax.view_init(30, 30)\n",
    "ax.plot(x[:, 0], x[:, 1], x[:, 2])\n",
    "ax.set_xlabel(\"x₁\")\n",
    "ax.set_ylabel(\"x₂\")\n",
    "ax.set_zlabel(\"x₃\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What conditions are necessary and sufficient for oscillations?\n",
    "\n",
    "Why does the system oscillate for some values of $n$ and $\\beta$ but not others? To find out, we need to use **linear stability analysis** to see how the values of the key biochemical parameters, $\\beta$ and $n$ control oscillations.\n",
    "\n",
    "As a first step, we must identify fixed points by solving $\\mathrm{d}x_i/\\mathrm{d}t = 0$ for each $x_i$. This is accomplished by appealing to **composite functions** in [Technical Appendix 9a](../technical_appendices/09a_composite_functions_to_find_fixed_point.ipynb). We find that there is a single fixed point, with $x_1 = x_2 = x_3 \\equiv x_0$, which satisfies\n",
    "\n",
    "\\begin{align}\n",
    "\\beta = x_0(1+x_0^n).\n",
    "\\end{align}\n",
    "\n",
    "If this fixed point is stable, then the system will sit there, constant in time. On the other hand, if it is unstable, then there is the potential for limit cycle oscillations. The question is: Under what conditions is this fixed point unstable?\n",
    "\n",
    "To answer this question, we assess the stability of the unique fixed point using **linear stability analysis**. The technique and the calculation for this system are described in [Technical Appendix 9b](../technical_appendices/09b_linear_stability_analysis.ipynb). The result is that the fixed point is unstable for $n > 2$ and\n",
    "\n",
    "\\begin{align}\n",
    "\\beta > \\frac{n}{2}\\left(\\frac{n}{2} - 1\\right)^{-\\frac{n+1}{n}}.\n",
    "\\end{align}\n",
    "\n",
    "We can display the regions in parameter space, that is in the $n\\text{-}\\beta$ plane, for which the system is stable and unstable in a **linear stability diagram**, shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-05T00:52:06.901929Z",
     "start_time": "2019-06-05T00:52:06.827613Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"6e638958-3b29-43e7-b9de-537975a589a3\" data-root-id=\"p1495\" style=\"display: contents;\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
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    "# Get bifurcation line\n",
    "n = np.linspace(2.01, 5, 200)\n",
    "beta = n / 2 * (n / 2 - 1) ** (-(1 + 1 / n))\n",
    "\n",
    "# Build the plot\n",
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    "    x_range=[2, 5],\n",
    "    y_range=[1, 2000],\n",
    ")\n",
    "p.patch(\n",
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    ")\n",
    "p.line(n, beta, line_width=4, color=\"black\")\n",
    "p.text(x=2.1, y=2, text=[\"stable\"])\n",
    "p.text(x=2.5, y=100, text=[\"unstable (limit cycle oscillations)\"])\n",
    "\n",
    "bokeh.io.show(p)"
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   "source": [
    "The linear stability diagram clearly shows that, from a design point of view, it is desirable to make both $n$ and $\\beta$ as high as possible."
   ]
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   "source": [
    "## Intuition from the protein-only model\n",
    "\n",
    "This analysis shows two conditions that favor oscillations:\n",
    "\n",
    "* Strong ultrasensitivity (large Hill coefficients)\n",
    "* Strong promoters\n",
    "\n",
    "In fact, these results can be understood intuitively: oscillations occur when the feedback \"overshoots.\" The sharper and stronger the response as one goes around the complete feedback loop, the longer and higher a pulse in one factor can grow before it is, inevitably, yanked back down by the feedback. Consistent with this view, there is a tradeoff between the length of the cycle (number of repressors in the loop) and the minimum Hill coefficient required ([Elowitz, 1999](https://catalog.princeton.edu/catalog/2244277))."
   ]
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   "source": [
    "## Including mRNA in the model provides additional insights\n",
    "\n",
    "In the above analysis, we only considered the three proteins themselves, and we neglected the mRNA dynamics. However, it would be of interest to understand how mRNA properties like stability and translation rate affect whether and how the circuit oscillations. Therefore, we will add additional equations for each mRNA species. We will continue to assume symmetry among species, with all mRNAs sharing the same parameter values. With this assumption, the dynamical equations become:\n",
    "\n",
    "\\begin{align}\n",
    "\\frac{\\mathrm{d}m_i}{\\mathrm{d}t} &= \\alpha + \\frac{\\beta_m}{1 + (x_j/k)^n} - \\gamma_m m_i,\\\\[1em]\n",
    "\\frac{\\mathrm{d}x_i}{\\mathrm{d}t} &= \\beta_p m_i - \\gamma_p x_i, \\\\[1em]\n",
    "\\end{align}\n",
    "\n",
    "with $i,j$ pairs $(1,3), (2,1), (3,2)$. Here, we have introduced $\\rho$ to allow for leaky transcription. In dimensionless units, these equations are\n",
    "\n",
    "\\begin{align}\n",
    "\\frac{\\mathrm{d}m_i}{\\mathrm{d}t} &= \\beta\\left(\\rho + \\frac{1}{1 + x_j^n}\\right) - m_i, \\\\[1em]\n",
    "\\gamma^{-1}\\,\\frac{\\mathrm{d}x_i}{\\mathrm{d}t} &= m_i - x_i,\n",
    "\\end{align}\n",
    "\n",
    "Here,\n",
    "\n",
    "- $\\gamma \\equiv \\gamma_p/\\gamma_m$ is the ratio of the two timescales in the system–the protein and mRNA degradation/decay rates.\n",
    "- $\\beta = \\beta_m\\beta_p/\\gamma_m\\gamma_p k$ is a dimensionless promoter strength.\n",
    "- $\\rho = \\alpha/\\beta_m$ is the relative strength of leaky versus regulated expression.\n",
    "\n",
    "As above, to start, we will solve this system numerically and explore its dynamics for different parameter values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-05T00:52:06.963778Z",
     "start_time": "2019-06-05T00:52:06.927633Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"4654b582-8ce0-48ee-820e-ec2c71e2d9a0\" data-root-id=\"p2032\" style=\"display: contents;\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "  const docs_json = {\"cadbc9c2-807e-4743-bc51-7d0ff074a973\":{\"version\":\"3.1.0\",\"title\":\"Bokeh 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\"},\"shape\":[1000],\"dtype\":\"float64\",\"order\":\"little\"}],[\"m1\",{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"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\"},\"shape\":[1000],\"dtype\":\"float64\",\"order\":\"little\"}],[\"m3\",{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"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\"},\"shape\":[1000],\"dtype\":\"float64\",\"order\":\"little\"}],[\"x1\",{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"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Math.pow(10, tick).toFixed(6)\"}},\"start\":-6,\"end\":0,\"value\":-3,\"step\":0.1}}],[\"gammaSlider\",{\"type\":\"object\",\"name\":\"Slider\",\"id\":\"p1916\",\"attributes\":{\"js_property_callbacks\":{\"type\":\"map\",\"entries\":[[\"change:value\",[{\"id\":\"p2033\"}]]]},\"width\":125,\"title\":\"\\u03b3\",\"format\":{\"type\":\"object\",\"name\":\"CustomJSTickFormatter\",\"id\":\"p1915\",\"attributes\":{\"code\":\"return Math.pow(10, tick).toFixed(3)\"}},\"start\":-3,\"end\":0,\"value\":0,\"step\":0.1}}],[\"nSlider\",{\"type\":\"object\",\"name\":\"Slider\",\"id\":\"p1919\",\"attributes\":{\"js_property_callbacks\":{\"type\":\"map\",\"entries\":[[\"change:value\",[{\"id\":\"p2033\"}]]]},\"width\":125,\"title\":\"n\",\"start\":1,\"end\":5,\"value\":3,\"step\":0.1}}]]},\"code\":\"\\nfunction rep_hill(x, n) {\\n\\treturn 1.0 / (1.0 + Math.pow(x, n));\\n}\\n\\n\\nfunction act_hill(x, n) {\\n\\treturn 1.0 - 1.0 / (1.0 + Math.pow(x, n));\\n}\\n\\n\\nfunction aa_and(x, y, nx, ny) {\\n\\tvar xnx = Math.pow(x, nx);\\n\\tvar yny = Math.pow(y, ny);\\n\\treturn xnx * yny / (1.0 + xnx) / (1.0 + yny);\\n}\\n\\n\\nfunction aa_or(x, y, nx, ny) {\\n\\tvar denom = (1.0 + Math.pow(x, nx)) * (1.0 + Math.pow(y, ny));\\n\\treturn (denom - 1.0) / denom;\\n}\\n\\n\\nfunction aa_or_single(x, y, nx, ny) {\\n\\tvar num = Math.pow(x, nx) + Math.pow(y, ny);\\n\\treturn num / (1.0 + num);\\n}\\n\\n\\nfunction rr_and(x, y, nx, ny) {\\n\\treturn 1.0 / (1.0 + Math.pow(x, nx)) / (1.0 + Math.pow(y, ny));\\n}\\n\\n\\nfunction rr_and_single(x, y, nx, ny) {\\n\\treturn 1.0 / (1.0 + Math.pow(x, nx) + Math.pow(y, ny));\\n}\\n\\n\\nfunction rr_or(x, y, nx, ny) {\\n\\tvar xnx = Math.pow(x, nx);\\n\\tvar yny = Math.pow(y, ny);\\n\\n\\treturn (1.0 + xnx + yny) / (1.0 + xnx) / (1.0 + yny);\\n}\\n\\n\\nfunction ar_and(x, y, nx, ny) {\\n\\txnx = Math.pow(x, nx);\\n\\treturn xnx / (1.0 + xnx) / (1.0 + Math.pow(y, ny));\\n}\\n\\n\\nfunction ar_or(x, y, nx, ny) {\\n\\tvar nxn = Math.pow(x, nx);\\n\\tvar yny = Math.pow(y, ny);\\n\\n\\treturn (1.0 + xnx * (1.0 + yny)) / (1.0 + xnx) / (1.0 + yny);\\n}\\n\\n\\nfunction ar_and_single(x, y, nx, ny) {\\n\\txnx = Math.pow(x, nx);\\n\\n\\treturn xnx / (1.0 + xnx + Math.pow(y, ny));\\n}\\n\\n\\nfunction ar_or_single(x, y, nx, ny) {\\n\\txnx = Math.pow(x, nx);\\n\\n\\treturn (1.0 + xnx) / (1.0 + xnx + Math.pow(y, ny));\\n}\\n\\n\\nfunction dActHill(x, n) {\\n\\txn = Math.pow(x, n);\\n\\n\\treturn n * Math.Pow(x, n - 1.0) / Math.pow((1 + Math.pow(x, n)), 2);\\n}\\n\\n\\nfunction dRepHill(x, n) {\\n\\txn = Math.pow(x, n);\\n\\n\\treturn -n * Math.Pow(x, n - 1.0) / Math.pow((1 + Math.pow(x, n)), 2);\\n}\\n\\n\\n// module.exports = {\\n//   rep_hill,\\n//   act_hill\\n// };\\n\\nfunction rkf45(\\n    f,\\n    initialCondition,\\n    timePoints,\\n    args,\\n    dt,\\n    tol,\\n    relStepTol,\\n    maxDeadSteps,\\n    sBounds,\\n    hMin,\\n    enforceNonnegative,\\n    debugMode,\\n) {\\n    // Set up return variables\\n    let tSol = [timePoints[0]];\\n    let t = timePoints[0];\\n    let iMax = timePoints.length;\\n    let y = [initialCondition];\\n    let y0 = initialCondition;\\n    let i = 1;\\n    let nDeadSteps = 0;\\n    let deadStep = false;\\n\\n    // DEBUG\\n    let nSteps = 0;\\n    // END EDEBUG\\n\\n    // Default parameters\\n    let h;\\n    if (dt === undefined) h = timePoints[1] - timePoints[0];\\n    else h = dt;\\n\\n    if (tol === undefined) tol = 1e-7;\\n    if (relStepTol === undefined) relStepTol = 0.0;\\n    if (sBounds === undefined) sBounds = [0.1, 10.0];\\n    if (hMin === undefined) hMin = 0.0;\\n    if (enforceNonnegative === undefined) enforceNonnegative = true;\\n    if (maxDeadSteps === undefined) maxDeadSteps = 10;\\n    if (debugMode === undefined) debugMode = false;\\n\\n    while (i < iMax && nDeadSteps < maxDeadSteps) {\\n        nDeadSteps = 0;\\n        while (t < timePoints[i] && nDeadSteps < maxDeadSteps) {\\n            [y0, t, h, deadStep] = rkf45Step(\\n                f,\\n                y0,\\n                t,\\n                args,\\n                h,\\n                tol,\\n                relStepTol,\\n                sBounds,\\n                hMin\\n            );\\n            nDeadSteps = deadStep ? nDeadSteps + 1 : 0;\\n            if (enforceNonnegative) {\\n                y0 = y0.map(function (x) {\\n                    if (x < 0.0) return 0.0;\\n                    else return x;\\n                });\\n            }\\n            // DEBUG\\n            nSteps += 1;\\n            // END DEBUG\\n        }\\n        if (t > tSol[tSol.length - 1]) {\\n            y.push(y0);\\n            tSol.push(t);\\n        }\\n        i += 1;\\n    }\\n\\n    // DEBUG\\n    if (debugMode) console.log(nSteps);\\n    // END DEBUG\\n\\n    let yInterp;\\n    if (nDeadSteps == maxDeadSteps) {\\n    \\tyInterp = nanArray(initialCondition.length, iMax);\\n    }\\n    else yInterp = interpolateSolution(timePoints, tSol, transpose(y));\\n\\n    return yInterp;\\n}\\n\\n\\nfunction rkf45Step(f, y, t, args, h, tol, relStepTol, sBounds, hMin) {\\n    let k1 = svMult(h, f(y, t, ...args));\\n\\n    let y2 = svMultAdd([0.25, 1.0], [k1, y]);\\n    let k2 = svMult(h, f(y2, t + 0.25 * h, ...args));\\n\\n    let y3 = svMultAdd([0.09375, 0.28125, 1.0], [k1, k2, y]);\\n    let k3 = svMult(h, f(y3, t + 0.375 * h, ...args));\\n\\n    let y4 = svMultAdd(\\n        [1932.0 / 2197.0, -7200.0 / 2197.0, 7296.0 / 2197.0, 1.0],\\n        [k1, k2, k3, y]\\n    );\\n    let k4 = svMult(h, f(y4, t + (12.0 * h) / 13.0, ...args));\\n\\n    let y5 = svMultAdd(\\n        [\\n            8341.0 / 4104.0,\\n            -32832.0 / 4104.0,\\n            29440.0 / 4104.0,\\n            -845.0 / 4104.0,\\n            1.0,\\n        ],\\n        [k1, k2, k3, k4, y]\\n    );\\n    let k5 = svMult(h, f(y5, t + h, ...args));\\n\\n    let y6 = svMultAdd(\\n        [\\n            -6080.0 / 20520.0,\\n            41040.0 / 20520.0,\\n            -28352.0 / 20520.0,\\n            9295.0 / 20520.0,\\n            -5643.0 / 20520.0,\\n            1.0,\\n        ],\\n        [k1, k2, k3, k4, k5, y]\\n    );\\n    let k6 = svMult(h, f(y6, t + h / 2.0, ...args));\\n\\n    // Calculate new step\\n    let yNew = svMultAdd(\\n        [\\n            2375.0 / 20520.0,\\n            11264.0 / 20520.0,\\n            10985.0 / 20520.0,\\n            -4104.0 / 20520.0,\\n            1.0,\\n        ],\\n        [k1, k3, k4, k5, y]\\n    );\\n\\n    // Relative difference between steps\\n\\tlet relChangeStep = norm(vectorAdd(yNew, svMult(-1.0, y))) / norm(yNew);\\n\\n    // Calculate error (note that k2's contribution to the error is zero)\\n    let errorVector = svMultAdd(\\n        [\\n            209.0 / 75240.0,\\n            -2252.8 / 75240.0,\\n            -2197.0 / 75240.0,\\n            1504.8 / 75240.0,\\n            2736.0 / 75240.0,\\n        ],\\n        [k1, k3, k4, k5, k6]\\n    );\\n    let error = Math.max(...absVector(errorVector));\\n\\n    // Either don't take a step or use the RK4 step\\n    let deadStep;\\n    if (error < tol || relChangeStep < relStepTol || h <= hMin) {\\n        t += h;\\n        deadStep = false;\\n    } else {\\n        yNew = y;\\n        deadStep = true;\\n    }\\n\\n    // Compute scaling for new step size\\n    let s;\\n    if (error === 0.0) {\\n        s = sBounds[1];\\n    } else {\\n        s = Math.pow((tol * h) / 2.0 / error, 0.25);\\n    }\\n    if (s < sBounds[0]) {\\n        s = sBounds[0];\\n    } else if (s > sBounds[1]) {\\n        s = sBounds[1];\\n    }\\n\\n    // Return new y-values, new time, and updated step size h\\n    return [yNew, t, Math.max(s * h, hMin), deadStep];\\n}\\n\\n\\nfunction dydtIMEX(y, t, f, cfun, Afun, fArgs, cfunArgs, AfunArgs, diagonalA) {\\n    /*\\n     * Right hand side of ODEs for initializing IMEX method with RKF.\\n     */\\n\\n    n = y.length;\\n    let rhs = zeros(n);\\n\\n    let A = Afun(t, ...AfunArgs);\\n    let c = cfun(t, ...cfunArgs);\\n\\n    // Linear part\\n    let nonConstantLinear = diagonalA\\n        ? elementwiseVectorMult(A, y)\\n        : mvMult(A, y, diagonalA);\\n    let linearPart = vectorAdd(nonConstantLinear, c);\\n\\n    // Nonlinear part\\n    let nonlinearPart = f(y, t, ...fArgs);\\n\\n    return vectorAdd(nonlinearPart, linearPart);\\n}\\n\\n\\nfunction cnab2Step(u, c, A, f1, f0, g1, omega, k, diagonalA) {\\n    /*\\n     * Take a CNAB2 step.\\n     *\\n     * - u is the current value of the solution.\\n     * - c is the constant term.\\n     * - A is the matrix for the linear function.\\n     * - f1 is the nonlinear function evaluated at the current value of y.\\n     * - f0 is the nonlinear function evaluated at the previous value of y.\\n     * - g1 is the linear function evaluated at the current value of y.\\n     * - omega is the ratio of the most recent step size to the one before that.\\n     * - k is the current step size.\\n     * - diagonalA is true if A is diagonal. This leads to a *much* faster time step.\\n     *   If diagonalA is true, then A is provided only as the diagonal.\\n     */\\n\\n    let invk = 1.0 / k;\\n    let b = vectorAdd(\\n        svMult(0.5, c),\\n        svMult(invk, u),\\n        svMult(1.0 + omega / 2.0, f1),\\n        svMult(-omega / 2.0, f0),\\n        svMult(0.5, g1)\\n    );\\n\\n    if (diagonalA) {\\n        let Aaug = svAdd(invk, svMult(-0.5, A));\\n        let result = elementwiseVectorDivide(b, Aaug);\\n    } else {\\n        let n = A.length;\\n        let Aaug = smMult(-0.5, A);\\n        for (i = 0; i < n; i++) {\\n            Aaug[i][i] += invk;\\n        }\\n        let result = solve(Aaug, b);\\n    }\\n\\n    return result;\\n}\\n\\n\\nfunction vsimexAdjustStepSizePID(\\n    k,\\n    relChange,\\n    relChangeStep,\\n    tol,\\n    kP,\\n    kI,\\n    kD,\\n    kBounds,\\n    sBounds\\n) {\\n    /*\\n     * Adjust step size using a PID controller.\\n     */\\n    let mult =\\n        Math.pow(relChange[1] / relChangeStep, kP) *\\n        Math.pow(tol / relChangeStep, kI) *\\n        Math.pow(Math.pow(relChange[0], 2) / relChange[1] / relChangeStep, kD);\\n    if (mult > sBounds[1]) mult = sBounds[1];\\n    else if (mult < sBounds[0]) mult = sBounds[0];\\n\\n    let newk = mult * k;\\n\\n    if (newk > kBounds[1]) newk = kBounds[1];\\n    else if (newk < kBounds[0]) newk = kBounds[0];\\n\\n    return newk;\\n}\\n\\n\\nfunction vsimexAdjustStepSizeRejectedStep(\\n    k,\\n    relChangeStep,\\n    tol,\\n    kBounds,\\n    sBounds\\n) {\\n    /*\\n     * Adjust step for rejected step\\n     */\\n\\n    let mult = tol / relChangeStep;\\n    if (mult < sBounds[0]) mult = sBounds[0];\\n\\n    let newk = mult * k;\\n    if (newk < kBounds[0]) newk = kBounds[0];\\n\\n    return newk;\\n}\\n\\n\\nfunction vsimexAdjustStepSizeFailedSolve(k, failedSolveS) {\\n    /*\\n     * Adjust step for failed solve. Bringing step size down will\\n     * eventually make matrix for linear solve positive definite.\\n     */\\n\\n    return k * failedSolveS;\\n}\\n\\n\\nfunction vsimex(\\n    f,\\n    cfun,\\n    Afun,\\n    initialCondition,\\n    timePoints,\\n    fArgs,\\n    cfunArgs,\\n    AfunArgs,\\n    diagonalA,\\n    k0,\\n    kBounds,\\n    tol,\\n    tolBuffer,\\n    kP,\\n    kI,\\n    kD,\\n    sBounds,\\n    failedSolveS,\\n    enforceNonnegative,\\n    maxDeadSteps\\n) {\\n    /*\\n     *\\n     */\\n\\n    // Defaults\\n    if (k0 === undefined) k0 = 1.0e-5;\\n    if (kBounds === undefined) kBounds = [1.0e-6, 100.0];\\n    if (tol === undefined) tol = 0.001;\\n    if (tolBuffer === undefined) tolBuffer = 0.01;\\n    if (kP === undefined) kP = 0.075;\\n    if (kI === undefined) kI = 0.175;\\n    if (kD === undefined) kD = 0.01;\\n    if (sBounds === undefined) sBounds = [0.1, 10.0];\\n    if (failedSolveS === undefined) failedSolveS = 0.1;\\n    if (enforceNonnegative == undefined) enforceNonnegative = true;\\n    if (maxDeadSteps === undefined) maxDeadSteps = 10;\\n\\n    // Do RKF to get the first few time points\\n    let rkf45TimePoints = [\\n        timePoints[0],\\n        timePoints[0] + k0,\\n        timePoints[0] + 2.0 * k0,\\n    ];\\n\\n    let args = [f, cfun, Afun, fArgs, cfunArgs, AfunArgs, diagonalA];\\n    let yRKF = rkf45(\\n        dydtIMEX,\\n        initialCondition,\\n        rkf45TimePoints,\\n        args,\\n        k0 / 10.0,\\n        tol,\\n        sBounds,\\n        0.0,\\n        enforceNonnegative,\\n        maxDeadSteps\\n    );\\n\\n    yRKF = transpose(yRKF);\\n\\n    // Set up variables for running CNAB2 VSIMEX\\n    let tSol = [timePoints[0]];\\n    let iMax = timePoints.length;\\n    let y = [initialCondition];\\n    let k = 2.0 * k0;\\n    let newk;\\n    let t = rkf45TimePoints[2];\\n    let y0 = yRKF[2];\\n    let i = 1;\\n    let nDeadSteps = 0;\\n    let deadStep = false;\\n    let c = cfun(t, ...cfunArgs);\\n    let A = Afun(t, ...AfunArgs);\\n    let f0 = f(initialCondition, timePoints[0], ...fArgs);\\n    let f1 = f(y0, t, ...fArgs);\\n    let g1 = vectorAdd(c, mvMult(A, y0, diagonalA));\\n    let omega = 1.0;\\n    let yStep;\\n    let relChangeStep;\\n    let relTol = tol * (1.0 + tolBuffer);\\n    let relChange = [\\n        norm(vectorAdd(y0, svMult(-1.0, yRKF[1]))) / norm(y0),\\n        norm(vectorAdd(yRKF[1], svMult(-1.0, initialCondition))) /\\n            norm(yRKF[1]),\\n    ];\\n\\n    // DEBUG\\n    let nSteps = 3;\\n    // END EDEBUG\\n\\n    while (i < iMax && nDeadSteps < maxDeadSteps) {\\n        nDeadSteps = 0;\\n        while (t < timePoints[i] && nDeadSteps < maxDeadSteps) {\\n            // Take CNAB2 step\\n            yStep = cnab2Step(y0, c, A, f1, f0, g1, omega, k, diagonalA);\\n\\n            // Reject the step if failed to solve\\n            if (yStep === null) {\\n                newk = vsimexAdjustStepSizeFailedSolve(k, failedSolveS);\\n                omega *= newk / k;\\n                k = newk;\\n                nDeadSteps += 1;\\n                console.log(\\\"null yStep\\\");\\n            } else {\\n                // Relative change\\n                relChangeStep =\\n                    norm(vectorAdd(yStep, svMult(-1.0, y0))) / norm(yStep);\\n\\n                // Take step if below tolerance\\n                if (relChangeStep <= relTol) {\\n                    f0 = f(y0, t, ...fArgs);\\n                    t += k;\\n                    y0 = yStep;\\n                    f1 = f(y0, t, ...fArgs);\\n                    c = cfun(t, ...cfunArgs);\\n                    A = Afun(t, ...AfunArgs);\\n                    g1 = vectorAdd(c, mvMult(A, y0, diagonalA));\\n                    newk = vsimexAdjustStepSizePID(\\n                        k,\\n                        relChange,\\n                        relChangeStep,\\n                        tol,\\n                        kP,\\n                        kI,\\n                        kD,\\n                        kBounds,\\n                        sBounds\\n                    );\\n                    relChange = [relChange[1], relChangeStep];\\n                    omega = newk / k;\\n                    k = newk;\\n                    nDeadSteps = 0;\\n                }\\n                // Reject the step is not within tolerance\\n                else {\\n                    newk = vsimexAdjustStepSizeRejectedStep(\\n                        k,\\n                        relChangeStep,\\n                        tol,\\n                        kBounds,\\n                        sBounds\\n                    );\\n                    omega *= newk / k;\\n                    k = newk;\\n                    nDeadSteps += 1;\\n                }\\n            }\\n            if (enforceNonnegative) {\\n                y0 = y0.map(function (x) {\\n                    if (x < 0.0) return 0.0;\\n                    else return x;\\n                });\\n            }\\n\\n            // DEBUG\\n\\t\\t    nSteps += 1;\\n\\t\\t    // END EDEBUG\\n        }\\n        if (t > tSol[tSol.length - 1]) {\\n            y.push(y0);\\n            tSol.push(t);\\n        }\\n        i += 1;\\n    }\\n\\n    // DEBUG\\n    console.log(nSteps);\\n    // END DEBUG\\n\\n    if (nDeadSteps == maxDeadSteps) {\\n        return nanArray(initialCondition, iMax);\\n    }\\n    let yInterp = interpolateSolution(timePoints, tSol, transpose(y));\\n\\n    return yInterp;\\n}\\n\\n\\nfunction interpolate1d(x, xs, ys) {\\n    let y2s = naturalSplineSecondDerivs(xs, ys);\\n\\n    let yInterp = x.map(function (xVal) {\\n        return splineEvaluate(xVal, xs, ys, y2s);\\n    });\\n\\n    return yInterp;\\n}\\n\\n\\nfunction interpolateSolution(timePoints, t, y) {\\n    // Interpolate each row of y\\n    let yInterp = y.map(function (yi) {\\n        return interpolate1d(timePoints, t, yi);\\n    });\\n\\n    return yInterp;\\n}\\n\\n\\nfunction naturalSplineSecondDerivs(xs, ys) {\\n    /*\\n     * Compute the second derivatives for a cubic spline data\\n     * measured at positions xs, ys.\\n     *\\n     * The second derivatives are then used to evaluate the spline.\\n     */\\n\\n    let n = xs.length;\\n\\n    // Storage used in tridiagonal solve\\n    let u = zeros(n);\\n\\n    // Return value\\n    let y2s = zeros(n);\\n\\n    // Solve trigiadonal matrix by decomposition\\n    for (let i = 1; i < n - 1; i++) {\\n        let fracInterval = (xs[i] - xs[i - 1]) / (xs[i + 1] - xs[i - 1]);\\n        let p = fracInterval * y2s[i - 1] + 2.0;\\n        y2s[i] = (fracInterval - 1.0) / p;\\n        u[i] =\\n            (ys[i + 1] - ys[i]) / (xs[i + 1] - xs[i]) -\\n            (ys[i] - ys[i - 1]) / (xs[i] - xs[i - 1]);\\n        u[i] =\\n            ((6.0 * u[i]) / (xs[i + 1] - xs[i - 1]) - fracInterval * u[i - 1]) /\\n            p;\\n    }\\n\\n    // Tridiagonal solve back substitution\\n    for (let k = n - 2; k >= 0; k--) {\\n        y2s[k] = y2s[k] * y2s[k + 1] + u[k];\\n    }\\n\\n    return y2s;\\n}\\n\\n\\nfunction splineEvaluate(x, xs, ys, y2s) {\\n    /*\\n     * Evaluate a spline computed from points xs, ys, with second derivatives\\n     * y2s, as compute by naturalSplineSecondDerivs().\\n     *\\n     * Assumes that x and xs are sorted.\\n     */\\n    let n = xs.length;\\n\\n    // Indices bracketing where x is\\n    let lowInd = 0;\\n    let highInd = n - 1;\\n\\n    // Perform bisection search to find index of x\\n    while (highInd - lowInd > 1) {\\n        let i = (highInd + lowInd) >> 1;\\n        if (xs[i] > x) {\\n            highInd = i;\\n        } else {\\n            lowInd = i;\\n        }\\n    }\\n    let h = xs[highInd] - xs[lowInd];\\n    let a = (xs[highInd] - x) / h;\\n    let b = (x - xs[lowInd]) / h;\\n\\n    let y = a * ys[lowInd] + b * ys[highInd];\\n    y +=\\n        (((Math.pow(a, 3) - a) * y2s[lowInd] + (Math.pow(b, 3) - b) * y2s[highInd]) * Math.pow(h, 2)) /\\n        6.0;\\n\\n    return y;\\n}\\n\\n\\n// module.exports = {\\n//   vsimex,\\n//   rkf45,\\n//   zeros, \\n//   linspace\\n// };\\n\\n// vsimex(lotkaVolterra, [1.0, 3.0], linspace(0.0, 20.0, 200), [1.0, 2.0, 3.0, 4.0], 0.01, 1e-7, [0.1, 10.0], 0.0)\\n// let lv = lotkaVolterraIMEX(1.0, 2.0, 3.0, 4.0);\\n// let sol = vsimex(lv.f, lv.cfun, lv.Afun, [1.0, 3.0], linspace(0.0, 20.0, 200), [], [], [], lv.diagonalA)\\n\\n\\nfunction lotkaVolterra(xy, t, alpha, beta, gamma, delta) {\\n\\t// Unpack\\n\\tvar [x, y] = xy;\\n\\n\\tvar dxdt = alpha * x - beta * x * y;\\n\\tvar dydt = delta * x * y - gamma * y;\\n\\n\\treturn [dxdt, dydt];\\n}\\n\\n\\nfunction lotkaVolterraIMEX(alpha, beta, gamma, delta) {\\n\\tf = function(xy) {\\n\\t\\tvar [x, y] = xy;\\n\\t\\treturn [-beta * x * y, delta * x * y];\\n\\t}\\n\\n\\tcfun = (x) => [0.0, 0.0];\\n\\n\\tAfun = (x) => [alpha, -gamma];\\n\\n\\tdiagonalA = true;\\n\\n\\treturn {f: f, cfun: cfun, Afun: Afun, diagonalA: true};\\n}\\n\\n\\nfunction cascade(yz, t, beta, gamma, n_x, n_y, x_fun, x_args) {\\n\\t// Unpack\\n\\tvar [y, z] = yz;\\n\\n\\tvar x = x_fun(t, ...x_args);\\n\\n\\tvar dy_dt = beta * act_hill(x, n_x) - y;\\n\\tvar dz_dy = gamma * (act_hill(y, n_y) - z)\\n\\n\\treturn [dy_dt, dz_dt];\\n}\\n\\n\\nfunction repressilator(x, t, beta, n) {\\n\\t// Unpack\\n\\tvar [x1, x2, x3] = x;\\n\\n\\treturn [\\n\\t\\tbeta * rep_hill(x3, n) - x1,\\n\\t\\tbeta * rep_hill(x1, n) - x2,\\n\\t\\tbeta * rep_hill(x2, n) - x3\\n\\t]\\n}\\n\\n\\n// module.exports = {\\n//   repressilator,\\n//   lotkaVolterra,\\n//   lotkaVolterraIMEX\\n// };\\n\\nfunction ij(i, j, n) {\\n    /*\\n     * Lexicographic indexing of 2D array represented as 1D.\\n     */\\n\\n    return i * n + j;\\n}\\n\\n\\nfunction twoDto1D(A) {\\n    /*\\n     * Convert a 2D matrix to a 1D representation with row-based (C)\\n     * lexicographic ordering.\\n     */\\n\\n    var m = A.length;\\n    var n = A[0].length;\\n\\n    var A1d = [];\\n    for (var i = 0; i < m; i++) {\\n        for (var j = 0; j < n; j++) {\\n            A1d.push(A[i][j]);\\n        }\\n    }\\n\\n    return A1d;\\n}\\n\\n\\nfunction linspace(start, stop, n) {\\n    var x = [];\\n    var currValue = start;\\n    var step = (stop - start) / (n - 1);\\n    for (var i = 0; i < n; i++) {\\n        x.push(currValue);\\n        currValue += step;\\n    }\\n    return x;\\n}\\n\\n\\nfunction zeros(n) {\\n    var x = [];\\n    for (var i = 0; i < n; i++) x.push(0.0);\\n    return x;\\n}\\n\\n\\nfunction shallowCopyMatrix(A) {\\n    /*\\n     * Make a shallow copy of a matrix.\\n     */\\n\\n    var Ac = [];\\n    var n = A.length;\\n    for (i = 0; i < n; i++) {\\n        Ac.push([...A[i]]);\\n    }\\n\\n    return Ac;\\n}\\n\\n\\nfunction nanArray() {\\n    /*\\n     * Return a NaN array of shape given by arguments.\\n     */\\n    if (arguments.length == 1) {\\n        var x = [];\\n        for (var i = 0; i < arguments[0]; i++) x.push(NaN);\\n    }\\n    else if (arguments.length == 2) {\\n        var x = [];\\n        for (var i = 0; i < arguments[0]; i++) {\\n            var xRow = [];\\n            for (var j = 0; j < arguments[1]; j++) xRow.push(NaN);\\n            x.push(xRow);\\n        }\\n    }\\n    else {\\n        throw 'Must only have one or two arguments to nanArray().'\\n    }\\n\\n    return x;\\n}\\n\\nfunction transpose(A) {\\n    var m = A.length;\\n    var n = A[0].length;\\n    var AT = [];\\n\\n    for (var j = 0; j < n; j++) {\\n        var ATj = [];\\n        for (var i = 0; i < m; i++) {\\n            ATj.push(A[i][j]);\\n        }\\n        AT.push(ATj);\\n    }\\n\\n    return AT;\\n}\\n\\n\\nfunction dot(v1, v2) {\\n    /*\\n     * Compute dot product v1 . v2.\\n     */\\n\\n    var n = v1.length;\\n    var result = 0.0;\\n    for (var i = 0; i < n; i++) result += v1[i] * v2[i];\\n\\n    return result;\\n}\\n\\n\\nfunction norm(v) {\\n    /*\\n     * 2-norm of a vector\\n     */\\n\\n    return Math.sqrt(dot(v, v));\\n}\\n\\n\\nfunction mvMult(A, v, diagonalA) {\\n    /*\\n     * Compute dot product A . v, where A is a matrix.\\n     * If diagonalA is true, then A must be a 1-D array.\\n     */\\n\\n    if (diagonalA) return elementwiseVectorMult(A, v);\\n    else {\\n        return A.map(function (Arow) {\\n            return dot(Arow, v);\\n        });\\n    }\\n}\\n\\n\\nfunction svMult(a, v) {\\n    /*\\n     * Multiply vector v by scalar a.\\n     */\\n\\n    return v.map(function (x) {\\n        return a * x;\\n    });\\n}\\n\\n\\nfunction smMult(a, A) {\\n    /*\\n     * Multiply matrix A by scalar a.\\n     */\\n\\n    return A.map(function (Arow) {\\n        return svMult(a, Arow);\\n    });\\n}\\n\\n\\nfunction svAdd(a, v) {\\n    /*\\n     * Add a scalar a to every element of vector v.\\n     */\\n\\n    return v.map(function (x) {\\n        return a + x;\\n    });\\n}\\n\\n\\nfunction vectorAdd() {\\n    var m = arguments[0].length;\\n    var n = arguments.length;\\n\\n    var result = [];\\n    for (var i = 0; i < m; i++) {\\n        var element = 0.0;\\n        for (var j = 0; j < n; j++) {\\n            element += arguments[j][i];\\n        }\\n        result.push(element);\\n    }\\n\\n    return result;\\n}\\n\\n\\nfunction elementwiseVectorDivide(v1, v2) {\\n    /*\\n     * Compute v1 / v2 elementwise.\\n     */\\n\\n    var result = [];\\n    n = v1.length;\\n\\n    for (var i = 0; i < n; i++) {\\n        result.push(v1[i] / v2[i]);\\n    }\\n\\n    return result;\\n}\\n\\n\\nfunction elementwiseVectorMult(v1, v2) {\\n    /*\\n     * Compute v1 * v2 elementwise.\\n     */\\n\\n    var result = [];\\n    n = v1.length;\\n\\n    for (var i = 0; i < n; i++) {\\n        result.push(v1[i] * v2[i]);\\n    }\\n\\n    return result;\\n}\\n\\n\\nfunction svMultAdd(scalars, vectors) {\\n    /*\\n     * Add a set of vectors together, each multiplied by a scalar.\\n     */\\n\\n    var m = vectors[0].length;\\n    var n = scalars.length;\\n\\n    if (vectors.length != n) {\\n        console.warn(\\\"svMultAdd: Difference number of scalars and vectors.\\\");\\n        return null;\\n    }\\n\\n    var result = [];\\n    for (var i = 0; i < m; i++) {\\n        var element = 0.0;\\n        for (var j = 0; j < n; j++) {\\n            element += scalars[j] * vectors[j][i];\\n        }\\n        result.push(element);\\n    }\\n\\n    return result;\\n}\\n\\n\\nfunction absVector(v) {\\n    var result = [];\\n    for (var i = 0; i < v.length; i++) {\\n        result[i] = Math.abs(v[i]);\\n    }\\n\\n    return result;\\n}\\n\\n\\nfunction LUPDecompose(A, eps) {\\n    /*\\n     * LUP decomposition.\\n     */\\n\\n    var i, j, k, imax;\\n    var maxA, absA;\\n    var Arow;\\n    var p = [];\\n    var n = A.length;\\n    var LU = shallowCopyMatrix(A);\\n\\n    // Permutation matrix\\n    for (i = 0; i <= n; i++) p.push(i);\\n\\n    for (i = 0; i < n; i++) {\\n        maxA = 0.0;\\n        imax = i;\\n\\n        for (k = i; k < n; k++) {\\n            absA = Math.abs(LU[k][i]);\\n            if (absA > maxA) {\\n                maxA = absA;\\n                imax = k;\\n            }\\n        }\\n\\n        // Failure; singular matrix\\n        if (maxA < eps) return [null, null];\\n\\n        if (imax != i) {\\n            // Pivot\\n            j = p[i];\\n            p[i] = p[imax];\\n            p[imax] = j;\\n\\n            // Pivot rows of A\\n            Arow = LU[i];\\n            LU[i] = LU[imax];\\n            LU[imax] = Arow;\\n\\n            // Count pivots\\n            p[n]++;\\n        }\\n\\n        for (j = i + 1; j < n; j++) {\\n            LU[j][i] /= LU[i][i];\\n\\n            for (k = i + 1; k < n; k++) LU[j][k] -= LU[j][i] * LU[i][k];\\n        }\\n    }\\n\\n    return [LU, p];\\n}\\n\\nfunction LUPSolve(LU, p, b) {\\n    /*\\n     * Solve a linear system where LU and p are stored as the\\n     * output of LUPDecompose().\\n     */\\n\\n    var n = b.length;\\n    var x = [];\\n\\n    for (var i = 0; i < n; i++) {\\n        x.push(b[p[i]]);\\n        for (var k = 0; k < i; k++) x[i] -= LU[i][k] * x[k];\\n    }\\n\\n    for (i = n - 1; i >= 0; i--) {\\n        for (k = i + 1; k < n; k++) x[i] -= LU[i][k] * x[k];\\n\\n        x[i] /= LU[i][i];\\n    }\\n\\n    return x;\\n}\\n\\nfunction solve(A, b) {\\n    /*\\n     * Solve a linear system using LUP decomposition.\\n     *\\n     * Returns null if singular.\\n     */\\n\\n    var eps = 1.0e-14;\\n    var LU, p;\\n\\n    [LU, p] = LUPDecompose(A, eps);\\n\\n    // Return null if singular\\n    if (LU === null) return null;\\n\\n    return LUPSolve(LU, p, b);\\n}\\n\\n\\nfunction repressilator(x, t, beta, rho, gamma, n) {\\n\\t// Unpack\\n\\tlet [m1, m2, m3, x1, x2, x3] = x;\\n\\n\\treturn [\\n\\t\\tbeta * (rho + rep_hill(x3, n)) - m1,\\n\\t\\tbeta * (rho + rep_hill(x1, n)) - m2,\\n\\t\\tbeta * (rho + rep_hill(x2, n)) - m3,\\n\\t\\tgamma * (m1 - x1),\\n\\t\\tgamma * (m2 - x2),\\n\\t\\tgamma * (m3 - x3)\\n\\t];\\n}\\n\\n\\nfunction callback() {\\n\\tlet xRangeMax = xRange.end;\\n\\tlet dt = 0.01;\\n\\tlet x0 = [0.0, 0.0, 0.0, 1.0, 1.0, 1.2];\\n\\tlet beta = Math.pow(10, betaSlider.value);\\n\\tlet gamma = Math.pow(10, gammaSlider.value);\\n\\tlet rho = Math.pow(10, rhoSlider.value);\\n\\tlet n = nSlider.value;\\n\\n\\tlet t = linspace(0.0, xRangeMax, cds.data['t'].length);\\n\\tlet args = [beta, rho, gamma, n];\\n\\n\\t// Integrate ODES\\n\\tlet xSolve = rkf45(repressilator, x0, t, args, t[1] - t[0], 1e-7, 1e-3, 100);\\n\\n\\tcds.data['t'] = t;\\n\\tcds.data['m1'] = xSolve[0];\\n\\tcds.data['m2'] = xSolve[1];\\n\\tcds.data['m3'] = xSolve[2];\\n\\tcds.data['x1'] = xSolve[3];\\n\\tcds.data['x2'] = xSolve[4];\\n\\tcds.data['x3'] = xSolve[5];\\n\\n\\tcds.change.emit();\\n}\\n\\ncallback()\"}}]]]},\"width\":125,\"title\":\"\\u03b2\",\"format\":{\"type\":\"object\",\"name\":\"CustomJSTickFormatter\",\"id\":\"p1913\",\"attributes\":{\"code\":\"return Math.pow(10, 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       "  const render_items = [{\"docid\":\"cadbc9c2-807e-4743-bc51-7d0ff074a973\",\"roots\":{\"p2032\":\"4654b582-8ce0-48ee-820e-ec2c71e2d9a0\"},\"root_ids\":[\"p2032\"]}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    let attempts = 0;\n",
       "    const timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "p2032"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sliders\n",
    "beta_slider = bokeh.models.Slider(\n",
    "    title=\"β\",\n",
    "    start=0,\n",
    "    end=4,\n",
    "    step=0.1,\n",
    "    value=1,\n",
    "    width=125,\n",
    "    format=bokeh.models.CustomJSTickFormatter(code=\"return Math.pow(10, tick).toFixed(2)\"),\n",
    ")\n",
    "gamma_slider = bokeh.models.Slider(\n",
    "    title=\"γ\",\n",
    "    start=-3,\n",
    "    end=0,\n",
    "    step=0.1,\n",
    "    value=0,\n",
    "    width=125,\n",
    "    format=bokeh.models.CustomJSTickFormatter(code=\"return Math.pow(10, tick).toFixed(3)\"),\n",
    ")\n",
    "rho_slider = bokeh.models.Slider(\n",
    "    title=\"ρ\",\n",
    "    start=-6,\n",
    "    end=0,\n",
    "    step=0.1,\n",
    "    value=-3,\n",
    "    width=125,\n",
    "    format=bokeh.models.CustomJSTickFormatter(code=\"return Math.pow(10, tick).toFixed(6)\"),\n",
    ")\n",
    "n_slider = bokeh.models.Slider(title=\"n\", start=1, end=5, step=0.1, value=3, width=125)\n",
    "\n",
    "def repressilator_rhs(mx, t, beta, gamma, rho, n):\n",
    "    \"\"\"\n",
    "    Returns 6-array of (dm_1/dt, dm_2/dt, dm_3/dt, dx_1/dt, dx_2/dt, dx_3/dt)\n",
    "    \"\"\"\n",
    "    m_1, m_2, m_3, x_1, x_2, x_3 = mx\n",
    "    return np.array(\n",
    "        [\n",
    "            beta * (rho + 1 / (1 + x_3 ** n)) - m_1,\n",
    "            beta * (rho + 1 / (1 + x_1 ** n)) - m_2,\n",
    "            beta * (rho + 1 / (1 + x_2 ** n)) - m_3,\n",
    "            gamma * (m_1 - x_1),\n",
    "            gamma * (m_2 - x_2),\n",
    "            gamma * (m_3 - x_3),\n",
    "        ]\n",
    "    )\n",
    "\n",
    "\n",
    "# Initial condiations\n",
    "x0 = np.array([0, 0, 0, 1, 1.1, 1.2])\n",
    "\n",
    "# Number of points to use in plots\n",
    "n_points = 1000\n",
    "\n",
    "\n",
    "# Solve for species concentrations\n",
    "def _solve_repressilator(log_beta, log_gamma, log_rho, n, t_max):\n",
    "    beta = 10 ** log_beta\n",
    "    gamma = 10 ** log_gamma\n",
    "    rho = 10 ** log_rho\n",
    "    t = np.linspace(0, t_max, n_points)\n",
    "    x = scipy.integrate.odeint(repressilator_rhs, x0, t, args=(beta, gamma, rho, n))\n",
    "    m1, m2, m3, x1, x2, x3 = x.transpose()\n",
    "    return t, m1, m2, m3, x1, x2, x3\n",
    "\n",
    "\n",
    "t, m1, m2, m3, x1, x2, x3 = _solve_repressilator(\n",
    "    beta_slider.value,\n",
    "    gamma_slider.value,\n",
    "    rho_slider.value,\n",
    "    n_slider.value,\n",
    "    40.0,\n",
    ")\n",
    "\n",
    "cds = bokeh.models.ColumnDataSource(\n",
    "    dict(t=t, m1=m1, m2=m2, m3=m3, x1=x1, x2=x2, x3=x3)\n",
    ")\n",
    "\n",
    "p_rep = bokeh.plotting.figure(\n",
    "    frame_width=500,\n",
    "    frame_height=200,\n",
    "    x_axis_label=\"t\",\n",
    "    x_range=[0, 40.0],\n",
    ")\n",
    "\n",
    "colors = bokeh.palettes.d3[\"Category20\"][6]\n",
    "m1_line = p_rep.line(source=cds, x=\"t\", y=\"m1\", line_width=2, color=colors[1])\n",
    "x1_line = p_rep.line(source=cds, x=\"t\", y=\"x1\", line_width=2, color=colors[0])\n",
    "m2_line = p_rep.line(source=cds, x=\"t\", y=\"m2\", line_width=2, color=colors[3])\n",
    "x2_line = p_rep.line(source=cds, x=\"t\", y=\"x2\", line_width=2, color=colors[2])\n",
    "m3_line = p_rep.line(source=cds, x=\"t\", y=\"m3\", line_width=2, color=colors[5])\n",
    "x3_line = p_rep.line(source=cds, x=\"t\", y=\"x3\", line_width=2, color=colors[4])\n",
    "\n",
    "legend_items = [\n",
    "    (\"m₁\", [m1_line]),\n",
    "    (\"x₁\", [x1_line]),\n",
    "    (\"m₂\", [m2_line]),\n",
    "    (\"x₂\", [x2_line]),\n",
    "    (\"m₃\", [m3_line]),\n",
    "    (\"x₃\", [x3_line]),\n",
    "]\n",
    "legend = bokeh.models.Legend(items=legend_items)\n",
    "\n",
    "p_rep.add_layout(legend, \"right\")\n",
    "\n",
    "# Build the layout\n",
    "layout = bokeh.layouts.column(\n",
    "    bokeh.layouts.row(\n",
    "        beta_slider,\n",
    "        gamma_slider,\n",
    "        rho_slider,\n",
    "        n_slider,\n",
    "        width=575,\n",
    "    ),\n",
    "    bokeh.layouts.Spacer(height=10),\n",
    "    p_rep,\n",
    ")\n",
    "\n",
    "# Set up callbacks\n",
    "def _callback(attr, old, new):\n",
    "    t, m1, m2, m3, x1, x2, x3 = _solve_repressilator(\n",
    "        beta_slider.value,\n",
    "        gamma_slider.value,\n",
    "        rho_slider.value,\n",
    "        n_slider.value,\n",
    "        p_rep.x_range.end,\n",
    "    )\n",
    "    cds.data = dict(t=t, m1=m1, m2=m2, m3=m3, x1=x1, x2=x2, x3=x3)\n",
    "\n",
    "beta_slider.on_change(\"value\", _callback)\n",
    "gamma_slider.on_change(\"value\", _callback)\n",
    "rho_slider.on_change(\"value\", _callback)\n",
    "n_slider.on_change(\"value\", _callback)\n",
    "p_rep.x_range.on_change(\"end\", _callback)\n",
    "\n",
    "# Build the app\n",
    "def repressilator_app(doc):\n",
    "    doc.add_root(layout)\n",
    "\n",
    "    \n",
    "# Display\n",
    "if interactive_python_plots:\n",
    "    bokeh.io.show(repressilator_app, notebook_url=notebook_url)\n",
    "else:\n",
    "    bokeh.io.show(biocircuits.jsplots.repressilator())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using a similar technique as for the simplified three-component system, we can show that there is a unique fixed point with $m_i = x_i = x_0$ for all $i$ with\n",
    "\n",
    "\\begin{align}\n",
    "(x_0 - \\beta\\rho)(1+x_0^n) = \\beta.\n",
    "\\end{align}\n",
    "\n",
    "We can perform linear stability for this fixed point. The linear stability matrix is now 6 by 6 rather than 3 by 3. Nonetheless, we can derive analytically that when we get an eigenvalue with a positive real part, it also has a nonzero imaginary part, which means that the instability is oscillatory. We get (not derived here; you can try it in the problems) an eigenvalue with positive real part when\n",
    "\n",
    "\\begin{align}\n",
    "\\left(\\sqrt{\\gamma} + \\sqrt{\\gamma^{-1}}\\right)^2 < \\frac{3f_0^2}{4+2f_0},\n",
    "\\end{align}\n",
    "\n",
    "where\n",
    "\n",
    "\\begin{align}\n",
    "f_0 = \\frac{\\beta n x_0^{n-1}}{(1+x_0^n)^2}.\n",
    "\\end{align}\n",
    "\n",
    "Note something interesting here: $x_0$ is independent of $\\gamma$, while the $\\left(\\sqrt{\\gamma} + \\sqrt{\\gamma^{-1}}\\right)^2$ term is invariant to exchanging $\\gamma \\leftrightarrow \\gamma^{-1}$. This is telling us that the magnitude of the ratio matters, but not whether protein or mRNA is more stable. \n",
    "\n",
    "We can compute the phase boundary to be the line for which the inequality above becomes an equality. To compute this, we first need to find the fixed point $x_0$ as a function of $\\beta$, $\\rho$, and $n$ for the six-component system. This cannot be done analytically as in the case of the simplified three-component system we considered before. We therefore introduce a numerical technique to compute a fixed point in [Technical Appendix 3c](../technical_appendices/09c_1d_root_finding.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The linear stability diagram\n",
    "\n",
    "Now that we can numerically compute the fixed point, we can proceed to construct the linear stability diagram showing the phase boundary (bifurcation line) for various n.\n",
    "\n",
    "We write a function to compute the values of $\\gamma$ along the bifurcation line for various values of $\\beta$. It is useful to rewrite the expression for the bifurcation.\n",
    "\n",
    "\\begin{align}\n",
    "\\gamma -\\xi\\sqrt{\\gamma} + 1 = 0,\n",
    "\\end{align}\n",
    "\n",
    "where\n",
    "\n",
    "\\begin{align}\n",
    "\\xi = \\sqrt{\\frac{3f_0^2}{4+2f_0}}.\n",
    "\\end{align}\n",
    "\n",
    "Then, we have\n",
    "\n",
    "\\begin{align}\n",
    "\\gamma = \\frac{1}{4}\\left(\\xi \\pm \\sqrt{\\xi^2-4}\\right)^2.\n",
    "\\end{align}\n",
    "\n",
    "Evidently, if $\\xi < 2$, there is no value of $\\gamma$ that satisfies this relation. There is no problem with this; it just says that there are regions in parameter space where oscillations cannot occur.\n",
    "\n",
    "When we make a plot of the bifurcation, we will only show the plot for $\\gamma > 1$ because of the symmetry of $\\gamma$ and $1/\\gamma$ we have described.\n",
    "\n",
    "For each bifurcation line, the oscillatory region is below and to the right of the boundary. We will start with $\\rho = 0$ and will vary $n$. In the following plot, we see something interesting: when $n>2$, the boundary approaches a vertical asymptote. This means that for strong enough promoters, one can get oscillations for _any_ decay rates. By contrast when $n<2$ the boundary approaches a horizontal asymptote. In this regime, it is essential the mRNA and protein decay rates must be similar enough to one another ($\\gamma$ must be below the line) or no oscillations are possible at any promoter strength. In between these regimes is the critical case of $n=2$, where either larger $\\beta$ and  $\\gamma$ closer to 1 both favor oscillations. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-05T00:52:07.720392Z",
     "start_time": "2019-06-05T00:52:06.981558Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
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oF0XQJX9bLpmchdA0SMpayuHF0COH/+u7psXQHhmnX6wsBdAQsyr0nDFF0CVvcujL9oXQMh4mOrs7hdA1Eenn6gDGEAXt4e7YhgYQC3NwzYbLRhA9EDgCdJBGED3rVwth1YYQBXKs5k6axhAFJhbR+x/GEBbm8UunJQYQCgJX0hKqRhA1PqQjPa9GECSncDzoNIYQPRiT3ZJ5xhAKS+bDPD7GEAA8/2ulBAZQM8v0FU3JRlAhaJl+dc5GUDFpA+Sdk4ZQFgSHRgTYxlAyULag613GUBoLpHNRYwZQE6hie3boBlAOmUJ3G+1GUC1alSRAcoZQJ3wrAWR3hlA5atTMR7zGUCr74cMqQcaQFvSh48xHBpAD1WQsrcwGkA5iN1tO0UaQIqxqrm8WRpAS28yjjtuGkCtFa/jt4IaQILPWbIxlxpAUods8qirGkB3qyCcHcAaQJ6Yr6eP1BpAFetSDf/oGkBBi0TFa/0aQIjOvsfVERtAfJj8DD0mG0C6eTmNoTobQOfQsUADTxtAyuiiH2JjG0D7F0sivncbQP7e6UAXjBtAqgbAc22gG0CTvQ+zwLQbQLW1HPcQyRtA7UAsOF7dG0B3boVuqPEbQComcZLvBRxAhEU6nDMaHEDVzC2EdC4cQEqzmkKyQhxAH2rSz+xWHEB5sygkJGscQBjN8zdYfxxAkYqMA4mTHECCbk5/tqccQLLEl6PguxxA4rnJaAfQHEBPdUjHKuQcQCcxe7dK+BxAJlLMMWcMHUAR3qgugCAdQAQag6aVNB1AR9fOkadIHUCQEQTptVwdQGxjnqTAcB1ALxQdvceEHUAROQMry5gdQCHb1+bKrB1AFu4l6cbAHUCTcnwqv9QdQA+KbqOz6B1AtYyTTKT8HUDmHYcekRAeQIw/6RF6JB5AW2deH184HkBMko8/QEweQIBXKmsdYB5AivzgmvZzHkBgiGrHy4ceQLPVgumcmx5A4KXq+WmvHkC6s2fxMsMeQPjExMj31h5A5rzReLjqHkDBrWP6dP4eQDTqVEYtEh9AmRaFVeElH0B9OtkgkTkfQJnQO6E8TR9Awticz+NgH0DO5vGkhnQfQDk0NholiB9AHK9qKL+bH0DfCpbIVK8fQKLOxPPlwh9AHWYJo3LWH0BuL3zP+ukfQPGKO3J+/R9ABfU1wn4IIECg7pv/OxIgQHgR6O32GyBA29q1ia8lIEAC7aPPZS8gQDAWVLwZOSBAN1drTMtCIECr6pF8ekwgQAJLc0knViBAizm+r9FfIEB6xCSseWkgQK1NXDsfcyBApZAdWsJ8IEDGqCQFY4YgQJEXMTkBkCBAT8oF85yZIEBUIGkvNqMgQNrwJOvMrCBALZAGI2G2IED71d7T8r8gQJsigvqBySBAhGTIkw7TIECxHY2cmNwgQDFprxEg5iBA2v8R8KTvIEAbPps0J/kgQEEoNdymAiFAZnDN4yMMIUCQelVInhUhQKthwgYWHyFAt/wMHIsoIUDb4jGF/TEhQEFwMT9tOyFA2MoPR9pEIUAl5tSZRE4hQBiIjDSsVyFAQE1GFBFhIUDTrBU2c2ohQMv8EZfScyFATXZWNC99IUDqOAILiYYhQIJPOBjgjyFAB7MfWTSZIUAFT+PKhaIhQNYEsmrUqyFAia++NSC1IUBdJ0Apab4hQMBEcUKvxyFAYOSQfvLQIUAh6uHaMtohQE9Eq1Rw4yFAzO436arsIUBc9taV4vUhQHR721cX/yFAJrWcLEkIIkBR9HUReBEiQCmmxgOkGiJAKlfyAM0jIkCGtWAG8ywiQOiTfREWNiJAZ+y4HzY/IkAf4oYuU0giQFXEXzttUSJAvBDAQ4RaIkBLdShFmGMiQP7SHT2pbCJAuz8pKbd1IkC3CNgGwn4iQFyzu9PJhyJAlwFqjc6QIkCt8Hwx0JkiQMO9kr3OoiJAOOZNL8qrIkDnKVWEwrQiQPaLU7q3vSJAGlb4zqnGIkAUGPe/mM8iQMa8B4uE2CJAN0fmLW3hIkAKNFOmUuoiQAQ+E/I08yJArm3vDhT8IkDBGrX67wQjQPnsNbPIDSNAit1HNp4WI0CFN8WBcB8jQFKZjJM/KCNAiPWAaQsxI0Btk4kB1DkjQCwQklmZQiNAP1+Kb1tLI0Amy2ZBGlQjQBv2H83VXCNA7tqyEI5lI0CszCAKQ24jQC54b7f0diNAvuOoFqN/I0Dhb9slTogjQCx9GeP1kCNAaNV6TJqZI0DJhxpgO6IjQItOGBzZqiNAjl+YfnOzI0BjOsOFCrwjQJS5xS+exCNAjhLRei7NI0Ba1Rplu9UjQIbs3OxE3iNAA51VEMvmI0DahcfNTe8jQIWfeSPN9yNAbTy3D0kAJEBfB9CQwQgkQCMEGKU2ESRAFY7nSqgZJEBPWJuAFiIkQP58lESBKiRAais4legyJEAgSvBwTDskQC3TKtasQyRANiVawwlMJECo8fQ2Y1QkQKk8di+5XCRAnltdqwtlJEBk9C2pWm0kQGX8byemdSRARbevJO59JEA/tn2fMoYkQKLWbpZzjiRA0UAcCLGWJEAhZyPz6p4kQEgEJlYhpyRA+BzKL1SvJECl9Ll+g7ckQAwapEGvvyRAR107d9fHJEDfzzYe/M8kQLnDUTUd2CRAlMlLuzrgJEAVr+iuVOgkQHt98A5r8CRAJHgv2n34JECOGnYPjQAlQIwWma2YCCVA91Jxs6AQJUCV6dsfpRglQDQluvGlICVA9n/xJ6MoJUCEoWvBnDAlQJpcFr2SOCVA763jGYVAJUCJucnWc0glQLnIwvJeUCVAZ0jNbEZYJUDQa+tDKmAlQCGWJHcKaCVAnDaDBedvJUCAMhbuv3clQGmH8C+VfyVAQD0pymaHJUAJhdu7NI8lQHuZJgT/liVAFMMtosWeJUCSVxiViKYlQAm3EdxHriVA7ElJdgO2JUDGffJiu70lQH7DRKFvxSVAs4x7MCDNJUCRSNYPzdQlQPVhmD523CVAWjwJvBvkJUCFMXSHveslQKaOKKBb8yVAb5F5Bfb6JUAZZr62jAImQJojUrMfCiZAxcmT+q4RJkDpPeaLOhkmQHJIsGbCICZAe5FcikYoJkCfnln2xi8mQJ3PGapDNyZAEFsTpbw+JkDKTMDmMUYmQC+Bnm6jTSZAraMvPBFVJkBqKvlOe1wmQHpUhKbhYyZAKSZeQkRrJkBxZRcio3ImQN+XREX+eSZA1P59q1WBJkBelF9UqYgmQFAIiT/5jyZADL2dbEWXJkDdw0TbjZ4mQJXaKIvSpSZAKmf4exOtJkB0dWWtULQmQHazJR+KuyZALm7y0L/CJkACjojC8ckmQKOTqPMf0SZA2pQWZErYJkCbOJoTcd8mQE20/gGU5iZA5McSL7PtJkDAuqiazvQmQONXlkTm+yZAyuq0LPoCJ0ArPOFSCgonQHuN+7YWESdAAJfnWB8YJ0Cvgow4JB8nQLLp1FUlJidAddCusCItJ0A1owtJHDQnQKwy4B4SOydAi68kMgRCJ0Djp9SC8kgnQGMD7xDdTydARf913MNWJ0DcKm/lpl0nQCtk4yuGZCdAzdPer2FrJ0Ck6XBxOXInQCZZrHANeSdAaxWnrd1/J0DDTXooqoYnQKNpQuFyjSdAZwUf2DeUJ0Af7jIN+ZonQCEepIC2oSdA+7ibMnCoJ0AtCEYjJq8nQNx20lLYtSdAO45zwYa8J0AN8l5vMcMnQA1czVzYySdATZn6iXvQJ0BQhSX3GtcnQL0GkKS23SdAkgt/kk7kJ0AhhTrB4uonQDpkDTFz8SdAhpVF4v/3J0BX/TPViP4nQDd0LAoOBShAsMKFgY8LKEBvnZk7DRIoQMehxDiHGChAMVFmef0eKEAmDuH9byUoQHgXmsbeKyhAWoX500kyKEAqRGomsTgoQKgRWr4UPyhAB3k5nHRFKEAcznvA0EsoQFEqlyspUihAaWgE3n1YKEBUID/Yzl4oQAqkxRocZShA5/oYpmVrKEBc3bx6q3EoQJ2yN5ntdyhAoYoSAix+KED/G9m1ZoQoQPW+GbWdiihABpdlANGQKEBa5E+YAJcoQETybn0snShAwHlbsFSjKEAKwrAxeakoQKWcDAKaryhAemEPIre1KEA061uS0LsoQIKSl1PmwShASCtqZvjHKECS/33LBs4oQKjMf4MR1ChAUL4ejxjaKECbawzvG+AoQEvS/KMb5ihAPv2lrhfsKECxWcEPEPIoQBOtCcgE+ChAYE482PX9KEADFxlB4wMpQD4cYgPNCSlAx73bH7MPKUAZo0yXlRUpQCO3fWp0GylAayQ6mk8hKUC7UU8nJycpQELdjBL7LClAgZnEXMsyKUD9iMoGmDgpQKPadBFhPilAbeWbfSZEKUATJRpM6EkpQIU1zH2mTylA+s+QE2FVKUALxkgOGFspQJP+1m7LYClARnEgNntmKUCFIwxlJ2wpQKcjg/zPcSlABoZw/XR3KUCNYMFoFn0pQBjHZD+0gilAkcdLgk6IKUAyZmky5Y0pQASaslB4kylAFkge3geZKUCuQKXbk54pQG88QkocpClAv9PxKqGpKUDEgbJ+Iq8pQHOchEagtClAllFqgxq6KUD9omc2kb8pQHpjgmAExSlAdjLCAnTKKUDxdzAe4M8pQEth2LNI1SlAud3GxK3aKUAVmgpSD+ApQDH9s1xt5SlAjiTV5cfqKUBK4IHuHvApQHuvz3dy9SlA9bzVgsL6KUCb2qwQDwAqQPN+byJYBSpABME5uZ0KKkDxUynW3w8qQJmEXXoeFSpAVjX3plkaKkA02hhdkR8qQNR15p3FJCpAVJWFavYpKkDB+hzEIy8qQAzj1atNNCpApRPaInQ5KkAyIVUqlz4qQBIZdMO2QypAI3dl79JIKkC5MFmv600qQPasgAQBUypALLoO8BJYKkDqjDdzIV0qQE++MI8sYipAiUcxRTRnKkDofnGWOGwqQPQTK4Q5cSpAaQyZDzd2KkDPwPc5MXsqQFfYhAQogCpAtEZ/cBuFKkALRyd/C4oqQElavjH4jipAf0KHieGTKkB8AMaHx5gqQEHPvy2qnSpA4SG7fImiKkBjn/91ZacqQBUg1ho+rCpAZqmIbBOxKkAMa2Js5bUqQHe8rxu0uipAmhe+e3+/KkAAGNyNR8QqQO11WVMMySpAmgOHzc3NKkDBqbb9i9IqQF9lO+VG1ypABkNphf7bKkDoXJXfsuAqQMbWFfVj5SpAi9tBxxHqKkBcmXFXvO4qQA8//qZj8ypAAflBtwf4KkCO7ZeJqPwqQAY6XB9GAStANPDreeAFK0BmEqWadworQFeR5oILDytApEcQNJwTK0B7+IKvKRgrQL9LoPazHCtAgsrKCjshK0A23WXtviUrQCHH1Z8/KitATqV/I70uK0DHacl5NzMrQHDZGaSuNytAkYnYoyI8K0DV2216k0ArQPT7QikBRStA8tzBsWtJK0DwNVUV000rQC9/aFU3UitA/u9nc5hWK0AuesBw9lorQITJ305RXytA1T40D6ljK0CL7iyz/WcrQNGcOTxPbCtAErzKq51wK0DHaFED6XQrQKRnP0QxeStA3CIHcHZ9K0B3phuIuIErQKue8I33hStAelT6gjOKK0C1q61obI4rQEEfgECikitAI7/nC9WWK0C9LVvMBJsrQC+dUYMxnytAecxCMlujK0AQBafagacrQCoY932lqytAJVysHcavK0BCqkC747MrQI1bLlj+tytAYkbw9RW8K0CHvAGWKsArQOGH3jk8xCtALugC40rIK0DHkOuSVswrQK+lFUtf0CtAuLn+DGXUK0AHyyTaZ9grQONBBrRn3CtAWe0hnGTgK0BxAfeTXuQrQKkTBZ1V6CtARRrMuEnsK0A8aMzoOvArQCeshi4p9CtAD+17ixT4K0BkiC0B/fsrQHsvHZHi/ytAtOXMPMUDLEDP/b4FpQcsQDYXdu2BCyxAxBx19VsPLECQQT8fMxMsQLD+V2wHFyxAQRJD3tgaLEDqeoR2px4sQN13oDZzIixAq4UbIDwmLECNW3o0AiosQJUyQnXFLSxAFKH344UxLECvSyCCQzUsQCDBQVH+OCxATNHhUrY8LEBiXIaIa0AsQKp8tfMdRCxAZXb1lc1HLEAVtsxwekssQIfOwYUkTyxAPHZb1stSLEBOhiBkcFYsQB73lzASWixAmt9IPbFdLECHcrqLTWEsQOO1cx3nZCxAM538831oLEDEVNwQEmwsQAphmnWjbyxAAm++IzJzLECBL9AcvnYsQG9hV2JHeixA0M/b9c19LED/UOXYUYEsQFvD+wzThCxAMgynk1GILECyFW9uzYssQDbN255GjyxAuCF1Jr2SLEABAcMGMZYsQPNWTUGimSxARwuc1xCdLEDr/zbLfKAsQOQOph3moyxAAglx0EynLEC8sx/lsKosQEnIOV0SrixARPBGOnGxLEALxs59zbQsQHDRWCknuCxAv4ZsPn67LEAZRZG+0r4sQGdUTqskwixAWOQqBnTFLEAPCq7QwMgsQOO+XgwLzCxAs97DulLPLEBeJ2Tdl9IsQKUzxnXa1SxA/n1whRrZLEAJXekNWNwsQIEBtxCT3yxA13Vfj8viLECVm2iLAeYsQJYqWAY16SxAoK+zAWbsLEAiigB/lO8sQDzrw3/A8ixAp9SCBer1LEBhFsIREfksQBZOBqY1/CxAr+TTw1f/LEBWDq9sdwItQFbHG6KUBS1AgNSdZa8ILUC8v7i4xwstQB/Z75zdDi1AADPGE/ERLUCsor4eAhUtQLS9W78QGC1Ag9kf9xwbLUAzCY3HJh4tQAYdJTIuIS1A715pODMkLUBLmNvbNSctQPCF/B02Ki1Ajd5MADQtLUDnDk2ELzAtQKAzfasoMy1AkSJddx82LUBUaGzpEzktQLU+KgMGPC1A7o8VxvU+LUBj9awz40EtQCK2bk3ORC1AuMXYFLdHLUCQw2iLnUotQIb5m7KBTS1AeVrvi2NQLUBngd8YQ1MtQFaw6FogVi1AHc+GU/tYLUAVajUE1FstQNWxb26qXi1A53iwk35hLUDbM3J1UGQtQBj3LhUgZy1Ay3ZgdO1pLUB7BYCUuGwtQJ+SBneBby1AkapsHUhyLUDgcyqJDHUtQL2wt7vOdy1AHLuLto56LUBdhh17TH0tQGSc4woIgC1A4h1UZ8GCLUBxwOSReIUtQHfOCowtiC1AhSU7V+CKLUCYNer0kI0tQLMAjGY/kC1AJxmUreuSLUAioXXLlZUtQJlJo8E9mC1Ax1GPkeOaLUCohas8h50tQNg9acQooC1A3105KsiiLUAyVIxvZaUtQLkY0pUAqC1AaCx6npmqLUD8l/OKMK0tQI3rrFzFry1AtT0UFViyLUBcKpe16LQtQI/Soj93ty1AF9ujtAO6LUBbbAYWjrwtQJUwNmUWvy1AqFSeo5zBLUDLhanSIMQtQKPxwfOixi1AIEVRCCPJLUB/rMARocstQMLReBEdzi1A7dvhCJfQLUCWb2P5DtMtQFasZOSE1S1AtC1My/jXLUCPCYCvatotQPnPZZLa3C1AwolidUjfLUD9uNpZtOEtQHpXMkEe5C1ARNbMLIbmLUBcHQ0e7OgtQK2KVRZQ6y1A2vEHF7LtLUAem4UhEvAtQFRDLzdw8i1AwRplWcz0LUDixIaJJvctQO9X88h++S1Ar1sJGdX7LUCwySZ7Kf4tQDkMqfB7AC5AG/7seswCLkBj6U4bGwUuQN6HKtNnBy5AugHbo7IJLkCq7bqO+wsuQN9PJJVCDi5Ar5lwuIcQLkBAqfj5yhIuQLHIFFsMFS5AS64c3UsXLkANe2eBiRkuQBm7S0nFGy5AaGQfNv8dLkCq1zdJNyAuQNXd6YNtIi5A5KmJ56EkLkCN1mp11CYuQNpm4C4FKS5A08U8FTQrLkBfxdEpYS0uQC+e8G2MLy5AzO/p4rUxLkAavw2K3TMuQFp3q2QDNi5AnugRdCc4LkA0SI+5STouQF0vcTZqPC5AnZsE7Ig+LkDU7pXbpUAuQKHtcAbBQi5Atr/gbdpELkAFJzAT8kYuQCqiqPcHSS5AQbeTHBxLLkATGjqDLk0uQCDk4yw/Ty5AkYLYGk5RLkAKx15OW1MuQOvnvMhmVS5AB3o4i3BXLkCLchaXeFkuQEUmm+1+Wy5A4kkKkINdLkAB8KZ/hl8uQD2Ls72HYS5Au+xxS4djLkDCDSMqhWUuQN/nB1uBZy5AXSZg33tpLkAQEWu4dGsuQAlUZ+drbS5ABfKSbWFvLkAeSitMVXEuQO4YbYRHcy5A+3aUFzh1LkCX2NwGJ3cuQGkPgVMUeS5AOUe7/v96LkDfCMUJ6nwuQC0413XSfi5A3xMqRLmALkAKNvV1noIuQO6TbwyChC5A3nzPCGSGLkBPm0psRIguQEz0FTgjii5AIeZlbQCMLkBwKm4N3I0uQOHTYRm2jy5Agk9zko6RLkCaY9R5ZZMuQGowttA6lS5AUC9JmA6XLkBCM73R4JguQO5nQX6xmi5ATVIEn4CcLkCZ0DM1Tp4uQKUZ/UEaoC5ARb2MxuShLkC2og7EraMuQIQLrjt1pS5AVpCVLjunLkBXI++d/6guQBIO5IrCqi5A1PKc9oOsLkDcy0HiQ64uQJvr+U4CsC5Ao/zrPb+xLkDiAD6werMuQO9SFac0tS5ABaWWI+22LkCMAOYmpLguQODHJrJZui5ADrR7xg28LkBx1gZlwL0uQIuX6Y5xvy5ACbhERSHBLkCqTziJz8IuQFbO41t8xC5AFvtlvifGLkBF9Nyx0ccuQH8wZjd6yS5A3X0eUCHLLkC40CH9xswuQOQGjD9rzi5AzsR3GA7QLkDNNf+Ir9EuQODdO5JP0y5A6JVGNe7ULkC9jDdzi9YuQA9RJk0n2C5Ac8UpxMHZLkBgIljZWtsuQGr5xo3y3C5AjTOL4ojeLkApEbnYHeAuQGcqZHGx4S5A7m+frUPjLkBYKX2O1OQuQG73DhVk5i5AZdFlQvLnLkChB5IXf+kuQGxCo5UK6y5AA4KovZTsLkDmHrCQHe4uQPXJxw+l7y5AR4z8OyvxLkCJx1oWsPIuQPo17p8z9C5AderB2bX1LkBlUODENvcuQK0rU2K2+C5AxJkjszT6LkCIEFq4sfsuQLde/nIt/S5ApawX5Kf+LkDVe6wMIQAvQCinwu2YAS9AEmJfiA8DL0DuOofdhAQvQOEYPu74BS9AAT2Hu2sHL0AVQmVG3QgvQDYd2o9NCi9AeB3nmLwLL0DZ64xiKg0vQMKMy+2WDi9Avl2iOwIQL0CGFxBNbBEvQJrNEiPVEi9AcO6nvjwUL0CUQ8wgoxUvQDjxe0oIFy9AGXeyPGwYL0CssGr4zhkvQKnUnn4wGy9AmXVI0JAcL0C5gWDu7x0vQMND39lNHy9A7mG8k6ogL0B03+4cBiIvQGwbbXZgIy9AMNIsobkkL0BEHCOeESYvQNtvRG5oJy9Ai5+EEr4oL0Dn29aLEiovQISyLdtlKy9AwA57AbgsL0AQOrD/CC4vQE/bvdZYLy9AKPiTh6cwL0Aw9CET9TEvQJyRVnpBMy9AnvEfvow0L0DEk2vf1jUvQIhXJt8fNy9AfXo8vmc4L0Dbmpl9rjkvQNq1KB70Oi9AcijUoDg8L0BrsIUGfD0vQLlqJlC+Pi9AfNWefv8/L0CFz9aSP0EvQPuXtY1+Qi9AM88hcLxDL0A7dwE7+UQvQPjyOe80Ri9AZQewjW9HL0Av20cXqUgvQPD25IzhSS9AAUZq7xhLL0DGFbo/T0wvQMsWtn6ETS9ACVw/rbhOL0DpWzbM608vQIHwetwdUS9A41bs3k5SL0BsMGnUflMvQGyCz72tVC9Aarb8m9tVL0Acms1vCFcvQJlgHjo0WC9AZqHK+15ZL0BNWa21iFovQPfqoGixWy9A1x1/FdlcL0BpICG9/10vQMWFX2AlXy9AfUgSAEpgL0CryRCdbWEvQNfQMTiQYi9AjoxL0rFjL0AqkzNs0mQvQP7hvgbyZS9AZt7BohBnL0CnVRBBLmgvQFF9feJKaS9AOxvch2ZqL0D55v0xgWsvQD54tOGabC9ANq7Ql7NtL0B/ySJVy24vQEp0ehriby9AtMam6PdwL0BMP3bADHIvQLbKtqIgcy9AKr81kDN0L0A73r+JRXUvQGRUIZBWdi9AT7olpGZ3L0A0FJjGdXgvQNWrQviDeS9A8KrvOZF6L0DjNGiMnXsvQAr2dPCofC9AfBXeZrN9L0CFJmvwvH4vQM4m443Ffy9AbIIMQM2AL0BvE60H1IEvQCUiiuXZgi9AQGZo2t6DL0BtBQzn4oQvQG2VOAzmhS9APhuxSuiGL0BXCzij6YcvQP9KjxbqiC9A3C54pemJL0A0fbNQ6IovQDxsARnmiy9AzKMh/+KML0AuPdMD340vQDPD1Cfaji9ArjLka9SPL0CJ+r7QzZAvQLj8IVfGkS9AS43J/72SL0DWc3HLtJMvQBvr1LqqlC9A4aGuzp+VL0ANurgHlJYvQDrKrGaHly9An91D7HmYL0C9czaZa5kvQHWBPG5cmi9An3ENbEybL0BUImCTO5wvQPLp6uQpnS9A0ZNjYReeL0CTYn8JBJ8vQG4O893vny9Aqsdy39qgL0AVNbIOxaEvQHd1ZGyuoi9AWh88+ZajL0BdQeu1fqQvQFliI6NlpS9AiYKVwUumL0A6GvIRMacvQLUb6ZQVqC9Ap/IpS/moL0AyhGM13KkvQEkwRFS+qi9Ao9B5qJ+rL0BVurEygKwvQMG8mPNfrS9ATiPb6z6uL0AZtCQcHa8vQIqPIIX6ry9AIrl5J9ewL0B4SdoDs7EvQCz46xqOsi9AX/pXbWizL0BSAMf7QbQvQIc34cYatS9AFVFOz/K1L0BzeLUVyrYvQLtXvZqgty9ATBgMX3a4L0CYYUdjS7kvQIxbFKgfui9AoqwXLvO6L0Dte/X1xbsvQINwUQCYvC9APrLOTWm9L0AP6Q/fOb4vQM4+t7QJvy9AJF5mz9i/L0CRc74vp8AvQMUtYNZ0wS9AFr3rw0HCL0As1QD5DcMvQN6rPnbZwy9AZfpDPKTEL0By/a5LbsUvQDl1HaU3xi9ATKYsSQDHL0DtWHk4yMcvQIvan3OPyC9AKf07+1XJL0DYF+nPG8ovQJoHQvLgyi9AWS7hYqXLL0ABdWAiacwvQB1KWTEszS9ADaNkkO7NL0Ak/BpAsM4vQBtZFEFxzy9Ar0TokzHQL0DR0S058dAvQCWbezGw0S9ATcRnfW7SL0Co+IcdLNMvQFBtcRLp0y9AbuC4XKXUL0BtmfL8YNUvQN1psvMb1i9A/6yLQdbWL0ApSRHnj9cvQI2u1eRI2C9ACdlqOwHZL0BkT2LruNkvQA0kTfVv2i9AKfW7WSbbL0BA7T4Z3NsvQCXDZTSR3C9AXrq/q0XdL0CVo9t/+d0vQFncR7Gs3i9ApFCSQF/fL0AQeUguEeAvQJRd93rC4C9A7pMrJ3PhL0DbQHEzI+IvQNwXVKDS4i9A/VtfboHjL0C93x2eL+QvQNEFGjDd5C9Am8DdJIrlL0BHk/J8NuYvQJCR4Tji5i9AeWAzWY3nL0AfNnDeN+gvQF3aH8nh6C9AoabJGYvpL0AJh/TQM+ovQJH5Ju/b6i9AYQ/ndIPrL0BtbLpiKuwvQIpIJrnQ7C9AA26veHbtL0ALPNqhG+4vQKmlKjXA7i9AITIkM2TvL0Cd/UmcB/AvQHa5HnGq8C9AqqskskzxL0DesN1f7vEvQHk6y3qP8i9A9VBuAzDzL0BLkkf6z/MvQKkz119v9C9AVwGdNA71L0DLXhh5rPUvQENGyC1K9i9ATUsrU+f2L0AjmL/pg/cvQBjwAvIf+C9A7a5ybLv4L0CWyYtZVvkvQN/Nyrnw+S9A6OKrjYr6L0BNyqrVI/svQF3fQpK8+y9AGxfvw1T8L0ANAipr7PwvQAjLbYiD/S9ADzg0HBr+L0DuqvYmsP4vQGsgLqlF/y9ALTJTo9r/L0CbCu+KNwAwQMhNo4CBADBA9hkCM8sAMEBtAkeiFAEwQJFArc5dATBAGwVwuKYBMEAVQ8pf7wEwQAC+9sQ3AjBA9gMw6H8CMED6dLDJxwIwQHBAsmkPAzBAaGVvyFYDMEB6syHmnQMwQKHHAsPkAzBAgBFMXysEMEC/zza7cQQwQG8R/Na3BDBATbbUsv0EMEDRYPlOQwUwQP+soquIBTBA794Iyc0FMEAtFWSnEgYwQNBF7EZXBjBAMTbZp5sGMEBefGLK3wYwQLd/v64jBzBAonknVWcHMEDpdNG9qgcwQGBO9OjtBzBAlbTG1jAIMEDAKH+HcwgwQL39U/u1CDBAVll7MvgIMECLMystOgkwQBtXmet7CTBAYWH7bb0JMEAHw4a0/gkwQDa/cL8/CjBAtGzujoAKMEBntTQjwQowQLRWeHwBCzBAUOHtmkELMED5ucl+gQswQBIZQCjBCzBA1AqFlwAMMECXb8zMPwwwQP77Sch+DDBACTkxir0MMEDEg7US/AwwQF4OCmI6DTBAKt9heHgNMEC50e9Vtg0wQH+W5vrzDTBA/7J4ZzEOMECAgdibbg4wQAAyOJirDjBAFcrJXOgOMECtJL/pJA8wQHLyST9hDzBAyLmbXZ0PMEBY1+VE2Q8wQHh9WfUUEDBA3rQnb1AQMECLXIGyixAwQO4pl7/GEDBAMqmZlgERMEDEPLk3PBEwQEMeJqN2ETBAC14Q2bARMEC546fZ6hEwQLBtHKUkEjBAHJKdO14SMEAPvlqdlxIwQJQ2g8rQEjBAPQxGwwkTMEAzS9KHQhMwQJKzVhh7EzBAjOkBdbMTMECSaQKe6xMwQAiHhpMjFDBAJG+8VVsUMEDmJ9LkkhQwQEaP9UDKFDBA/VtUagEVMECaHRxhOBUwQKY8eiVvFTBAy/qbt6UVMECscq4X3BUwQFKY3kUSFjBAazlZQkgWMEAU/UoNfhYwQLhj4KazFjBAB8hFD+kWMEBOXqdGHhcwQAA1MU1TFzBAdDQPI4gXMEBwH23IvBcwQBKTdj3xFzBA5gZXgiUYMEDxzDmXWRgwQAQSSnyNGDBA1N2yMcEYMECrEp+39BgwQD5uOQ4oGTBARImsNVsZMECF1yIujhkwQHyoxvfAGTBArybCkvMZMECrWD//JRowQIYgaD1YGjBAITxmTYoaMEAhRWMvvBowQIexiOPtGjBARtP/aR8bMECy2PHCUBswQCLMh+6BGzBA25Tq7LIbMEB19kK+4xswQFyRuWIUHDBAr+J22kQcMEBqRKMldRwwQLrtZkSlHDBAzvLpNtUcMEATRVT9BB0wQFGzzZc0HTBAzel9BmQdMEBNcoxJkx0wQCq0IGHCHTBAtvRhTfEdMED5VncOIB4wQBnch6ROHjBARGO6D30eMEDMqTVQqx4wQFlLIGbZHjBA3MGgUQcfMEAFZt0SNR8wQB5v/KliHzBA3PIjF5AfMEAO5nlavR8wQEIcJHTqHzBAJ0hIZBcgMECi+wsrRCAwQNKnlMhwIDBAw5wHPZ0gMEB4CoqIySAwQFgAQav1IDBAQm1RpSEhMEAFIOB2TSEwQCPHESB5ITBATPEKoaQhMED3DPD5zyEwQOVo5Sr7ITBAAzQPNCYiMECpfZEVUSIwQMs1kM97IjBAkiwvYqYiMEDwEpLN0CIwQMN63BH7IjBAc9YxLyUjMECuebUlTyMwQOiYivV4IzBA5EnUnqIjMECSg7UhzCMwQCAeUX71IzBAWdPJtB4kMECUPkLFRyQwQJbc3K9wJDBA3Qu8dJkkMEAKDQIUwiQwQFIC0Y3qJDBAJvBK4hIlMED2vJEROyUwQFYxxxtjJTBAPfgMAYslMEArn4TBsiUwQOOVT13aJTBAxi6P1AEmMEARn2QnKSYwQHr+8FVQJjBA20dVYHcmMED3WLJGniYwQGnyKAnFJjBAHrjZp+smMEBEMeUiEicwQDvIa3o4JzBACcuNrl4nMEDaamu/hCcwQO+8JK2qJzBAu7nZd9AnMEC9Paof9icwQGYJtqQbKDBAVMocB0EoMEAH9v1GZigwQFcCeWSLKDBA4kGtX7AoMEDp6Lk41SgwQDIQvu/5KDBAGLbYhB4pMEAUvSj4QikwQIHszElnKTBAb/DjeYspMEDTWoyIrykwQAag5HXTKTBAxxsLQvcpMED7DR7tGiowQEGcO3c+KjBAx8eB4GEqMEBRkw4phSowQP/K/1CoKjBA5iZzWMsqMEAaSoY/7iowQOq6VgYRKzBAjuUBrTMrMEAeHKUzViswQKaWXZp4KzBAPXNI4ZorMEBXtoIIvSswQFxKKRDfKzBAGQBZ+AAsMEAMjy7BIiwwQMOUxmpELDBAppU99WUsMEC2/K9ghywwQKMbOq2oLDBAISv42sksMEB6Sgbq6iwwQFaAgNoLLTBACbqCrCwtMEBRzChgTS0wQA9zjvVtLTBAnlHPbI4tMECQ8gbGri0wQN7HUAHPLTBAWCvIHu8tMEBBXogeDy4wQIiJrAAvLjBAJr5PxU4uMECg9Ixsbi4wQM4Nf/aNLjBAQdJAY60uMECx8uyyzC4wQEEInuXrLjBAmpRu+wovMEBzAHn0KS8wQGOe19BILzBAD6mkkGcvMEBzQ/ozhi8wQE958rqkLzBAQj+nJcMvMECncjJ04S8wQNfZrab/LzBA/iMzvR0wMEBa6du3OzAwQEmrwZZZMDBAe9T9WXcwMEDMuKkBlTAwQHiV3o2yMDBAEZG1/s8wMEC7u0dU7TAwQGIPro4KMTBAHG8BricxMEDzn1qyRDEwQE5p0pthMTBAp2OBan4xMEC5GYAemzEwQBgA57e3MTBApnTONtQxMEA6wU6b8DEwQAAZgOUMMjBAupl6FSkyMEAwS1YrRTIwQKgfKydhMjBAGPQQCX0yMEAAkB/RmDIwQH6lbn+0MjBAg9EVFNAyMEC8myyP6zIwQL52yvAGMzBAFcAGOSIz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\"},\"shape\":[2000],\"dtype\":\"float64\",\"order\":\"little\"}]]}}},\"view\":{\"type\":\"object\",\"name\":\"CDSView\",\"id\":\"p2408\",\"attributes\":{\"filter\":{\"type\":\"object\",\"name\":\"AllIndices\",\"id\":\"p2409\"}}},\"glyph\":{\"type\":\"object\",\"name\":\"Line\",\"id\":\"p2404\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"x\"},\"y\":{\"type\":\"field\",\"field\":\"y\"},\"line_color\":\"#deebf7\",\"line_width\":2}},\"nonselection_glyph\":{\"type\":\"object\",\"name\":\"Line\",\"id\":\"p2405\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"x\"},\"y\":{\"type\":\"field\",\"f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       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
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       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
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   "source": [
    "# Our beta range\n",
    "beta = np.logspace(0, 5, 2000)\n",
    "\n",
    "# Color according to n\n",
    "colors = bokeh.palettes.Blues9[1:-1]\n",
    "\n",
    "def fixed_point(beta, n, rho):\n",
    "    \"\"\"Function derived in technical appendix 9c for finding fixed point.\"\"\"\n",
    "    def _root_function(x):\n",
    "        return beta - (x - beta * rho) * (1 + x ** n)\n",
    "\n",
    "    return scipy.optimize.brentq(_root_function, 0, beta * (1 + rho))\n",
    "\n",
    "\n",
    "def gamma_bifurcation(beta, n, rho):\n",
    "    # Initialize gamma\n",
    "    gamma = np.empty_like(beta)\n",
    "\n",
    "    for i, b in enumerate(beta):\n",
    "        x0 = fixed_point(b, n, rho)\n",
    "\n",
    "        f0 = -b * n * x0 ** (n - 1) / (1 + x0 ** n) ** 2\n",
    "        \n",
    "        if f0 < -2:\n",
    "            gamma[i] = np.nan\n",
    "        else:\n",
    "            xi = np.sqrt(3 * f0 ** 2 / (4 + 2 * f0))\n",
    "\n",
    "            if xi < 2:\n",
    "                gamma[i] = np.nan\n",
    "            else:\n",
    "                gamma[i] = (xi + np.sqrt(xi ** 2 - 4)) ** 2 / 4\n",
    "\n",
    "    return gamma\n",
    "\n",
    "p = bokeh.plotting.figure(\n",
    "    width=400,\n",
    "    height=300,\n",
    "    x_axis_label=\"β\",\n",
    "    y_axis_label=\"γ\",\n",
    "    x_axis_type=\"log\",\n",
    "    y_axis_type=\"log\",\n",
    "    y_range=[1.3, 1e3],\n",
    "    x_range=[1, 1e5],\n",
    ")\n",
    "\n",
    "# Make the plots\n",
    "for n, color in zip([1.5, 1.75, 1.95, 2, 2.05, 2.5, 3][::-1], colors):\n",
    "    gamma = gamma_bifurcation(beta, n, 0)\n",
    "    p.line(beta, gamma, line_width=2, color=color, legend_label=f\"n = {n}\")\n",
    "\n",
    "bokeh.io.show(p)"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also see how leaky expression will affect the phase diagram. We will set $n = 2$ and make a plot of the phase boundary for different values of $\\rho$. As you can see in this plot, leaky transcription kills the oscillations roughly when the amount of leaky protein production becomes sufficient to shut off expression of the next repressor. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-05T00:52:08.092256Z",
     "start_time": "2019-06-05T00:52:07.722315Z"
    },
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
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mLvS9Ur87QHKwGwpa6DtAvrnqAJ4RPEBtnxX8Hjs8QAFb1VXdZDxALE7oaNmOPEAsCJOQE7k8QCcMoSiM4zxAq5hljUMOPUB1cLwbOjk9QESkCjFwZD1A410/K+aPPUBJrNRonLs9QBlR0EiT5z1AII/EKssTPkAM+tBuREA+QH1Ho3X/bD5AGyF4oPyZPkDx9xtRPMc+QBzZ6+m+9D5AkEPWzYQiP0As/1tgjlA/QPv0kAXcfj9A2AgdIm6tP0Az9DwbRdw/QAqRYauwBUBASEaMnWEdQECqzZ6XNTVAQM2BZc0sTUBAolT5ckdlQEDCQMC8hX1AQEm7bd/nlUBAYSYDEG6uQECMRNCDGMdAQHysc3Dn30BAoz3bC9v4QEBzlUSM8xFBQEiFPSgxK0FA+4ikFpREQUAmPqmOHF5BQCXczMfKd0FAuazi+Z6RQUBdhRBdmatBQGJBzym6xUFAqDzrmAHgQUASz4Tjb/pBQMXIEEMFFUJA/+5Y8cEvQkDDeXwopkpCQCCS8CKyZUJAWdGAG+aAQkCtwE9NQpxCQNlZ1/PGt0JAdojpSnTTQkD4q7COSu9CQHEasPtJC0NAL6TEznInQ0D8FyVFxUNDQDjIYpxBYENAohBqEuh8Q0AJ3YLluJlDQKIwUVS0tkNALq7VndrTQ0AAIW4BLPFDQLYG1r6oDkRAthknFlEsRECd3NlHJUpEQFcmxpQlaERAGK8jPlKGREAdnoqFq6REQEwY9Kwxw0RAnc+69uThREBKk5ulxQBFQPrgtfzTH0VAnHaMPxA/RUAj5QWyel5FQDQkbZgTfkVAjSZyN9udRUBdbyrU0b1FQGSoEbT33UVAETkKHU3+RUBm3l1V0h5GQLZDvqOHP0ZAapxFT21gRkCCPnefg4FGQAc+QNzKokZAdQn4TUPERkDzBmE97eVGQIIyqfPIB0dABb1qutYpR0BWrKzbFkxHQCh846GJbkdA1b/xVy+RR0BAxShJCLRHQII4ScEU10dAjciDDFX6R0DizHl3yR1IQBfsPU9yQUhAZ8NU4U9lSEAsj7V7YolIQG3UymyqrUhASAtzAyjSSEBfSgGP2/ZIQFvzPV/FG0lAUGBnxOVASUAjkjIPPWZJQBLgy5DLi0lAGajXmpGxSUBzAHN/j9dJQA9qNJHF/UlAKoQsIzQkSkDZwOaI20pKQJkaaha8cUpAD8s5INaYSkCyAlb7KcBKQJahPP2350pAO/Hpe4APS0CHX9nNgzdLQMQ6BkrCX0tAn27sRzyIS0B1Qokf8rBLQIoYXCnk2UtAXi5nvhIDTEBEXjA4fixMQOrhwfAmVkxAIharQg2ATECrPwGJMapMQEpRYB+U1ExA5LLrYTX/TEC4CU+tFSpNQPIBv141VU1ANhn605SATUBhaklrNKxNQJR6gYMU2E1ASgcDfDUETkC41bu0lzBOQESDJ447XU5AV1dQaSGKTkBMFtCnSbdOQIXV0Ku05E5A8dAN2GIST0CZQdSPVEBPQHY1BDeKbk9AomgRMgSdT0CtHwTmwstPQDwDerjG+k9Ac37TBwgVUEC6CiupzyxQQCUq9XO6RFBAENAvnMhcUECM0CVW+nRQQA5Sb9ZPjVBAtz/yUcmlUEBivOL9Zr5QQEOWww8p11BAPLtmvQ/wUEDWre08GwlRQPv6ycRLIlFARLC9i6E7UUAB09vIHFVRQP3XiLO9blFA4Rt7g4SIUUBKXLtwcaJRQKUxpbOEvFFAq4nnhL7WUUCZIoUdH/FRQB0H1bamC1JABAuDilUmUkCSSJDSK0FSQJSeU8kpXFJAPy96qU93UkC43weunZJSQFXYVxIUrlJAswUdErPJUkB1mmLpeuVSQM6RjNRrAVNAvjJYEIYdU0AslNzZyTlTQKwhi243VlNABSEwDM9yU0CZOPPwkI9TQH32V1t9rFNAU1g+ipTJU0AIVOO81uZTQERh4TJEBFRAsQMxLN0hVEAAVinpoT9UQNeVgKqSXVRAdrBMsa97VEAp0AM/+ZlUQKnqfJVvuFRAMVDw9hLXVEBqO/il4/VUQEZikeXhFFVAkIcb+Q00VUBpDVokaFNVQI2IdKvwclVAiFT30qeSVUC3KNTfjbJVQBauYhej0lVAGBZhv+fyVUAwsvQdXBNWQFmMqnkANFZAZAB4GdVUVkBKVrtE2nVWQE1dPEMQl1ZA8QctXXe4VkAGCSrbD9pWQHRxOwba+1ZA9E7VJ9YdV0DRS9iJBEBXQHRPknZlYldA7x+/OPmEV0BqBIkbwKdXQJdoiWq6yldAAoHJcejtV0BS8MJ9ShFYQJhtYNvgNFhAfWv+16tYWEBkwGvBq3xYQJ5P6uXgoFhAf7MvlEvFWEB96GUb7OlYQD/5K8vCDllAtKuW888zWUA1LzHlE1lZQI/L/fCOfllAH5F2aEGkWUDyCY6dK8pZQOfrr+JN8FlA5cvBiqgWWkDs0SPpOz1aQIZusVEIZFpA8hDCGA6LWkCB3imTTbJaQAJrOhbH2VpAN3LD93oBW0BlkhOOaSlbQNsH+S+TUVtAy2nCNPh5W0D5Zz/0mKJbQKmJwcZ1y1tAmO0cBY/0W0AeC6kI5R1cQEl0QSt4R1xAXJlGx0hxXEAtjZ43V5tcQMDKtdejxVxACvx/Ay/wXEDQwXgX+RpdQL98pHACRl1AeReRbEtxXUAq0lZp1JxdQPMOmcWdyF1ArB+H4Kf0XUDLFN0Z8yBeQIiN5NF/TV5ALol1aU56XkCEOfdBX6deQLvWYL2y1F5AQnQ6PkkCX0AA150nIzBfQMJMN91AXl9A8IRGw6KMX0BVap8+SbtfQIT+qrQ06l9AFBu0xbIMYEBka7YUbiRgQOAq83pMPGBAS1xNLE5UYEBJu/Rcc2xgQL4tZkG8hGBABzZsDimdYECnZR/5ubVgQLnQ5jZvzmBABYJ4/UjnYEDE79mCRwBhQBJxYP1qGWFA+rOxo7MyYUBdNMSsIUxhQFiz30+1ZWFAdq+dxG5/YUCP3elCTplhQFSiAgNUs2FAm4x5PYDNYUBC0DMr0+dhQPzBagVNAmJAolOsBe4cYkBWkdtltjdiQFYfMWCmUmJAl7g7L75tYkD2reAN/ohiQGRmXDdmpGJAkd9C5/a/YkB4L4BZsNtiQKEGWcqS92JAJDNrdp4TY0B0JK6a0y9jQNFvc3QyTGNAtFVnQbtoY0DOR5E/boVjQN9vVK1LomNAWDdwyVO/Y0DDzwDThtxjQPe7fwnl+WNAAFrErG4XZEAGbgT9IzVkQM6t1DoFU2RAIU0ppxJxZED+ilaDTI9kQKo/ERGzrWRAZGtvkkbMZEA8xuhJB+tkQHFQV3r1CWVAzuP3ZhEpZUDQxWpTW0hlQK86tIPTZ2VANxk9PHqHZUBuX9PBT6dlQELIqllUx2VA6GFdSYjnZUAqJezW6wdmQJaNv0h/KGZAjDKo5UJJZkA3Yd/0NmpmQES3B75bi2ZAxb4tibGsZkC0isieOM5mQINUukfx72ZAihpRzdsRZ0BpP0d5+DNnQDEqxJVHVmdAxOdcbcl4Z0DUzBRLfptnQAEZXnpmvmdA2JoaR4LhZ0DJVJz90QRoQBUjpupVKGhAj2JsWw5MaECemJWd+29oQOsbO/8dlGhALr7pznW4aEDxdqJbA91oQEsP2/TGAWlAqM5+6sAmaUBgKO+M8UtpQKdqBC1ZcWlAJm4OHPiWaUDKRtWrzrxpQIr1mS7d4mlAPRsX9yMJakBSrIFYoy9qQOCliaZbVmpAccNaNU19akAHNp1ZeKRqQCBcdmjdy2pAz3qJt3zzakDwd/icVhtrQECVZG9rQ2tA5Czvhbtra0Cubjo4R5RrQKUeat4OvWtAo1Qk0RLma0ADPZJpUw9sQIPaYAHROGxAEMnB8otibEAHAmyYhIxsQEyhnE27tmxApKsXbjDhbEAy1ihW5AttQBxPpGLXNm1AXYfn8AlibUCY/dlefI1tQHAK7govuW1Asa0hVCLlbUDeXP+ZVhFuQNzSnjzMPW5A4+ClnINqbkBvQEkbfZduQMpmTRq5xG5AalkH/DfybkDAg10j+h9vQDeOyPP/TW9AbDZU0Ul8b0C2KKAg2KpvQL7a4Ear2W9A2jPw1GEEcEC8tv/XEBxwQJP1GuDiM3BAexAKIdhLcECruOHO8GNwQLKhAx4tfHBARfMeQ42UcEDeuzBzEa1wQN1jhOO5xXBAZCG0yYbecEDgbKlbePdwQD52nc+OEHFAuZoZXMopcUCA2/c3K0NxQOZUY5qxXHFAUbbYul12cUDYuibRL5BxQJWibhUoqnFAr6wkwEbEcUD8kRAKjN5xQJQATiz4+HFA2BdNYIsTckBc5dLfRS5yQHfi+eQnSXJAlHIyqjFkckBAYkNqY39yQNpmSmC9mnJANJ+8xz+2ckDCFGfc6tFyQJg9b9q+7XJAMX9T/rsJc0DuseuE4iVzQEulaasyQnNA/6RZr6xec0C6/qLOUHtzQLSIiEcfmHNACimpWBi1c0DfXQBBPNJzQEjG5j+L73NA7asSlQUNdECpjZiAqyp0QMCq60J9SHRAAo/eHHtmdECxn6NPpYR0QD2pzRz8onRA121Qxn/BdEC2NIGOMOB0QGxaF7gO/3RA2uEshhoedUAQBj88VD11QAXNLh68XHVALZtBcFJ8dUDFxyF3F5x1QEMy33cLvHVAWNjvty7cdUD6bDB9gfx1QD7w5A0EHXZAHki5sLY9dkAX2sGsmV52QJslfEmtf3ZAnV/PzvGgdkDEDg2FZ8J2QKuo8bQO5HZA+y+lp+cFd0Bz07um8id3QOSNNvwvSndA88aD8p9sd0AR9X/UQo93QBlAdu0YsndABiUhiSLVd0CTGqvzX/h3QMs2r3nRG3hAmtU5aHc/eEAqQMkMUmN4QIlVTrVhh3hA9jMtsKareEBR4z1MIdB4QIMAzdjR9HhA5WmcpbgZeUCE7OMC1j55QLHyUUEqZHlAQjMMsrWJeUAFYrCmeK95QCjhVHFz1XlAr3OJZKb7eUDv8FfTESJ6QPn4RBG2SHpAUapQcpNvekBtWPdKqpZ6QGVDMvD6vXpAplB4t4XlekDExL72Sg17QFb+eQRLNXtAyjGeN4Zde0CZJqDn/IV7QEj1dWyvrntAo8aXHp7Xe0AQlABXyQB8QATpLm8xKnxAcKUlwdZTfECbwWynuX18QNISEn3ap3xAWhGqnTnSfECKn1Bl1/x8QAPSqTC0J31AHLniXNBSfUBMK7JHLH59QBiRWU/IqX1A0rGl0qTVfUC4ge8wwgF+QDzxHMogLn5Adb2h/sBafkDZQYAvo4d+QPxKSr7HtH5A2eohDS/ifkAHTrp+2Q9/QFCSWHbHPX9Ae57UV/lrf0Bg+5mHb5p/QBOuqGoqyX9AphOWZir4f0Dt3sbwtxOAQKAoKaF9K4BATTIfeGZDgEDJsKKpcluAQFYz+mmic4BAOpW57fWLgEAlcMJpbaSAQBGPRBMJvYBA52G+H8nVgEDJcf3Ere6AQAbWHjm3B4FAwKmPsuUggUAugg1oOTqBQLLlppCyU4FAeMO7Y1FtgUDd6/0YFoeBQH2JcegAoYFA/pptChK7gUB6bZy3SdWBQMMX/Cio74FAPfbely0KgkB1J+w92iSCQH4JIFWuP4JA9rfMF6pagkDbiprAzXWCQPGViIoZkYJAJyntsI2sgkB8UXZvKsiCQMVaKgLw44JAIFJopd7/gkA6ieiV9huDQE4avRA4OINA02xSU6NUg0Ahu2+bOHGDQKuYNyf4jYNAE3koNeKqg0ADOB0E98eDQNehTdM25YNABf1O4qEChEBJlBRxOCCEQMRB8L/6PYRAu/qSD+lbhEA9XA2hA3qEQJI40LVKmIRAhSWtj762hEBgC9dwX9WEQPe04pst9IRATGDHUykThUArUN/bUjKFQI5e6HeqUYVA5Y8EbDBxhUA1p7r85JCFQPm69m7IsIVAF8sKCNvQhUB6V68NHfGFQKr3A8aOEYZAN/OPdzAyhkAQ20JpAlOGQLcjdeIEdIZARcDoKjiVhkCLvsmKnLaGQNzjrkoy2IZA4Uqas/n5hkBMAvoO8xuHQIGsqKYePodABCDuxHxgh0AfCYC0DYOHQCyMgsDRpYdA8+iINMnIh0D3HpZc9OuHQLGSHYVTD4hAybMD++YyiEAfpJ4Lr1aIQBLgtgSseohAgOeHNN6eiEDX58DpRcOIQCZnhXPj54hAIfBtIbcMiUA0v4hDwTGJQGxwWioCV4lAqa7eJnp8iUCO44iKKaKJQJfoRKcQyIlAMLl3zy/uiUDWJQBWhxSKQCOIN44XO4pAJXjyy+BhikCDgoFj44iKQMjfsakfsIpAvSzO85XXikDUI5+XRv+KQKdXbOsxJ4tAa+78RVhPi0DBXpj+uXeLQFMtB21XoItArauT6TDJi0AvuArNRvKLQBN/vHCZG4xAnTx9LilFjEBAAKZg9m6MQDdxFWIBmYxA75MwjkrDjEC3kONA0u2MQJh7otaYGI1AVx1qrJ5DjUB2vcAf5G6NQLXtto5pmo1AblboVy/GjUBChHzaNfKNQPm2J3Z9Ho5AirEriwZLjkBgi1h60XeOQLSCDaXepI5AY9A5bS7SjkC5fF01wf+OQJM1imCXLY9AviVkUrFbj0CQzSJvD4qPQM7ckRuyuI9ApA0SvZnnj0CdAM1cYwuQQJ2O27scI5BAYzVHL/k6kEBYu+7q+FKQQH+Z/SIca5BA5GzsC2ODkECVaIHazZuQQH7I0MNctJBAsEQ9/Q/NkEB9hXi85+WQQCiYgzfk/pBAVGSvpAUYkUADIp06TDGRQHTQPjC4SpFAga3XvElkkUDErfwXAX6RQG/1lHnel5FAzVHaGeKxkUCDs1kxDMyRQGup8/hc5pFAVtzcqdQAkkBSi559cxuSQMQIF645NpJALzh6dSdRkkDDDFIOPWySQJsIf7N6h5JAprw4oOCikkCISQ4Qb76SQPPg5j4m2pJA70cCaQb2kkDMWfnKDxKTQOOLvqFCLpNA/3GeKp9Kk0C+Q0CjJWeTQIJipknWg5NAQeAuXLGgk0AaB5QZt72TQLDh7MDn2pNAWcStkUP4k0Dz1qjLyhWUQMOfDq99M5RA7o5ufFxRlEDNird0Z2+UQB59ONmejZRA9OCg6wKslECPUQHuk8qUQNoZzCJS6ZRABMXVzD0IlUCxr1UvVyeVQBma5o2eRpVABDuHLBRmlUCb05pPuIWVQPnD6TuLpZVA3CCiNo3FlUDmSViFvuWVQOyAB24fBpZAEYISN7AmlkDPHEQncUeWQNnNz4ViaJZAz1lSmoSJlkAOadKs16qWQCskwQVczJZAcdH67RHulkBHc8eu+Q+XQHtn25ETMpdAgQdY4V9Ul0CCSczn3naXQJJiNfCQmZdAnGn/RXa8l0Bh+wU1j9+XQFfflAncAphAka1oEF0mmEBxda+WEkqYQJplCer8bZhAknSJWBySmECACrYwcbaYQOGricH72phAOaVzWrz/mEDGt1hLsySZQA/Hk+TgSZlAwIf2dkVvmUA7L8pT4ZSZQFUk0My0uplAC7FCNMDgmUBHtdXcAweaQK5atxmALZpAUcmQPjVUmkC83YafI3uaQL/fOpFLoppAcDrLaK3JmkAzNdR7SfGaQNStcCAgGZtAwNM6rTFBm0Ah5Ex5fmmbQGznQdwGkptAsm82Lsu6m0AtWMnHy+ObQOqFHAIJDZxAlKnVNoM2nEAxAh/AOmCcQFYhqPgvipxAKrCmO2O0nECxNdfk1N6cQD3efVCFCZ1A+0Nn23Q0nUC6OOnio1+dQK2Q48QSi51Are7A38G2nUBdkXeSseKdQJghijziDp5AGIIIPlQ7nkBDoJD3B2ieQEJGT8r9lJ5AFO4AGDbCnkBBlvJCse+eQGGXAq5vHZ9AFHuhvHFLn0Av1NLSt3mfQCsYLlVCqJ9Aq3nfqBHXn0BUYtQZEwOgQL4d8S3AGqBAsro9RJAyoEAkH36Qg0qgQOW7wkaaYqBA1P1om9R6oECjvxvDMpOgQG280/K0q6BAzQLYX1vEoECzaL4/Jt2gQOD/a8gV9qBADosVMCoPoUDN8z+tYyihQPLAwHbCQaFA5I2+w0ZboUB1grHL8HShQHLLY8bAjqFA8RPy67aooUBN/8t008KhQMCjtJkW3aFA6QXDk4D3oUDRlGKcERKiQL+mU+3JLKJAx/arwKlHokAOI9dQsWKiQMwrl9jgfaJA8fIEkziZokDCvJC7uLSiQPOwAo5h0KJApVx7RjPsokAZNXQhLgijQCgbwFtSJKNAgN+LMqBAo0CIx17jF12jQEcTG6y5eaNAzYP+yoWWo0CP4qJ+fLOjQHOJ/gWe0KNAu+tkoOrto0CVH4eNYgukQLJodA0GKaRAccOaYNVGpEDxcMfH0GSkQPSDJ4T4gqRAim5I10yhpECakBgDzr+kQBrH50l83qRAW/xn7lf9pEDtuK0zYRylQHm1MF2YO6VAZm3Mrv1apUBhssBskXqlQL1AsttTmqVAlVSrQEW6pUADQBzhZdqlQPwB3AK2+qVAJ94o7DUbpkCS9ajj5TumQETgajDGXKZAuUfmGdd9pkArgvznGJ+mQPEu+eKLwKZAlNOSUzDipkDneeuCBgSnQP1OkboOJqdAF0N/RElIp0Beqh1rtmqnQMneQnlWjadAseIzuimwp0B4BKV5MNOnQByDugNr9qdAuTMJpdkZqEAMKJeqfD2oQMdV3GFUYahAHz/DGGGFqEAWnKkdo6moQNkEYb8azqhAF50vTcjyqEBXwNAWrBepQFCvdWzGPKlAIj7GnhdiqUDUg+H+n4epQI6KXt5fralABAFNj1fTqUDR7DVkh/mpQO9dHLDvH6pACiN+xpBGqkAvf1T7am2qQDngFKN+lKpAb5axEsy7qkAxjZqfU+OqQLAEvp8VC6tAwUyJaRIzq0CigOlTSlurQB1ETLa9g6tAeIGg6Gysq0CgKFdDWNWrQHTvYx+A/qtAFxM+1uQnrEB9GuHBhlGsQOSZzTxme6xAvvcJooOlrEBzMiNN38+sQG6nLZp5+qxARdvF5VIlrUAXQxGNa1CtQOMOv+3De61AZPUIZlynrUDFALRUNdOtQKlcERlP/61AZSX/EqorrkBiOOmiRliuQMAFyiklha5ACGMrCUayrkBmXyejqd+uQMcYaVpQDa9AYJItkjo7r0BqjESuaGmvQBhdERPbl69A3sqLJZLGr0DF50BLjvWvQDT3KfVnErBA6Y+/tCsqsEBvUQqYEkKwQGWy/9IcWrBAlf3hmUpysEB9w0AhnIqwQKxM+Z0Ro7BAmAw3Rau7sEA8FXRMadSwQFaLeelL7bBAURtgUlMGsUDobpC9fx+xQFKjw2HROLFAW8ADdkhSsUDyL6wx5WuxQIY2asynhbFAD2w9fpCfsUDGNXh/n7mxQJxAwAjV07FAS/wOUzHusUBMF7KXtAiyQFX7SxBfI7JAqUrU9jA+skAZXpiFKlmyQMTDO/dLdLJAkr64hpWPskBWxmBvB6uyQN8I3eyhxrJAlusuO2XiskDzjbCWUf6yQK9MFTxnGrNAwEVqaKY2s0D23BZZD1OzQJtB3Uuib7NAnPTafl+Ms0CVT4kwR6mzQKMMvp9ZxrNA+M6rC5fjs0BGrOKz/wC0QNK2UNiTHrRAkohCuVM8tEDazmOXP1q0QPjWv7NXeLRAnRvCT5yWtEAM0zatDbW0QDF+Sw6s07RAXHiPtXfytEAeiPTlcBG1QLNwz+KXMLVAZ4TY7+xPtUDMNyxRcG+1QNG1S0sij7VAlnQdIwOvtUBRy+0dE8+1QOSIb4FS77VAYIu8k8EPtkBnWFabYDC2QHi2Jt8vUbZAFUeApi9ytkC6IR85YJO2QPFvKd/BtLZAEQow4VTWtkACFS+IGfi2QOagjh0QGrdArkgj6zg8t0Cc0i47lF63QJXRYFgigbdAqUfXjeOjt0A5SR8n2Ma3QEahNXAA6rdAlnaHtVwNuEDl8fJD7TC4QN/kx2iyVLhAXHLIcax4uEBKtymt25y4QLp0lGlAwbhA27ol9trluED5lG+iqwq5QHa2eb6yL7lAryjCmvBUuUAa+j2IZXq5QCvuWdgRoLlAWy773PXFuUAs/H/oEey5QDpkwE1mErpAWvIOYPM4ukCcZjlzuV+6QKNridu4hrpAxE3F7fGtukBWszD/ZNW6QAtWjWUS/bpAZr0bd/oku0AX+puKHU27QMNiTvd7dbtAjVH0FBaeu0Dk4tA77Ma7QGC1qcT+77tAvarHCE4ZvED3qfdh2kK8QGRiiyqkbLxAOBBavauWvEDgQcF18cC8QKeepa9167xAda5zxzgWvUDAoiAaO0G9QKAgKwV9bL1A8guc5v6XvUDwUwcdwcO9QKzAjAfE771A38HYBQgcvkDiPiV4jUi+QOFnOr9Udb5AQ4hvPF6ivkAj2qtRqs++QFZbZ2E5/b5AWqOrzgsrv0CmuhT9IVm/QDPz0VB8h79AUMKmLhu2v0CIm+v7/uS/QJNmRw8UCsBA1a6KfsshwEBU9U3/pTnAQHjEa8WjUcBAAlMLBcVpwEBv9aDyCYLAQOiP7sJymsBAFQkEq/+ywEBdvT/gsMvAQPTyTpiG5MBAhE4uCYH9wECQSCppoBbBQHuj3+7kL8FAM+I70U5JwUCvv31H3mLBQPumNYmTfMFA/ytGzm6WwUACheROcLDBQNgEmUOYysFAuZU/5ebkwUD0NAhtXP/BQC5vdxT5GcJAct1mFb00wkDuogWqqE/CQHLr2Ay8asJArGq8ePeFwkAD3OIoW6HCQGqD1ljnvMJAtK55RJzYwkDNNwcoevTCQKUHE0CBEMNA3pmKybEsw0BHgbUBDEnDQPzsNSaQZcNAfy4JdT6Cw0BrQIgsF5/DQOlNaIsavMNAODu70EjZw0B5LvA7ovbDQO4Z1AwnFMRAS0aSg9cxxECW3rTgs0/EQCF8JWW8bcRAx7MtUvGLxED9o3fpUqrEQD2DDm3hyMRA+i9fH53nxEChwDhDhgbFQDcVzRudJcVAGWmx7OFExUAF5t75VGTFQAY4s4f2g8VAvSHx2sajxUA2EsE4xsPFQOa6seb048VAOae4KlMExkBY1DJL4STGQDxK5Y6fRcZAsbX9PI5mxkC1AhOdrYfGQIH4Jff9qMZApNWhk3/KxkDa7Vy7MuzGQApImbcXDsdApT0F0i4wx0ABG7xUeFLHQBXARor0dMdA4kKcvaOXx0AIkiI6hrrHQBQZr0uc3cdA+GSHPuYAyEAJymFfZCTIQOUKZvsWSMhAy/8tYP5ryEC+P8bbGpDIQM/JrrxstMhA56/bUfTYyECZwrXqsf3IQFk9G9elIslAk3RgZ9BHyUDRg1DsMW3JQNT9LbfKkslA1pyzGZu4yUBa9BRmo97JQA8k/+7jBMpAHouZB10rykBpfYYDD1LKQN344zb6eMpAyVxM9h6gykBdIdeWfcfKQM+QGW4W78pAmYEn0ukWy0AmEZQZ+D7LQIZgcptBZ8tAN1FWr8aPy0AGRFWth7jLQBjYBu6E4ctAtKuFyr4KzEAhHnCcNTTMQAkS6b3pXcxA7LGYiduHzECuNK1aC7LMQFOk24x53MxA5aRgfCYHzUAnPQGGEjLNQHSgCwc+Xc1AHvlXXamIzUDyNEnnVLTNQOXRzQNB4M1A86xgEm4MzkA60Qlz3DjOQPBIX4aMZc5Aiu+FrX6SzkByRDJKs7/OQPY/qb4q7c5AbijBbeUaz0BKaeK640jPQGBrCAomd89AzG3Cv6ylz0ARYTRBeNTPQDbhC3rEAdBAwzxen28Z0EAu2wTEPTHQQJJovxsvSdBAxhWa2kNh0EA5Ce40fHnQQBHRYV/YkdBATNXpjliq0EAwy8j4/MLQQMsokNLF29BAhZkgUrP00EBbc6qtxQ3RQGcsrhv9JtFAqtH80llA0UDmfbgK3FnRQMjRVPqDc9FAKWyX2VGN0UBwY5jgRafRQJy/wkdgwdFAofTUR6Hb0UAYXuEZCfbRQPy6TveXENJAwqrYGU4r0kCSKpC7K0bSQKMT3BYxYdJAS5p5Zl580kBrzXzls5fSQCoXUc8xs9JA0L25X9jO0kAHZtLSp+rSQDOVD2WgBtNABTU/U8Ii00DHF4naDT/TQP58bziDW9NAe5fPqiJ400B/E+Jv7JTTQFKeO8bgsdNAAm7N7P/O00BYyuUiSuzTQJaWMKi/CdRAk9u3vGAn1EBOU+SgLUXUQJn0fZUmY9RAG4Cs20uB1EAJDvi0nZ/UQFOcSWMcvtRARJ7rKMjc1EBAjIpIofvUQBB1NQWoGtVAXI9eotw51UBkzNtjP1nVQKFr543QeNVAwI4gZZCY1UAuz4suf7jVQL/Tky+d2NVA+OcJrur41UB8kybwZxnWQNsyijwVOtZAPJE92vJa1kBvgrIQAXzWQKd+xCdAndZAQT65Z7C+1kBMV0EZUuDWQDLbeIUlAtdAyfXn9Sok10BXjYO0YkbXQPnirQvNaNdAwTQ3RmqL10DrX16vOq7XQN2E0ZI+0ddAS6uuPHb010DYZ4T54RfYQKqCUhaCO9hAbp6K4FZf2EAg4RCmYIPYQOycPLWfp9hA7frYXBTM2EATpiXsvvDYQJN317KfFdlAWCQZAbc62UDh6osnBWDZQP1CSHeKhdlAsI3eQUer2UCsxlfZO9HZQNA2NpBo99lAIid2uc0d2kCnlY6oa0TaQFzqcbFCa9pAMK6OKFOS2kAdQtBinbnaQPWXn7Uh4dpAOOzjduAI20BpgAP92TDbQFdX5J4OWdtAivHss36B20C9CwWUKqrbQI1dlpcS09tA0VmNFzf820Af8FltmCXcQMhO8PI2T9xA4KbJAhN53EBs8OT3LKPcQLOwxy2FzdxAwcB+ABz43ECwFZ/M8SLdQBuKRu8GTt1AG6gcxlt53UBbdVOv8KTdQF0/qAnG0N1A8WlkNNz83UDfPV6PMyneQHS5+XrMVd5APGIpWKeC3kBLF2+IxK/eQL3l3G0k3d5AWt0Va8cK30CS507jrTjfQKeeTzrYZt9AyCZz1EaV30B0CKkW+sPfQGULdmby8t9A6In6FBgR4EDi/+vj2SjgQINDtNO+QOBAh41EGMdY4ECK5Nrl8nDgQLiOAnFCieBAvoOU7rWh4EAB4LeTTbrgQN1X4pUJ0+BA/qvYKurr4EBdHq+I7wThQI7nyeUZHuFASK3deGk34UAE+e943lDhQOSvVx15auFAw4q9nTmE4UBGjxwyIJ7hQLOJwhItuOFADodQeGDS4UCEULubuuzhQOfmS7Y7B+JAjf+fAeQh4kBCgaq3szziQGMCtBKrV+JAr0dbTcpy4kBrw5WiEY7iQOMVsE2BqeJA/Y1OihnF4kAyq22U2uDiQK+fYqjE/OJAmNPbAtgY40ATaeHgFDXjQKnA1X97UeNADv91HQxu40D/ktr3xorjQJS8d02sp+NAuRQeXbzE40DcFftl9+HjQGOlmadd/+NAgZ3iYe8c5EB+WB3VrDrkQBU88EGWWORAXEZh6at25EDNmtYM7pTkQJAQF+5cs+RAocFKz/jR5EBGmvvywfDkQB3qFZy4D+VAOfXoDd0u5UCdhieML07lQI2D6FqwbeVAN3+nvl+N5UD4T0X8Pa3lQLOkCFlLzeVAzJueGojt5UBJWhuH9A3mQEuk+uSQLuZAc3Yge11P5kCcn9mQWnDmQEdc3G2IkeZAEPJIWuey5kDoTKqed9TmQGic9oM59uZAkvKPUy0Y50CB40RXUzrnQH4lUdmrXOdAyTJeJDd/50B564OD9aHnQCs5SULnxOdAzLKkrAzo50DoQf0OZgvoQOXIKrbzLuhAs8l277VS6EA4Dp0IrXboQOFQzE/ZmuhAAeemEzu/6EBja0Oj0uPoQF9qLU6gCOlA8A5mZKQt6UA70GQ231LpQO8gGBVReOlAvh7mUfqd6UDDQ60+28PpQBgYxS306elA/uT+cUUQ6kATaaZezzbqQPyMgkeSXepA+hnWgI6E6kCecGBfxKvqQH1BXjg00+pADkaKYd766kA0+x0xwyLrQOFc0v3iSutAKqLgHj5z60Bi+wLs1JvrQGBQdb2nxOtAfAD267bt60Cro8bQAhfsQBTMrMWLQOxAusnyJFJq7EA8bmhJVpTsQMbSY46YvuxAKB7CTxnp7EC4TOjp2BPtQGT5w7nXPu1AOSfMHBZq7UAaDQJxlJXtQJPh8RRTwe1A6aizZ1Lt7UBXA+zIkhnuQDD9zJgURu5ALeAWONhy7kBFBRkI3p/uQMWosmomze5Agb5TwrH67kBkyP1xgCjvQCWtRN2SVu9A+5BPaOmE70CGr9l3hLPvQEs2M3Fk4u9AwRAh3cQI8ECEjMFceiDwQNsnBetSOPBAgTHCu05Q8EBBnhsDbmjwQHB6gfWwgPBApVuxxxeZ8ECs07auorHwQIrj699RyvBAwm/5kCXj8EC8tNf3HfzwQCy8zko7FfFAOtN2wH0u8UDgALmP5UfxQKB9z+9yYfFARytGGCZ78UDNDftA/5TxQObEHqL+rvFA3gU1dCTJ8UDJFhXwcOPxQLxJ6k7k/fFAbXk0yn4Y8kDihcibQDPyQE3S0P0pTvJAmsPNKjtp8kBRP5ZddITyQNYrWNHVn/JAtvCYwV+78kBh+DVqEtfyQAUyZQfu8vJAi5S11fIO80Bkog8SISvzQKDttfl4R/NAcJ1Fyvpj80DB87bBpoDzQEvUXR59nfNAvkvqHn6680ArGGkCqtfzQDQyRAgB9fNAmVZDcIMS9EA4kYx6MTD0QCPIpGcLTvRAPUhweBFs9ED8UTPuQ4r0QGunkgqjqPRA/xqUDy/H9EDMHp8/6OX0QEBVfd3OBPVA7CFbLOMj9UDyO8hvJUP1QItAuOuVYvVA4EaD5DSC9UDHdOaeAqL1QNOTBGD/wfVABqhmbSvi9UCWhvwMhwL2QFZuHYUSI/ZASKC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= 0.01\"},\"renderers\":[{\"id\":\"p2852\"}]}},{\"type\":\"object\",\"name\":\"LegendItem\",\"id\":\"p2866\",\"attributes\":{\"label\":{\"type\":\"value\",\"value\":\"\\u03c1 = 0.001\"},\"renderers\":[{\"id\":\"p2863\"}]}},{\"type\":\"object\",\"name\":\"LegendItem\",\"id\":\"p2876\",\"attributes\":{\"label\":{\"type\":\"value\",\"value\":\"\\u03c1 = 0.0001\"},\"renderers\":[{\"id\":\"p2873\"}]}},{\"type\":\"object\",\"name\":\"LegendItem\",\"id\":\"p2886\",\"attributes\":{\"label\":{\"type\":\"value\",\"value\":\"\\u03c1 = 0\"},\"renderers\":[{\"id\":\"p2883\"}]}}]}}]}}],\"callbacks\":{\"type\":\"map\"}}};\n",
       "  const render_items = [{\"docid\":\"4829c6e6-3757-4f9d-b1af-4bbfafc7b77d\",\"roots\":{\"p2800\":\"419a133f-aec2-45a2-8693-84c61cdf45e7\"},\"root_ids\":[\"p2800\"]}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    let attempts = 0;\n",
       "    const timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "p2800"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = bokeh.plotting.figure(\n",
    "    width=400,\n",
    "    height=300,\n",
    "    x_axis_label=\"β\",\n",
    "    y_axis_label=\"γ\",\n",
    "    x_axis_type=\"log\",\n",
    "    y_axis_type=\"log\",\n",
    "    y_range=[1, 1e4],\n",
    "    x_range=[1, 1e5],\n",
    ")\n",
    "\n",
    "for rho, color in zip([1e-2, 1e-3, 1e-4, 0], colors[::2]):\n",
    "    gamma = gamma_bifurcation(beta, 2, rho)\n",
    "    p.line(beta, gamma, line_width=2, color=color, legend_label=f\"ρ = {rho}\")\n",
    "\n",
    "p.legend.location = \"top_left\"\n",
    "\n",
    "bokeh.io.show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The repressilator design objectives\n",
    "\n",
    "Compared to the protein-only analysis, including the mRNAs explicitly provided additional insight. We learned that when the protein and mRNA decay times are comparable, the delay around the loop is effectively longer, reducing the minimum Hill coefficient and promoter strength required for oscillations. From this analysis we can extract several design objectives to maximize the chances of achieving self-sustaining oscillations in an experimental realization of the circuit: \n",
    "\n",
    "1. Low \"leakiness:\" $\\rho \\ll 1$ ⟶ *Use tight, artificial promoters that can be fully repressed.*\n",
    "\n",
    "2. Strong promoters: $\\beta \\gg 1$ ⟶ *Can be achieved using strong promoters derived from phages that produce high protein levels*\n",
    "\n",
    "3. Similar protein and mRNA decay rates $\\gamma\\approx 1$ ⟶ *Destabilize repressors to increase their decay rates to be more comparable to those of mRNA. This can be done by adding destabilizing C-terminal tags based on the ssrA protein degradation system ([Karzai, et al., 2000](https://doi.org/10.1038/75843)).*\n",
    "\n",
    "4. Ultrasensitive repression curves, ideally $n > 1.5$ or $2$, or as large as possible ⟶ *Use intrinsically cooperative repression mechanisms, such as those from phage λ, or those that incorporate multiple binding sites, such as those in the TetR system. The phage λ* $P_R$ *promoter architecture provides a high regulatory range, and can be adapted to work with binding sites for LacI and TetR* ([Lutz and Bujard, 1997](https://doi.org/10.1093/nar/25.6.1203)).\n",
    " \n",
    "In addition, there are also biological design rules as well:\n",
    "\n",
    "5. To minimize toxicity from overexpressing repressors, put the circuit on a low copy plasmid (pSC101)...\n",
    "\n",
    "6. ...But to maximize the readout, put a fluorescent reporter gene on a higher copy number plasmid (ColE1).\n",
    "\n",
    "7. Destabilize the fluorescent protein so that it can track the circuit activity\n",
    "\n",
    "8. Avoid \"read through\" from one operon to the next ⟶ add transcriptional terminators between promoter-repressor units.\n",
    "\n",
    "Based on these considerations, we designed the repressilator as a two plasmid system to be used in an *E. coli* strain deleted for the natural *lac* operon.\n",
    "\n",
    "<div style=\"margin: auto; width: 400px;\">\n",
    "\n",
    "![repressilator plasmids](figs/repressilator_plasmids.png)\n",
    "\n",
    "</div>\n",
    "\n",
    "Here, the repressilator consists of three repressors on the low copy pSC101 plasmid, with TetR additionally repressing a green fluorescent protein reporter on the higher copy ColE1 plasmid. The _lite_ suffix on the repressors signifies that they have a destruction tag to decrease their stability. The _aav_ suffix on the GFP indicates that it incorporates a less active degradation tag variant. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Will it work?\n",
    "\n",
    "It took a long time to design and then construct the repressilator. In addition to the molecular cloning, there were many steps to characterize the repressors and promoters and make sure signals could propagate through sequential repressor cascades. \n",
    "\n",
    "After all of this, there was more than a little uncertainty as to whether the system would, indeed, exhibit self-sustaining oscillations.\n",
    "\n",
    "To see how the circuit behaved, we used time-lapse imaging to record movies of individual repressilator-containing cells growing into microcolonies. (Lacking automated autofocus systems, one author slept near the microscope with an alarm clock to refocus the microscope every hour, all night long, exemplifying another important role for clocks in science and technology.) \n",
    "\n",
    "In [these movies](figs/repressilator_movie.mov), the changing fluorescence intensity in each cell provided a glimpse into the state of the oscillator over time:\n",
    "\n",
    "<div style=\"margin: auto; width: 600px;\">\n",
    "\n",
    "<video src=\"figs/repressilator_movie.mov\" style=\"width: 600px;\" controls>Your browser does not support display of this video.</video>\n",
    "\n",
    "</div>\n",
    "\n",
    "This movie shows both clear oscillations in individual cells, as well as variability among cells in the amplitude, phase, and duration of each pulse. Analyzing these movies was done by manually tracking each cell backwards in time. This would now be done in a more automated fashion.\n",
    "\n",
    "This procedure revealed clear oscillations in most cells, such as this:\n",
    "\n",
    "<div style=\"margin: auto; width: 600px;\">\n",
    "\n",
    "![Repressilator trace](figs/repressilator_trace.png)\n",
    "\n",
    "</div>\n",
    "\n",
    "Analysis of many cells showed a typical repressilator period of 160 ± 40 min (SD, *n* = 63), with a cell division time of ≈50-60 min at 30°C. Sibling cells desynchronized with one another over about two cell cycles (95 ± 10 min). "
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "Evidently, a simple three element negative feedback loop can indeed generate oscillations. But those oscillations are variable. Do the dynamics have to be so variable? Next, we will see that oscillations can be improved dramatically, and even used as a timer to record bacterial growth in the mammalian gut."
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "## Improving the repressilator\n",
    "\n",
    "In Potvin-Trottier et al, [Nature, 2016](https://doi.org/10.1038/nature19841), Johan Paulsson and colleagues asked what accounted for the repressilator's variability and whether it could be further improved (see [Gao & Elowitz, Nature, 2016](https://doi.org/10.1038/nature19478) for a summary of this work).  \n",
    "\n",
    "* **Observation method**. Instead of growth on agarose pads, where waste products can build up and influence cell growth and behavior, they switched to a microfluidic device developed by [Suckjoon Jun](https://jun.ucsd.edu/) termed \"the mother machine,\" ([Wang, et al., 2010](https://doi.org/10.1016/j.cub.2010.04.045))which allows continuous observation of single cells over hundreds of generations by trapping it at the end of a channel (for more information, see [Suckjoon Jun's website](https://jun.ucsd.edu/mother_machine.php)). This revealed that, despite its variability, the original repressilator exhibited self-sustaining oscillations that never terminate.\n",
    "\n",
    "* **Integration of reporter**. Now able to analyze the dynamics in more constant conditions, they found that much of this variation could be attributed to the reporter plasmid itself. Integrating the reporter into the repressilator plasmid reduced this variability, as seen in the [movie](figs/integrated_reporter_movie.mov) and traces below. (In this movie, a cyan fluorescent protein, shown in the blue channel, is expressed at a constant level to allow image analysis, while the mVenus fluorescent protein, shown in the green channel, reports on the state of the repressilator.)\n",
    "\n",
    "<div style=\"margin: auto; width: 300px;\">\n",
    "\n",
    "![reporter integrated repressilator](figs/reporter_integrated_repressilator.png)\n",
    "\n",
    "</div>\n",
    "\n",
    "<div style=\"margin: auto; width: 600px;\">\n",
    "\n",
    "<video src=\"figs/integrated_reporter_movie.mov\" style=\"width: 600px;\" controls>Your browser does not support display of this video.</video>\n",
    "\n",
    "</div>\n",
    "\n",
    "\n",
    "<div style=\"margin: auto; width: 600px;\">\n",
    "\n",
    "![integrated reporter traces](figs/integrated_reporter_traces.png)\n",
    "\n",
    "</div>\n",
    "\n",
    "* **The problem with TetR: It's too good**. A nice property of TetR is that it binds extremely tightly to its operator site. But this tightness of binding creates a problem as well, when one considers the discreteness of molecules and the stochasticity of their removal. For a DNA binding protein with a weaker affinity for its site (higher $K$), de-repression occurs at a relatively high concentration of the repressor, where discreteness can be ignored, and concentration dynamics are approximately continuous, as we have assumed. But when binding is very tight, the timing of de-repression by TetR depends sensitively on when the final molecules of TetR are degraded or diluted from the cell, as shown in the following figure from Potvin-Trottier et al. This leads to variability in the overall period. The authors showed that this effect can be mitigated by inclusion of a \"DNA sponge\"–a plasmid containing extra TetR binding sites. The presence of these extra, competing sites, pushes the effective threshold for de-repression to higher concentrations. (In the original repressilator design the reporter plasmid fortuitously played a similar role.) We will analyze stochasticity in more detailed in coming chapters.\n",
    "\n",
    "\n",
    "\n",
    "<div style=\"margin: auto; width: 400px;\">\n",
    "\n",
    "![TetR decay](figs/TetR_decay.png)\n",
    "\n",
    "</div>"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "* **Eliminating enzymatic degradation makes the repressilator an astonishingly precise clock**. Finally, an additional source of variability stemmed from fluctuations in the enzymatic protein degradation machinery. This source of variation could be circumvented by reducing the amounts of repressor made, or more dramatically by eliminating their degradation altogether, allowing dilution to dominate protein removal. Without protein degradation, oscillation periods stretched out to as much as 14 cell cycles. However, their precision increased dramatically, with a standard deviation in period duration of only about 14%, and an incredibly regular pulse shape and amplitude, as you can see in the image below. In fact, the repressilator became so precise that it would take on average 180 cell cycles to accumulate even a half-period of phase drift! \n",
    "\n",
    "Here, with three colors to allow simultaneous observation of all three repressors, is one of the final repressilator designs as [a movie](figs/repressilator_titration_sponge_movie.mov) and a typical trace.\n",
    "\n",
    "<div style=\"margin: auto; width: 600px;\">\n",
    "\n",
    "<video src=\"figs/repressilator_titration_sponge_movie.mov\" style=\"width: 600px;\" controls>Your browser does not support display of this video.</video>\n",
    "\n",
    "</div>\n",
    "\n",
    "\n",
    "<div style=\"margin: auto; width: 400px;\">\n",
    "\n",
    "![accurate repressilator trace](figs/accurate_repressilator_trace.png)\n",
    "\n",
    "</div>\n",
    "\n",
    "In fact, this is so accurate that you can [see it in a test tube](figs/repressilator_in_a_tube.mp4). Here, the cells have no means of synchronization, yet they stay in phase for multiple oscillations.\n",
    "\n",
    "<div style=\"margin: auto; width: 400px;\">\n",
    "\n",
    "<video src=\"figs/repressilator_in_a_tube.mp4\" style=\"width: 400px;\" controls>Your browser does not support display of this video.</video>\n",
    "\n",
    "</div>\n",
    "\n",
    "The dilution-based repressilator circuit is so precise that even though cells are not synchronized with one another, they stay in sync over many generations of growth. When the cells grow as a colony on a plate, most growth occurs at the leading edge of the colony. Behind that front, cells stop growing, effectively leaving a record of the state of their repressilators at each point in time, somewhat like tree rings, as shown in this image (red, green, and blue channels show fluorescence from three reporters, one expressed with each repressor):\n",
    "\n",
    "\n",
    "<div style=\"width: 450px; margin: auto;\">\n",
    "\n",
    "![repressilator colony](figs/RepressilatorColony.png)\n",
    "    \n",
    "</div>\n",
    "\n",
    "\n",
    "These improvements are summarized in this image:\n",
    "\n",
    "<div style=\"width: 600px; margin: auto;\">\n",
    "\n",
    "![repressilator watch](figs/RepressilatorWatch.png)\n",
    "    \n",
    "</div>\n",
    "\n",
    "\n",
    "This is a great example of a \"less is more\" principle of circuit design. One might have imagined that increasing the precision of the repressilator would require additional regulatory circuitry, but this does not seem to be the case, at least under these well-controlled conditions. As we wrote in an accompanying piece, \"Evidently, precision does not necessarily demand circuit complexity and, in this case, even seems to benefit from minimalism.\" ([Gao and Elowitz, 2016](https://doi.org/10.1038/nature19478)) At the same time, achieving a constant period irrespective of growth conditions will likely require compensation mechanisms, much as early navigational clocks required additional systems to compensate for the effects of varying temperature on clock period ([Love, 2016](https://www.theatlantic.com/technology/archive/2016/01/pendulum-clock-john-harrison/424614/)). "
   ]
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   "source": [
    "## Precision repressilators can record bacterial growth in the mammalian gut\n",
    "\n",
    "Riglar and coworkers took advantage of the incredible precision offered by this circuit to interrogate bacterial growth dynamics in the mammalian gut, which we describe in this section. ([Riglar, et al., 2019](https://doi.org/10.1038/s41467-019-12638-z)).\n",
    "\n",
    "The approach of Riglar and coworkers relied on the ability to read out repressilator **phase** from colony ring phases. More specifically, they found that the **phase** of the repressilator in the initial cell that seeded the colony controls the radial phasing of the rings in the colony. Cells that are plated at the peak of reporter fluorescence produce colonies with a high intensity \"dot\" at the center, whereas cells plated at the trough of the oscillations have rings shifted by 180 degrees. This is shown schematically in part c of the image below. They also took advantage of the ability to modulate the phase of the repressilator directly with two small molecule drugs, anhydrotetracycline (aTc) and IPTG, which respectively inhibit TetR and LacI proteins (a, below). After using these drugs to synchronize a population of cells at one phase, they could observe a beautiful linear relationship between the repressilator phase (as determined by the phasing of colony rings) and the elapsed growth of the cells (bottom-right plot, below). Further, they verified that it was really growth and not time per se that correlated with phase (see Figure 3 of [the paper](https://doi.org/10.1038/s41467-019-12638-z)).\n",
    "\n",
    "\n",
    "\n",
    "<div style=\"width: 600px; margin: auto;\">\n",
    "\n",
    "![repressilator phase](figs/RepressilatorPhase.png)\n",
    "   \n",
    "</div>\n",
    "\n",
    "\n",
    "_Colony phasing indicates cumulative cell growth. (a) The repressilator can be perturbed by adding the drugs aTc and IPTG, which inhibit TetR and LacI, respectively. This allows one to put the system into a defined phase. (b) Colony showing the rings of fluorescent protein expression that indicate phase. (c) Schematic indicating how repressilator phase correlates with colony ring phase. (lower left) Examples of colonies generated by cells exposed to either aTc or IPTG. Note the distinct phasing. (lower right) Colony ring phase correlates with number of generations of growth._\n",
    "\n",
    "Taken together, this means that _the phase of the oscillator can be used as a measure of bacterial growth_. \n",
    "\n",
    "They wondered whether this might allow one to recover the history of cell growth in an environment that would otherwise be inaccessible. _E. coli_ normally spend part of their life cycle within the mammalian gut, an environment that is difficult to observe directly. Ideally, one would like ot know how much bacterial cells typically grow within the gut, how much cell-cell variability there is in that growth, and how the amount of growth depends on conditions in the gut, including which other bacteria are present, or whether it has been recently depleted of bacteria by antibiotics. Using the repressilator as a growth recorder, they identified conditions, such as inflammation, that led to greater variability in growth. They also saw that growth varied between different spatial areas or niches in the gut. \n"
   ]
  },
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   "metadata": {},
   "source": [
    "## Conclusions\n",
    "\n",
    "The process of designing and building a synthetic oscillator in bacteria taught us many things about oscillatory dynamics, and synthetic circuit design:\n",
    "\n",
    "* Limit cycles are stable orbits in phase space. Circuits that generate limit cycle dynamics are ideal for self-sustaining oscillations in living cells.\n",
    "* The repressilator uses a cycle of three repressors to create a delayed negative feedback loop.\n",
    "* Linear stability analysis allows us to determine the stability of a fixed point.\n",
    "* The simplified protein-only repressilator model has a single fixed point, and generates limit cycle oscillations when this point becomes unstable. \n",
    "* High Hill coefficients and strong promoters favor oscillations.\n",
    "* Including mRNA dynamics in the model revealed that comparable mRNA and protein decay rates favors oscillations and allows them to occur with less ultrasensitivity. This suggests destabilizing proteins to make their decay rates more similar to those of mRNA. \n",
    "* A repressilator designed to meet these conditions shows sustained oscillations in individual *E. coli* cells, with significant variability. \n",
    "\n",
    "Improving the repressilator revealed additional conclusions:\n",
    "\n",
    "* Even a relatively simple synthetic circuit of three genes can operate with incredible precision in a living cell.\n",
    "* The three-repressor structure of the repressilator, and the way its dynamics progress sequentially from expression of one repressor to the next, means that one can define the phase of the oscillator. Phase is a critical aspect of many natural biological oscillators. For example, the cell cycle is controlled by an oscillator that advances a cell from one phase to the next, in a sequential cycle, with each phase performing unique activities. These phases begin with growth during G1 phase, followed by DNA replication during S phase, a second G phase (G2), and finally an M phase corresponding to mitosis. \n",
    "* The accurate \"2.0\" repressilator can provide insight into bacterial growth dynamics within the gut. \n",
    "\n",
    "One could imagine further exploring how engineered probiotic cells behave when introduced into animals.\n",
    "\n",
    "Taken together, this work shows the complementary power of thoughtful rational design and engineering in creating dynamic cellular circuits. Can our own rationally designed circuits eventually  compete those produced by evolution, the \"blind watchmaker\" itself? Only time will tell. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "## References\n",
    "\n",
    "- Czeisler, C. A., et al., Stability, precision, and near-24-hour period of the human circadian pacemaker, _Science_, 284, 2177–2181, 1999. ([link](https://doi.org/10.1126/science.284.5423.2177))\n",
    "- Dawkins, R., _The Blind Watchmaker_, W. W. Norton & Company, 1986 ([link](https://en.wikipedia.org/wiki/The_Blind_Watchmaker))\n",
    "- Elowitz, M. B. and Leibler, S., A synthetic oscillatory network of transcriptional regulators, _Nature_, 403, 335–338, 2000. ([link](https://doi.org/10.1038/35002125))\n",
    "- Elowitz, M. B., _Transport, assembly, and dynamics in systems of interacting proteins_, Ph.D. thesis, Princeton University, 1999. ([link](https://catalog.princeton.edu/catalog/2244277))\n",
    "- Gao, X. J. and Elowitz, M. B., Precision timing in a cell, _Nature_, 538, 462–463, 2016. ([link](https://doi.org/10.1038/nature19478))\n",
    "- Karzai, A. W., Roche, E. D., Sauer, R. T., The SsrA–SmpB system for protein tagging, directed degradation and ribosome rescue, _Nat. Struct. Biol._, 7, 449–455, 2000. ([link](https://doi.org/10.1038/75843))\n",
    "- Konopka, R. J. and Benzer, S., Clock mutants of _Drosphila melanogaster_, _Proc. Natl. Acad. Sci. USA, 68, 2112–2116, 1971. ([link](https://doi.org/10.1073/pnas.68.9.2112))\n",
    "- Love, S., Building an impossible clock, _The Atlantic_, January 19, 2016. ([link](https://www.theatlantic.com/technology/archive/2016/01/pendulum-clock-john-harrison/424614/))\n",
    "- Lutz, R. and Bujard, H., Independent and tight regulation of transcriptional units in _Escherichia coli_ via the LacR/O, the TetR/O and AraC/I₁-I₂ regulatory elements, _Nucl. Acid Res._, 25, 1203–1210, 1997. ([link](https://doi.org/10.1093/nar/25.6.1203))\n",
    "- Mihalcescu, I., Hsing, W., Leiber, S., Resilient circadian oscillator revealed in individual cyanobacteria, _Nature_, 430, 81–85, 2004. ([link](https://doi.org/10.1038/nature02533))\n",
    "- Nakajima, et al., Reconstitution of circadian oscillation of cyanobacterial KaiC phosphorylation in vitro, _Science_, 308, 414–415, 2005.([link](https://doi.org/10.1126/science.1108451))\n",
    "- Potvin-Trottier, L., et al., Synchronous long-term oscillations in a synthetic gene circuit, _Nature_, 538, 514–517, 2016 ([link](https://doi.org/10.1038/nature19841))\n",
    "- Riglar, D. T., Bacterial variability in the mammalian gut captured by a single-cell synthetic oscillator, _Nature Comm._, 10, 4665, 2019. ([link](https://doi.org/10.1038/s41467-019-12638-z))\n",
    "- Rust, M. J., et al., Ordered phosphorylation governs oscillation of a three-protein circadian clock, _Science_, 318, 809–812, 2007. ([link](https://doi.org/10.1126/science.1148596))\n",
    "- Strogatz, S. H., _Nonlinear Dynamics and Chaos With Applications to Physics, Biology, Chemistry, and Engineering, 2nd Ed._, CRC Press, 2015. ([link](https://doi.org/10.1201/9780429492563))\n",
    "- Wang, P., et al., Robust growth of _Escherichia coli_, _Curr. Biol._, 20, 1099–1103, 2010. ([link](https://doi.org/10.1016/j.cub.2010.04.045))\n",
    "- Welsh, D. K., et al., Individual neurons dissociated from rat suprachiasmatic nucleus express independently phased circadian firing rhythms, _Neuron_, 14, 697–706, 1995. ([link](https://doi.org/10.1016/0896-6273%2895%2990214-7))\n",
    "- Welsh, D. K., Bioluminescence imaging of individual fibroblasts reveals persistent, independently phased circadian rhythms of clock gene expression, _Curr. Biol._, 14, 2289–2295, 2004. ([link](https://doi.org/10.1016/j.cub.2004.11.057))\n"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "## Technical appendices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbsphinx-toctree": {
     "maxdepth": 1
    },
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   },
   "source": [
    "- [9a. Fixed points and composite functions](../technical_appendices/09a_composite_functions_to_find_fixed_point.ipynb)\n",
    "- [9b. Linear stability analysis](../technical_appendices/09b_linear_stability_analysis.ipynb)\n",
    "- [9c. Numerical one-dimensional bounded root finding](../technical_appendices/09c_1d_root_finding.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "<hr>\n",
    "\n",
    "## Problems"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbsphinx-toctree": {},
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   },
   "source": [
    "- [9.1: Coupled repressilators](../problems/09/problem_9.1.ipynb)\n",
    "- [9.2: The KaiABC clock](../problems/09/problem_9.2.ipynb)\n",
    "- [9.3: Linear stability analysis of the repressilator with mRNA](../problems/09/problem_9.3.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Computing environment"
   ]
  },
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      "Python implementation: CPython\n",
      "Python version       : 3.10.10\n",
      "IPython version      : 8.12.0\n",
      "\n",
      "numpy      : 1.23.5\n",
      "scipy      : 1.10.1\n",
      "bokeh      : 3.1.0\n",
      "matplotlib : 3.7.1\n",
      "colorcet   : 3.0.1\n",
      "biocircuits: 0.1.11\n",
      "jupyterlab : 3.5.3\n",
      "\n"
     ]
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   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -v -p numpy,scipy,bokeh,matplotlib,colorcet,biocircuits,jupyterlab"
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