{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multiple Occupancy in TX/TL\n",
    "\n",
    "Contributed by: Matthieu Kratz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Why this model?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Like most genetic circuit models, you typically start with a model that captures the two fundamental processes of transcription and translation. These processes can modelled with varying degrees of complexity, ranging from a basic tx/tl model (__Equation 1__) to complex models that simulate tx/tl at a base pair level. For my project, it was of particular importance that I accurately model the sequestration of transcriptional machinery. Further, I was working without RPU data, making typical models that assume some maximum steady state saturation e.g. positive proportional hill intractable for my system. \n",
    "\n",
    "\\begin{align}\n",
    "\\\\ \\\\\n",
    "&G \\xrightarrow{ktx} T + G \\\\ \\\\ \n",
    "&\\textbf{Equation 1.} \\ \\ \\text{Simple Transcription}\n",
    "\\\\\n",
    "\\end{align}\n",
    "\n",
    "\n",
    "\n",
    "Hence, I needed a model that gave reasonable steady state transcription and translation dynamics from non-RPU parameters, all while accurately tracking the sequestration of the relevant machinery. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Multi-TX Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model CRN is below and the mechanism effectively relies on accounting for all possible RNAp occupancy states, including the various transitions between these occupancy states.\n",
    "\n",
    "\\begin{align}\n",
    "\\\\ \\\\\n",
    "&\\textbf{1. Binding} \\\\ \n",
    "&T7_p + T7_p:G_n  \\underset{kT72}{\\overset{kT71}\\rightleftharpoons} T7_p:G_{n\\alpha}  \\ \\ , \\   \\text{n $\\neq$ $n_{max}$} \\\\ \\\\\n",
    "&\\textbf{2. Closed --> Open} \\\\ \n",
    "&T7_p:G_{n\\alpha}  \\xrightarrow{k_{iso}} T7_p:G_{n+1}  \\ \\ , \\   \\text{n $\\neq$ $n_{max}$} \\\\ \\\\\n",
    "&\\textbf{3. TX} \\\\ \n",
    "&T7_p:G_{n\\alpha} \\xrightarrow{ktx} nT7_m + nT7_p + T7_p:G_{0\\alpha} \\ \\ , \\  \\text{n $\\neq$ 0} \\\\ \n",
    "&T7_p:G_n \\xrightarrow{ktx} nT7_m + nT7_p + T7_p:G_{0} \\ \\ , \\  \\text{n $\\neq$ 0} \\\\\n",
    "\\end{align}\n",
    "\n",
    "kT71 --> Promoter binding rate constant (bimolecular)\n",
    "\n",
    "kT72 --> Promoter unbinding rate constant (unimolecular)\n",
    "\n",
    "k_iso --> Closed to open complex transition rate constant (unimolecular)\n",
    "\n",
    "ktx --> Single polymerase mRNA synthesis rate constant (unimolecular)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A couple of comments:\n",
    "- We explicitly model the process of transitioning from the closed ($T7_p:G_{n\\alpha}$) to open complex ($T7_p:G_{n+1}$). This a pretty slow reaction in vivo, and in the various iterations of this model, including this seemed to be key in accurately reflecting transcription dynamics.\n",
    "\n",
    "- $n_{max}$ refers to the maximum possible occupancy of the gene. This is the physical limit which is ultimately determined by the footprint of the polymerase and the length of the gene. In relation to the point above, without isomerization, this physical limit is always met. With the explicit isomerization, this physical saturation is not met (with my parameter set at least).\n",
    "\n",
    "- Polymerase can only be added one at a time to existing genes i.e. cannot have multiple binding events or closed complexes simultaneously.\n",
    "\n",
    "- We have two TX reactions, one from the closed state and the other from the open state, both with $N$ actively transcribing polymerases. We decided to allow release from the closed state as there is no reason why one polymerase in the closed form should inhibit of other activitely transcribing polymerases. Further, at a modelling level, not allowing release from the closed state results in excessive sequestration of polymerase due to the long time scale of isomerization.\n",
    "\n",
    "- In both TX reactions, the entirety of the $N$ polymerases (bar the one in the closed state) are simultaneously release, along with $N$ transcripts and the unoccupied gene ($T7_p:G_{0}$)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Biocrnpyler multi_tx Mechanism subclass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Subclass should be availabe via `from biocrnpyler.mechanisms import multi_tx`, code is below for clarity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Couple of comments:\n",
    "- The subclass has been designed to be used in concert with the Promoter and DNA assemblies subclasses\n",
    "\n",
    "- Have to define a maximum occupancy (int) and cognate polymerase (species or str) when instantiating \n",
    "\n",
    "- The various complex species are generated within the subclass from the names of the polymerase and dna objects in the DNAassembly object. They are defined as species with DNA material types.\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example: T7 Polymerase Transcription of GFP mRNA\n",
    "\n",
    "Now we define a DNA assembly that use our mechanism in the following steps:\n",
    "- Create a species for the relevant polymerase\n",
    "- Create multi_tx Mechanism, give a maximum occupancy and polymerase (must be species)\n",
    "- Associate this mechanism with a promoter\n",
    "- Place this promoter into a DNA assembly\n",
    "\n",
    "And voila, the cassette regulated by T7p is ready to use!\n",
    "\n",
    "This DNAassembly will be placed in a SimpleTxTlExtract which only dilutes mRNA, not proteins. I haven't used protein dilution as it makes it easier to glean the degree of sequestration present with this mechanism. In practical use, you would of course allow your polymerase to be diluted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Species(N = 26) = {\n",
      "    complex[dna[PFL]:9x_protein[T7p](closed)] (@ 0),  \n",
      "    complex[dna[PFL]:9x_protein[T7p](open)] (@ 0),  \n",
      "    complex[dna[PFL]:8x_protein[T7p](closed)] (@ 0),  \n",
      "    complex[dna[PFL]:8x_protein[T7p](open)] (@ 0),  \n",
      "    complex[dna[PFL]:7x_protein[T7p](closed)] (@ 0),  \n",
      "    complex[dna[PFL]:7x_protein[T7p](open)] (@ 0),  \n",
      "    complex[dna[PFL]:6x_protein[T7p](closed)] (@ 0),  \n",
      "    complex[dna[PFL]:6x_protein[T7p](open)] (@ 0),  \n",
      "    complex[dna[PFL]:5x_protein[T7p](closed)] (@ 0),  \n",
      "    complex[dna[PFL]:5x_protein[T7p](open)] (@ 0),  \n",
      "    complex[dna[PFL]:4x_protein[T7p](closed)] (@ 0),  \n",
      "    complex[dna[PFL]:4x_protein[T7p](open)] (@ 0),  \n",
      "    complex[dna[PFL]:3x_protein[T7p](closed)] (@ 0),  \n",
      "    complex[dna[PFL]:3x_protein[T7p](open)] (@ 0),  \n",
      "    complex[dna[PFL]:2x_protein[T7p](closed)] (@ 0),  \n",
      "    complex[dna[PFL]:2x_protein[T7p](open)] (@ 0),  \n",
      "    complex[dna[PFL]:10x_protein[T7p](closed)] (@ 0),  \n",
      "    complex[dna[PFL]:10x_protein[T7p](open)] (@ 0),  \n",
      "    complex[dna[PFL]:protein[T7p](closed)] (@ 0),  \n",
      "    complex[dna[PFL]:protein[T7p](open)] (@ 0),  \n",
      "    protein[T7p] (@ 0),  \n",
      "    rna[T7_const] (@ 0),  \n",
      "    dna[T7_const] (@ 0),  \n",
      "    rna[PFL] (@ 0),  \n",
      "    dna[PFL] (@ 0),  \n",
      "    protein[GFP] (@ 0),  \n",
      "}\n",
      "\n",
      "Reactions (48) = [\n",
      "0. protein[T7p]+complex[dna[PFL]:protein[T7p](open)] <--> complex[dna[PFL]:2x_protein[T7p](closed)]\n",
      "1. protein[T7p]+complex[dna[PFL]:2x_protein[T7p](open)] <--> complex[dna[PFL]:3x_protein[T7p](closed)]\n",
      "2. protein[T7p]+complex[dna[PFL]:3x_protein[T7p](open)] <--> complex[dna[PFL]:4x_protein[T7p](closed)]\n",
      "3. protein[T7p]+complex[dna[PFL]:4x_protein[T7p](open)] <--> complex[dna[PFL]:5x_protein[T7p](closed)]\n",
      "4. protein[T7p]+complex[dna[PFL]:5x_protein[T7p](open)] <--> complex[dna[PFL]:6x_protein[T7p](closed)]\n",
      "5. protein[T7p]+complex[dna[PFL]:6x_protein[T7p](open)] <--> complex[dna[PFL]:7x_protein[T7p](closed)]\n",
      "6. protein[T7p]+complex[dna[PFL]:7x_protein[T7p](open)] <--> complex[dna[PFL]:8x_protein[T7p](closed)]\n",
      "7. protein[T7p]+complex[dna[PFL]:8x_protein[T7p](open)] <--> complex[dna[PFL]:9x_protein[T7p](closed)]\n",
      "8. protein[T7p]+complex[dna[PFL]:9x_protein[T7p](open)] <--> complex[dna[PFL]:10x_protein[T7p](closed)]\n",
      "9. complex[dna[PFL]:protein[T7p](closed)] --> complex[dna[PFL]:protein[T7p](open)]\n",
      "10. complex[dna[PFL]:2x_protein[T7p](closed)] --> complex[dna[PFL]:2x_protein[T7p](open)]\n",
      "11. complex[dna[PFL]:3x_protein[T7p](closed)] --> complex[dna[PFL]:3x_protein[T7p](open)]\n",
      "12. complex[dna[PFL]:4x_protein[T7p](closed)] --> complex[dna[PFL]:4x_protein[T7p](open)]\n",
      "13. complex[dna[PFL]:5x_protein[T7p](closed)] --> complex[dna[PFL]:5x_protein[T7p](open)]\n",
      "14. complex[dna[PFL]:6x_protein[T7p](closed)] --> complex[dna[PFL]:6x_protein[T7p](open)]\n",
      "15. complex[dna[PFL]:7x_protein[T7p](closed)] --> complex[dna[PFL]:7x_protein[T7p](open)]\n",
      "16. complex[dna[PFL]:8x_protein[T7p](closed)] --> complex[dna[PFL]:8x_protein[T7p](open)]\n",
      "17. complex[dna[PFL]:9x_protein[T7p](closed)] --> complex[dna[PFL]:9x_protein[T7p](open)]\n",
      "18. complex[dna[PFL]:10x_protein[T7p](closed)] --> complex[dna[PFL]:10x_protein[T7p](open)]\n",
      "19. complex[dna[PFL]:protein[T7p](open)] --> protein[T7p]+rna[PFL]+dna[PFL]\n",
      "20. complex[dna[PFL]:2x_protein[T7p](open)] --> 2protein[T7p]+2rna[PFL]+dna[PFL]\n",
      "21. complex[dna[PFL]:3x_protein[T7p](open)] --> 3protein[T7p]+3rna[PFL]+dna[PFL]\n",
      "22. complex[dna[PFL]:4x_protein[T7p](open)] --> 4protein[T7p]+4rna[PFL]+dna[PFL]\n",
      "23. complex[dna[PFL]:5x_protein[T7p](open)] --> 5protein[T7p]+5rna[PFL]+dna[PFL]\n",
      "24. complex[dna[PFL]:6x_protein[T7p](open)] --> 6protein[T7p]+6rna[PFL]+dna[PFL]\n",
      "25. complex[dna[PFL]:7x_protein[T7p](open)] --> 7protein[T7p]+7rna[PFL]+dna[PFL]\n",
      "26. complex[dna[PFL]:8x_protein[T7p](open)] --> 8protein[T7p]+8rna[PFL]+dna[PFL]\n",
      "27. complex[dna[PFL]:9x_protein[T7p](open)] --> 9protein[T7p]+9rna[PFL]+dna[PFL]\n",
      "28. complex[dna[PFL]:10x_protein[T7p](open)] --> 10protein[T7p]+10rna[PFL]+dna[PFL]\n",
      "29. complex[dna[PFL]:2x_protein[T7p](closed)] --> protein[T7p]+rna[PFL]+complex[dna[PFL]:protein[T7p](closed)]\n",
      "30. complex[dna[PFL]:3x_protein[T7p](closed)] --> 2protein[T7p]+2rna[PFL]+complex[dna[PFL]:protein[T7p](closed)]\n",
      "31. complex[dna[PFL]:4x_protein[T7p](closed)] --> 3protein[T7p]+3rna[PFL]+complex[dna[PFL]:protein[T7p](closed)]\n",
      "32. complex[dna[PFL]:5x_protein[T7p](closed)] --> 4protein[T7p]+4rna[PFL]+complex[dna[PFL]:protein[T7p](closed)]\n",
      "33. complex[dna[PFL]:6x_protein[T7p](closed)] --> 5protein[T7p]+5rna[PFL]+complex[dna[PFL]:protein[T7p](closed)]\n",
      "34. complex[dna[PFL]:7x_protein[T7p](closed)] --> 6protein[T7p]+6rna[PFL]+complex[dna[PFL]:protein[T7p](closed)]\n",
      "35. complex[dna[PFL]:8x_protein[T7p](closed)] --> 7protein[T7p]+7rna[PFL]+complex[dna[PFL]:protein[T7p](closed)]\n",
      "36. complex[dna[PFL]:9x_protein[T7p](closed)] --> 8protein[T7p]+8rna[PFL]+complex[dna[PFL]:protein[T7p](closed)]\n",
      "37. complex[dna[PFL]:10x_protein[T7p](closed)] --> 9protein[T7p]+9rna[PFL]+complex[dna[PFL]:protein[T7p](closed)]\n",
      "38. dna[PFL]+protein[T7p] <--> complex[dna[PFL]:protein[T7p](closed)]\n",
      "39. rna[PFL] --> rna[PFL]+protein[GFP]\n",
      "40. dna[T7_const] --> dna[T7_const]+rna[T7_const]\n",
      "41. rna[T7_const] --> rna[T7_const]+protein[T7p]\n",
      "42. protein[GFP] --> \n",
      "43. rna[PFL] --> \n",
      "44. protein[T7p] --> \n",
      "45. rna[T7_const] --> \n",
      "46. rna[PFL] --> \n",
      "47. rna[T7_const] --> \n",
      "]\n",
      "[DNAassembly: PFL\n",
      "\tpT7\n",
      "\ttranscript = rna_PFL\n",
      "\tweak\n",
      "\tprotein = protein_GFP, DNAassembly: T7_const\n",
      "\tweak\n",
      "\ttranscript = rna_T7_const\n",
      "\tweak\n",
      "\tprotein = protein_T7p]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/murray/Library/CloudStorage/Dropbox/macosx/src/biocrnpyler/biocrnpyler/core/parameter.py:1550: UserWarning: parameter file contains no unit column! Please add a column named ['unit', 'units'].\n",
      "  warn(\n"
     ]
    }
   ],
   "source": [
    "from biocrnpyler.core import Species\n",
    "from biocrnpyler.components import Promoter, DNAassembly\n",
    "from biocrnpyler.mechanisms import multi_tx\n",
    "from biocrnpyler.mixtures import SimpleTxTlDilutionMixture\n",
    " \n",
    "\n",
    "#the most important parameters for multi_tx\n",
    "#max_occ is the number of ribosomes that can bind to a transcript\n",
    "#k_iso is the rate of isomerization\n",
    "parameters = {\"max_occ\":10, \"k_iso\":10}\n",
    "\n",
    "# Define Polymerase, and max occupancy and instatiate Mechanism Object\n",
    "T7P = Species('T7p','protein')\n",
    "#Multi-tx mechanism\n",
    "MX = multi_tx(pol=T7P,name='MX')\n",
    "\n",
    "# create promoter object, associated MX and params with it\n",
    "pT7_mx = Promoter(name='pT7',mechanisms={'transcription':MX})\n",
    "\n",
    "# place promoter object into DNA assembly with GFP reporter\n",
    "GFP = Species(\"GFP\", material_type = \"protein\")\n",
    "PFL_mx = DNAassembly('PFL', promoter=pT7_mx, rbs = \"weak\", protein=GFP)\n",
    "\n",
    "# create simple promoter and DNA assembly objects that synthesize polymerase\n",
    "SC = DNAassembly('T7_const', promoter=\"weak\", rbs = \"weak\", protein=T7P)\n",
    "\n",
    "# make extract with T7p source and GFP and compile CRN \n",
    "mixture = SimpleTxTlDilutionMixture(components=[PFL_mx, SC],\n",
    "                                    parameter_file = \"default_parameters.txt\",\n",
    "                                    parameters = parameters)\n",
    "\n",
    "CRN1 = mixture.compile_crn()\n",
    "\n",
    "print(CRN1.pretty_print(show_rates = False))\n",
    "print(mixture.components)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "try:\n",
    "    import numpy as np\n",
    "    import matplotlib.pyplot as plt\n",
    "\n",
    "    ts = np.arange(0,5000,1)\n",
    "\n",
    "    x0_dict = {repr(PFL_mx.dna):1.,repr(SC.dna):.1}\n",
    "\n",
    "    R1 = CRN1.simulate_with_bioscrape_via_sbml(ts, initial_condition_dict = x0_dict, stochastic = False)\n",
    "    if R1 is not None:\n",
    "        fig, ax = plt.subplots(1,2,figsize=(18,8))\n",
    "        ax[0].set_title('Polymerase Levels',pad=20,fontdict={'fontsize':18})\n",
    "        ax[0].plot(R1[str(T7P)],linewidth=3)\n",
    "        ax[0].set_xlabel('Time (sec)',labelpad=15,fontdict={'fontsize':14})\n",
    "        ax[0].set_ylabel('Polymerase Count',labelpad=15,fontdict={'fontsize':14})\n",
    "\n",
    "        ax[1].set_title('GFP Levels',pad=20,fontdict={'fontsize':18})\n",
    "        ax[1].plot(R1[str(GFP)],linewidth=3,c='k')\n",
    "        ax[1].set_xlabel('Time (sec)',labelpad=15,fontdict={'fontsize':14})\n",
    "        ax[1].set_ylabel('[GFP]',labelpad=15,fontdict={'fontsize':14})\n",
    "except ModuleNotFoundError:\n",
    "    print('please install the plotting libraries: pip install biocrnpyler[all]')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparison of Transcript SS with RPU Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we will do a head to head comparison with a simple transcription model built using RPU data. I consider this to effectively be the ground truth for the SS Transcript Count, although it should be noted this comparison does not necessary validate any of the pre-SS dynamics as the simple transcription model assumes immediate saturation. RPU data is from Qi et al.(2012) and RPU standard is from supplement of Nielsen et al. (2016)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/murray/Library/CloudStorage/Dropbox/macosx/src/biocrnpyler/biocrnpyler/core/parameter.py:1550: UserWarning: parameter file contains no unit column! Please add a column named ['unit', 'units'].\n",
      "  warn(\n"
     ]
    }
   ],
   "source": [
    "from biocrnpyler.mechanisms import Transcription_MM\n",
    "# place promoter object into DNA assembly\n",
    "mech_default = Transcription_MM(rnap=T7P,name='MX')\n",
    "pT7 = Promoter(\"pT7\", mechanisms = [mech_default])\n",
    "PFL_default = DNAassembly('PFL',dna='T7',promoter=pT7,rbs = \"weak\", protein = GFP)\n",
    "\n",
    "# make extract with T7p source and GFP and compile CRN \n",
    "Test_EX = SimpleTxTlDilutionMixture(components=[PFL_default, SC],parameter_file = \"default_parameters.txt\")\n",
    "CRN2 = Test_EX.compile_crn()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " \n",
      "\n",
      "MX Model predicts 240754.0 GFP and RPU Model predicts 24745.0 GFP at SS \n",
      " \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "try:\n",
    "    import numpy as np\n",
    "    import matplotlib.pyplot as plt\n",
    "\n",
    "    ts = np.arange(0,5000,1)\n",
    "    x0_dict = {PFL_default.dna:1, SC.dna:.1}\n",
    "\n",
    "    R2 = CRN2.simulate_with_bioscrape_via_sbml(ts, initial_condition_dict = x0_dict, stochastic = False,)\n",
    "    if R2 is not None:\n",
    "        fig = plt.figure(figsize=(10,6))\n",
    "        plt.title('GFP Levels',pad=20,fontdict={'fontsize':18})\n",
    "        plt.plot(R1[str(GFP)],linewidth=3,c='k',label='MTX Model')\n",
    "        plt.plot(R2[str(GFP)],linewidth=3,c='b', label='RPU Model')\n",
    "        plt.xlabel('Time (sec)',labelpad=15,fontdict={'fontsize':14})\n",
    "        plt.ylabel('[GFP]',labelpad=15,fontdict={'fontsize':14})\n",
    "        plt.legend(fontsize=14)\n",
    "\n",
    "        print('\\n \\n')\n",
    "        print(f\"MX Model predicts {np.round(R1[str(str(GFP))].iloc[-1])} GFP and RPU Model predicts {np.round(R2[str(str(GFP))].iloc[-1])} GFP at SS \\n \\n\")\n",
    "except ModuleNotFoundError:\n",
    "    print('please install the plotting libraries: pip install biocrnpyler[all]')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The two models seem to agree pretty well, with some minor difference. This is pretty cool given the fact that MX model uses indirect parameters (promoter affinity, isomerization and tx rates) to predict this SS value. Of course, it's also important to keep in mind that alternative models typically do to not account for the constant sequestration of the polymerase machinery to sustain this SS transcript level. As such, they may not reflect key dynamics resulting from machinery allocation/sharing e.g. retroactivity in tx/tl."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Putative Multi-TL Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The general model of binding, isomerization and production can be readily extended to the process of translation. However, there are several caveats for translation. \n",
    "\n",
    "- First of all, I don't have any direct data like RPU experiments to compare my model with. All I can say is that with biologically reasonable parameter sets, you get something like a few 100 proteins per mRNA (RBZ in excess) and e. coli has a protein to mRNA ratio in that range (100:1 - 1000:1) (Taniguchi et al.(2010) or bionumbers book). \n",
    "\n",
    "- Second of all, since we're no longer working with DNA complexes, the mRNA-RBZ complexes should be subject to degradation/dilution. However, it seems that at the level of the biology, initiation can have a stabilizing effect and for elongation it is uncertain (Roy et al. (2013)). In my simulations, I've effectively done as if only dilution is applied to these complexes, but there is an argument to include some form of active degradation (at a reduced rate). Depending on how this implemented, one may need to make a new degradation mechanism where the complexes effectively releases the ribosomes i.e. only mRNA degraded. This is also complicated by the fact that using subclasses like ComplexSpecies() results in inheritance of degradation/material properties. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Quick Example \n",
    "PFL is transcribed through simple transcription and translated through my multi_tl mechanism. We also have a constitutive source of our RBZ being produced to provide a constant saturating concentration of RBZ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Species(N = 29) = {\n",
      "    protein[Ribo(machinery)] (@ 120.0),  \n",
      "    found_key=(mech=None, partid=e coli, name=Ribo).\n",
      "    search_key=(mech=initial concentration, partid=e coli, name=Ribo).\n",
      "\n",
      "    protein[RNAase(machinery)] (@ 30.0),  \n",
      "    found_key=(mech=None, partid=e coli, name=RNAase).\n",
      "    search_key=(mech=initial concentration, partid=e coli, name=RNAase).\n",
      "\n",
      "    protein[RNAP(machinery)] (@ 15.0),  \n",
      "    found_key=(mech=None, partid=e coli, name=RNAP).\n",
      "    search_key=(mech=initial concentration, partid=e coli, name=RNAP).\n",
      "\n",
      "    complex[protein[Ribo]:rna[cellular_processes](closed)] (@ 0),  \n",
      "    complex[protein[Ribo]:rna[cellular_processes](open)] (@ 0),  \n",
      "    complex[protein[Ribo]:rna[GFP](closed)] (@ 0),  \n",
      "    complex[protein[Ribo]:rna[GFP](open)] (@ 0),  \n",
      "    complex[2x_protein[Ribo]:rna[cellular_processes](closed)] (@ 0),  \n",
      "    complex[2x_protein[Ribo]:rna[cellular_processes](open)] (@ 0),  \n",
      "    complex[2x_protein[Ribo]:rna[GFP](closed)] (@ 0),  \n",
      "    complex[2x_protein[Ribo]:rna[GFP](open)] (@ 0),  \n",
      "    complex[protein[RNAase]:rna[cellular_processes]] (@ 0),  \n",
      "    complex[protein[RNAase]:rna[GFP]] (@ 0),  \n",
      "    complex[dna[cellular_processes]:protein[RNAP]] (@ 0),  \n",
      "    complex[dna[T7]:protein[RNAP]] (@ 0),  \n",
      "    complex[complex[protein[Ribo]:rna[cellular_processes]]:protein[RNAase]] (@ 0),  \n",
      "    complex[complex[protein[Ribo]:rna[cellular_processes]]:protein[RNAase]] (@ 0),  \n",
      "    complex[complex[protein[Ribo]:rna[GFP]]:protein[RNAase]] (@ 0),  \n",
      "    complex[complex[protein[Ribo]:rna[GFP]]:protein[RNAase]] (@ 0),  \n",
      "    complex[complex[2x_protein[Ribo]:rna[cellular_processes]]:protein[RNAase]] (@ 0),  \n",
      "    complex[complex[2x_protein[Ribo]:rna[cellular_processes]]:protein[RNAase]] (@ 0),  \n",
      "    complex[complex[2x_protein[Ribo]:rna[GFP]]:protein[RNAase]] (@ 0),  \n",
      "    complex[complex[2x_protein[Ribo]:rna[GFP]]:protein[RNAase]] (@ 0),  \n",
      "    protein[cellular_processes] (@ 0),  \n",
      "    rna[cellular_processes] (@ 0),  \n",
      "    dna[cellular_processes] (@ 0),  \n",
      "    dna[T7] (@ 0),  \n",
      "    protein[GFP] (@ 0),  \n",
      "    rna[GFP] (@ 0),  \n",
      "}\n",
      "\n",
      "Reactions (42) = [\n",
      "0. dna[T7]+protein[RNAP(machinery)] <--> complex[dna[T7]:protein[RNAP]]\n",
      "1. complex[dna[T7]:protein[RNAP]] --> dna[T7]+rna[GFP]+protein[RNAP(machinery)]\n",
      "2. rna[GFP]+protein[Ribo(machinery)] <--> complex[protein[Ribo]:rna[GFP](closed)]\n",
      "3. complex[protein[Ribo]:rna[GFP](closed)] --> complex[protein[Ribo]:rna[GFP](open)]\n",
      "4. complex[2x_protein[Ribo]:rna[GFP](closed)] --> complex[2x_protein[Ribo]:rna[GFP](open)]\n",
      "5. protein[Ribo(machinery)]+complex[protein[Ribo]:rna[GFP](open)] <--> complex[2x_protein[Ribo]:rna[GFP](closed)]\n",
      "6. complex[protein[Ribo]:rna[GFP](open)] --> protein[Ribo(machinery)]+protein[GFP]+rna[GFP]\n",
      "7. complex[2x_protein[Ribo]:rna[GFP](open)] --> 2protein[Ribo(machinery)]+2protein[GFP]+rna[GFP]\n",
      "8. complex[2x_protein[Ribo]:rna[GFP](closed)] --> protein[Ribo(machinery)]+protein[GFP]+complex[protein[Ribo]:rna[GFP](closed)]\n",
      "9. dna[cellular_processes]+protein[RNAP(machinery)] <--> complex[dna[cellular_processes]:protein[RNAP]]\n",
      "10. complex[dna[cellular_processes]:protein[RNAP]] --> dna[cellular_processes]+rna[cellular_processes]+protein[RNAP(machinery)]\n",
      "11. rna[cellular_processes]+protein[Ribo(machinery)] <--> complex[protein[Ribo]:rna[cellular_processes](closed)]\n",
      "12. complex[protein[Ribo]:rna[cellular_processes](closed)] --> complex[protein[Ribo]:rna[cellular_processes](open)]\n",
      "13. complex[2x_protein[Ribo]:rna[cellular_processes](closed)] --> complex[2x_protein[Ribo]:rna[cellular_processes](open)]\n",
      "14. protein[Ribo(machinery)]+complex[protein[Ribo]:rna[cellular_processes](open)] <--> complex[2x_protein[Ribo]:rna[cellular_processes](closed)]\n",
      "15. complex[protein[Ribo]:rna[cellular_processes](open)] --> protein[Ribo(machinery)]+protein[cellular_processes]+rna[cellular_processes]\n",
      "16. complex[2x_protein[Ribo]:rna[cellular_processes](open)] --> 2protein[Ribo(machinery)]+2protein[cellular_processes]+rna[cellular_processes]\n",
      "17. complex[2x_protein[Ribo]:rna[cellular_processes](closed)] --> protein[Ribo(machinery)]+protein[cellular_processes]+complex[protein[Ribo]:rna[cellular_processes](closed)]\n",
      "18. complex[protein[Ribo]:rna[cellular_processes](closed)]+protein[RNAase(machinery)] <--> complex[complex[protein[Ribo]:rna[cellular_processes]]:protein[RNAase]]\n",
      "19. complex[complex[protein[Ribo]:rna[cellular_processes]]:protein[RNAase]] --> protein[Ribo(machinery)]+protein[RNAase(machinery)]\n",
      "20. complex[2x_protein[Ribo]:rna[cellular_processes](open)]+protein[RNAase(machinery)] <--> complex[complex[2x_protein[Ribo]:rna[cellular_processes]]:protein[RNAase]]\n",
      "21. complex[complex[2x_protein[Ribo]:rna[cellular_processes]]:protein[RNAase]] --> 2protein[Ribo(machinery)]+protein[RNAase(machinery)]\n",
      "22. complex[protein[Ribo]:rna[GFP](closed)]+protein[RNAase(machinery)] <--> complex[complex[protein[Ribo]:rna[GFP]]:protein[RNAase]]\n",
      "23. complex[complex[protein[Ribo]:rna[GFP]]:protein[RNAase]] --> protein[Ribo(machinery)]+protein[RNAase(machinery)]\n",
      "24. complex[2x_protein[Ribo]:rna[GFP](closed)]+protein[RNAase(machinery)] <--> complex[complex[2x_protein[Ribo]:rna[GFP]]:protein[RNAase]]\n",
      "25. complex[complex[2x_protein[Ribo]:rna[GFP]]:protein[RNAase]] --> 2protein[Ribo(machinery)]+protein[RNAase(machinery)]\n",
      "26. complex[protein[Ribo]:rna[GFP](open)]+protein[RNAase(machinery)] <--> complex[complex[protein[Ribo]:rna[GFP]]:protein[RNAase]]\n",
      "27. complex[complex[protein[Ribo]:rna[GFP]]:protein[RNAase]] --> protein[Ribo(machinery)]+protein[RNAase(machinery)]\n",
      "28. rna[GFP]+protein[RNAase(machinery)] <--> complex[protein[RNAase]:rna[GFP]]\n",
      "29. complex[protein[RNAase]:rna[GFP]] --> protein[RNAase(machinery)]\n",
      "30. rna[cellular_processes]+protein[RNAase(machinery)] <--> complex[protein[RNAase]:rna[cellular_processes]]\n",
      "31. complex[protein[RNAase]:rna[cellular_processes]] --> protein[RNAase(machinery)]\n",
      "32. complex[protein[Ribo]:rna[cellular_processes](open)]+protein[RNAase(machinery)] <--> complex[complex[protein[Ribo]:rna[cellular_processes]]:protein[RNAase]]\n",
      "33. complex[complex[protein[Ribo]:rna[cellular_processes]]:protein[RNAase]] --> protein[Ribo(machinery)]+protein[RNAase(machinery)]\n",
      "34. complex[2x_protein[Ribo]:rna[cellular_processes](closed)]+protein[RNAase(machinery)] <--> complex[complex[2x_protein[Ribo]:rna[cellular_processes]]:protein[RNAase]]\n",
      "35. complex[complex[2x_protein[Ribo]:rna[cellular_processes]]:protein[RNAase]] --> 2protein[Ribo(machinery)]+protein[RNAase(machinery)]\n",
      "36. complex[2x_protein[Ribo]:rna[GFP](open)]+protein[RNAase(machinery)] <--> complex[complex[2x_protein[Ribo]:rna[GFP]]:protein[RNAase]]\n",
      "37. complex[complex[2x_protein[Ribo]:rna[GFP]]:protein[RNAase]] --> 2protein[Ribo(machinery)]+protein[RNAase(machinery)]\n",
      "38. protein[cellular_processes] --> \n",
      "39. protein[GFP] --> \n",
      "40. rna[GFP] --> \n",
      "41. rna[cellular_processes] --> \n",
      "]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/murray/Library/CloudStorage/Dropbox/macosx/src/biocrnpyler/biocrnpyler/core/parameter.py:1550: UserWarning: parameter file contains no unit column! Please add a column named ['unit', 'units'].\n",
      "  warn(\n"
     ]
    }
   ],
   "source": [
    "from biocrnpyler.mixtures import TxTlDilutionMixture\n",
    "from biocrnpyler.mechanisms import multi_tl\n",
    "\n",
    "#the most important parameters for multi_tl\n",
    "#max_occ is the number of ribosomes that can bind to a transcript\n",
    "#k_iso is the rate of isomerization\n",
    "parameters = {\"max_occ\":2, \"k_iso\":10}\n",
    "\n",
    "#Create a DNA assembly and Mixture\n",
    "PFL = DNAassembly('PFL', dna='T7', rbs='medium', promoter='medium',transcript='GFP',protein='GFP')\n",
    "EM = TxTlDilutionMixture(name = 'e coli',components=[PFL], parameter_file = \"default_parameters.txt\", parameters = parameters)\n",
    "\n",
    "# Instantiate mechanism and pass it the ribosome\n",
    "ML = multi_tl(EM.ribosome.get_species())\n",
    "\n",
    "#Overwrite the translation mechanism\n",
    "EM.add_mechanism(ML, overwrite = True)\n",
    "\n",
    "CRN3 = EM.compile_crn()\n",
    "print(CRN3.pretty_print(show_rates = False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "try:\n",
    "\n",
    "    import numpy as np\n",
    "    import matplotlib.pyplot as plt\n",
    "\n",
    "    ts = np.arange(0,10000,1)\n",
    "    x0_dict = {PFL.dna:1, EM.ribosome.get_species():100,  EM.rnap.get_species():20}\n",
    "\n",
    "    R3 = CRN3.simulate_with_bioscrape_via_sbml(ts, initial_condition_dict = x0_dict, stochastic = False,)\n",
    "\n",
    "    if R3 is not None:\n",
    "        fig, ax = plt.subplots(1,2,figsize=(18,8))\n",
    "        ax[0].set_title('GFP Protein Levels',pad=20,fontdict={'fontsize':18})\n",
    "        ax[0].plot(R3[str(PFL.protein)],linewidth=3)\n",
    "        #ax[0].plot(R3[str(EM.ribosome.get_species())],linewidth=3)\n",
    "        ax[0].set_xlabel('Time (sec)',labelpad=15,fontdict={'fontsize':14})\n",
    "        ax[0].set_ylabel('GFP Protein Count',labelpad=15,fontdict={'fontsize':14})\n",
    "\n",
    "        ax[1].set_title('GFP Transcript Levels',pad=20,fontdict={'fontsize':18})\n",
    "        ax[1].plot(R3[str(PFL.transcript)],linewidth=3,c='k')\n",
    "        ax[1].set_xlabel('Time (sec)',labelpad=15,fontdict={'fontsize':14})\n",
    "        ax[1].set_ylabel('GFP Transcript Count',labelpad=15,fontdict={'fontsize':14})\n",
    "except ModuleNotFoundError:\n",
    "    print('please install the plotting libraries: pip install biocrnpyler[all]')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Future Work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Want to do more rigorous validation of multi-tx mechanism. As part of that, I first plan on comparing RPU data of existing consitutive promoters and non-native polymerase expression systems e.g. T5 with what the multi-tx model would predict. \n",
    "- Is there data out there that would help validate the predicted RNAp occupancy of genes from the multi-tx model (same story for multi-tl model)?\n",
    "- Eventually want to develop a multi-tx mechanism that can be used for TF-mediated transcription.\n",
    "- Start looking towards validating multi-tl model, currently plan on seeing what data and parameters I could scrape from existing resource e.g. BCD RBS binding rates from biocrnpyler. \n",
    "- Do some model comparison using bioscrape inference, where I generate parameters with RPU data model and fit parameters (vary known and unknown parameters) from the MTX model. Definitely very interesting for deriving isomerization rates as that seems hard to come by in literature and seems to be a key part of the model in determining system behaviour (Pre-SS and SS dynamics)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### References"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "T7 parameters:\n",
    "- Promoter Binding and Unbinding: Jia, Y., Kumar, A., & Patel, S. S. (1996). Equilibrium and Stopped-flow Kinetic Studies of Interaction between T7 RNA Polymerase and Its Promoters Measured by Protein and 2-Aminopurine Fluorescence Changes. Journal of Biological Chemistry , 271(48), 30451–30458. https://doi.org/10.1074/jbc.271.48.30451 \n",
    "- Isomerization Rate: Skinner, G. M., Baumann, C. G., Quinn, D. M., Molloy, J. E., & Hoggett, J. G. (2004). Promoter Binding, Initiation, and Elongation By Bacteriophage T7 RNA Polymerase: A SINGLE-MOLECULE VIEW OF THE TRANSCRIPTION CYCLE . Journal of Biological Chemistry , 279(5), 3239–3244. https://doi.org/10.1074/jbc.M310471200 \n",
    "- Translation Rate (T7p is VERY fast): Kochetkov, S. N., Rusakova, E. E., & Tunitskaya, V. L. (1998). Recent studies of T7 RNA polymerase mechanism. FEBS Letters, 440(3), 264–267. https://doi.org/https://doi.org/10.1016/S0014-5793(98)01484-7\n",
    "   \n",
    "Translation Parameters:\n",
    "- RBS Binding and Unbinding: Chandra, F., & Del Vecchio, D. (2016). The Effects of Ribosome Autocatalysis and Negative Feedback in Resource Competition. bioRxiv. https://doi.org/10.1101/042127\n",
    "- Isomerization Rate: Draper, D. E. (1993). Mechanisms of Translational Initiation and Repression in Prokaryotes BT  - The Translational Apparatus: Structure, Function, Regulation, Evolution. In K. H. Nierhaus, F. Franceschi, A. R. Subramanian, V. A. Erdmann, & B. Wittmann-Liebold (Eds.) (pp. 197–207). Boston, MA: Springer US. https://doi.org/10.1007/978-1-4615-2407-6_19\n",
    "\n",
    "Misc:\n",
    "- mRNA Degradation: Roy, B., & Jacobson, A. (2013). The intimate relationships of mRNA decay and translation. Trends in Genetics : TIG, 29(12), 691–699. https://doi.org/10.1016/j.tig.2013.09.002\n",
    "- RPU Data: Nielsen, A. A. K., Der, B. S., Shin, J., Vaidyanathan, P., Paralanov, V., Strychalski, E. A., … Voigt, C. A. (2016). Genetic circuit design automation. Science, 352(6281), aac7341. https://doi.org/10.1126/science.aac7341 and Qi, L., Haurwitz, R. E., Shao, W., Doudna, J. A., & Arkin, A. P. (2012). RNA processing enables predictable programming of gene expression. Nature Biotechnology, 30(10), 1002–1006. https://doi.org/10.1038/nbt.2355\n",
    "- Protein:mRNA ratios: Taniguchi, Y., Choi, P. J., Li, G.-W., Chen, H., Babu, M., Hearn, J., … Xie, X. S. (2010). Quantifying E. coli Proteome and Transcriptome with Single-Molecule Sensitivity in Single Cells. Science, 329(5991), 533 LP – 538. https://doi.org/10.1126/science.1188308\n",
    "    "
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