{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Global Mechanisms\n",
    "Global mechanisms provide a framework to have certain reactions work on specific subsets of species in a CRN, applied at the end of compilation to all species created by Component and Mixture Mechanisms.\n",
    "\n",
    "Under the hood, global mechanisms work just like normal mechanisms - they take a set of species and compile them into CRNs. Global mechanisms are called at the end of the compilation, so they apply to all species generated by all local mechanisms. \n",
    "\n",
    "### Keywords:\n",
    "__filter_dict__ {str : True/False} is used to make global mechanisms selective. The dictionaries keys (strings) can be species' names, types, or attributes. The mechanism is then applied or not based upon the value of that attribute in the filter_dict. For exampe filter_dict = {\"dna\":False} would not apply its mechanism to any \"dna\" species. \n",
    "\n",
    "__default_on__ True / False: For species with no attributes in the filter_dict, a global mechanism defaults to its default_on keyword, which can be True or False.\n",
    "\n",
    "__recursive_species_filtering__ True / False: When applying a filter dictionary to a ComplexSpecies, this keyword determines if the filter will be applied recursively to all species inside that complex species. For example, consider the filter dict filter_dict = {\"dna\":False}. A ComplexSpecies (material_type=\"complex\") consisting of a species of type material_type=\"dna\" and a species of type material_type=\"protein\" will not be effected by this filter unless recursive_species_filtering = True. Note that attributes are automatically inheritted recursively, so this keyword only matters for name and material_type filters.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example 1: Global Dilution using attributes\n",
    "\n",
    "In the following example, a model will be set up where global mechanisms cause degradation by dilution on all species which are do not have the attributes \"genomic\" or \"machinery\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Species(N = 17) = {\n",
      "    complex[protein[Ribo]:rna[G2]] (@ 0),  \n",
      "    complex[protein[Ribo]:rna[G1]] (@ 0),  \n",
      "    complex[protein[RNAase]:rna[G2]] (@ 0),  \n",
      "    complex[protein[RNAase]:rna[G1]] (@ 0),  \n",
      "    complex[dna[G2]:protein[RNAP]] (@ 0),  \n",
      "    complex[dna[G1]:protein[RNAP]] (@ 0),  \n",
      "    complex[complex[protein[Ribo]:rna[G2]]:protein[RNAase]] (@ 0),  \n",
      "    complex[complex[protein[Ribo]:rna[G1]]:protein[RNAase]] (@ 0),  \n",
      "    protein[Ribo] (@ 0),  \n",
      "    protein[RNAase] (@ 0),  \n",
      "    protein[RNAP] (@ 0),  \n",
      "    protein[G2] (@ 0),  \n",
      "    rna[G2] (@ 0),  \n",
      "    dna[G2(genomic)] (@ 0),  \n",
      "    protein[G1] (@ 0),  \n",
      "    rna[G1] (@ 0),  \n",
      "    dna[G1] (@ 0),  \n",
      "}\n",
      "\n",
      "Reactions (27) = [\n",
      "0. dna[G1]+protein[RNAP] <--> complex[dna[G1]:protein[RNAP]]\n",
      " Kf=k_forward * dna_G1 * protein_RNAP\n",
      " Kr=k_reverse * complex_dna_G1_protein_RNAP_\n",
      "  k_forward=100\n",
      "  k_reverse=20\n",
      "\n",
      "1. complex[dna[G1]:protein[RNAP]] --> dna[G1]+rna[G1]+protein[RNAP]\n",
      " Kf=k_forward * complex_dna_G1_protein_RNAP_\n",
      "  k_forward=3\n",
      "\n",
      "2. rna[G1]+protein[Ribo] <--> complex[protein[Ribo]:rna[G1]]\n",
      " Kf=k_forward * rna_G1 * protein_Ribo\n",
      " Kr=k_reverse * complex_protein_Ribo_rna_G1_\n",
      "  k_forward=100\n",
      "  k_reverse=20\n",
      "\n",
      "3. complex[protein[Ribo]:rna[G1]] --> rna[G1]+protein[G1]+protein[Ribo]\n",
      " Kf=k_forward * complex_protein_Ribo_rna_G1_\n",
      "  k_forward=2\n",
      "\n",
      "4. dna[G2(genomic)]+protein[RNAP] <--> complex[dna[G2]:protein[RNAP]]\n",
      " Kf=k_forward * dna_G2_genomic * protein_RNAP\n",
      " Kr=k_reverse * complex_dna_G2_genomic_protein_RNAP_\n",
      "  k_forward=100\n",
      "  k_reverse=20\n",
      "\n",
      "5. complex[dna[G2]:protein[RNAP]] --> dna[G2(genomic)]+rna[G2]+protein[RNAP]\n",
      " Kf=k_forward * complex_dna_G2_genomic_protein_RNAP_\n",
      "  k_forward=3\n",
      "\n",
      "6. rna[G2]+protein[Ribo] <--> complex[protein[Ribo]:rna[G2]]\n",
      " Kf=k_forward * rna_G2 * protein_Ribo\n",
      " Kr=k_reverse * complex_protein_Ribo_rna_G2_\n",
      "  k_forward=100\n",
      "  k_reverse=20\n",
      "\n",
      "7. complex[protein[Ribo]:rna[G2]] --> rna[G2]+protein[G2]+protein[Ribo]\n",
      " Kf=k_forward * complex_protein_Ribo_rna_G2_\n",
      "  k_forward=2\n",
      "\n",
      "8. complex[protein[Ribo]:rna[G2]] --> \n",
      " Kf=k_forward * complex_protein_Ribo_rna_G2_\n",
      "  k_forward=0.5\n",
      "\n",
      "9. protein[G1] --> \n",
      " Kf=k_forward * protein_G1\n",
      "  k_forward=0.5\n",
      "\n",
      "10. complex[dna[G1]:protein[RNAP]] --> \n",
      " Kf=k_forward * complex_dna_G1_protein_RNAP_\n",
      "  k_forward=0.5\n",
      "\n",
      "11. dna[G1] --> \n",
      " Kf=k_forward * dna_G1\n",
      "  k_forward=0.5\n",
      "\n",
      "12. protein[G2] --> \n",
      " Kf=k_forward * protein_G2\n",
      "  k_forward=0.5\n",
      "\n",
      "13. complex[protein[Ribo]:rna[G1]] --> \n",
      " Kf=k_forward * complex_protein_Ribo_rna_G1_\n",
      "  k_forward=0.5\n",
      "\n",
      "14. rna[G2] --> \n",
      " Kf=k_forward * rna_G2\n",
      "  k_forward=0.5\n",
      "\n",
      "15. protein[RNAP] --> \n",
      " Kf=k_forward * protein_RNAP\n",
      "  k_forward=0.5\n",
      "\n",
      "16. protein[RNAase] --> \n",
      " Kf=k_forward * protein_RNAase\n",
      "  k_forward=0.5\n",
      "\n",
      "17. protein[Ribo] --> \n",
      " Kf=k_forward * protein_Ribo\n",
      "  k_forward=0.5\n",
      "\n",
      "18. rna[G1] --> \n",
      " Kf=k_forward * rna_G1\n",
      "  k_forward=0.5\n",
      "\n",
      "19. complex[protein[Ribo]:rna[G2]]+protein[RNAase] <--> complex[complex[protein[Ribo]:rna[G2]]:protein[RNAase]]\n",
      " Kf=k_forward * complex_protein_Ribo_rna_G2_ * protein_RNAase\n",
      " Kr=k_reverse * complex_complex_protein_Ribo_rna_G2__protein_RNAase_\n",
      "  k_forward=100\n",
      "  k_reverse=20\n",
      "\n",
      "20. complex[complex[protein[Ribo]:rna[G2]]:protein[RNAase]] --> protein[Ribo]+protein[RNAase]\n",
      " Kf=k_forward * complex_complex_protein_Ribo_rna_G2__protein_RNAase_\n",
      "  k_forward=0.5\n",
      "\n",
      "21. complex[protein[Ribo]:rna[G1]]+protein[RNAase] <--> complex[complex[protein[Ribo]:rna[G1]]:protein[RNAase]]\n",
      " Kf=k_forward * complex_protein_Ribo_rna_G1_ * protein_RNAase\n",
      " Kr=k_reverse * complex_complex_protein_Ribo_rna_G1__protein_RNAase_\n",
      "  k_forward=100\n",
      "  k_reverse=20\n",
      "\n",
      "22. complex[complex[protein[Ribo]:rna[G1]]:protein[RNAase]] --> protein[Ribo]+protein[RNAase]\n",
      " Kf=k_forward * complex_complex_protein_Ribo_rna_G1__protein_RNAase_\n",
      "  k_forward=0.5\n",
      "\n",
      "23. rna[G2]+protein[RNAase] <--> complex[protein[RNAase]:rna[G2]]\n",
      " Kf=k_forward * rna_G2 * protein_RNAase\n",
      " Kr=k_reverse * complex_protein_RNAase_rna_G2_\n",
      "  k_forward=100\n",
      "  k_reverse=20\n",
      "\n",
      "24. complex[protein[RNAase]:rna[G2]] --> protein[RNAase]\n",
      " Kf=k_forward * complex_protein_RNAase_rna_G2_\n",
      "  k_forward=0.5\n",
      "\n",
      "25. rna[G1]+protein[RNAase] <--> complex[protein[RNAase]:rna[G1]]\n",
      " Kf=k_forward * rna_G1 * protein_RNAase\n",
      " Kr=k_reverse * complex_protein_RNAase_rna_G1_\n",
      "  k_forward=100\n",
      "  k_reverse=20\n",
      "\n",
      "26. complex[protein[RNAase]:rna[G1]] --> protein[RNAase]\n",
      " Kf=k_forward * complex_protein_RNAase_rna_G1_\n",
      "  k_forward=0.5\n",
      "\n",
      "]\n",
      "Simulating with BioSCRAPE\n",
      "Simulating with BioSCRAPE\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/murray/Library/CloudStorage/Dropbox/macosx/src/biocrnpyler/biocrnpyler/core/chemical_reaction_network.py:512: UserWarning: Trying to set species that is not in model: protein_Ribo_machinery\n",
      "  m.set_species(processed)\n",
      "/Users/murray/Library/CloudStorage/Dropbox/macosx/src/biocrnpyler/biocrnpyler/core/chemical_reaction_network.py:512: UserWarning: Trying to set species that is not in model: protein_RNAP_machinery\n",
      "  m.set_species(processed)\n",
      "/Users/murray/Library/CloudStorage/Dropbox/macosx/src/biocrnpyler/biocrnpyler/core/chemical_reaction_network.py:512: UserWarning: Trying to set species that is not in model: protein_RNAase_machinery\n",
      "  m.set_species(processed)\n",
      "/Users/murray/Library/CloudStorage/Dropbox/macosx/src/biocrnpyler/biocrnpyler/core/chemical_reaction_network.py:512: UserWarning: Trying to set species that is not in model: protein_Ribo_machinery\n",
      "  m.set_species(processed)\n",
      "/Users/murray/Library/CloudStorage/Dropbox/macosx/src/biocrnpyler/biocrnpyler/core/chemical_reaction_network.py:512: UserWarning: Trying to set species that is not in model: protein_RNAP_machinery\n",
      "  m.set_species(processed)\n",
      "/Users/murray/Library/CloudStorage/Dropbox/macosx/src/biocrnpyler/biocrnpyler/core/chemical_reaction_network.py:512: UserWarning: Trying to set species that is not in model: protein_RNAase_machinery\n",
      "  m.set_species(processed)\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1400x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from biocrnpyler.components import DNAassembly\n",
    "from biocrnpyler.mechanisms import Dilution\n",
    "from biocrnpyler.mixtures import TxTlExtract\n",
    "\n",
    "#We will only use default parameters in this model, for simplicity.\n",
    "kb, ku, ktx, ktl, kdeg, kdil = 100, 20, 3, 2, .5, .5\n",
    "parameters = {\"kb\":kb, \"ku\":ku, \"ktx\":ktx, \"ktl\":ktl, \"kdeg\": kdeg, \"kdil\":kdil}\n",
    "\n",
    "#Creates a global dilution mechanism that acts on all species generated except for\n",
    "# those with the type or attribute \"genome\" or \"machinery\"\n",
    "dilution_mechanism = Dilution(filter_dict = {\"genomic\":False, \"machinery\":False}, default_on = True)\n",
    "\n",
    "#Add this mechanism to a dictionary which is passed into the Mixture txtl.TxTlExtract\n",
    "global_mechanisms = {\"dilution\":dilution_mechanism}\n",
    "myMixture = TxTlExtract(name = \"txtl\", parameters = parameters, global_mechanisms = global_mechanisms)\n",
    "\n",
    "#Add machinery attributes to species I want constiutively expressed at the dilution rate\n",
    "myMixture.rnap.add_attribute(\"machinery\")\n",
    "myMixture.rnaase.add_attribute(\"machinery\")\n",
    "myMixture.ribosome.add_attribute(\"machinery\")\n",
    "\n",
    "#Creates a dna assembly. This assembly is type \"dna\" so it will be degraded\n",
    "A_dna = DNAassembly(name = \"G1\", promoter = \"pBest\", rbs = \"BCD2\")\n",
    "\n",
    "#Create another dna assembly but set its internal specie's attributes to contain \"genomic\" so it will not be degraded\n",
    "#Note: this only protects the dna_G2 species encoded by this assembly as well as complex species (eg rnap:DNA) which inherit their subspecies attributes.\n",
    "A_genome = DNAassembly(name = \"G2\", promoter = \"pBest\", rbs = \"BCD2\", attributes = [\"genomic\"])\n",
    "\n",
    "myMixture.add_components(A_dna)\n",
    "myMixture.add_components(A_genome)\n",
    "myCRN = myMixture.compile_crn()\n",
    "print(myCRN.pretty_print(show_rates=True, show_material=True,\n",
    "                         show_attributes=True, show_keys=False))\n",
    "\n",
    "print(\"Simulating with BioSCRAPE\")\n",
    "try:\n",
    "    print(\"Simulating with BioSCRAPE\")\n",
    "    import numpy as np\n",
    "    import pylab as plt\n",
    "    timepoints = np.arange(0, 50, .1)\n",
    "\n",
    "    x0_dict = {myMixture.ribosome.get_species():100,\n",
    "               myMixture.rnap.get_species():20,\n",
    "               myMixture.rnaase.get_species():10,\n",
    "               A_dna.dna:20,\n",
    "               A_genome.dna:20}\n",
    "\n",
    "    full_result_sto = myCRN.simulate_with_bioscrape_via_sbml(timepoints,\n",
    "                                                             initial_condition_dict = x0_dict,\n",
    "                                                             stochastic = True)\n",
    "    full_result_det = myCRN.simulate_with_bioscrape_via_sbml(timepoints,\n",
    "                                                             initial_condition_dict = x0_dict,\n",
    "                                                             stochastic = False)\n",
    "\n",
    "    if (full_result_det is not None) and (full_result_sto is not None):\n",
    "        #chemical_reaction_network.get_all_species_containing is a useful shortcut to get lists of species\n",
    "        tot_A_dna_det = np.sum(full_result_det[myCRN.get_all_species_containing(A_dna.dna, return_as_strings=True)], 1)\n",
    "        tot_A_genome_det = np.sum(full_result_det[myCRN.get_all_species_containing(A_genome.dna, return_as_strings=True)], 1)\n",
    "        tot_A_dna_sto = np.sum(full_result_sto[myCRN.get_all_species_containing(A_dna.dna, return_as_strings=True)], 1)\n",
    "        tot_A_genome_sto = np.sum(full_result_sto[myCRN.get_all_species_containing(A_genome.dna, return_as_strings=True)], 1)\n",
    "\n",
    "        tot_A_dna_rna_det = np.sum(full_result_det[myCRN.get_all_species_containing(A_dna.transcript, return_as_strings=True)], 1)\n",
    "        tot_A_genome_rna_det = np.sum(full_result_det[myCRN.get_all_species_containing(A_genome.protein, return_as_strings=True)], 1)\n",
    "        tot_A_dna__rna_sto = np.sum(full_result_sto[myCRN.get_all_species_containing(A_dna.transcript, return_as_strings=True)], 1)\n",
    "        tot_A_genome_rna_sto = np.sum(full_result_sto[myCRN.get_all_species_containing(A_genome.protein, return_as_strings=True)], 1)\n",
    "\n",
    "        plt.figure(figsize = (14, 10))\n",
    "        plt.subplot(131)\n",
    "        plt.plot(timepoints, tot_A_dna_det, color = \"blue\", label = \"Non-Genomic DNA (deterministic)\")\n",
    "        plt.plot(timepoints, tot_A_genome_det, color = \"cyan\", label = \"Genomic DNA (deterministic)\")\n",
    "        plt.plot(timepoints, tot_A_dna_sto, \":\", color = \"blue\", label = \"Non-Genomic DNA (stochastic)\")\n",
    "        plt.plot(timepoints, tot_A_genome_sto, \":\", color = \"cyan\", label = \"Genomic DNA (stochastic)\")\n",
    "        plt.legend()\n",
    "        plt.xlabel(\"Time\")\n",
    "        plt.title(\"DNA\")\n",
    "        plt.ylabel(\"Concentration or Count\")\n",
    "\n",
    "        plt.subplot(132)\n",
    "        plt.plot(timepoints, tot_A_dna_rna_det, color = \"blue\", label = \"Non-Genomic RNA (deterministic)\")\n",
    "        plt.plot(timepoints, tot_A_genome_rna_det, color = \"cyan\", label = \"Genomic RNA (deterministic)\")\n",
    "        plt.plot(timepoints, tot_A_dna__rna_sto, \":\", color = \"blue\", label = \"Non-Genomic RNA (stochastic)\")\n",
    "        plt.plot(timepoints, tot_A_genome_rna_sto, \":\", color = \"cyan\", label = \"Genomic RNA (stochastic)\")\n",
    "        plt.legend()\n",
    "        plt.xlabel(\"Time\")\n",
    "        plt.title(\"RNA\")\n",
    "        plt.ylabel(\"Concentration / Count\")\n",
    "\n",
    "        plt.subplot(133)\n",
    "        plt.plot(timepoints, full_result_det[str(A_dna.protein)], color = \"blue\", label = \"Non-Genomic Protein (deterministic)\")\n",
    "        plt.plot(timepoints, full_result_det[str(A_genome.protein)], color = \"cyan\", label = \"Genomic Protein (deterministic)\")\n",
    "        plt.plot(timepoints, full_result_sto[str(A_dna.protein)], \":\", color = \"blue\", label = \"Non-Genomic Protein (stochastic)\")\n",
    "        plt.plot(timepoints, full_result_sto[str(A_genome.protein)], \":\", color = \"cyan\", label = \"Genomic Protein (stochastic)\")\n",
    "        plt.legend()\n",
    "        plt.title(\"Protein\")\n",
    "        plt.xlabel(\"Time\")\n",
    "        plt.ylabel(\"Concentration / Count\")\n",
    "\n",
    "        plt.show()\n",
    "except ModuleNotFoundError:\n",
    "    print('please install the plotting libraries: pip install biocrnpyler[all]')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example 2: Global Dilution using material_type with and without recursive_species_filtering\n",
    "\n",
    "In this example a very simple model of a piece of DNA $G$ binding to a protein $P$ to form a complex will be considered. Dilution will be applied to all species excent those of type \"dna\". When recursive_species_filtering is False, the protein AND the DNA-protein complex are both diluted. When recursive_species_filtering is True, only the protein is diluted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CRN: recursive_species_filtering = False:\n",
      " Species = protein_A, dna_G, complex_dna_G_protein_A_\n",
      "Reactions = [\n",
      "\tprotein[A]+dna[G] <--> complex[dna[G]:protein[A]]\n",
      "\tprotein[A] --> \n",
      "\tcomplex[dna[G]:protein[A]] --> \n",
      "]\n",
      "\n",
      "CRN: recursive_species_filtering = True:\n",
      " Species(N = 3) = {\n",
      "    complex[dna[G]:protein[A]] (@ 0),  \n",
      "    dna[G] (@ 0),  \n",
      "    protein[A] (@ 0),  \n",
      "}\n",
      "\n",
      "Reactions (2) = [\n",
      "0. protein[A]+dna[G] <--> complex[dna[G]:protein[A]]\n",
      " Kf=k_forward * protein_A * dna_G\n",
      " Kr=k_reverse * complex_dna_G_protein_A_\n",
      "  k_forward=100\n",
      "  found_key=(mech=None, partid=None, name=kb).\n",
      "  search_key=(mech=one_step_binding, partid=dna_G_protein_A, name=kb).\n",
      "  k_reverse=20\n",
      "  found_key=(mech=None, partid=None, name=ku).\n",
      "  search_key=(mech=one_step_binding, partid=dna_G_protein_A, name=ku).\n",
      "\n",
      "1. protein[A] --> \n",
      " Kf=k_forward * protein_A\n",
      "  k_forward=0.5\n",
      "  found_key=(mech=None, partid=None, name=kdil).\n",
      "  search_key=(mech=global_degradation_via_dilution, partid=protein_A, name=kdil).\n",
      "\n",
      "]\n"
     ]
    }
   ],
   "source": [
    "from biocrnpyler.core import Species\n",
    "from biocrnpyler.components import ChemicalComplex\n",
    "from biocrnpyler.mixtures import ExpressionExtract\n",
    "\n",
    "G = Species(\"G\", material_type = \"dna\")\n",
    "A = Species(\"A\", material_type = \"protein\")\n",
    "C1 = ChemicalComplex([G, A])\n",
    "\n",
    "dilution_mechanism_no_recursion = Dilution(filter_dict = {\"dna\":False}, default_on = True, recursive_species_filtering = False)\n",
    "M_no_recursion = ExpressionExtract(components = [C1], global_mechanisms = {\"dilution\":dilution_mechanism_no_recursion}, parameters = parameters)\n",
    "CRN_no_recursion = M_no_recursion.compile_crn()\n",
    "print(\"CRN: recursive_species_filtering = False:\\n\", CRN_no_recursion)\n",
    "\n",
    "dilution_mechanism_recursion = Dilution(filter_dict = {\"dna\":False}, default_on = True, recursive_species_filtering = True)\n",
    "M_recursion = ExpressionExtract(components = [C1], global_mechanisms = {\"dilution\":dilution_mechanism_recursion}, parameters = parameters)\n",
    "CRN_recursion = M_recursion.compile_crn()\n",
    "print(\"\\nCRN: recursive_species_filtering = True:\\n\", CRN_recursion.pretty_print(show_keys = True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example 3: Global Degradation of Linear DNA Strands by RecBCD inhibited by GamS\n",
    "\n",
    "In this example, the degradation of linear DNA strands in an extract is modeled along with the possibility of inhibiting this degradation with GamS, as described by [Sun et al. 2014.](https://pubs.acs.org/doi/abs/10.1021/sb400131a)\n",
    "\n",
    "This model will use a global degradation mechanism that applies to all DNA without the attribute 'circular'. This example will create a warning because some Species will have both material_type = 'dna' and 'circular' in which case the default_on = False keyword will be used to turn off degradation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Species(N = 10) = {\n",
      "    protein[RFP] (@ 1.0),  \n",
      "    found_key=(mech=initial concentration, partid=None, name=RFP).\n",
      "    search_key=(mech=initial concentration, partid=Mixture 1, name=RFP).\n",
      "\n",
      "    rna[RFP] (@ 1.0),  \n",
      "    found_key=(mech=initial concentration, partid=None, name=RFP).\n",
      "    search_key=(mech=initial concentration, partid=Mixture 1, name=RFP).\n",
      "\n",
      "    dna[RFP(circular)] (@ 1.0),  \n",
      "    found_key=(mech=initial concentration, partid=None, name=RFP).\n",
      "    search_key=(mech=initial concentration, partid=Mixture 1, name=RFP).\n",
      "\n",
      "    protein[GFP] (@ 1.0),  \n",
      "    found_key=(mech=initial concentration, partid=None, name=GFP).\n",
      "    search_key=(mech=initial concentration, partid=Mixture 1, name=GFP).\n",
      "\n",
      "    rna[GFP] (@ 1.0),  \n",
      "    found_key=(mech=initial concentration, partid=None, name=GFP).\n",
      "    search_key=(mech=initial concentration, partid=Mixture 1, name=GFP).\n",
      "\n",
      "    dna[GFP] (@ 1.0),  \n",
      "    found_key=(mech=initial concentration, partid=None, name=GFP).\n",
      "    search_key=(mech=initial concentration, partid=Mixture 1, name=GFP).\n",
      "\n",
      "    recBCD (@ 0),  \n",
      "    complex[dna[GFP]:recBCD] (@ 0),  \n",
      "    complex[2x_GamS:recBCD] (@ 0),  \n",
      "    GamS (@ 0),  \n",
      "}\n",
      "\n",
      "Reactions (9) = [\n",
      "0. 2GamS+recBCD <--> complex[2x_GamS:recBCD]\n",
      " Kf=k_forward * GamS^2 * recBCD\n",
      " Kr=k_reverse * complex_GamS_2x_recBCD_\n",
      "  k_forward=100\n",
      "  found_key=(mech=None, partid=None, name=kb).\n",
      "  search_key=(mech=one_step_binding, partid=GamS_2x_recBCD, name=kb).\n",
      "  k_reverse=10\n",
      "  found_key=(mech=None, partid=None, name=ku).\n",
      "  search_key=(mech=one_step_binding, partid=GamS_2x_recBCD, name=ku).\n",
      "\n",
      "1. dna[GFP] --> dna[GFP]+rna[GFP]\n",
      " Kf=k_forward * dna_GFP\n",
      "  k_forward=0.5\n",
      "  found_key=(mech=None, partid=None, name=ktx).\n",
      "  search_key=(mech=simple_transcription, partid=strong, name=ktx).\n",
      "\n",
      "2. rna[GFP] --> rna[GFP]+protein[GFP]\n",
      " Kf=k_forward * rna_GFP\n",
      "  k_forward=1.5\n",
      "  found_key=(mech=None, partid=None, name=ktl).\n",
      "  search_key=(mech=simple_translation, partid=weak, name=ktl).\n",
      "\n",
      "3. dna[RFP(circular)] --> dna[RFP(circular)]+rna[RFP]\n",
      " Kf=k_forward * dna_RFP_circular\n",
      "  k_forward=0.5\n",
      "  found_key=(mech=None, partid=None, name=ktx).\n",
      "  search_key=(mech=simple_transcription, partid=strong, name=ktx).\n",
      "\n",
      "4. rna[RFP] --> rna[RFP]+protein[RFP]\n",
      " Kf=k_forward * rna_RFP\n",
      "  k_forward=1.5\n",
      "  found_key=(mech=None, partid=None, name=ktl).\n",
      "  search_key=(mech=simple_translation, partid=weak, name=ktl).\n",
      "\n",
      "5. dna[GFP]+recBCD <--> complex[dna[GFP]:recBCD]\n",
      " Kf=k_forward * dna_GFP * recBCD\n",
      " Kr=k_reverse * complex_dna_GFP_recBCD_\n",
      "  k_forward=100\n",
      "  found_key=(mech=None, partid=None, name=kb).\n",
      "  search_key=(mech=deg_tagged_degradation, partid=dna_GFP, name=kb).\n",
      "  k_reverse=10\n",
      "  found_key=(mech=None, partid=None, name=ku).\n",
      "  search_key=(mech=deg_tagged_degradation, partid=dna_GFP, name=ku).\n",
      "\n",
      "6. complex[dna[GFP]:recBCD] --> recBCD\n",
      " Kf=k_forward * complex_dna_GFP_recBCD_\n",
      "  k_forward=0.5\n",
      "  found_key=(mech=None, partid=None, name=kdeg).\n",
      "  search_key=(mech=deg_tagged_degradation, partid=dna_GFP, name=kdeg).\n",
      "\n",
      "7. rna[GFP] --> \n",
      " Kf=k_forward * rna_GFP\n",
      "  k_forward=0.05\n",
      "  found_key=(mech=None, partid=None, name=kdil).\n",
      "  search_key=(mech=rna_degradation, partid=rna_GFP, name=kdil).\n",
      "\n",
      "8. rna[RFP] --> \n",
      " Kf=k_forward * rna_RFP\n",
      "  k_forward=0.05\n",
      "  found_key=(mech=None, partid=None, name=kdil).\n",
      "  search_key=(mech=rna_degradation, partid=rna_RFP, name=kdil).\n",
      "\n",
      "]\n",
      "Simulating with BioSCRAPE\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/murray/Library/CloudStorage/Dropbox/macosx/src/biocrnpyler/biocrnpyler/mechanisms/global_mechanisms.py:106: UserWarning: species dna_RFP_circular has multiple attributes(or material type) which conflict with global mechanism filter deg_tagged_degradation. Using default value False.\n",
      "  warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from biocrnpyler.mechanisms import Deg_Tagged_Degradation\n",
    "from biocrnpyler.mixtures import SimpleTxTlExtract\n",
    "parameters = {\"kb\": 100, \"ku\": 10,\"kdeg\": .5, \"ktx\": .5, \"ktl\": 1.5, \"kdil\": .05}\n",
    "\n",
    "recBCD = Species(\"recBCD\")\n",
    "gamS = Species(\"GamS\")\n",
    "\n",
    "linear_dna_degradation = Deg_Tagged_Degradation(\n",
    "    degradase=recBCD, filter_dict={\"dna\":True, \"circular\":False}, default_on=False)\n",
    "\n",
    "inhibited_recBCD = ChemicalComplex([recBCD]+2*[gamS])\n",
    "\n",
    "Assembly_linear = DNAassembly(\"GFP\", promoter=\"strong\", rbs=\"weak\", initial_concentration=1.0)\n",
    "Assembly_circular = DNAassembly(\"RFP\", promoter=\"strong\", rbs=\"weak\", initial_concentration=1.0, attributes=[\"circular\"])\n",
    "\n",
    "M = SimpleTxTlExtract(\"Mixture 1\", \n",
    "                      components = [inhibited_recBCD, Assembly_linear, Assembly_circular],\n",
    "                     global_mechanisms = [linear_dna_degradation], parameters = parameters)\n",
    "\n",
    "CRN = M.compile_crn()\n",
    "print(CRN.pretty_print())\n",
    "\n",
    "try:\n",
    "    %matplotlib inline\n",
    "    print(\"Simulating with BioSCRAPE\")\n",
    "    import numpy as np\n",
    "    import pylab as plt\n",
    "    timepoints = np.arange(0, 100, .1)\n",
    "    \n",
    "    #Simulate with no gamS\n",
    "    x0 = {str(recBCD):1, str(gamS):0, str(Assembly_linear.dna):1.0, str(Assembly_circular.dna):1.0}\n",
    "    R = CRN.simulate_with_bioscrape_via_sbml(timepoints, initial_condition_dict = x0)\n",
    "    \n",
    "    #Simulate with GamS\n",
    "    x0 = {str(recBCD):1, str(gamS):20, str(Assembly_linear.dna):1.0, str(Assembly_circular.dna):1.0}\n",
    "    R2 = CRN.simulate_with_bioscrape_via_sbml(timepoints, initial_condition_dict = x0)\n",
    "    \n",
    "    if R is not None and R2 is not None:\n",
    "        plt.subplot(121)\n",
    "        plt.title(\"No GamS\")\n",
    "        plt.plot(timepoints, R[str(Assembly_linear.protein)], label = \"GFP (linear)\")\n",
    "        plt.plot(timepoints, R[str(Assembly_circular.protein)], label = \"RFP (circular)\")\n",
    "        plt.legend()\n",
    "\n",
    "        plt.subplot(122)\n",
    "        plt.title(\"With GamS\")\n",
    "        plt.plot(timepoints, R2[str(Assembly_linear.protein)], label = \"GFP (linear)\")\n",
    "        plt.plot(timepoints, R2[str(Assembly_circular.protein)], label = \"RFP (circular)\")\n",
    "        plt.legend()\n",
    "\n",
    "        plt.show()\n",
    "\n",
    "except ModuleNotFoundError:\n",
    "    print('please install the plotting libraries: pip install biocrnpyler[all]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# End"
   ]
  }
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