{
 "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 degredation 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),  complex[protein[Ribo]:rna[G1]] (@ 0),  complex[protein[RNAase]:rna[G2]] (@ 0),  complex[protein[RNAase]:rna[G1]] (@ 0),  complex[dna[G2]:protein[RNAP]] (@ 0),  complex[dna[G1]:protein[RNAP]] (@ 0),  complex[complex[protein[Ribo]:rna[G2]]:protein[RNAase]] (@ 0),  complex[complex[protein[Ribo]:rna[G1]]:protein[RNAase]] (@ 0),  protein[Ribo] (@ 0),  protein[RNAase] (@ 0),  protein[RNAP] (@ 0),  protein[G2] (@ 0),  rna[G2] (@ 0),  dna[G2(genomic)] (@ 0),  protein[G1] (@ 0),  rna[G1] (@ 0),  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. protein[RNAP] --> \n",
      " Kf=k_forward * protein_RNAP\n",
      "  k_forward=0.5\n",
      "\n",
      "9. protein[Ribo] --> \n",
      " Kf=k_forward * protein_Ribo\n",
      "  k_forward=0.5\n",
      "\n",
      "10. protein[RNAase] --> \n",
      " Kf=k_forward * protein_RNAase\n",
      "  k_forward=0.5\n",
      "\n",
      "11. dna[G1] --> \n",
      " Kf=k_forward * dna_G1\n",
      "  k_forward=0.5\n",
      "\n",
      "12. rna[G1] --> \n",
      " Kf=k_forward * rna_G1\n",
      "  k_forward=0.5\n",
      "\n",
      "13. complex[dna[G1]:protein[RNAP]] --> \n",
      " Kf=k_forward * complex_dna_G1_protein_RNAP_\n",
      "  k_forward=0.5\n",
      "\n",
      "14. protein[G1] --> \n",
      " Kf=k_forward * protein_G1\n",
      "  k_forward=0.5\n",
      "\n",
      "15. complex[protein[Ribo]:rna[G1]] --> \n",
      " Kf=k_forward * complex_protein_Ribo_rna_G1_\n",
      "  k_forward=0.5\n",
      "\n",
      "16. rna[G2] --> \n",
      " Kf=k_forward * rna_G2\n",
      "  k_forward=0.5\n",
      "\n",
      "17. protein[G2] --> \n",
      " Kf=k_forward * protein_G2\n",
      "  k_forward=0.5\n",
      "\n",
      "18. complex[protein[Ribo]:rna[G2]] --> \n",
      " Kf=k_forward * complex_protein_Ribo_rna_G2_\n",
      "  k_forward=0.5\n",
      "\n",
      "19. 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",
      "20. complex[protein[RNAase]:rna[G1]] --> protein[RNAase]\n",
      " Kf=k_forward * complex_protein_RNAase_rna_G1_\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. 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",
      "26. 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",
      "]\n",
      "Simulating with BioSCRAPE\n",
      "Simulating with BioSCRAPE\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ayush\\Box\\Research\\bioCRNpyler\\biocrnpyler\\core\\chemical_reaction_network.py:362: UserWarning: Trying to set species that is not in model: protein_Ribo_machinery\n",
      "  m.set_species(processed)\n",
      "C:\\Users\\ayush\\Box\\Research\\bioCRNpyler\\biocrnpyler\\core\\chemical_reaction_network.py:362: UserWarning: Trying to set species that is not in model: protein_RNAP_machinery\n",
      "  m.set_species(processed)\n",
      "C:\\Users\\ayush\\Box\\Research\\bioCRNpyler\\biocrnpyler\\core\\chemical_reaction_network.py:362: UserWarning: Trying to set species that is not in model: protein_RNAase_machinery\n",
      "  m.set_species(processed)\n",
      "C:\\Users\\ayush\\Box\\Research\\bioCRNpyler\\biocrnpyler\\core\\chemical_reaction_network.py:362: UserWarning: Trying to set species that is not in model: protein_Ribo_machinery\n",
      "  m.set_species(processed)\n",
      "C:\\Users\\ayush\\Box\\Research\\bioCRNpyler\\biocrnpyler\\core\\chemical_reaction_network.py:362: UserWarning: Trying to set species that is not in model: protein_RNAP_machinery\n",
      "  m.set_species(processed)\n",
      "C:\\Users\\ayush\\Box\\Research\\bioCRNpyler\\biocrnpyler\\core\\chemical_reaction_network.py:362: 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",
    "\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_materials = True,\n",
    "                         show_attributes = True, show_keys = False))\n",
    "\n",
    "\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": 24,
   "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),  dna[G] (@ 0),  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_degredation_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 Degredation of Linear DNA Strands by RecBCD inhibited by GamS\n",
    "\n",
    "In this example, the degredation of linear DNA strands in an extract is modeled along with the possibility of inhibiting this degredation 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 degredation 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 degredation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Species(N = 10) = {\n",
      "recBCD (@ 0),  complex[dna[GFP]:recBCD] (@ 0),  protein[RFP] (@ 0),  rna[RFP] (@ 0),  dna[RFP(circular)] (@ 0),  complex[2x_GamS:recBCD] (@ 0),  GamS (@ 0),  protein[GFP] (@ 0),  rna[GFP] (@ 0),  dna[GFP] (@ 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_degredation, partid=dna_GFP, name=kb).\n",
      "  k_reverse=10\n",
      "  found_key=(mech=None, partid=None, name=ku).\n",
      "  search_key=(mech=deg_tagged_degredation, 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_degredation, 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_degredation, 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_degredation, partid=rna_RFP, name=kdil).\n",
      "\n",
      "]\n",
      "Simulating with BioSCRAPE\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ayush\\Box\\Research\\bioCRNpyler\\biocrnpyler\\mechanisms\\global_mechanisms.py:104: UserWarning: species dna_RFP_circular has multiple attributes(or material type) which conflict with global mechanism filter deg_tagged_degredation. Using default value False.\n",
      "  warn(f\"species {repr(s)} has multiple attributes(or material type) which conflict with global mechanism filter {repr(self)}. Using default value {self.default_on}.\")\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_Degredation\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_degredation = Deg_Tagged_Degredation(degredase = recBCD, \n",
    "                                                filter_dict = {\"dna\":True, \"circular\":False}, \n",
    "                                                default_on = False)\n",
    "\n",
    "inhibited_recBCD = ChemicalComplex([recBCD]+2*[gamS])\n",
    "\n",
    "Assembly_linear = DNAassembly(\"GFP\", promoter = \"strong\", rbs = \"weak\", initial_conc = 1.0)\n",
    "Assembly_circular = DNAassembly(\"RFP\", promoter = \"strong\", rbs = \"weak\", initial_conc = 1.0, attributes = [\"circular\"])\n",
    "\n",
    "M = SimpleTxTlExtract(\"Mixture 1\", \n",
    "                      components = [inhibited_recBCD, Assembly_linear, Assembly_circular],\n",
    "                     global_mechanisms = [linear_dna_degredation], 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": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# End"
   ]
  }
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