{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DNA Assemblies, gene expression, transcription, and translation\n",
    "The central Dogma - namely that DNA is transcribed to mRNA which is translated to proteins - is a key part of modelling many biochemical processes, especially in synthetic biology. Towards enabling easy modeling of transcription and translation in diverse context, BioCRNpyler has a number of Mixtures, Components and Mechanisms to produce models which include the central dogma."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 1: Creating a DNAassembly\n",
    "DNAassembly is a Component consisting of 2 sub Components: Promoter and RBS (ribosome binding site). DNAassembly automatically produces transcription and translation reactions from simple specifications: a DNAassembly X will produce 3 species dna_X, rna_X, and protein_X. These species can also be renamed manually if desired."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "repr(Mixture) gives a printout of what is in a mixture and what it's Mechanisms are:\n",
      " Mixture: Catalysis Mixture\n",
      "Components = [\n",
      "\tDNAassembly: X ]\n",
      "Mechanisms = {\n",
      "\ttranscription:simple_transcription\n",
      "\ttranslation:simple_translation } \n",
      "\n",
      "Pretty_print representation of the CRN:\n",
      " Species(N = 3) = {\n",
      "protein[X] (@ 0),  rna[X] (@ 0),  dna[X] (@ 0),  \n",
      "}\n",
      "\n",
      "Reactions (2) = [\n",
      "0. dna[X] --> dna[X]+rna[X]\n",
      " Kf=k_forward * dna_X\n",
      "  k_forward=0.5\n",
      "  found_key=(mech=None, partid=None, name=ktx).\n",
      "  search_key=(mech=simple_transcription, partid=P, name=ktx).\n",
      "\n",
      "1. rna[X] --> rna[X]+protein[X]\n",
      " Kf=k_forward * rna_X\n",
      "  k_forward=2\n",
      "  found_key=(mech=None, partid=None, name=ktl).\n",
      "  search_key=(mech=simple_translation, partid=RBS, name=ktl).\n",
      "\n",
      "]\n"
     ]
    }
   ],
   "source": [
    "from biocrnpyler import *\n",
    "\n",
    "#promoter is the name of the promoter or a Promoter component\n",
    "#RBS is the name of the RBS or an RBS Component\n",
    "#   RBS = None means there will be no translation\n",
    "#By default (if transcript and protein are None), the transgript and protein will have the same name as an assmebly\n",
    "#   Users can also assign Species or String names to transcript and protein\n",
    "G = DNAassembly(\"X\", promoter = \"P\", rbs = \"RBS\", transcript = None, protein = None)\n",
    "\n",
    "#create a transcription and translation Mechanisms. \n",
    "mech_tx = SimpleTranscription()\n",
    "mech_tl = SimpleTranslation()\n",
    "\n",
    "#place that mechanism in a dictionary: {\"transcription\":mech_tx, \"translation\":mech_tl}\n",
    "default_mechanisms = {mech_tx.mechanism_type:mech_tx, mech_tl.mechanism_type:mech_tl}\n",
    "\n",
    "#Use default parameters for conviencience\n",
    "default_parameters = {\"kb\":100, \"ku\":10, \"ktx\":.5, \"ktl\":2}\n",
    "#Create a mixture.\n",
    "M = Mixture(\"Catalysis Mixture\", components = [G], parameters = default_parameters, mechanisms = default_mechanisms)\n",
    "    \n",
    "print(\"repr(Mixture) gives a printout of what is in a mixture and what it's Mechanisms are:\\n\", repr(M),\"\\n\")\n",
    "\n",
    "#Compile the CRN with Mixture.compile_crn\n",
    "CRN = M.compile_crn()\n",
    "\n",
    "print(\"Pretty_print representation of the CRN:\\n\",\n",
    "      CRN.pretty_print(show_rates = True, show_attributes = True, show_materials = True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 2: Placing a DNAassmebly in different Cell-like Mixtures for Transcription and Translation\n",
    "BioCRNpyler contains many pre-built Mixtures to model Transcription and Translation at different levels of detail in different contexts. Cell-like Mixtures are designed to more accurately model the internal environment of a cell.\n",
    "\n",
    "## Cell-Like Mixtures:\n",
    "__ExpressionDilutionMixture__ uses the Mechanisms: OneStepGeneExpression\n",
    "* Genes express in a single step $G \\to G + P_G$. \n",
    "* Proteins are degraded: $P_G \\to \\emptyset$.\n",
    "\n",
    "__SimpleTxTlDilutionMixture__ uses the Mechanisms: SimpleTranscription, SimpleTranslation, and Dilution (A global Mechanisms)\n",
    "* Genes transcribe via simple catalysis $G_X \\to G_X + T_X$\n",
    "* mRNAs translate via simple catalyis $T_X \\to T_X + P_X$. \n",
    "* Proteins and mRNA are degraded/diluted at different rates: $T_X \\to \\emptyset$, $P_X \\to \\emptyset$.\n",
    "\n",
    "__TxTlDilutionMixture__ uses the Mechanisms: Transcription_MM, Translation_MM, Degredation_mRNA_MM and Dilution (A global Mechanisms)\n",
    "* Genes transcribe via RNApolymerase (RNAP) $G_X + RNAP \\rightleftarrows G_X:RNAP \\to G_X + RNAP + T_X$\n",
    "* mRNAs translate via Ribosomes (Ribo) $T_X + Ribo \\rightleftarrows T_X:Ribo \\to T_X + Ribo + P_X$\n",
    "* mRNAs are degraded via endonucleases (Endo): $T_X + Endo \\rightleftarrows T_X:Endo \\to Endo$\n",
    "* Proteins & mRNA are Diluted: $P_X \\to \\emptyset$, $T_X \\to \\emptyset$\n",
    "* A Background DNAassembly $G_\\textrm{cellular_processes}$ puts loading on all the cellular resources.\n",
    "\n",
    "In the following examples, the parameter file default_parameters.txt will be used to quickly produce more complex models with realistic parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ExpressionDilutionMixture: ExpressionDilution\n",
      "Components = [\n",
      "\tDNAassembly: X ]\n",
      "Mechanisms = {\n",
      "\ttranscription:gene_expression\n",
      "\ttranslation:dummy_translation\n",
      "\tcatalysis:basic_catalysis\n",
      "\tbinding:one_step_binding }\n",
      "Global Mechanisms = {\n",
      "\tdilution:dilution } \n",
      " Species(N = 2) = {\n",
      "protein[X] (@ 0),  dna[X] (@ 0),  \n",
      "}\n",
      "\n",
      "Reactions (2) = [\n",
      "0. dna[X] --> dna[X]+protein[X]\n",
      " Kf=k_forward * dna_X\n",
      "  k_forward=0.28125\n",
      "  found_key=(mech=gene_expression, partid=None, name=kexpress).\n",
      "  search_key=(mech=gene_expression, partid=strong, name=kexpress).\n",
      "\n",
      "1. protein[X] --> \n",
      " Kf=k_forward * protein_X\n",
      "  k_forward=0.001\n",
      "  found_key=(mech=None, partid=None, name=kdil).\n",
      "  search_key=(mech=dilution, partid=protein_X, name=kdil).\n",
      "\n",
      "] \n",
      "\n",
      "\n",
      "SimpleTxTlDilutionMixture: SimpleTxTl\n",
      "Components = [\n",
      "\tDNAassembly: X ]\n",
      "Mechanisms = {\n",
      "\ttranscription:simple_transcription\n",
      "\ttranslation:simple_translation\n",
      "\tcatalysis:basic_catalysis\n",
      "\tbinding:one_step_binding }\n",
      "Global Mechanisms = {\n",
      "\tdilution:global_degredation_via_dilution\n",
      "\trna_degredation:rna_degredation } \n",
      " Species(N = 3) = {\n",
      "protein[X] (@ 0),  rna[X] (@ 0),  dna[X] (@ 0),  \n",
      "}\n",
      "\n",
      "Reactions (5) = [\n",
      "0. dna[X] --> dna[X]+rna[X]\n",
      " Kf=k_forward * dna_X\n",
      "  k_forward=0.4775625\n",
      "  found_key=(mech=simple_transcription, partid=strong, name=ktx).\n",
      "  search_key=(mech=simple_transcription, partid=strong, name=ktx).\n",
      "\n",
      "1. rna[X] --> rna[X]+protein[X]\n",
      " Kf=k_forward * rna_X\n",
      "  k_forward=0.06\n",
      "  found_key=(mech=simple_translation, partid=weak, name=ktl).\n",
      "  search_key=(mech=simple_translation, partid=weak, name=ktl).\n",
      "\n",
      "2. rna[X] --> \n",
      " Kf=k_forward * rna_X\n",
      "  k_forward=0.001\n",
      "  found_key=(mech=None, partid=None, name=kdil).\n",
      "  search_key=(mech=global_degredation_via_dilution, partid=rna_X, name=kdil).\n",
      "\n",
      "3. protein[X] --> \n",
      " Kf=k_forward * protein_X\n",
      "  k_forward=0.001\n",
      "  found_key=(mech=None, partid=None, name=kdil).\n",
      "  search_key=(mech=global_degredation_via_dilution, partid=protein_X, name=kdil).\n",
      "\n",
      "4. rna[X] --> \n",
      " Kf=k_forward * rna_X\n",
      "  k_forward=0.001\n",
      "  found_key=(mech=rna_degredation, partid=None, name=kdil).\n",
      "  search_key=(mech=rna_degredation, partid=rna_X, name=kdil).\n",
      "\n",
      "] \n",
      "\n",
      "\n",
      "TxTlDilutionMixture: e coli\n",
      "Components = [\n",
      "\tDNAassembly: X\n",
      "\tProtein: RNAP\n",
      "\tProtein: Ribo\n",
      "\tProtein: RNAase\n",
      "\tDNAassembly: cellular_processes ]\n",
      "Mechanisms = {\n",
      "\ttranscription:transcription_mm\n",
      "\ttranslation:translation_mm\n",
      "\tcatalysis:michalis_menten\n",
      "\tbinding:one_step_binding }\n",
      "Global Mechanisms = {\n",
      "\trna_degredation:rna_degredation_mm\n",
      "\tdilution:global_degredation_via_dilution } \n",
      " Species(N = 17) = {\n",
      "dna[cellular_processes] (@ 5.0),  \n",
      "   found_key=(mech=None, partid=e coli, name=cellular_processes).\n",
      "   search_key=(mech=initial concentration, partid=e coli, name=cellular_processes).\n",
      "complex[protein[Ribo]:rna[cellular_processes]] (@ 0),  complex[protein[Ribo]:rna[X]] (@ 0),  complex[protein[RNAase]:rna[cellular_processes]] (@ 0),  complex[protein[RNAase]:rna[X]] (@ 0),  complex[dna[cellular_processes]:protein[RNAP]] (@ 0),  complex[dna[X]:protein[RNAP]] (@ 0),  complex[complex[protein[Ribo]:rna[cellular_processes]]:protein[RNAase]] (@ 0),  complex[complex[protein[Ribo]:rna[X]]:protein[RNAase]] (@ 0),  protein[cellular_processes] (@ 0),  rna[cellular_processes] (@ 0),  protein[X] (@ 0),  rna[X] (@ 0),  dna[X] (@ 0),  protein[Ribo(machinery)] (@ 0),  protein[RNAase(machinery)] (@ 0),  protein[RNAP(machinery)] (@ 0),  \n",
      "}\n",
      "\n",
      "Reactions (20) = [\n",
      "0. dna[X]+protein[RNAP(machinery)] <--> complex[dna[X]:protein[RNAP]]\n",
      " Kf=k_forward * dna_X * protein_RNAP_machinery\n",
      " Kr=k_reverse * complex_dna_X_protein_RNAP_machinery_\n",
      "  k_forward=100.0\n",
      "  found_key=(mech=None, partid=None, name=kb).\n",
      "  search_key=(mech=transcription_mm, partid=strong, name=kb).\n",
      "  k_reverse=0.5\n",
      "  found_key=(mech=None, partid=strong, name=ku).\n",
      "  search_key=(mech=transcription_mm, partid=strong, name=ku).\n",
      "\n",
      "1. complex[dna[X]:protein[RNAP]] --> dna[X]+rna[X]+protein[RNAP(machinery)]\n",
      " Kf=k_forward * complex_dna_X_protein_RNAP_machinery_\n",
      "  k_forward=3.926187672\n",
      "  found_key=(mech=None, partid=strong, name=ktx).\n",
      "  search_key=(mech=transcription_mm, partid=strong, name=ktx).\n",
      "\n",
      "2. rna[X]+protein[Ribo(machinery)] <--> complex[protein[Ribo]:rna[X]]\n",
      " Kf=k_forward * rna_X * protein_Ribo_machinery\n",
      " Kr=k_reverse * complex_protein_Ribo_machinery_rna_X_\n",
      "  k_forward=100.0\n",
      "  found_key=(mech=None, partid=None, name=kb).\n",
      "  search_key=(mech=translation_mm, partid=weak, name=kb).\n",
      "  k_reverse=5.0\n",
      "  found_key=(mech=None, partid=weak, name=ku).\n",
      "  search_key=(mech=translation_mm, partid=weak, name=ku).\n",
      "\n",
      "3. complex[protein[Ribo]:rna[X]] --> rna[X]+protein[X]+protein[Ribo(machinery)]\n",
      " Kf=k_forward * complex_protein_Ribo_machinery_rna_X_\n",
      "  k_forward=0.05\n",
      "  found_key=(mech=None, partid=None, name=ktl).\n",
      "  search_key=(mech=translation_mm, partid=weak, name=ktl).\n",
      "\n",
      "4. dna[cellular_processes]+protein[RNAP(machinery)] <--> complex[dna[cellular_processes]:protein[RNAP]]\n",
      " Kf=k_forward * dna_cellular_processes * protein_RNAP_machinery\n",
      " Kr=k_reverse * complex_dna_cellular_processes_protein_RNAP_machinery_\n",
      "  k_forward=500\n",
      "  found_key=(mech=transcription, partid=None, name=kb).\n",
      "  search_key=(mech=transcription_mm, partid=average_promoter, name=kb).\n",
      "  k_reverse=50\n",
      "  found_key=(mech=transcription, partid=None, name=ku).\n",
      "  search_key=(mech=transcription_mm, partid=average_promoter, name=ku).\n",
      "\n",
      "5. complex[dna[cellular_processes]:protein[RNAP]] --> dna[cellular_processes]+rna[cellular_processes]+protein[RNAP(machinery)]\n",
      " Kf=k_forward * complex_dna_cellular_processes_protein_RNAP_machinery_\n",
      "  k_forward=0.1\n",
      "  found_key=(mech=transcription, partid=None, name=ktx).\n",
      "  search_key=(mech=transcription_mm, partid=average_promoter, name=ktx).\n",
      "\n",
      "6. rna[cellular_processes]+protein[Ribo(machinery)] <--> complex[protein[Ribo]:rna[cellular_processes]]\n",
      " Kf=k_forward * rna_cellular_processes * protein_Ribo_machinery\n",
      " Kr=k_reverse * complex_protein_Ribo_machinery_rna_cellular_processes_\n",
      "  k_forward=500\n",
      "  found_key=(mech=translation, partid=None, name=kb).\n",
      "  search_key=(mech=translation_mm, partid=average_rbs, name=kb).\n",
      "  k_reverse=5\n",
      "  found_key=(mech=translation, partid=None, name=ku).\n",
      "  search_key=(mech=translation_mm, partid=average_rbs, name=ku).\n",
      "\n",
      "7. complex[protein[Ribo]:rna[cellular_processes]] --> rna[cellular_processes]+protein[cellular_processes]+protein[Ribo(machinery)]\n",
      " Kf=k_forward * complex_protein_Ribo_machinery_rna_cellular_processes_\n",
      "  k_forward=0.1\n",
      "  found_key=(mech=translation, partid=None, name=ktl).\n",
      "  search_key=(mech=translation_mm, partid=average_rbs, name=ktl).\n",
      "\n",
      "8. rna[X]+protein[RNAase(machinery)] <--> complex[protein[RNAase]:rna[X]]\n",
      " Kf=k_forward * rna_X * protein_RNAase_machinery\n",
      " Kr=k_reverse * complex_protein_RNAase_machinery_rna_X_\n",
      "  k_forward=100.0\n",
      "  found_key=(mech=None, partid=None, name=kb).\n",
      "  search_key=(mech=rna_degredation_mm, partid=rna_X, name=kb).\n",
      "  k_reverse=10.0\n",
      "  found_key=(mech=None, partid=None, name=ku).\n",
      "  search_key=(mech=rna_degredation_mm, partid=rna_X, name=ku).\n",
      "\n",
      "9. complex[protein[RNAase]:rna[X]] --> protein[RNAase(machinery)]\n",
      " Kf=k_forward * complex_protein_RNAase_machinery_rna_X_\n",
      "  k_forward=0.001\n",
      "  found_key=(mech=rna_degredation_mm, partid=None, name=kdeg).\n",
      "  search_key=(mech=rna_degredation_mm, partid=rna_X, name=kdeg).\n",
      "\n",
      "10. complex[protein[Ribo]:rna[X]]+protein[RNAase(machinery)] <--> complex[complex[protein[Ribo]:rna[X]]:protein[RNAase]]\n",
      " Kf=k_forward * complex_protein_Ribo_machinery_rna_X_ * protein_RNAase_machinery\n",
      " Kr=k_reverse * complex_complex_protein_Ribo_machinery_rna_X__protein_RNAase_machinery_\n",
      "  k_forward=100.0\n",
      "  found_key=(mech=None, partid=None, name=kb).\n",
      "  search_key=(mech=rna_degredation_mm, partid=complex_protein_Ribo_machinery_rna_X_, name=kb).\n",
      "  k_reverse=10.0\n",
      "  found_key=(mech=None, partid=None, name=ku).\n",
      "  search_key=(mech=rna_degredation_mm, partid=complex_protein_Ribo_machinery_rna_X_, name=ku).\n",
      "\n",
      "11. complex[complex[protein[Ribo]:rna[X]]:protein[RNAase]] --> protein[Ribo(machinery)]+protein[RNAase(machinery)]\n",
      " Kf=k_forward * complex_complex_protein_Ribo_machinery_rna_X__protein_RNAase_machinery_\n",
      "  k_forward=0.001\n",
      "  found_key=(mech=rna_degredation_mm, partid=None, name=kdeg).\n",
      "  search_key=(mech=rna_degredation_mm, partid=complex_protein_Ribo_machinery_rna_X_, name=kdeg).\n",
      "\n",
      "12. rna[cellular_processes]+protein[RNAase(machinery)] <--> complex[protein[RNAase]:rna[cellular_processes]]\n",
      " Kf=k_forward * rna_cellular_processes * protein_RNAase_machinery\n",
      " Kr=k_reverse * complex_protein_RNAase_machinery_rna_cellular_processes_\n",
      "  k_forward=100.0\n",
      "  found_key=(mech=None, partid=None, name=kb).\n",
      "  search_key=(mech=rna_degredation_mm, partid=rna_cellular_processes, name=kb).\n",
      "  k_reverse=10.0\n",
      "  found_key=(mech=None, partid=None, name=ku).\n",
      "  search_key=(mech=rna_degredation_mm, partid=rna_cellular_processes, name=ku).\n",
      "\n",
      "13. complex[protein[RNAase]:rna[cellular_processes]] --> protein[RNAase(machinery)]\n",
      " Kf=k_forward * complex_protein_RNAase_machinery_rna_cellular_processes_\n",
      "  k_forward=0.001\n",
      "  found_key=(mech=rna_degredation_mm, partid=None, name=kdeg).\n",
      "  search_key=(mech=rna_degredation_mm, partid=rna_cellular_processes, name=kdeg).\n",
      "\n",
      "14. complex[protein[Ribo]:rna[cellular_processes]]+protein[RNAase(machinery)] <--> complex[complex[protein[Ribo]:rna[cellular_processes]]:protein[RNAase]]\n",
      " Kf=k_forward * complex_protein_Ribo_machinery_rna_cellular_processes_ * protein_RNAase_machinery\n",
      " Kr=k_reverse * complex_complex_protein_Ribo_machinery_rna_cellular_processes__protein_RNAase_machinery_\n",
      "  k_forward=100.0\n",
      "  found_key=(mech=None, partid=None, name=kb).\n",
      "  search_key=(mech=rna_degredation_mm, partid=complex_protein_Ribo_machinery_rna_cellular_processes_, name=kb).\n",
      "  k_reverse=10.0\n",
      "  found_key=(mech=None, partid=None, name=ku).\n",
      "  search_key=(mech=rna_degredation_mm, partid=complex_protein_Ribo_machinery_rna_cellular_processes_, name=ku).\n",
      "\n",
      "15. complex[complex[protein[Ribo]:rna[cellular_processes]]:protein[RNAase]] --> protein[Ribo(machinery)]+protein[RNAase(machinery)]\n",
      " Kf=k_forward * complex_complex_protein_Ribo_machinery_rna_cellular_processes__protein_RNAase_machinery_\n",
      "  k_forward=0.001\n",
      "  found_key=(mech=rna_degredation_mm, partid=None, name=kdeg).\n",
      "  search_key=(mech=rna_degredation_mm, partid=complex_protein_Ribo_machinery_rna_cellular_processes_, name=kdeg).\n",
      "\n",
      "16. rna[X] --> \n",
      " Kf=k_forward * rna_X\n",
      "  k_forward=0.001\n",
      "  found_key=(mech=None, partid=None, name=kdil).\n",
      "  search_key=(mech=global_degredation_via_dilution, partid=rna_X, name=kdil).\n",
      "\n",
      "17. protein[X] --> \n",
      " Kf=k_forward * protein_X\n",
      "  k_forward=0.001\n",
      "  found_key=(mech=None, partid=None, name=kdil).\n",
      "  search_key=(mech=global_degredation_via_dilution, partid=protein_X, name=kdil).\n",
      "\n",
      "18. rna[cellular_processes] --> \n",
      " Kf=k_forward * rna_cellular_processes\n",
      "  k_forward=0.001\n",
      "  found_key=(mech=None, partid=None, name=kdil).\n",
      "  search_key=(mech=global_degredation_via_dilution, partid=rna_cellular_processes, name=kdil).\n",
      "\n",
      "19. protein[cellular_processes] --> \n",
      " Kf=k_forward * protein_cellular_processes\n",
      "  k_forward=0.001\n",
      "  found_key=(mech=None, partid=None, name=kdil).\n",
      "  search_key=(mech=global_degredation_via_dilution, partid=protein_cellular_processes, name=kdil).\n",
      "\n",
      "] \n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel\\ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\biocrnpyler-1.1.0-py3.7.egg\\biocrnpyler\\parameter.py:507: UserWarning: parameter file contains no unit column! Please add a column named ['unit', 'units'].\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\biocrnpyler-1.1.0-py3.7.egg\\biocrnpyler\\parameter.py:507: UserWarning: parameter file contains no unit column! Please add a column named ['unit', 'units'].\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\biocrnpyler-1.1.0-py3.7.egg\\biocrnpyler\\parameter.py:507: UserWarning: parameter file contains no unit column! Please add a column named ['unit', 'units'].\n"
     ]
    }
   ],
   "source": [
    "#Changing the promoter and RBS name will result in different parameters\n",
    "#Here are some parameters loaded into default_parameters.txt\n",
    "#Promoter Names: strong, medium, weak correspond to bicistronic RBSs: BCD2, BCD8 and BCD12\n",
    "#RBS Names: strong, medium, Weak correspond to Anderson Promoters: J23100, J23106, and J23103\n",
    "G = DNAassembly(\"X\", promoter = \"strong\", rbs = \"weak\", transcript = None, protein = None)\n",
    "#Also notice that the names of transcript and protein can be changed, or set to Species.\n",
    "\n",
    "#Compare the Following Mixtures and Resulting CRNs\n",
    "M1 = ExpressionDilutionMixture(\"ExpressionDilution\", components = [G], parameter_file = \"default_parameters.txt\")\n",
    "CRN1 = M1.compile_crn()\n",
    "print(repr(M1),\"\\n\", CRN1.pretty_print(show_attributes = True, show_material = True, show_rates = True),\"\\n\\n\")\n",
    "\n",
    "#Components should be reinstantiated before passing them into a new Mixture\n",
    "G = DNAassembly(\"X\", promoter = \"strong\", rbs = \"weak\", transcript = None, protein = None)\n",
    "M2 = SimpleTxTlDilutionMixture(\"SimpleTxTl\", components = [G], parameter_file = \"default_parameters.txt\")\n",
    "CRN2 = M2.compile_crn()\n",
    "print(repr(M2),\"\\n\", CRN2.pretty_print(show_attributes = True, show_material = True, show_rates = True),\"\\n\\n\")\n",
    "\n",
    "G = DNAassembly(\"X\", promoter = \"strong\", rbs = \"weak\", transcript = None, protein = None)\n",
    "M3 = TxTlDilutionMixture(\"e coli\", components = [G], parameter_file = \"default_parameters.txt\")\n",
    "\n",
    "CRN3 = M3.compile_crn()\n",
    "print(repr(M3),\"\\n\", CRN3.pretty_print(show_attributes = True, show_material = True, show_rates = True),\"\\n\\n\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 3: Placing a DNAassmebly in different Extract-like Mixtures for Transcription and Translation\n",
    "BioCRNpyler contains many pre-built Mixtures to model Transcription and Translation at different levels of detail in different contexts. Cell-like Mixtures are designed to more accurately model the internal environment of a cell.\n",
    "\n",
    "## Extract-Like Mixtures:\n",
    "__ExpressionDilutionMixture__ uses the Mechanisms: OneStepGeneExpression\n",
    "* Genes express in a single step $G \\to G + P_G$. \n",
    "__SimpleTxTlExtract__ uses the Mechanisms: SimpleTranscription, SimpleTranslation, and Dilution (A global Mechanisms)\n",
    "* Genes transcribe via simple catalysis $G_X \\to G_X + T_X$\n",
    "* mRNAs translate via simple catalyis $T_X \\to T_X + P_X$. \n",
    "* mRNA are degraded: $T_X \\to \\emptyset$\n",
    "\n",
    "__TxTlExtract__ uses the Mechanisms: Transcription_MM, Translation_MM, Degredation_mRNA_MM and Dilution (A global Mechanisms)\n",
    "* Genes transcribe via RNApolymerase (RNAP) $G_X + RNAP \\rightleftarrows G_X:RNAP \\to G_X + RNAP + T_X$\n",
    "* mRNAs translate via Ribosomes (Ribo) $T_X + Ribo \\rightleftarrows T_X:Ribo \\to T_X + Ribo + P_X$\n",
    "* mRNAs are degraded via endonucleases (Endo): $T_X + Endo \\rightleftarrows T_X:Endo \\to Endo$\n",
    "\n",
    "In the following examples, the parameter file default_parameters.txt will be used to quickly produce more complex models with realistic parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ExpressionExtract: ExpressionExtract\n",
      "Components = [\n",
      "\tDNAassembly: X ]\n",
      "Mechanisms = {\n",
      "\ttranscription:gene_expression\n",
      "\ttranslation:dummy_translation\n",
      "\tcatalysis:basic_catalysis\n",
      "\tbinding:one_step_binding } \n",
      " Species(N = 2) = {\n",
      "protein[X] (@ 0),  dna[X] (@ 0),  \n",
      "}\n",
      "\n",
      "Reactions (1) = [\n",
      "0. dna[X] --> dna[X]+protein[X]\n",
      " Kf=k_forward * dna_X\n",
      "  k_forward=0.28125\n",
      "  found_key=(mech=gene_expression, partid=None, name=kexpress).\n",
      "  search_key=(mech=gene_expression, partid=strong, name=kexpress).\n",
      "\n",
      "] \n",
      "\n",
      "\n",
      "SimpleTxTlExtract: SimpleTxTlExtract\n",
      "Components = [\n",
      "\tDNAassembly: X ]\n",
      "Mechanisms = {\n",
      "\ttranscription:simple_transcription\n",
      "\ttranslation:simple_translation\n",
      "\tcatalysis:basic_catalysis\n",
      "\tbinding:one_step_binding }\n",
      "Global Mechanisms = {\n",
      "\trna_degredation:rna_degredation } \n",
      " Species(N = 3) = {\n",
      "protein[X] (@ 0),  rna[X] (@ 0),  dna[X] (@ 0),  \n",
      "}\n",
      "\n",
      "Reactions (3) = [\n",
      "0. dna[X] --> dna[X]+rna[X]\n",
      " Kf=k_forward * dna_X\n",
      "  k_forward=0.4775625\n",
      "  found_key=(mech=simple_transcription, partid=strong, name=ktx).\n",
      "  search_key=(mech=simple_transcription, partid=strong, name=ktx).\n",
      "\n",
      "1. rna[X] --> rna[X]+protein[X]\n",
      " Kf=k_forward * rna_X\n",
      "  k_forward=0.06\n",
      "  found_key=(mech=simple_translation, partid=weak, name=ktl).\n",
      "  search_key=(mech=simple_translation, partid=weak, name=ktl).\n",
      "\n",
      "2. rna[X] --> \n",
      " Kf=k_forward * rna_X\n",
      "  k_forward=0.001\n",
      "  found_key=(mech=rna_degredation, partid=None, name=kdil).\n",
      "  search_key=(mech=rna_degredation, partid=rna_X, name=kdil).\n",
      "\n",
      "] \n",
      "\n",
      "\n",
      "TxTlExtract: e coli extract\n",
      "Components = [\n",
      "\tDNAassembly: X\n",
      "\tProtein: RNAP\n",
      "\tProtein: Ribo\n",
      "\tProtein: RNAase ]\n",
      "Mechanisms = {\n",
      "\ttranscription:transcription_mm\n",
      "\ttranslation:translation_mm\n",
      "\tcatalysis:michalis_menten\n",
      "\tbinding:one_step_binding }\n",
      "Global Mechanisms = {\n",
      "\trna_degredation:rna_degredation_mm } \n",
      " Species(N = 10) = {\n",
      "protein[Ribo] (@ 24.0),  \n",
      "   found_key=(mech=None, partid=e coli extract, name=protein_Ribo).\n",
      "   search_key=(mech=initial concentration, partid=e coli extract, name=protein_Ribo).\n",
      "protein[RNAase] (@ 6.0),  \n",
      "   found_key=(mech=None, partid=e coli extract, name=protein_RNAase).\n",
      "   search_key=(mech=initial concentration, partid=e coli extract, name=protein_RNAase).\n",
      "protein[RNAP] (@ 3.0),  \n",
      "   found_key=(mech=None, partid=e coli extract, name=protein_RNAP).\n",
      "   search_key=(mech=initial concentration, partid=e coli extract, name=protein_RNAP).\n",
      "complex[protein[Ribo]:rna[X]] (@ 0),  complex[protein[RNAase]:rna[X]] (@ 0),  complex[dna[X]:protein[RNAP]] (@ 0),  complex[complex[protein[Ribo]:rna[X]]:protein[RNAase]] (@ 0),  protein[X] (@ 0),  rna[X] (@ 0),  dna[X] (@ 0),  \n",
      "}\n",
      "\n",
      "Reactions (8) = [\n",
      "0. dna[X]+protein[RNAP] <--> complex[dna[X]:protein[RNAP]]\n",
      " Kf=k_forward * dna_X * protein_RNAP\n",
      " Kr=k_reverse * complex_dna_X_protein_RNAP_\n",
      "  k_forward=100.0\n",
      "  found_key=(mech=None, partid=None, name=kb).\n",
      "  search_key=(mech=transcription_mm, partid=strong, name=kb).\n",
      "  k_reverse=0.5\n",
      "  found_key=(mech=None, partid=strong, name=ku).\n",
      "  search_key=(mech=transcription_mm, partid=strong, name=ku).\n",
      "\n",
      "1. complex[dna[X]:protein[RNAP]] --> dna[X]+rna[X]+protein[RNAP]\n",
      " Kf=k_forward * complex_dna_X_protein_RNAP_\n",
      "  k_forward=3.926187672\n",
      "  found_key=(mech=None, partid=strong, name=ktx).\n",
      "  search_key=(mech=transcription_mm, partid=strong, name=ktx).\n",
      "\n",
      "2. rna[X]+protein[Ribo] <--> complex[protein[Ribo]:rna[X]]\n",
      " Kf=k_forward * rna_X * protein_Ribo\n",
      " Kr=k_reverse * complex_protein_Ribo_rna_X_\n",
      "  k_forward=100.0\n",
      "  found_key=(mech=None, partid=None, name=kb).\n",
      "  search_key=(mech=translation_mm, partid=weak, name=kb).\n",
      "  k_reverse=5.0\n",
      "  found_key=(mech=None, partid=weak, name=ku).\n",
      "  search_key=(mech=translation_mm, partid=weak, name=ku).\n",
      "\n",
      "3. complex[protein[Ribo]:rna[X]] --> rna[X]+protein[X]+protein[Ribo]\n",
      " Kf=k_forward * complex_protein_Ribo_rna_X_\n",
      "  k_forward=0.05\n",
      "  found_key=(mech=None, partid=None, name=ktl).\n",
      "  search_key=(mech=translation_mm, partid=weak, name=ktl).\n",
      "\n",
      "4. rna[X]+protein[RNAase] <--> complex[protein[RNAase]:rna[X]]\n",
      " Kf=k_forward * rna_X * protein_RNAase\n",
      " Kr=k_reverse * complex_protein_RNAase_rna_X_\n",
      "  k_forward=100.0\n",
      "  found_key=(mech=None, partid=None, name=kb).\n",
      "  search_key=(mech=rna_degredation_mm, partid=rna_X, name=kb).\n",
      "  k_reverse=10.0\n",
      "  found_key=(mech=None, partid=None, name=ku).\n",
      "  search_key=(mech=rna_degredation_mm, partid=rna_X, name=ku).\n",
      "\n",
      "5. complex[protein[RNAase]:rna[X]] --> protein[RNAase]\n",
      " Kf=k_forward * complex_protein_RNAase_rna_X_\n",
      "  k_forward=0.001\n",
      "  found_key=(mech=rna_degredation_mm, partid=None, name=kdeg).\n",
      "  search_key=(mech=rna_degredation_mm, partid=rna_X, name=kdeg).\n",
      "\n",
      "6. complex[protein[Ribo]:rna[X]]+protein[RNAase] <--> complex[complex[protein[Ribo]:rna[X]]:protein[RNAase]]\n",
      " Kf=k_forward * complex_protein_Ribo_rna_X_ * protein_RNAase\n",
      " Kr=k_reverse * complex_complex_protein_Ribo_rna_X__protein_RNAase_\n",
      "  k_forward=100.0\n",
      "  found_key=(mech=None, partid=None, name=kb).\n",
      "  search_key=(mech=rna_degredation_mm, partid=complex_protein_Ribo_rna_X_, name=kb).\n",
      "  k_reverse=10.0\n",
      "  found_key=(mech=None, partid=None, name=ku).\n",
      "  search_key=(mech=rna_degredation_mm, partid=complex_protein_Ribo_rna_X_, name=ku).\n",
      "\n",
      "7. complex[complex[protein[Ribo]:rna[X]]:protein[RNAase]] --> protein[Ribo]+protein[RNAase]\n",
      " Kf=k_forward * complex_complex_protein_Ribo_rna_X__protein_RNAase_\n",
      "  k_forward=0.001\n",
      "  found_key=(mech=rna_degredation_mm, partid=None, name=kdeg).\n",
      "  search_key=(mech=rna_degredation_mm, partid=complex_protein_Ribo_rna_X_, name=kdeg).\n",
      "\n",
      "] \n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel\\ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\biocrnpyler-1.1.0-py3.7.egg\\biocrnpyler\\parameter.py:507: UserWarning: parameter file contains no unit column! Please add a column named ['unit', 'units'].\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\biocrnpyler-1.1.0-py3.7.egg\\biocrnpyler\\parameter.py:507: UserWarning: parameter file contains no unit column! Please add a column named ['unit', 'units'].\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\biocrnpyler-1.1.0-py3.7.egg\\biocrnpyler\\parameter.py:507: UserWarning: parameter file contains no unit column! Please add a column named ['unit', 'units'].\n"
     ]
    }
   ],
   "source": [
    "#Compare the Following Mixtures and Resulting CRNs\n",
    "\n",
    "#Components should be reinstantiated before passing them into a new Mixture\n",
    "G = DNAassembly(\"X\", promoter = \"strong\", rbs = \"weak\", transcript = None, protein = None)\n",
    "M1 = ExpressionExtract(\"ExpressionExtract\", components = [G], parameter_file = \"default_parameters.txt\")\n",
    "CRN1 = M1.compile_crn()\n",
    "print(repr(M1),\"\\n\", CRN1.pretty_print(show_attributes = True, show_material = True, show_rates = True),\"\\n\\n\")\n",
    "\n",
    "G = DNAassembly(\"X\", promoter = \"strong\", rbs = \"weak\", transcript = None, protein = None)\n",
    "M2 = SimpleTxTlExtract(\"SimpleTxTlExtract\", components = [G], parameter_file = \"default_parameters.txt\")\n",
    "CRN2 = M2.compile_crn()\n",
    "print(repr(M2),\"\\n\", CRN2.pretty_print(show_attributes = True, show_material = True, show_rates = True),\"\\n\\n\")\n",
    "\n",
    "G = DNAassembly(\"X\", promoter = \"strong\", rbs = \"weak\", transcript = None, protein = None)\n",
    "M3 = TxTlExtract(\"e coli extract\", components = [G], parameter_file = \"default_parameters.txt\")\n",
    "CRN3 = M3.compile_crn()\n",
    "print(repr(M3),\"\\n\", CRN3.pretty_print(show_attributes = True, show_material = True, show_rates = True),\"\\n\\n\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 4: Retroactivity and Loading Using a Custom Promoter Object\n",
    "Most of the default Mixtures in BioCRNpyler include transcription, translation, and degredation machinery such as RNAP (RNA Polymerase), Ribosomes, and RNAases. In the following example, we will illustrate loading effects due to competition over this machinery. For this example, we will use the following model of constituitive transcription and translation:\n",
    "\n",
    "$G_i + \\textrm{RNAP} \\leftrightarrow G_i:\\textrm{RNAP} \\rightarrow G_i + \\textrm{RNAP} + T_i$\n",
    "\n",
    "$T_i + \\textrm{Ribosome} \\leftrightarrow T_i:\\textrm{Ribosome} \\rightarrow T_i + \\textrm{Ribosome} + P_i$\n",
    "\n",
    "$T_i + \\textrm{RNAase} \\leftrightarrow T_i:\\textrm{RNAase} \\rightarrow \\textrm{RNAase}$\n",
    "\n",
    "Here $G_i$, $T_i$ and $P_i$ are gene, transcript, and protein $i$, respectively. In the example that follows, we will allow different RNA polymerases for different genes. Also, some genes may not be translated at all. By using orthogonal polymerases and/or loads without translation, we will see different kinds of loading effects.\n",
    "\n",
    "We will create 4 different DNA assemblies. The reference assembly, \"ref\", will have a RNAP promoter and an RBS. We will examine the output of this reporter as a function of the amount of various load assemblies.\n",
    "* The \"Load\" assembly will be identical to the \"ref\" assembly; this assembly will put load on all parts of transcription, RNA degredation, and translation for the ref assembly.\n",
    "* The \"TxLoad\" assembly will have an RNAP promoter, but no RBS, ensuring that only RNAP and RNAases experience loading, not ribosomes.\n",
    "* The \"T7Load\" assembly will have a T7 promoter and an RBS so there will be no loading on polymerases.\n",
    "* The \"T7TxLoad\" assmebly will have a T7 promoter and no RBS, so there will only be load on the RNAases.\n",
    "\n",
    "The creation of these assemblies highlights the flexible, object oriented nature of bioCRNpyler."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel\\ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "pretty_print gives a nicely formatted repesentation of the CRNS, reactions, and species. The names of species are formatted for clarity, but are not the actual species representations. Additionally a number of printing options are available. \n",
      " Species(N = 33) = {\n",
      "protein[ref] (@ 0),  rna[ref] (@ 0),  dna[ref] (@ 0),  complex[protein[Ribo]:rna[ref]] (@ 0),  complex[protein[Ribo]:rna[T7Load]] (@ 0),  complex[protein[Ribo]:rna[Load]] (@ 0),  complex[protein[RNAase]:rna[ref]] (@ 0),  complex[protein[RNAase]:rna[TxLoad]] (@ 0),  complex[protein[RNAase]:rna[T7TxLoad]] (@ 0),  complex[protein[RNAase]:rna[T7Load]] (@ 0),  complex[protein[RNAase]:rna[Load]] (@ 0),  complex[dna[ref]:protein[RNAP]] (@ 0),  complex[dna[TxLoad]:protein[RNAP]] (@ 0),  complex[dna[T7TxLoad]:protein[T7]] (@ 0),  complex[dna[T7Load]:protein[T7]] (@ 0),  complex[dna[Load]:protein[RNAP]] (@ 0),  complex[complex[protein[Ribo]:rna[ref]]:protein[RNAase]] (@ 0),  complex[complex[protein[Ribo]:rna[T7Load]]:protein[RNAase]] (@ 0),  complex[complex[protein[Ribo]:rna[Load]]:protein[RNAase]] (@ 0),  rna[TxLoad] (@ 0),  dna[TxLoad] (@ 0),  rna[T7TxLoad] (@ 0),  dna[T7TxLoad] (@ 0),  protein[T7Load] (@ 0),  rna[T7Load] (@ 0),  dna[T7Load] (@ 0),  protein[T7] (@ 0),  protein[Ribo] (@ 0),  protein[RNAase] (@ 0),  protein[RNAP] (@ 0),  protein[Load] (@ 0),  rna[Load] (@ 0),  dna[Load] (@ 0),  \n",
      "}\n",
      "\n",
      "Reactions (32) = [\n",
      "0. dna[ref]+protein[RNAP] <--> complex[dna[ref]:protein[RNAP]]\n",
      " Kf=k_forward * dna_ref * protein_RNAP\n",
      " Kr=k_reverse * complex_dna_ref_protein_RNAP_\n",
      "  k_forward=100\n",
      "  k_reverse=10\n",
      "\n",
      "1. complex[dna[ref]:protein[RNAP]] --> dna[ref]+rna[ref]+protein[RNAP]\n",
      " Kf=k_forward * complex_dna_ref_protein_RNAP_\n",
      "  k_forward=3.0\n",
      "\n",
      "2. rna[ref]+protein[Ribo] <--> complex[protein[Ribo]:rna[ref]]\n",
      " Kf=k_forward * rna_ref * protein_Ribo\n",
      " Kr=k_reverse * complex_protein_Ribo_rna_ref_\n",
      "  k_forward=100\n",
      "  k_reverse=10\n",
      "\n",
      "3. complex[protein[Ribo]:rna[ref]] --> rna[ref]+protein[ref]+protein[Ribo]\n",
      " Kf=k_forward * complex_protein_Ribo_rna_ref_\n",
      "  k_forward=5.0\n",
      "\n",
      "4. dna[Load]+protein[RNAP] <--> complex[dna[Load]:protein[RNAP]]\n",
      " Kf=k_forward * dna_Load * protein_RNAP\n",
      " Kr=k_reverse * complex_dna_Load_protein_RNAP_\n",
      "  k_forward=100\n",
      "  k_reverse=10\n",
      "\n",
      "5. complex[dna[Load]:protein[RNAP]] --> dna[Load]+rna[Load]+protein[RNAP]\n",
      " Kf=k_forward * complex_dna_Load_protein_RNAP_\n",
      "  k_forward=3.0\n",
      "\n",
      "6. rna[Load]+protein[Ribo] <--> complex[protein[Ribo]:rna[Load]]\n",
      " Kf=k_forward * rna_Load * protein_Ribo\n",
      " Kr=k_reverse * complex_protein_Ribo_rna_Load_\n",
      "  k_forward=100\n",
      "  k_reverse=10\n",
      "\n",
      "7. complex[protein[Ribo]:rna[Load]] --> rna[Load]+protein[Load]+protein[Ribo]\n",
      " Kf=k_forward * complex_protein_Ribo_rna_Load_\n",
      "  k_forward=5.0\n",
      "\n",
      "8. dna[T7Load]+protein[T7] <--> complex[dna[T7Load]:protein[T7]]\n",
      " Kf=k_forward * dna_T7Load * protein_T7\n",
      " Kr=k_reverse * complex_dna_T7Load_protein_T7_\n",
      "  k_forward=100\n",
      "  k_reverse=10\n",
      "\n",
      "9. complex[dna[T7Load]:protein[T7]] --> dna[T7Load]+rna[T7Load]+protein[T7]\n",
      " Kf=k_forward * complex_dna_T7Load_protein_T7_\n",
      "  k_forward=3.0\n",
      "\n",
      "10. rna[T7Load]+protein[Ribo] <--> complex[protein[Ribo]:rna[T7Load]]\n",
      " Kf=k_forward * rna_T7Load * protein_Ribo\n",
      " Kr=k_reverse * complex_protein_Ribo_rna_T7Load_\n",
      "  k_forward=100\n",
      "  k_reverse=10\n",
      "\n",
      "11. complex[protein[Ribo]:rna[T7Load]] --> rna[T7Load]+protein[T7Load]+protein[Ribo]\n",
      " Kf=k_forward * complex_protein_Ribo_rna_T7Load_\n",
      "  k_forward=5.0\n",
      "\n",
      "12. dna[TxLoad]+protein[RNAP] <--> complex[dna[TxLoad]:protein[RNAP]]\n",
      " Kf=k_forward * dna_TxLoad * protein_RNAP\n",
      " Kr=k_reverse * complex_dna_TxLoad_protein_RNAP_\n",
      "  k_forward=100\n",
      "  k_reverse=10\n",
      "\n",
      "13. complex[dna[TxLoad]:protein[RNAP]] --> dna[TxLoad]+rna[TxLoad]+protein[RNAP]\n",
      " Kf=k_forward * complex_dna_TxLoad_protein_RNAP_\n",
      "  k_forward=3.0\n",
      "\n",
      "14. dna[T7TxLoad]+protein[T7] <--> complex[dna[T7TxLoad]:protein[T7]]\n",
      " Kf=k_forward * dna_T7TxLoad * protein_T7\n",
      " Kr=k_reverse * complex_dna_T7TxLoad_protein_T7_\n",
      "  k_forward=100\n",
      "  k_reverse=10\n",
      "\n",
      "15. complex[dna[T7TxLoad]:protein[T7]] --> dna[T7TxLoad]+rna[T7TxLoad]+protein[T7]\n",
      " Kf=k_forward * complex_dna_T7TxLoad_protein_T7_\n",
      "  k_forward=3.0\n",
      "\n",
      "16. rna[ref]+protein[RNAase] <--> complex[protein[RNAase]:rna[ref]]\n",
      " Kf=k_forward * rna_ref * protein_RNAase\n",
      " Kr=k_reverse * complex_protein_RNAase_rna_ref_\n",
      "  k_forward=100\n",
      "  k_reverse=10\n",
      "\n",
      "17. complex[protein[RNAase]:rna[ref]] --> protein[RNAase]\n",
      " Kf=k_forward * complex_protein_RNAase_rna_ref_\n",
      "  k_forward=2\n",
      "\n",
      "18. complex[protein[Ribo]:rna[ref]]+protein[RNAase] <--> complex[complex[protein[Ribo]:rna[ref]]:protein[RNAase]]\n",
      " Kf=k_forward * complex_protein_Ribo_rna_ref_ * protein_RNAase\n",
      " Kr=k_reverse * complex_complex_protein_Ribo_rna_ref__protein_RNAase_\n",
      "  k_forward=100\n",
      "  k_reverse=10\n",
      "\n",
      "19. complex[complex[protein[Ribo]:rna[ref]]:protein[RNAase]] --> protein[Ribo]+protein[RNAase]\n",
      " Kf=k_forward * complex_complex_protein_Ribo_rna_ref__protein_RNAase_\n",
      "  k_forward=2\n",
      "\n",
      "20. rna[Load]+protein[RNAase] <--> complex[protein[RNAase]:rna[Load]]\n",
      " Kf=k_forward * rna_Load * protein_RNAase\n",
      " Kr=k_reverse * complex_protein_RNAase_rna_Load_\n",
      "  k_forward=100\n",
      "  k_reverse=10\n",
      "\n",
      "21. complex[protein[RNAase]:rna[Load]] --> protein[RNAase]\n",
      " Kf=k_forward * complex_protein_RNAase_rna_Load_\n",
      "  k_forward=2\n",
      "\n",
      "22. complex[protein[Ribo]:rna[Load]]+protein[RNAase] <--> complex[complex[protein[Ribo]:rna[Load]]:protein[RNAase]]\n",
      " Kf=k_forward * complex_protein_Ribo_rna_Load_ * protein_RNAase\n",
      " Kr=k_reverse * complex_complex_protein_Ribo_rna_Load__protein_RNAase_\n",
      "  k_forward=100\n",
      "  k_reverse=10\n",
      "\n",
      "23. complex[complex[protein[Ribo]:rna[Load]]:protein[RNAase]] --> protein[Ribo]+protein[RNAase]\n",
      " Kf=k_forward * complex_complex_protein_Ribo_rna_Load__protein_RNAase_\n",
      "  k_forward=2\n",
      "\n",
      "24. rna[T7Load]+protein[RNAase] <--> complex[protein[RNAase]:rna[T7Load]]\n",
      " Kf=k_forward * rna_T7Load * protein_RNAase\n",
      " Kr=k_reverse * complex_protein_RNAase_rna_T7Load_\n",
      "  k_forward=100\n",
      "  k_reverse=10\n",
      "\n",
      "25. complex[protein[RNAase]:rna[T7Load]] --> protein[RNAase]\n",
      " Kf=k_forward * complex_protein_RNAase_rna_T7Load_\n",
      "  k_forward=2\n",
      "\n",
      "26. complex[protein[Ribo]:rna[T7Load]]+protein[RNAase] <--> complex[complex[protein[Ribo]:rna[T7Load]]:protein[RNAase]]\n",
      " Kf=k_forward * complex_protein_Ribo_rna_T7Load_ * protein_RNAase\n",
      " Kr=k_reverse * complex_complex_protein_Ribo_rna_T7Load__protein_RNAase_\n",
      "  k_forward=100\n",
      "  k_reverse=10\n",
      "\n",
      "27. complex[complex[protein[Ribo]:rna[T7Load]]:protein[RNAase]] --> protein[Ribo]+protein[RNAase]\n",
      " Kf=k_forward * complex_complex_protein_Ribo_rna_T7Load__protein_RNAase_\n",
      "  k_forward=2\n",
      "\n",
      "28. rna[TxLoad]+protein[RNAase] <--> complex[protein[RNAase]:rna[TxLoad]]\n",
      " Kf=k_forward * rna_TxLoad * protein_RNAase\n",
      " Kr=k_reverse * complex_protein_RNAase_rna_TxLoad_\n",
      "  k_forward=100\n",
      "  k_reverse=10\n",
      "\n",
      "29. complex[protein[RNAase]:rna[TxLoad]] --> protein[RNAase]\n",
      " Kf=k_forward * complex_protein_RNAase_rna_TxLoad_\n",
      "  k_forward=2\n",
      "\n",
      "30. rna[T7TxLoad]+protein[RNAase] <--> complex[protein[RNAase]:rna[T7TxLoad]]\n",
      " Kf=k_forward * rna_T7TxLoad * protein_RNAase\n",
      " Kr=k_reverse * complex_protein_RNAase_rna_T7TxLoad_\n",
      "  k_forward=100\n",
      "  k_reverse=10\n",
      "\n",
      "31. complex[protein[RNAase]:rna[T7TxLoad]] --> protein[RNAase]\n",
      " Kf=k_forward * complex_protein_RNAase_rna_T7TxLoad_\n",
      "  k_forward=2\n",
      "\n",
      "]\n",
      "Simulating\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\scipy\\integrate\\odepack.py:247: ODEintWarning: Excess work done on this call (perhaps wrong Dfun type). Run with full_output = 1 to get quantitative information.\n",
      "  warnings.warn(warning_msg, ODEintWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\scipy\\integrate\\odepack.py:247: ODEintWarning: Excess work done on this call (perhaps wrong Dfun type). Run with full_output = 1 to get quantitative information.\n",
      "  warnings.warn(warning_msg, ODEintWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from biocrnpyler import *\n",
    "\n",
    "#Because we are lazy, all parameters will use the default \"param_name\"-->value key mapping.\n",
    "#Parameter warnings will be later suppressed in the Mixture constructor\n",
    "#For details on how parameter loading and defaulting works, see the Parameter notebook.\n",
    "kb, ku, ktx, ktl, kdeg = 100, 10, 3.0, 5.0, 2\n",
    "parameters = {\"kb\":kb, \"ku\":ku, \"ktx\":ktx, \"ktl\":ktl, \"kdeg\":kdeg}\n",
    "\n",
    "#A constituitively expressed reporter\n",
    "#By default the promoter 'P' will use the polymerase 'rnap'\n",
    "reference_assembly = DNAassembly(name = \"ref\", promoter = \"P\", rbs = \"BCD\")\n",
    "#A constiuitively expressed load (RNA and Protein)\n",
    "full_load_assembly = DNAassembly(name = \"Load\", promoter = \"P\", rbs = \"BCD\")\n",
    "#A constiutively transcribed (but not translated) load\n",
    "#By putting rbs = None, DNAassembly automatically knows not to include translation\n",
    "RNA_load_assembly = DNAassembly(name = \"TxLoad\", promoter = \"P\", rbs = None)\n",
    "\n",
    "#Load genes on orthogonal polymerases\n",
    "T7 = Protein(\"T7\") #Create a new protein (polymerase) called 'T7'\n",
    "\n",
    "#Create a custom promoter with a custom mechanism that uses T7 instead of RNAP\n",
    "#instantiate a new mechanism Transcription_MM with its own name and overwrote the default parameter rnap\n",
    "mechanism_txt7 = Transcription_MM(name = \"T7_transcription_mm\", rnap=T7.get_species())\n",
    "#Create an instance of a promoter with this mechanism for transcription\n",
    "T7P = Promoter(\"T7P\", mechanisms={\"transcription\":mechanism_txt7})\n",
    "#Create A load assembly with the custom T7 promoter\n",
    "T7_load_assembly = DNAassembly(name = \"T7Load\", promoter = T7P, rbs = \"BCD\")\n",
    "\n",
    "#Each new assembly requires its own promoter instance - so here I create another here\n",
    "T7P = Promoter(\"T7P\", mechanisms={\n",
    "    \"transcription\":Transcription_MM(name = \"T7_transcription_mm\", rnap=T7.get_species())})\n",
    "#A load assembly with the custom T7 promoter and no RBS\n",
    "T7RNA_load_assembly = DNAassembly(name = \"T7TxLoad\", promoter = T7P, rbs = None)\n",
    "\n",
    "#Add all the assemblies to a mixture\n",
    "components = [reference_assembly, full_load_assembly, T7_load_assembly, T7, RNA_load_assembly, T7RNA_load_assembly]\n",
    "myMixture = TxTlExtract(name = \"txtl\", parameters = parameters, components = components)\n",
    "\n",
    "#Print the CRN\n",
    "myCRN = myMixture.compile_crn()\n",
    "\n",
    "#The Species, Reaction, and CRN pretty_print functions return text which has been formated with a number of formatting options\n",
    "print(\"\\npretty_print gives a nicely formatted repesentation of the CRNS, reactions, and species. The names of species are formatted for clarity, but are not the actual species representations. Additionally a number of printing options are available.\",\n",
    "      \"\\n\", myCRN.pretty_print(show_material = True, show_rates = True, show_attributes = True, show_keys = False))\n",
    "\n",
    "#Simulate with BioSCRAPE if installed\n",
    "\n",
    "print(\"Simulating\")\n",
    "try:\n",
    "    %matplotlib inline\n",
    "    import numpy as np\n",
    "    import pylab as plt\n",
    "    timepoints = np.arange(0, 3, .01)\n",
    "    stochastic = False #Whether to use ODE models or Stochastic SSA models\n",
    "    plt.figure(figsize = (16, 8))\n",
    "    plt.subplot(221)\n",
    "    plt.title(\"Load on a RNAP Promoter\")\n",
    "    loads = [0, 1.0, 5., 10., 50, 100, 500, 1000]\n",
    "    for dna_Load in loads:\n",
    "        #print(\"Simulating for dna_Load=\", dna_Load)\n",
    "        x0_dict = {\"protein_T7\": 10., \"protein_RNAP\":10., \"protein_RNAase\":5.0, \"protein_Ribo\":50.,\n",
    "                   'dna_ref':5., 'dna_Load':dna_Load}\n",
    "\n",
    "        results = myCRN.simulate_with_bioscrape_via_sbml(timepoints, initial_condition_dict = x0_dict, stochastic = stochastic)\n",
    "        if results is not None:\n",
    "            plt.plot(timepoints, results[\"protein_ref\"], label = \"Load = \"+str(dna_Load))\n",
    "\n",
    "    plt.xlim(0, 5)\n",
    "    #plt.xlabel(\"time\")\n",
    "    plt.ylabel(\"Reference Protein\")\n",
    "    plt.legend()\n",
    "\n",
    "    plt.subplot(222)\n",
    "    plt.title(\"Load on a T7 Promotoer\")\n",
    "    for dna_Load in loads:\n",
    "        #print(\"Simulating for dna_T7Load=\", dna_Load)\n",
    "        x0_dict = {\"protein_T7\": 10., \"protein_RNAP\":10., \"protein_RNAase\":5.0, \"protein_Ribo\":50.,\n",
    "                   'dna_ref':5., 'dna_T7Load':dna_Load}\n",
    "        results = myCRN.simulate_with_bioscrape_via_sbml(timepoints, initial_condition_dict = x0_dict, stochastic = stochastic)\n",
    "        if results is not None:\n",
    "            plt.plot(timepoints, results[\"protein_ref\"], label=\"Load = \" + str(dna_Load))\n",
    "    plt.xlim(0, 5)\n",
    "    #plt.xlabel(\"time\")\n",
    "    plt.ylabel(\"Reference Protein\")\n",
    "    plt.legend()\n",
    "\n",
    "    plt.subplot(223)\n",
    "    plt.title(\"Load on a RNAP Promotoer, No RBS\")\n",
    "    for dna_Load in loads:\n",
    "        #print(\"Simulating for dna_TxLoad=\", dna_Load)\n",
    "        x0_dict = {\"protein_T7\": 10., \"protein_RNAP\":10., \"protein_RNAase\":5.0, \"protein_Ribo\":50.,\n",
    "                   'dna_ref':5., 'dna_TxLoad':dna_Load}\n",
    "        results = myCRN.simulate_with_bioscrape_via_sbml(timepoints, initial_condition_dict = x0_dict, stochastic = stochastic)\n",
    "        if results is not None:\n",
    "            plt.plot(timepoints, results[\"protein_ref\"], label=\"Load = \" + str(dna_Load))\n",
    "    plt.xlim(0, 5)\n",
    "    plt.xlabel(\"time\")\n",
    "    plt.ylabel(\"Reference Protein\")\n",
    "    plt.legend()\n",
    "\n",
    "    plt.subplot(224)\n",
    "    plt.title(\"Load on a T7 Promotoer, No RBS\")\n",
    "    for dna_Load in loads:\n",
    "        #print(\"Simulating for dna_T7TxLoad=\", dna_Load)\n",
    "        x0_dict = {\"protein_T7\": 10., \"protein_RNAP\":10., \"protein_RNAase\":5.0, \"protein_Ribo\":50.,\n",
    "                   'dna_ref':5., 'dna_T7TxLoad':dna_Load}\n",
    "        results = myCRN.simulate_with_bioscrape_via_sbml(timepoints, initial_condition_dict = x0_dict, stochastic = stochastic)\n",
    "        if results is not None:\n",
    "            plt.plot(timepoints, results[\"protein_ref\"], label=\"Load = \" + str(dna_Load))\n",
    "    plt.xlim(0, 5)\n",
    "    plt.xlabel(\"time\")\n",
    "    plt.ylabel(\"Reference Protein\")\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "except ModuleNotFoundError:\n",
    "    print('please install the plotting libraries: pip install biocrnpyler[all]')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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