{
 "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 modeling 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.core import Mixture\n",
    "from biocrnpyler.components.dna import DNAassembly\n",
    "from biocrnpyler.mechanisms import SimpleTranscription, SimpleTranslation\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 transcript and protein will have the same name as an assembly\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 convenience\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 DNAassembly 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 catalysis $T_X \\to T_X + P_X$. \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, Degradation_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 $G_{\\text{cellular\\_processes}}$ 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_degradation_via_dilution\n",
      "\trna_degradation:rna_degradation } \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_degradation_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_degradation_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_degradation, partid=None, name=kdil).\n",
      "  search_key=(mech=rna_degradation, 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_degradation:rna_degradation_mm\n",
      "\tdilution:global_degradation_via_dilution } \n",
      " Species(N = 17) = {\n",
      "protein[Ribo(machinery)] (@ 150.0),  \n",
      "   found_key=(mech=None, partid=e coli, name=Ribo).\n",
      "   search_key=(mech=initial concentration, partid=e coli, name=Ribo).\n",
      "protein[RNAase(machinery)] (@ 45.0),  \n",
      "   found_key=(mech=None, partid=e coli, name=RNAase).\n",
      "   search_key=(mech=initial concentration, partid=e coli, name=RNAase).\n",
      "protein[RNAP(machinery)] (@ 15.0),  \n",
      "   found_key=(mech=None, partid=e coli, name=RNAP).\n",
      "   search_key=(mech=initial concentration, partid=e coli, name=RNAP).\n",
      "protein[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",
      "rna[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",
      "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[X] (@ 0),  rna[X] (@ 0),  dna[X] (@ 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. 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_degradation_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_degradation_mm, partid=complex_protein_Ribo_machinery_rna_X_, name=ku).\n",
      "\n",
      "9. 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_degradation_mm, partid=None, name=kdeg).\n",
      "  search_key=(mech=rna_degradation_mm, partid=complex_protein_Ribo_machinery_rna_X_, name=kdeg).\n",
      "\n",
      "10. 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_degradation_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_degradation_mm, partid=complex_protein_Ribo_machinery_rna_cellular_processes_, name=ku).\n",
      "\n",
      "11. 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_degradation_mm, partid=None, name=kdeg).\n",
      "  search_key=(mech=rna_degradation_mm, partid=complex_protein_Ribo_machinery_rna_cellular_processes_, name=kdeg).\n",
      "\n",
      "12. 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",
      "13. 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",
      "14. 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",
      "15. 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",
      "16. 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",
      "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_degradation_via_dilution, partid=protein_X, name=kdil).\n",
      "\n",
      "18. 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",
      "19. 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_degradation_via_dilution, partid=rna_cellular_processes, name=kdil).\n",
      "\n",
      "] \n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ayush\\Box\\Research\\bioCRNpyler\\biocrnpyler\\core\\parameter.py:507: UserWarning: parameter file contains no unit column! Please add a column named ['unit', 'units'].\n",
      "  warn(f\"parameter file contains no {accepted_name} column! Please add a \"\n",
      "C:\\Users\\ayush\\Box\\Research\\bioCRNpyler\\biocrnpyler\\core\\parameter.py:507: UserWarning: parameter file contains no unit column! Please add a column named ['unit', 'units'].\n",
      "  warn(f\"parameter file contains no {accepted_name} column! Please add a \"\n",
      "C:\\Users\\ayush\\Box\\Research\\bioCRNpyler\\biocrnpyler\\core\\parameter.py:507: UserWarning: parameter file contains no unit column! Please add a column named ['unit', 'units'].\n",
      "  warn(f\"parameter file contains no {accepted_name} column! Please add a \"\n"
     ]
    }
   ],
   "source": [
    "from biocrnpyler.mixtures import ExpressionDilutionMixture, SimpleTxTlDilutionMixture, TxTlDilutionMixture\n",
    "#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 re-instantiated 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 DNAassembly 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 catalysis $T_X \\to T_X + P_X$. \n",
    "* mRNA are degraded: $T_X \\to \\emptyset$\n",
    "\n",
    "__TxTlExtract__ uses the Mechanisms: Transcription_MM, Translation_MM, Degradation_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_degradation:rna_degradation } \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_degradation, partid=None, name=kdil).\n",
      "  search_key=(mech=rna_degradation, 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_degradation:rna_degradation_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. 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_degradation_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_degradation_mm, partid=complex_protein_Ribo_rna_X_, name=ku).\n",
      "\n",
      "5. 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_degradation_mm, partid=None, name=kdeg).\n",
      "  search_key=(mech=rna_degradation_mm, partid=complex_protein_Ribo_rna_X_, name=kdeg).\n",
      "\n",
      "6. 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",
      "7. 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",
      "] \n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ayush\\Box\\Research\\bioCRNpyler\\biocrnpyler\\core\\parameter.py:507: UserWarning: parameter file contains no unit column! Please add a column named ['unit', 'units'].\n",
      "  warn(f\"parameter file contains no {accepted_name} column! Please add a \"\n",
      "C:\\Users\\ayush\\Box\\Research\\bioCRNpyler\\biocrnpyler\\core\\parameter.py:507: UserWarning: parameter file contains no unit column! Please add a column named ['unit', 'units'].\n",
      "  warn(f\"parameter file contains no {accepted_name} column! Please add a \"\n",
      "C:\\Users\\ayush\\Box\\Research\\bioCRNpyler\\biocrnpyler\\core\\parameter.py:507: UserWarning: parameter file contains no unit column! Please add a column named ['unit', 'units'].\n",
      "  warn(f\"parameter file contains no {accepted_name} column! Please add a \"\n"
     ]
    }
   ],
   "source": [
    "from biocrnpyler.mixtures import ExpressionExtract, SimpleTxTlExtract, TxTlExtract\n",
    "#Compare the Following Mixtures and Resulting CRNs\n",
    "\n",
    "#Components should be re-instantiated 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 degradation 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 constitutive 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 degradation, 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\" assembly 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": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "pretty_print gives a nicely formatted representation 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. 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",
      "17. 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",
      "18. 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",
      "19. complex[protein[RNAase]:rna[ref]] --> protein[RNAase]\n",
      " Kf=k_forward * complex_protein_RNAase_rna_ref_\n",
      "  k_forward=2\n",
      "\n",
      "20. 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",
      "21. 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",
      "22. 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",
      "23. complex[protein[RNAase]:rna[T7Load]] --> protein[RNAase]\n",
      " Kf=k_forward * complex_protein_RNAase_rna_T7Load_\n",
      "  k_forward=2\n",
      "\n",
      "24. 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",
      "25. complex[protein[RNAase]:rna[T7TxLoad]] --> protein[RNAase]\n",
      " Kf=k_forward * complex_protein_RNAase_rna_T7TxLoad_\n",
      "  k_forward=2\n",
      "\n",
      "26. 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",
      "27. 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",
      "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[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",
      "31. complex[protein[RNAase]:rna[Load]] --> protein[RNAase]\n",
      " Kf=k_forward * complex_protein_RNAase_rna_Load_\n",
      "  k_forward=2\n",
      "\n",
      "]\n",
      "Simulating\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\ayush\\anaconda3\\envs\\py310\\lib\\site-packages\\scipy\\integrate\\_odepack_py.py:248: 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:\\Users\\ayush\\anaconda3\\envs\\py310\\lib\\site-packages\\scipy\\integrate\\_odepack_py.py:248: 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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",
      "text/plain": [
       "<Figure size 1600x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from biocrnpyler.components import Protein, Promoter\n",
    "from biocrnpyler.mechanisms import Transcription_MM\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": [
    "# End"
   ]
  }
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