{
 "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),  \n",
      "    rna[X] (@ 0),  \n",
      "    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),  \n",
      "    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),  \n",
      "    rna[X] (@ 0),  \n",
      "    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:michaelis_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",
      "\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",
      "\n",
      "    protein[RNAP(machinery)] (@ 15.0),  \n",
      "    found_key=(mech=None, partid=e coli, name=RNAP).\n",
      "    search_key=(mech=initial concentration, partid=e coli, name=RNAP).\n",
      "\n",
      "    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",
      "\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",
      "\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",
      "\n",
      "    complex[protein[Ribo]:rna[cellular_processes]] (@ 0),  \n",
      "    complex[protein[Ribo]:rna[X]] (@ 0),  \n",
      "    complex[protein[RNAase]:rna[cellular_processes]] (@ 0),  \n",
      "    complex[protein[RNAase]:rna[X]] (@ 0),  \n",
      "    complex[dna[cellular_processes]:protein[RNAP]] (@ 0),  \n",
      "    complex[dna[X]:protein[RNAP]] (@ 0),  \n",
      "    complex[complex[protein[Ribo]:rna[cellular_processes]]:protein[RNAase]] (@ 0),  \n",
      "    complex[complex[protein[Ribo]:rna[X]]:protein[RNAase]] (@ 0),  \n",
      "    protein[X] (@ 0),  \n",
      "    rna[X] (@ 0),  \n",
      "    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. 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_degradation_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_degradation_mm, partid=rna_cellular_processes, name=ku).\n",
      "\n",
      "11. 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_degradation_mm, partid=None, name=kdeg).\n",
      "  search_key=(mech=rna_degradation_mm, partid=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_degradation_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_degradation_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_degradation_mm, partid=None, name=kdeg).\n",
      "  search_key=(mech=rna_degradation_mm, partid=rna_X, 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_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",
      "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_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",
      "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_degradation_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[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",
      "19. 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",
      "] \n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/murray/Library/CloudStorage/Dropbox/macosx/src/biocrnpyler/biocrnpyler/core/parameter.py:678: UserWarning: parameter file contains no unit column! Please add a column named ['unit', 'units'].\n",
      "  warn(\n",
      "/Users/murray/Library/CloudStorage/Dropbox/macosx/src/biocrnpyler/biocrnpyler/core/parameter.py:678: UserWarning: parameter file contains no unit column! Please add a column named ['unit', 'units'].\n",
      "  warn(\n",
      "/Users/murray/Library/CloudStorage/Dropbox/macosx/src/biocrnpyler/biocrnpyler/core/parameter.py:678: UserWarning: parameter file contains no unit column! Please add a column named ['unit', 'units'].\n",
      "  warn(\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),  \n",
      "    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),  \n",
      "    rna[X] (@ 0),  \n",
      "    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:michaelis_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",
      "\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",
      "\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",
      "\n",
      "    complex[protein[Ribo]:rna[X]] (@ 0),  \n",
      "    complex[protein[RNAase]:rna[X]] (@ 0),  \n",
      "    complex[dna[X]:protein[RNAP]] (@ 0),  \n",
      "    complex[complex[protein[Ribo]:rna[X]]:protein[RNAase]] (@ 0),  \n",
      "    protein[X] (@ 0),  \n",
      "    rna[X] (@ 0),  \n",
      "    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_degradation_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_degradation_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_degradation_mm, partid=None, name=kdeg).\n",
      "  search_key=(mech=rna_degradation_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_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",
      "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_degradation_mm, partid=None, name=kdeg).\n",
      "  search_key=(mech=rna_degradation_mm, partid=complex_protein_Ribo_rna_X_, name=kdeg).\n",
      "\n",
      "] \n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/murray/Library/CloudStorage/Dropbox/macosx/src/biocrnpyler/biocrnpyler/core/parameter.py:678: UserWarning: parameter file contains no unit column! Please add a column named ['unit', 'units'].\n",
      "  warn(\n",
      "/Users/murray/Library/CloudStorage/Dropbox/macosx/src/biocrnpyler/biocrnpyler/core/parameter.py:678: UserWarning: parameter file contains no unit column! Please add a column named ['unit', 'units'].\n",
      "  warn(\n",
      "/Users/murray/Library/CloudStorage/Dropbox/macosx/src/biocrnpyler/biocrnpyler/core/parameter.py:678: UserWarning: parameter file contains no unit column! Please add a column named ['unit', 'units'].\n",
      "  warn(\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 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),  \n",
      "    rna[ref] (@ 0),  \n",
      "    dna[ref] (@ 0),  \n",
      "    complex[protein[Ribo]:rna[ref]] (@ 0),  \n",
      "    complex[protein[Ribo]:rna[T7Load]] (@ 0),  \n",
      "    complex[protein[Ribo]:rna[Load]] (@ 0),  \n",
      "    complex[protein[RNAase]:rna[ref]] (@ 0),  \n",
      "    complex[protein[RNAase]:rna[TxLoad]] (@ 0),  \n",
      "    complex[protein[RNAase]:rna[T7TxLoad]] (@ 0),  \n",
      "    complex[protein[RNAase]:rna[T7Load]] (@ 0),  \n",
      "    complex[protein[RNAase]:rna[Load]] (@ 0),  \n",
      "    complex[dna[ref]:protein[RNAP]] (@ 0),  \n",
      "    complex[dna[TxLoad]:protein[RNAP]] (@ 0),  \n",
      "    complex[dna[T7TxLoad]:protein[T7]] (@ 0),  \n",
      "    complex[dna[T7Load]:protein[T7]] (@ 0),  \n",
      "    complex[dna[Load]:protein[RNAP]] (@ 0),  \n",
      "    complex[complex[protein[Ribo]:rna[ref]]:protein[RNAase]] (@ 0),  \n",
      "    complex[complex[protein[Ribo]:rna[T7Load]]:protein[RNAase]] (@ 0),  \n",
      "    complex[complex[protein[Ribo]:rna[Load]]:protein[RNAase]] (@ 0),  \n",
      "    rna[TxLoad] (@ 0),  \n",
      "    dna[TxLoad] (@ 0),  \n",
      "    rna[T7TxLoad] (@ 0),  \n",
      "    dna[T7TxLoad] (@ 0),  \n",
      "    protein[T7Load] (@ 0),  \n",
      "    rna[T7Load] (@ 0),  \n",
      "    dna[T7Load] (@ 0),  \n",
      "    protein[T7] (@ 0),  \n",
      "    protein[Ribo] (@ 0),  \n",
      "    protein[RNAase] (@ 0),  \n",
      "    protein[RNAP] (@ 0),  \n",
      "    protein[Load] (@ 0),  \n",
      "    rna[Load] (@ 0),  \n",
      "    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[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",
      "19. complex[protein[RNAase]:rna[TxLoad]] --> protein[RNAase]\n",
      " Kf=k_forward * complex_protein_RNAase_rna_TxLoad_\n",
      "  k_forward=2\n",
      "\n",
      "20. 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",
      "21. complex[protein[RNAase]:rna[T7Load]] --> protein[RNAase]\n",
      " Kf=k_forward * complex_protein_RNAase_rna_T7Load_\n",
      "  k_forward=2\n",
      "\n",
      "22. 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",
      "23. complex[protein[RNAase]:rna[T7TxLoad]] --> protein[RNAase]\n",
      " Kf=k_forward * complex_protein_RNAase_rna_T7TxLoad_\n",
      "  k_forward=2\n",
      "\n",
      "24. 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",
      "25. 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",
      "26. 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",
      "27. complex[protein[RNAase]:rna[ref]] --> protein[RNAase]\n",
      " Kf=k_forward * complex_protein_RNAase_rna_ref_\n",
      "  k_forward=2\n",
      "\n",
      "28. 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",
      "29. complex[protein[RNAase]:rna[Load]] --> protein[RNAase]\n",
      " Kf=k_forward * complex_protein_RNAase_rna_Load_\n",
      "  k_forward=2\n",
      "\n",
      "30. 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",
      "31. 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",
      "]\n",
      "Simulating\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": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# End"
   ]
  }
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