{
 "cells": [
  {
   "attachments": {
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compiling Chemical Reaction Networks\n",
    "__Overview:__ The main utility of BioCRNpyler is to compile complex CRNs from simple specifications. BioCRNpyler compilation is summarized in the below figure:\n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "Mixtures represent context and contain Components and default Mechanisms. Components (biochemical parts) call Mechanisms which they inherit from Mixture or override internally. Mechanisms are reaction schemas (abstract functions that produce CRN Species and Reactions) that represent different biochemical processes. Mechanisms find reaction parameters they need from a parameter file. More details on parameter loading can be seen in the parameter ipython notebook. \n",
    "\n",
    "In this notebook, a number of basic BioCRNpyler models are compiled involving enzymes and molecular binding. This notebook highlights the structure of BioCRNpyler and how to control compilation. Other notebooks highlight the available Mixtures, Components, and Mechanisms through detailed examples."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 1: Enzymes & Default Mechanisms\n",
    "\n",
    "In this example, we examine two different mechanisms for modeling an Enzyme $E$ which converts a substrate $S$ into a product $P$ in an in-vitro context (no dilution of any species):\n",
    "\n",
    "In this context, Enzyme is a Component which looks for a 'catalysis' Mechanism. Two different Mixtures will be compared with different default 'catalysis' Mechanisms.\n",
    "\n",
    "1. Baisc Catalysis: $E + S \\xrightarrow{k_{cat}} E + P$\n",
    "2. Michaelis Menten Catalysis: $E + S \\underset{k_u}{\\overset{k_b}{\\rightleftharpoons}}E:S \\xrightarrow{k_{cat}} E + P$\n",
    "\n",
    "By default, the Enzyme Component will inherit the Mechanism in its Mixture."
   ]
  },
  {
   "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",
      "\tEnzyme: E ]\n",
      "Mechanisms = {\n",
      "\tcatalysis:basic_catalysis } \n",
      "\n",
      "String representation of a CRN shows the string names of all species:\n",
      " Species = protein_E, S, P\n",
      "Reactions = [\n",
      "\tprotein[E]+S --> protein[E]+P\n",
      "] \n",
      "\n",
      "\n",
      "Pretty_print representation of a CRN has formatting options and is easier to read:\n",
      " Species(N = 3) = {\n",
      "S (@ 0),  P (@ 0),  protein[E] (@ 0),  \n",
      "}\n",
      "\n",
      "Reactions (1) = [\n",
      "0. protein[E]+S --> protein[E]+P\n",
      " Kf=k_forward * protein_E * S\n",
      "  k_forward=1.0\n",
      "\n",
      "]\n"
     ]
    }
   ],
   "source": [
    "from biocrnpyler import *\n",
    "\n",
    "#We will use default parameter names\n",
    "default_parameters = {\"kb\":100, \"ku\":10, \"kcat\":1.}\n",
    "\n",
    "#Mixture 1 (M1) will contain an Enzyme E1\n",
    "E = Enzyme(\"E\", substrates = \"S\", products = \"P\")\n",
    "\n",
    "#Choose a catalysis mechanism by commenting out one of them.\n",
    "mech_cat = BasicCatalysis()\n",
    "#mech_cat = MichaelisMenten()\n",
    "\n",
    "#place that mechanism in a dictionary: \"catalysis\":mech_cat\n",
    "default_mechanisms = {mech_cat.mechanism_type:mech_cat}\n",
    "\n",
    "#Create a mixture.\n",
    "#Components is a list of Components in the mixture\n",
    "#parameters is a dictionary of parameters. Can also accept parameter_file.\n",
    "#default_mechanisms = dict sets the default_mechanisms in the Mixture\n",
    "M = Mixture(\"Catalysis Mixture\", components = [E], parameters = default_parameters, mechanisms = default_mechanisms)\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",
    "#CRNs can be printed in two ways\n",
    "print(\"String representation of a CRN shows the string names of all species:\\n\",CRN, \"\\n\\n\")\n",
    "print(\"Pretty_print representation of a CRN has formatting options and is easier to read:\\n\",\n",
    "      CRN.pretty_print(show_rates = True, show_attributes = True, show_materials = True, show_keys = False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 2: Combining Multiple Enzymes into a Pathway Using Default Parameters for All Enzymes\n",
    "Next, we will combine 3 enzymes together into a pathway:\n",
    "1. $E_1$ converts $A$ to $B$\n",
    "2. $E_2$ converts $C$ to $D$\n",
    "3. $E_3$ is a MultiEnzyme which converts $B$ and $D$ to $F$\n",
    "\n",
    "MultiEnzyme is a more general Enzyme Component which can have multiple substrates and products. Notice how all the Enzymes use the same default catalysis Mechanism which can be changed by changing the what is passed into default_mechanisms in the Mixture.\n",
    "\n",
    "Finally, we show how specific parameters can be used for each enzyme or default parameters shared between all Components.\n",
    "Default parameters can be given in a dictionary using the name of the parameter as a keyword:\n",
    "\n",
    "    \"parameter name\" : value\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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: Default Param Pathway\n",
      "Components = [\n",
      "\tEnzyme: E1\n",
      "\tEnzyme: E2\n",
      "\tEnzyme: E3 ]\n",
      "Mechanisms = {\n",
      "\tcatalysis:basic_catalysis } \n",
      "\n",
      "Species(N = 8) = {\n",
      "F (@ 0),  protein[E3] (@ 0),  protein[E2] (@ 0),  protein[E1] (@ 0),  D (@ 0),  C (@ 0),  B (@ 0),  A (@ 0),  \n",
      "}\n",
      "\n",
      "Reactions (3) = [\n",
      "0. protein[E1]+A --> protein[E1]+B\n",
      " Kf=k_forward * protein_E1 * A\n",
      "  k_forward=1.0\n",
      "\n",
      "1. protein[E2]+C --> protein[E2]+D\n",
      " Kf=k_forward * protein_E2 * C\n",
      "  k_forward=1.0\n",
      "\n",
      "2. protein[E3]+B+D --> protein[E3]+F\n",
      " Kf=k_forward * protein_E3 * B * D\n",
      "  k_forward=1.0\n",
      "\n",
      "]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel\\ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    }
   ],
   "source": [
    "#These simple parameters can be used by default for all enzymes\n",
    "default_parameters = {\"kb\":100, \"ku\":10, \"kcat\":1.}\n",
    "\n",
    "E1 = Enzyme(\"E1\", substrates = \"A\", products = \"B\")\n",
    "E2 = Enzyme(\"E2\", substrates = \"C\", products = \"D\")\n",
    "E3 = Enzyme(\"E3\", substrates = [\"B\", \"D\"], products = [\"F\"])\n",
    "\n",
    "#creeate a catalysis mechanism. \n",
    "mech_cat = BasicCatalysis()\n",
    "#mech_cat = MichaelisMenten()\n",
    "\n",
    "#place that mechanism in a dictionary: \"catalysis\":mech_cat\n",
    "default_mechanisms = {mech_cat.mechanism_type:mech_cat}\n",
    "\n",
    "#Create a mixture.\n",
    "#Components is a list of Components in the mixture\n",
    "#parameters is a dictionary of parameters. Can also accept parameter_file.\n",
    "#default_mechanisms = dict sets the default_mechanisms in the Mixture\n",
    "M = Mixture(\"Default Param Pathway\", components = [E1, E2, E3], parameters = default_parameters, mechanisms = default_mechanisms)\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",
    "print(CRN.pretty_print(show_rates = True, show_attributes = True, show_materials = True, show_keys = False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 3: A Pathway of Enzymes with Specific Parameters for Each Enzyme\n",
    "In the previous example, the same default parameters used by all the Enzymes. It is also easy to have specific parameters for each enzyme. Specific parameters can be given in a dictionary or parameter file using a variety of keys (see the Parameters notebook for details). The simplest, however, is to make a dictionary of ParameterKeys:\n",
    "\n",
    "    ParameterKey(mechanism = \"mechanism name/type\", part_id = \"component name\", name = \"parameter name\") : value\n",
    "\n",
    "Notice that by switching the catalysis Mechanism, different parameters are used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using Specific Parameters for all reactions:\n",
      "Viewing the ParameterKeys used in a CRN can be toggled with the show_keys=True/False keyword in CRN.pretty_print.\n",
      "\n",
      "Species(N = 11) = {\n",
      "F (@ 0),  protein[E3] (@ 0),  protein[E2] (@ 0),  protein[E1] (@ 0),  D (@ 0),  complex[C:protein[E2]] (@ 0),  C (@ 0),  complex[B:D:protein[E3]] (@ 0),  B (@ 0),  complex[A:protein[E1]] (@ 0),  A (@ 0),  \n",
      "}\n",
      "\n",
      "Reactions (6) = [\n",
      "0. A+protein[E1] <--> complex[A:protein[E1]]\n",
      " Kf=k_forward * A * protein_E1\n",
      " Kr=k_reverse * complex_A_protein_E1_\n",
      "  k_forward=111\n",
      "  found_key=(mech=michalis_menten, partid=E1, name=kb).\n",
      "  search_key=(mech=michalis_menten, partid=E1, name=kb).\n",
      "  k_reverse=11\n",
      "  found_key=(mech=michalis_menten, partid=E1, name=ku).\n",
      "  search_key=(mech=michalis_menten, partid=E1, name=ku).\n",
      "\n",
      "1. complex[A:protein[E1]] --> B+protein[E1]\n",
      " Kf=k_forward * complex_A_protein_E1_\n",
      "  k_forward=1.11\n",
      "  found_key=(mech=michalis_menten, partid=E1, name=kcat).\n",
      "  search_key=(mech=michalis_menten, partid=E1, name=kcat).\n",
      "\n",
      "2. C+protein[E2] <--> complex[C:protein[E2]]\n",
      " Kf=k_forward * C * protein_E2\n",
      " Kr=k_reverse * complex_C_protein_E2_\n",
      "  k_forward=222\n",
      "  found_key=(mech=michalis_menten, partid=E2, name=kb).\n",
      "  search_key=(mech=michalis_menten, partid=E2, name=kb).\n",
      "  k_reverse=22\n",
      "  found_key=(mech=michalis_menten, partid=E2, name=ku).\n",
      "  search_key=(mech=michalis_menten, partid=E2, name=ku).\n",
      "\n",
      "3. complex[C:protein[E2]] --> D+protein[E2]\n",
      " Kf=k_forward * complex_C_protein_E2_\n",
      "  k_forward=2.22\n",
      "  found_key=(mech=michalis_menten, partid=E2, name=kcat).\n",
      "  search_key=(mech=michalis_menten, partid=E2, name=kcat).\n",
      "\n",
      "4. B+D+protein[E3] <--> complex[B:D:protein[E3]]\n",
      " Kf=k_forward * B * D * protein_E3\n",
      " Kr=k_reverse * complex_B_D_protein_E3_\n",
      "  k_forward=333\n",
      "  found_key=(mech=michalis_menten, partid=E3, name=kb).\n",
      "  search_key=(mech=michalis_menten, partid=E3, name=kb).\n",
      "  k_reverse=33\n",
      "  found_key=(mech=michalis_menten, partid=E3, name=ku).\n",
      "  search_key=(mech=michalis_menten, partid=E3, name=ku).\n",
      "\n",
      "5. complex[B:D:protein[E3]] --> F+protein[E3]\n",
      " Kf=k_forward * complex_B_D_protein_E3_\n",
      "  k_forward=3.33\n",
      "  found_key=(mech=michalis_menten, partid=E3, name=kcat).\n",
      "  search_key=(mech=michalis_menten, partid=E3, name=kcat).\n",
      "\n",
      "]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel\\ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    }
   ],
   "source": [
    "#Or specific parameters can be made which give different rates for each enzyme\n",
    "#The first row of parameters is used by BasicCatalysis\n",
    "#The second-fourth row of parameters is used my MichaelisMenten catalysis\n",
    "specific_parameters = {\n",
    "    ParameterKey(mechanism = \"basic_catalysis\", part_id = \"E1\", name = \"kcat\"):100,\n",
    "    ParameterKey(mechanism = \"basic_catalysis\", part_id = \"E2\", name = \"kcat\"):200, \n",
    "    ParameterKey(mechanism = \"basic_catalysis\", part_id = \"E3\", name = \"kcat\"):300,\n",
    "    ParameterKey(mechanism = \"michalis_menten\", part_id = \"E1\", name = \"kb\"):111,\n",
    "    ParameterKey(mechanism = \"michalis_menten\", part_id = \"E1\", name = \"ku\"):11, \n",
    "    ParameterKey(mechanism = \"michalis_menten\", part_id = \"E1\", name = \"kcat\"): 1.11,\n",
    "    ParameterKey(mechanism = \"michalis_menten\", part_id = \"E2\", name = \"kb\"):222, \n",
    "    ParameterKey(mechanism = \"michalis_menten\", part_id = \"E2\", name = \"ku\"):22, \n",
    "    ParameterKey(mechanism = \"michalis_menten\", part_id = \"E2\", name = \"kcat\"): 2.22,\n",
    "    ParameterKey(mechanism = \"michalis_menten\", part_id = \"E3\", name = \"kb\"):333, \n",
    "    ParameterKey(mechanism = \"michalis_menten\", part_id = \"E3\", name = \"ku\"):33, \n",
    "    ParameterKey(mechanism = \"michalis_menten\", part_id = \"E3\", name = \"kcat\"): 3.33\n",
    "}\n",
    "\n",
    "E1 = Enzyme(\"E1\", substrates = \"A\", products = \"B\")\n",
    "E2 = Enzyme(\"E2\", substrates = \"C\", products = \"D\")\n",
    "E3 = Enzyme(\"E3\", substrates = [\"B\", \"D\"], products = [\"F\"])\n",
    "\n",
    "#choose a catalysis mechanism. \n",
    "#mech_cat = BasicCatalysis()\n",
    "mech_cat = MichaelisMenten()\n",
    "\n",
    "#place that mechanism in a dictionary: \"catalysis\":mech_cat\n",
    "default_mechanisms = {mech_cat.mechanism_type:mech_cat}\n",
    "\n",
    "#To change the parameters, pass in a different dictionary using the parameter keyword\n",
    "M = Mixture(\"Catalysis Mixture\", components = [E1, E2, E3], parameters = specific_parameters, mechanisms = default_mechanisms)\n",
    "CRN = M.compile_crn()\n",
    "print(\"Using Specific Parameters for all reactions:\")\n",
    "print(\"Viewing the ParameterKeys used in a CRN can be toggled with the show_keys=True/False keyword in CRN.pretty_print.\\n\")\n",
    "print(CRN.pretty_print(show_rates = True, show_keys = True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 4: Adding a Custom Mechanism to a Component\n",
    "Notice that in the above CRN, the enzymatic process is irreversible. However, many biochemists would argue that irreversibility is actually an approximation. In reality, the products of the reaction can bind to the enzyme and, if the chemical potential is high enough, cause the reverse reaction to occur. In many cases, it might be desirable to to only include the reverse reaction for some of the enzymatic reactions being in the Model. In BioCRNpyler, this can be done easily by adding a custom Mechanism to an individual Component which will override the default Mechanism provided by the Mixture.\n",
    "\n",
    "This is illustrated on the pathway built above, where here Enzyme 3 will be given a new catalysis mechanism called MichaelisMentenReversible: $E + S \\rightleftarrows E:S \\rightleftarrows E:P \\rightleftarrows E + P$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Notice that there are additional reactions involving E3:\n",
      " Species(N = 12) = {\n",
      "complex[F:protein[E3]] (@ 0),  F (@ 0),  protein[E3] (@ 0),  protein[E2] (@ 0),  protein[E1] (@ 0),  D (@ 0),  complex[C:protein[E2]] (@ 0),  C (@ 0),  complex[B:D:protein[E3]] (@ 0),  B (@ 0),  complex[A:protein[E1]] (@ 0),  A (@ 0),  \n",
      "}\n",
      "\n",
      "Reactions (7) = [\n",
      "0. A+protein[E1] <--> complex[A:protein[E1]]\n",
      " Kf=k_forward * A * protein_E1\n",
      " Kr=k_reverse * complex_A_protein_E1_\n",
      "  k_forward=100\n",
      "  k_reverse=10\n",
      "\n",
      "1. complex[A:protein[E1]] --> B+protein[E1]\n",
      " Kf=k_forward * complex_A_protein_E1_\n",
      "  k_forward=1.0\n",
      "\n",
      "2. C+protein[E2] <--> complex[C:protein[E2]]\n",
      " Kf=k_forward * C * protein_E2\n",
      " Kr=k_reverse * complex_C_protein_E2_\n",
      "  k_forward=100\n",
      "  k_reverse=10\n",
      "\n",
      "3. complex[C:protein[E2]] --> D+protein[E2]\n",
      " Kf=k_forward * complex_C_protein_E2_\n",
      "  k_forward=1.0\n",
      "\n",
      "4. B+D+protein[E3] <--> complex[B:D:protein[E3]]\n",
      " Kf=k_forward * B * D * protein_E3\n",
      " Kr=k_reverse * complex_B_D_protein_E3_\n",
      "  k_forward=111\n",
      "  k_reverse=11\n",
      "\n",
      "5. F+protein[E3] <--> complex[F:protein[E3]]\n",
      " Kf=k_forward * F * protein_E3\n",
      " Kr=k_reverse * complex_F_protein_E3_\n",
      "  k_forward=22\n",
      "  k_reverse=22\n",
      "\n",
      "6. complex[B:D:protein[E3]] <--> complex[F:protein[E3]]\n",
      " Kf=k_forward * complex_B_D_protein_E3_\n",
      " Kr=k_reverse * complex_F_protein_E3_\n",
      "  k_forward=1.0\n",
      "  k_reverse=0.001\n",
      "\n",
      "]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel\\ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    }
   ],
   "source": [
    "#The MichaelisMentenReversible has different parameter names: kb1, ku1, kb2, ku2, and kcat_rev\n",
    "default_parameters = {\"kb\":100, \"ku\":10, \"kcat\":1., \"kb1\":111, \"kb2\":22, \"ku1\":11, \"ku2\":22, \"kcat_rev\":.001}\n",
    "\n",
    "#Enzymes 1 and 2 are the same as above\n",
    "E1 = Enzyme(\"E1\", substrates = \"A\", products = \"B\")\n",
    "E2 = Enzyme(\"E2\", substrates = \"C\", products = \"D\")\n",
    "\n",
    "#Create a dictionary of custom mechanisms to pass into E3 with the mechanisms keyword\n",
    "mm_reversible = MichaelisMentenReversible()\n",
    "custom_mechanisms = {mm_reversible.mechanism_type:mm_reversible}\n",
    "E3 = Enzyme(\"E3\", substrates = [\"B\", \"D\"], products = [\"F\"], mechanisms = custom_mechanisms)\n",
    "\n",
    "#choose a catalysis mechanism. \n",
    "#mech_cat = BasicCatalysis()\n",
    "mech_cat = MichaelisMenten()\n",
    "#place that mechanism in a dictionary: \"catalysis\":mech_cat\n",
    "default_mechanisms = {mech_cat.mechanism_type:mech_cat}\n",
    "#Make the Mixture\n",
    "M = Mixture(\"Catalysis Mixture\", components = [E1, E2, E3], parameters = default_parameters, mechanisms = default_mechanisms)\n",
    "#Compile the CRN\n",
    "CRN = M.compile_crn()\n",
    "\n",
    "print(\"Notice that there are additional reactions involving E3:\\n\", CRN.pretty_print(show_rates = True, show_keys = False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 5: Modelling an Enzyme which is also a ChemicalComplex (Binding)\n",
    "\n",
    "In this example, we will consider the followingbiochemical process: the tetramerization of the biosynthesis protein Inosine-5′monophosphate dehydrogenase (IMPDH) which is a homeotetramer that catalyzes the following reaction important in guanine biosynthesis:\n",
    "\n",
    "$\\textrm{inosine-5'-phosphate} + \\textrm{NAD}^+ + H_2O \\rightleftharpoons  \\textrm{xanthosine-5'-phosphate} + \\textrm{NADH} + H^+$\n",
    "\n",
    "The ChemicalComplex Component will represent the assembled protein Complex IMPDH formed by 4 identifical monomer subunits. ChemicalComplex is effectively a wrapper around a ComplexSpecies in order to provide binding reactions. The catalysis of the above reaction will be accomplished by simultaneously making IMPDH an Enzyme."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Species(N = 12) = {\n",
      "complex[F:protein[E3]] (@ 0),  F (@ 0),  protein[E3] (@ 0),  protein[E2] (@ 0),  protein[E1] (@ 0),  D (@ 0),  complex[C:protein[E2]] (@ 0),  C (@ 0),  complex[B:D:protein[E3]] (@ 0),  B (@ 0),  complex[A:protein[E1]] (@ 0),  A (@ 0),  \n",
      "}\n",
      "\n",
      "Reactions (7) = [\n",
      "0. A+protein[E1] <--> complex[A:protein[E1]]\n",
      " Kf=k_forward * A * protein_E1\n",
      " Kr=k_reverse * complex_A_protein_E1_\n",
      "  k_forward=100\n",
      "  k_reverse=10\n",
      "\n",
      "1. complex[A:protein[E1]] --> B+protein[E1]\n",
      " Kf=k_forward * complex_A_protein_E1_\n",
      "  k_forward=1.0\n",
      "\n",
      "2. C+protein[E2] <--> complex[C:protein[E2]]\n",
      " Kf=k_forward * C * protein_E2\n",
      " Kr=k_reverse * complex_C_protein_E2_\n",
      "  k_forward=100\n",
      "  k_reverse=10\n",
      "\n",
      "3. complex[C:protein[E2]] --> D+protein[E2]\n",
      " Kf=k_forward * complex_C_protein_E2_\n",
      "  k_forward=1.0\n",
      "\n",
      "4. B+D+protein[E3] <--> complex[B:D:protein[E3]]\n",
      " Kf=k_forward * B * D * protein_E3\n",
      " Kr=k_reverse * complex_B_D_protein_E3_\n",
      "  k_forward=111\n",
      "  k_reverse=11\n",
      "\n",
      "5. F+protein[E3] <--> complex[F:protein[E3]]\n",
      " Kf=k_forward * F * protein_E3\n",
      " Kr=k_reverse * complex_F_protein_E3_\n",
      "  k_forward=22\n",
      "  k_reverse=22\n",
      "\n",
      "6. complex[B:D:protein[E3]] <--> complex[F:protein[E3]]\n",
      " Kf=k_forward * complex_B_D_protein_E3_\n",
      " Kr=k_reverse * complex_F_protein_E3_\n",
      "  k_forward=1.0\n",
      "  k_reverse=0.001\n",
      "\n",
      "]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel\\ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    }
   ],
   "source": [
    "#Use default parameters for simplicity\n",
    "default_parameters = {\"kb\":100, \"ku\":10, \"kcat\":1., \"kb1\":111, \"kb2\":22, \"ku1\":11, \"ku2\":22, \"kcat_rev\":.001}\n",
    "\n",
    "#Create a single species to represent the monomer.\n",
    "monomer = Species(\"IMPDH\", material_type = \"subunit\")\n",
    "\n",
    "#A ChemicalComplex takes a list of species and allows them all to bind together.\n",
    "C = ChemicalComplex(species = [monomer]*4, name = \"IMPDH\")\n",
    "\n",
    "#Here, we set the internal species of MultiEnzyme (enzyme) to the the ComplexSpecies stored inside ChemicalComplex. \n",
    "#This allows the same formal CRN species to be represented by multiple components and therefore participate in more reactions.\n",
    "E = Enzyme(enzyme = C.get_species(), substrates = [\"isosine5phosphate\", \"NAD\", \"H2O\"], products = [\"xanthosine5phosphate\", \"NADH\", \"H\"])\n",
    "\n",
    "\n",
    "#choose a catalysis mechanism. \n",
    "#mech_cat = BasicCatalysis()\n",
    "mech_cat = MichaelisMenten()\n",
    "#mech_cat = MichaelisMentenReversible()\n",
    "\n",
    "#create a binding mechanism\n",
    "mech_bind = One_Step_Binding() #All species bind together in a single step\n",
    "\n",
    "#place that mechanism in a dictionary: {\"catalysis\":mech_cat, \"binding\":mech_bind}\n",
    "default_mechanisms = {mech_cat.mechanism_type:mech_cat, mech_bind.mechanism_type:mech_bind}\n",
    "\n",
    "#Make the Mixture\n",
    "M = Mixture(\"Catalysis Mixture\", components = [E1, E2, E3], parameters = default_parameters, mechanisms = default_mechanisms)\n",
    "#Compile the CRN\n",
    "CRN = M.compile_crn()\n",
    "print(CRN.pretty_print(show_rates = True, show_keys = False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 6a: Modeling Combinatorial Binding between Many Species with CombinatorialComplex\n",
    "\n",
    "CombinatorialComplex is a generalization of the ChemicalComplex Component that enumerates combinatorial sets of bound species and the reactions that convert between them. The Species X, Y, and Z can bind together in any order to form a complex X:Y:Z. This results in 5 total reactions which can be modeled simply.\n",
    "\n",
    "Note: detailed binding parameters can be set under the part_id name_binder_bindee. For example if X binds to Y for a combinatorial complex called CC, the part_id would be \"CC_Y_X\". "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Species(N = 7) = {\n",
      "Z (@ 0),  complex[Y:Z] (@ 0),  Y (@ 0),  complex[X:Z] (@ 0),  complex[X:Y:Z] (@ 0),  complex[X:Y] (@ 0),  X (@ 0),  \n",
      "}\n",
      "\n",
      "Reactions (6) = [\n",
      "0. Y+X <--> complex[X:Y]\n",
      " Kf=k_forward * Y * X\n",
      " Kr=k_reverse * complex_X_Y_\n",
      "  k_forward=100\n",
      "  k_reverse=10\n",
      "\n",
      "1. Z+complex[X:Y] <--> complex[X:Y:Z]\n",
      " Kf=k_forward * Z * complex_X_Y_\n",
      " Kr=k_reverse * complex_X_Y_Z_\n",
      "  k_forward=100\n",
      "  k_reverse=10\n",
      "\n",
      "2. Z+X <--> complex[X:Z]\n",
      " Kf=k_forward * Z * X\n",
      " Kr=k_reverse * complex_X_Z_\n",
      "  k_forward=100\n",
      "  k_reverse=10\n",
      "\n",
      "3. Y+complex[X:Z] <--> complex[X:Y:Z]\n",
      " Kf=k_forward * Y * complex_X_Z_\n",
      " Kr=k_reverse * complex_X_Y_Z_\n",
      "  k_forward=100\n",
      "  k_reverse=10\n",
      "\n",
      "4. Z+Y <--> complex[Y:Z]\n",
      " Kf=k_forward * Z * Y\n",
      " Kr=k_reverse * complex_Y_Z_\n",
      "  k_forward=100\n",
      "  k_reverse=10\n",
      "\n",
      "5. X+complex[Y:Z] <--> complex[X:Y:Z]\n",
      " Kf=k_forward * X * complex_Y_Z_\n",
      " Kr=k_reverse * complex_X_Y_Z_\n",
      "  k_forward=100\n",
      "  k_reverse=10\n",
      "\n",
      "]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel\\ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    }
   ],
   "source": [
    "from biocrnpyler import Species, CombinatorialComplex, One_Step_Binding, Mixture, Complex\n",
    "X, Y, Z = Species(\"X\"), Species(\"Y\"), Species(\"Z\")\n",
    "CC = CombinatorialComplex(final_states = [Complex([X, Y, Z])])\n",
    "\n",
    "mech_bind = One_Step_Binding()\n",
    "default_mechanisms = {mech_bind.mechanism_type:mech_bind}\n",
    "\n",
    "#Make the Mixture\n",
    "default_parameters = {\"kb\":100, \"ku\":10}\n",
    "M = Mixture(\"CombinatorialComlex Mixture\", components = [CC], parameters = default_parameters, mechanisms = default_mechanisms)\n",
    "\n",
    "CRN = M.compile_crn()\n",
    "print(CRN.pretty_print(show_rates = True, show_keys = False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 6b: Modeling Combinatorial Binding between Many Species with CombinatorialComplex Part 2.\n",
    "\n",
    "CombinatorialComplex is in fact quite a bit more powerful than the previous example. There can be multiple initial_states, final_states, intermediate_states, and excluded_states. Abstractly this can be written:\n",
    "\n",
    "## Initial States $\\rightleftharpoons$ Intermediate States $\\rightleftharpoons$ Final States\n",
    "\n",
    "Here $\\rightleftharpoons$ denote combinatorial binding. All Excluded States found during enumeration are skipped. If no initial_states are given, the CombinatorialComplex defaults to all Species inside Complexes in the Final States. If no intermediate states are given, initial states convert directly to final states.\n",
    "\n",
    "In the following example, we illustrate this functionality by considering a set of final states X:X:Y:Z, X:Y:Y:Z. We require that Z bind to dimers X:X and Y:Y before Y or X can bind to the opposite dimer using a mixture of excluded and intermediate states."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Species(N = 9) = {\n",
      "Z (@ 0),  complex[2x_Y:Z] (@ 0),  complex[2x_Y] (@ 0),  Y (@ 0),  complex[X:2x_Y:Z] (@ 0),  complex[2x_X:Z] (@ 0),  complex[2x_X:Y:Z] (@ 0),  complex[2x_X] (@ 0),  X (@ 0),  \n",
      "}\n",
      "\n",
      "Reactions (6) = [\n",
      "0. 2X <--> complex[2x_X]\n",
      " Kf=k_forward * X^2\n",
      " Kr=k_reverse * complex_X_2x_\n",
      "  k_forward=100\n",
      "  k_reverse=10\n",
      "\n",
      "1. Z+complex[2x_X] <--> complex[2x_X:Z]\n",
      " Kf=k_forward * Z * complex_X_2x_\n",
      " Kr=k_reverse * complex_X_2x_Z_\n",
      "  k_forward=100\n",
      "  k_reverse=10\n",
      "\n",
      "2. 2Y <--> complex[2x_Y]\n",
      " Kf=k_forward * Y^2\n",
      " Kr=k_reverse * complex_Y_2x_\n",
      "  k_forward=100\n",
      "  k_reverse=10\n",
      "\n",
      "3. Z+complex[2x_Y] <--> complex[2x_Y:Z]\n",
      " Kf=k_forward * Z * complex_Y_2x_\n",
      " Kr=k_reverse * complex_Y_2x_Z_\n",
      "  k_forward=100\n",
      "  k_reverse=10\n",
      "\n",
      "4. Y+complex[2x_X:Z] <--> complex[2x_X:Y:Z]\n",
      " Kf=k_forward * Y * complex_X_2x_Z_\n",
      " Kr=k_reverse * complex_X_2x_Y_Z_\n",
      "  k_forward=100\n",
      "  k_reverse=10\n",
      "\n",
      "5. X+complex[2x_Y:Z] <--> complex[X:2x_Y:Z]\n",
      " Kf=k_forward * X * complex_Y_2x_Z_\n",
      " Kr=k_reverse * complex_X_Y_2x_Z_\n",
      "  k_forward=100\n",
      "  k_reverse=10\n",
      "\n",
      "]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel\\ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    }
   ],
   "source": [
    "from biocrnpyler import Species, CombinatorialComplex, One_Step_Binding, Mixture, Complex\n",
    "X, Y, Z = Species(\"X\"), Species(\"Y\"), Species(\"Z\")\n",
    "\n",
    "final_states = [Complex([X, X, Y, Z]), Complex([X, Y, Y, Z])] #Two different final states\n",
    "excluded_states = [Complex([X, Y]), Complex([X, Z]), Complex([Y, Z])] #Exclude all hetereodimers\n",
    "intermediate_states = [Complex([X, X, Z]), Complex([Y, Y, Z])] #Force Z to bind to YY and XX before the final states can form\n",
    "\n",
    "CC = CombinatorialComplex(final_states = final_states, excluded_states = excluded_states, intermediate_states = intermediate_states)\n",
    "\n",
    "mech_bind = One_Step_Binding()\n",
    "default_mechanisms = {mech_bind.mechanism_type:mech_bind}\n",
    "\n",
    "#Make the Mixture\n",
    "default_parameters = {\"kb\":100, \"ku\":10}\n",
    "M = Mixture(\"CombinatorialComlex Mixture\", components = [CC], parameters = default_parameters, mechanisms = default_mechanisms)\n",
    "\n",
    "CRN = M.compile_crn()\n",
    "print(CRN.pretty_print(show_rates = True, show_keys = False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}