{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multi-state complexes\n",
    "\n",
    "<div class=\"admonition note\">\n",
    "**Topics**: Complexes, complex reactions.\n",
    "</div>\n",
    "\n",
    "In this chapter, we will introduce a concise way of declaring reactions between molecules that can be in a high number of distinct functional states.  We will use the `Complex` class and its subconstituents `SubUnit`s and `SubunitState`s to specify the state space of these molecules.\n",
    "\n",
    "We will first intoduce `Complex`es in a general way and compare them to other forms of rule-based modeling frameworks. We will then present their use in an IP3 receptor example that builds on the one used in [a previous chapter](STEPS_Tutorial_IP3.ipynb).\n",
    "\n",
    "## Complex declaration\n",
    "\n",
    "Complexes are composed of an arbitrary number of subunits that can themselves be in an arbitrary number of states. In this guide, we will represent complexes as collections of geometric shapes, like in the following examples:\n",
    "\n",
    "<img src=\"images/complex_examples.png\"/>\n",
    "\n",
    "Each complex consists of a list of subunits, represented by different geometrical shapes in the second column of the figure. These subunits can be in various states (represented by colors), as shown in the third column. Specific instances of complexes can thus be in various states, resulting from all the possible combinations of subunit states. The last column only shows a few examples of such states for each complex.\n",
    "\n",
    "In order to declare a complex, we first need to declare all its subunits along with their subunit states. We then need to provide a list of subunits that the complex is made of. Consider the following example, corresponding to the first row of the figure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import steps.interface\n",
    "\n",
    "from steps.model import *\n",
    "\n",
    "mdl = Model()\n",
    "\n",
    "with mdl:\n",
    "    B0, B1, B2 = SubUnitState.Create()\n",
    "    \n",
    "    BSU = SubUnit.Create([B0, B1, B2])\n",
    "    \n",
    "    CB = Complex.Create([BSU, BSU, BSU, BSU], statesAsSpecies=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As usual, we first need to import required modules and define a `Model` object. The creation of subunit states and subunit is then straighforward with the `SubUnitState` and `SubUnit` classes. `SubUnitState` behaves as `Species`, we do not need to specify any parameters for their creation. `SubUnit` takes a list of `SubUnitState`s as a parameter. Finally, the complex is created with the `Complex` class that takes a list of `SubUnit` objects as argument as well as the `statesAsSpecies` keyword argument that specifies that all states of the complex should be automatically declared in STEPS as `Species`. This keyword parameter is required in the current version of STEPS since multi-state complexes are not natively supported yet.\n",
    "\n",
    "Note that the list of `SubUnit` objects that is given to the `Complex` constructor can totally contain duplicates since complexes can be composed of several identical subunits. In addition, the order in which the subunits are given is important when these subunits are not identical as it will later be used to identify specific subunits in a complex. In our graphical representations, we will assume that the first element of this list is the subunit in the top right corner of the complex and the remaning subunits are read in clockwise order from there.\n",
    "\n",
    "We can then list all the states that this complex can be in with:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15 states\n",
      "CB[B0, B0, B0, B0]\n",
      "CB[B0, B0, B0, B1]\n",
      "CB[B0, B0, B0, B2]\n",
      "CB[B0, B0, B1, B1]\n",
      "CB[B0, B0, B1, B2]\n",
      "CB[B0, B0, B2, B2]\n",
      "CB[B0, B1, B1, B1]\n",
      "CB[B0, B1, B1, B2]\n",
      "CB[B0, B1, B2, B2]\n",
      "CB[B0, B2, B2, B2]\n",
      "CB[B1, B1, B1, B1]\n",
      "CB[B1, B1, B1, B2]\n",
      "CB[B1, B1, B2, B2]\n",
      "CB[B1, B2, B2, B2]\n",
      "CB[B2, B2, B2, B2]\n"
     ]
    }
   ],
   "source": [
    "def printStates(cs):\n",
    "    print(f'{len(cs)} states')\n",
    "    for state in cs:\n",
    "        print(state)\n",
    "        \n",
    "printStates(CB[...])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We used the square bracket notation after the complex name to access its states: `CB[...]`. This notation returns an object that describes a set of states of the complex, when using it only with the ellipsis `...` object, this corresponds to all possible states of the complex. We will see how to use this notation later in the chapter.\n",
    "\n",
    "Note that instead of the $3^4 = 81$ states that should result from all possible combinations of 3 subunit states for 4 subunits, we only have 15 states. This is due to the fact that, by default, complex states do not take the order of subunits into account. The state `CB_B0_B0_B0_B1` is equivalent to the state `CB_B0_B0_B1_B0` since they are both composed of 3 subunits in state `B0` and one subunit in state `B1`. Only one of the four equivalent states is conserved and declared in STEPS as a `Species`.\n",
    "\n",
    "### Complex ordering\n",
    "\n",
    "This behavior is however not always desirable as neighboring relations between subunits can sometimes be considered important. The `Complex` constructor can thus take an additional keyword argument `order`. This argument makes it possible to specify groups of complex states that will be considered equivalent. STEPS comes with 3 built-in choices for this parameter: `NoOrdering`, the default ; `StrongOrdering`, that considers all possible ordered states ; and `RotationalSymmetryOrdering`, that we will explain below. It is also possible to implement a custom order function, more details are given in [the documentation](API_model.rst#steps.API_2.model.Complex).\n",
    "\n",
    "The following figure shows how states are grouped in the 3 order functions for a complex with 4 identical subunits with 2 states:\n",
    "\n",
    "<img src=\"images/complex_states_1.png\"/>\n",
    "\n",
    "Columns correspond to the number of subunits in state S1 (dark blue), starting with all subunits in state S0 (light blue). The last two columns are ommited since they are identical to the first two if states are inverted. Grey lines represent which states are grouped together under the different ordering functions. The first row contains all the possible ordered states and the last one contains the unordered states. Since subunits can only be in two states, there are only 5 states under the `NoOrdering` function: all subunits in S0, one subunit in S1, two in S1, etc. The `RotationalSymmetryOrdering` function is a bit trickier, it groups all states that are identical under rotation. When only one subunit is in S1, all states can be made equivalent with quarter turn rotations. This is not the case when two subunits are in S1, there are then two distinct states that cannot be made identical with quarter turn rotations: a state in which the two subunits in S1 are adjacent, and another in which they are opposite. Note that this rotational symmetry still takes into account handedness:\n",
    "\n",
    "<img src=\"images/complex_states_2.png\"/>\n",
    "\n",
    "In the above figure, 4 identical subunits can be in 3 different states and we only consider the case in which two subunits are in S0 (light blue), one in S1 (dark blue) and one in S2 (teal). Note that under rotational symmetry, there are two complex states in which S1 and S2 are adjacent but these states are not identical: the left one has S1 then S2 while the other has S2 then S1 (in clockwise direction). When complexes contain different subunits, and depending in which order the subunits are declared in the complex, it becomes less likely for complex states to be rotationaly equivalent:\n",
    "\n",
    "<img src=\"images/complex_states_3.png\"/>\n",
    "\n",
    "We can declare the complex described in this last figure in STEPS with the rotational symmetry ordering function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "with mdl:\n",
    "    C0, C1, R0, R1 = SubUnitState.Create()\n",
    "    \n",
    "    CSU, RSU = SubUnit.Create([C0, C1], [R0, R1])\n",
    "    \n",
    "    CC = Complex.Create(\n",
    "        [CSU, RSU, CSU, RSU], statesAsSpecies=True, order=RotationalSymmetryOrdering\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then print all the corresponding states:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 states\n",
      "CC[C0, R0, C0, R0]\n",
      "CC[C0, R0, C0, R1]\n",
      "CC[C0, R0, C1, R0]\n",
      "CC[C0, R0, C1, R1]\n",
      "CC[C0, R1, C0, R1]\n",
      "CC[C0, R1, C1, R1]\n",
      "CC[C1, R0, C0, R1]\n",
      "CC[C1, R0, C1, R0]\n",
      "CC[C1, R0, C1, R1]\n",
      "CC[C1, R1, C1, R1]\n"
     ]
    }
   ],
   "source": [
    "printStates(CC[...])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We get 10 states, as expected from the figure. Again, we used the square bracket notation `CC[...]` to access complex states ; in the next section, we describe how this notation works.\n",
    "\n",
    "## Complex selectors\n",
    "\n",
    "A complex selector is an instance of the `ComplexSelector` class and is created when using the square bracket notation on a complex. Simply put, the square bracket notation allows to slice the complex state space in a way that is similar to array slicing in numpy (see [the numpy documentation](https://numpy.org/doc/1.18/reference/arrays.indexing.html) for more details). As we will see later, these complex selectors can then be used for declaring reactions that apply to a subset of complex states without having to enumerate all the states. The following figure shows how various square bracket notations select various part of the complex state space:\n",
    "\n",
    "<img src=\"images/complex_selectors.png\"/>\n",
    "\n",
    "For simplicity of representation, the complex used in these examples has 3 subunits: two identical subunits `S` and one subunit `T`, both these subunits can be in 3 different states. The same principles of course apply for complexes with more than 3 subunits. While in these examples, the full ordered state space is represented, the complex states selected by a complex selectors will depend on the specific ordering function used during the creation of the `Complex`. The states are organized spatially as if they were part of a three dimensional matrix, to make the analogy with numpy slicing easier to see.\n",
    "\n",
    "Let us declare this complex in STEPS and evaluate these complex selectors:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "with mdl:\n",
    "    S0, S1, S2, T0, T1, T2 = SubUnitState.Create()\n",
    "    \n",
    "    SSU, TSU = SubUnit.Create([S0, S1, S2], [T0, T1, T2])\n",
    "    \n",
    "    CD = Complex.Create([SSU, SSU, TSU], statesAsSpecies=True, order=StrongOrdering)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first example **A** corresponds to the complex selector we used so far, it returns all the possible states of the complex. Like for numpy slicing, the easiest way to select all 'dimensions' from the complex is to use a colon `:` for each dimension, meaning we want to select everything in this 'dimension'. The complex has 3 subunits / 'dimensions' so we need 3 colons in the square bracket: `CD[:, :, :]`. The order of dimensions is the same as the one used when declaring the complex. The ellipsis object `...` can be used, like in numpy, to avoid repeating colons when the number of subunits / dimensions is high. It is equivalent to typing comma separated colons for the remaning dimensions. Note however that only one ellipsis object can be used in a square bracket notation since using several could lead to ambiguities (in `CD[..., S0, ...]` it would not be clear which dimension should correspond to S0). If no ellipsis object is used, the number of comma separated values should always match the number of subunits in the complex.\n",
    "\n",
    "We can thus get all $3^3 = 27$ complex states with:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27 states\n",
      "CD[S0, S0, T0]\n",
      "CD[S0, S0, T1]\n",
      "CD[S0, S0, T2]\n",
      "CD[S0, S1, T0]\n",
      "CD[S0, S1, T1]\n",
      "CD[S0, S1, T2]\n",
      "CD[S0, S2, T0]\n",
      "CD[S0, S2, T1]\n",
      "CD[S0, S2, T2]\n",
      "CD[S1, S0, T0]\n",
      "CD[S1, S0, T1]\n",
      "CD[S1, S0, T2]\n",
      "CD[S1, S1, T0]\n",
      "CD[S1, S1, T1]\n",
      "CD[S1, S1, T2]\n",
      "CD[S1, S2, T0]\n",
      "CD[S1, S2, T1]\n",
      "CD[S1, S2, T2]\n",
      "CD[S2, S0, T0]\n",
      "CD[S2, S0, T1]\n",
      "CD[S2, S0, T2]\n",
      "CD[S2, S1, T0]\n",
      "CD[S2, S1, T1]\n",
      "CD[S2, S1, T2]\n",
      "CD[S2, S2, T0]\n",
      "CD[S2, S2, T1]\n",
      "CD[S2, S2, T2]\n"
     ]
    }
   ],
   "source": [
    "printStates(CD[...])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Examples **B** and **C** slice the state space in one dimension. The complex selector in example **B** has colons for the first two dimensions, meaning all subunit states are selected, and the last one has the `T1` `SubUnitState` object, indicating that only complex states in which the third subunit is in state `T1` should be selected. Again, the two colons can be replaced by an ellipsis object `...`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9 states\n",
      "CD[S0, S0, T1]\n",
      "CD[S0, S1, T1]\n",
      "CD[S0, S2, T1]\n",
      "CD[S1, S0, T1]\n",
      "CD[S1, S1, T1]\n",
      "CD[S1, S2, T1]\n",
      "CD[S2, S0, T1]\n",
      "CD[S2, S1, T1]\n",
      "CD[S2, S2, T1]\n"
     ]
    }
   ],
   "source": [
    "printStates(CD[:, :, T1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example **D** specifies two out of three dimensions, it selects all states in which the second `S` subunit is in state `S1` and the `T` subunit is in state `T1`. Note that if all subunits / 'dimensions' are uniquely specified, the square bracket notation returns a `ComplexState` instead of a `ComplexSelector`. As expected example **D** return 3 states:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 states\n",
      "CD[S0, S1, T2]\n",
      "CD[S1, S1, T2]\n",
      "CD[S2, S1, T2]\n"
     ]
    }
   ],
   "source": [
    "printStates(CD[:, S1, T2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example **E** combines two `SubUnitState` with the union operator `|` in order to select states for which the first subunit is either in state `S0` or `S2`. Alternatively, since there are only 3 possibles states for this subunit, we can use the negation operator `~S1` to select all subunit states that are not `S1`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6 states\n",
      "CD[S0, S0, T1]\n",
      "CD[S0, S1, T1]\n",
      "CD[S0, S2, T1]\n",
      "CD[S2, S0, T1]\n",
      "CD[S2, S1, T1]\n",
      "CD[S2, S2, T1]\n",
      "6 states\n",
      "CD[S0, S0, T1]\n",
      "CD[S0, S1, T1]\n",
      "CD[S0, S2, T1]\n",
      "CD[S2, S0, T1]\n",
      "CD[S2, S1, T1]\n",
      "CD[S2, S2, T1]\n"
     ]
    }
   ],
   "source": [
    "printStates(CD[S0 | S2, :, T1])\n",
    "printStates(CD[~S1, :, T1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not that both `|` and `~` operators return a `SubUnitSelector` object (see [documentation](API_model.rst#steps.API_2.model.SubUnitSelector)) that represent a subset of the `SubUnitState`s associated to a given `SubUnit`.\n",
    "\n",
    "Examples **F** and **G** illustrate the possibility to combine complex selectors. Example **F** shows the intersection between two result selectors with the `&` operator while example **G** shows the union with the `|` operator. In both cases the result object is a complex selector itself and can thus be further combined with other complex selectors. As expected, the union from example **G** yields 15 states:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15 states\n",
      "CD[S0, S0, T1]\n",
      "CD[S0, S1, T0]\n",
      "CD[S0, S1, T1]\n",
      "CD[S0, S1, T2]\n",
      "CD[S0, S2, T1]\n",
      "CD[S1, S0, T1]\n",
      "CD[S1, S1, T0]\n",
      "CD[S1, S1, T1]\n",
      "CD[S1, S1, T2]\n",
      "CD[S1, S2, T1]\n",
      "CD[S2, S0, T1]\n",
      "CD[S2, S1, T0]\n",
      "CD[S2, S1, T1]\n",
      "CD[S2, S1, T2]\n",
      "CD[S2, S2, T1]\n"
     ]
    }
   ],
   "source": [
    "printStates(CD[:, :, T1] | CD[:, S1, :])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that, while example **F** can also be written as a single complex selector, example **G** cannot. \n",
    "\n",
    "Example **H** illustrates the use of the `<<` operator to inject subunit states in a complex selector. `CD[..] << S0` should be read 'inject a subunit state `S0` in any available position'. Since, in `CD[...]`, there are 2 free positions that can be in state `S0`, it is equivalent to `CD[S0, :, :] | CD[:, S0, :]`. It is not very useful in our example but becomes convenient for bigger complexes. Note that the right hand side of the `<<` operator can also be a `SubUnitSelector`: `CD[...] << (S0 | S1)`. Finally, several subunit states can be injected at once with e.g. `CD[...] << 2 * S0`. Detailed explanations and examples are available in the [documentation](API_model.rst#steps.API_2.model.ComplexSelector). In example **H** we have:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15 states\n",
      "CD[S0, S0, T0]\n",
      "CD[S0, S0, T1]\n",
      "CD[S0, S0, T2]\n",
      "CD[S0, S1, T0]\n",
      "CD[S0, S1, T1]\n",
      "CD[S0, S1, T2]\n",
      "CD[S0, S2, T0]\n",
      "CD[S0, S2, T1]\n",
      "CD[S0, S2, T2]\n",
      "CD[S1, S0, T0]\n",
      "CD[S1, S0, T1]\n",
      "CD[S1, S0, T2]\n",
      "CD[S2, S0, T0]\n",
      "CD[S2, S0, T1]\n",
      "CD[S2, S0, T2]\n"
     ]
    }
   ],
   "source": [
    "printStates(CD[...] << S0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Complexes and rule based modeling\n",
    "\n",
    "Although STEPS complexes offer similar capabilities as rule-based modeling frameworks like [bionetgen](http://bionetgen.org/), they are not completely equivalent. STEPS complexes require the explicit declaration of all complexes before any simulation takes place. In contrast, bionetgen allows the creation of new complexes through the binding of smaller complexes. Thus, STEPS complexes are more suited to cases in which the complex has a set structure and its state space is known before simulation.\n",
    "\n",
    "Having introduced the main concepts relative to the `Complex` class, we can now use multi-state complexes in a full example. We first reset the jupyter kernel to start from scratch:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "%reset -f"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IP3 receptor model\n",
    "\n",
    "The corresponding python script: [STEPS_Tutorial_Complexes.py](https://github.com/CNS-OIST/STEPS_Example/tree/master/user_manual/source/API_2/scripts/STEPS_Tutorial_Complexes.py)\n",
    "\n",
    "In this section, we will implement the IP3 receptor model described in *De Young and Keizer, A single-pool inositol 1, 4, 5-trisphosphate-receptor-based model for agonist-stimulated oscillations in Ca2+ concentration, PNAS, 1992*. This model relies on a markov chain description of IP3R subunits in which each of the 4 identical subunits have 3 binding sites, one for IP3 and two for Ca2+, one activating, the other inactivating. This results in $2^3 = 8$ possible states per subunit and the whole channel is deemed open if at least three of the subunits are in the state in which one IP3 and the activating Ca2+ are bound. \n",
    "\n",
    "We first import the required modules and declare the parameters as specified in the original article:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import steps.interface\n",
    "\n",
    "from steps.model import *\n",
    "from steps.geom import *\n",
    "from steps.sim import *\n",
    "from steps.saving import *\n",
    "from steps.rng import *\n",
    "\n",
    "nAvog = 6.02214076e23 \n",
    "\n",
    "nbIP3R = 5\n",
    "nbPumps = 5\n",
    "\n",
    "c0 = 2e-6\n",
    "c1 = 0.185\n",
    "\n",
    "cytVol = 1.6572e-19\n",
    "ERVol = cytVol * c1\n",
    "\n",
    "a1 = 400e6\n",
    "a2 = 0.2e6\n",
    "a3 = 400e6\n",
    "a4 = 0.2e6\n",
    "a5 = 20e6\n",
    "\n",
    "b1 = 0.13e-6 * a1\n",
    "b2 = 1.049e-6 * a2\n",
    "b3 = 943.4e-9 * a3\n",
    "b4 = 144.5e-9 * a4\n",
    "b5 = 82.34e-9 * a5\n",
    "\n",
    "v1 = 6\n",
    "v2 = 0.11\n",
    "\n",
    "v3 = 0.9e-6\n",
    "k3 = 0.1e-6\n",
    "\n",
    "rp = v3 * 1e3 * cytVol * nAvog / nbPumps / 2\n",
    "rb = 10 * rp\n",
    "rf = (rb + rp) / (k3 ** 2)\n",
    "\n",
    "kip3 = 1e3 * nAvog * ERVol * v1 / nbIP3R"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then declare the model, the species and most importantly, the complex that we will use to simulate IP3 receptors. The following figure describes the IP3R complex:\n",
    "\n",
    "<img src=\"images/complex_ip3_structure.png\"/>\n",
    "\n",
    "As explained before, it is composed of 4 identical subunits which can be in 8 distinct states, we name the states according to what is bound to the subunit: for state $ijk$, $i$ is 1 if IP3 is bound, $j$ is 1 if the activating Ca2+ is bound, and $k$ is 1 if the inactivating Ca2+ is bound. State $110$ thus corresponds to the open state. Below the complex and its subunits, we represented the reaction network that governs the transitions between the subunit states. Each transition involves the binding or unbinding of either IP3 or Ca2+.\n",
    "\n",
    "We then proceed to declaring the IP3R complex:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "mdl = Model()\n",
    "r = ReactionManager()\n",
    "\n",
    "with mdl:\n",
    "    Ca, IP3, ERPump, ERPump2Ca = Species.Create()\n",
    "    \n",
    "    R000, R100, R010, R001, R110, R101, R111, R011 = SubUnitState.Create()\n",
    "    \n",
    "    IP3RSU = SubUnit.Create([R000, R100, R010, R001, R110, R101, R111, R011])\n",
    "    \n",
    "    IP3R = Complex.Create([IP3RSU, IP3RSU, IP3RSU, IP3RSU], statesAsSpecies=True)\n",
    "    \n",
    "    ssys = SurfaceSystem.Create()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The declaration of the complex itself follows what we saw in the first part of this chapter. We can count the number of distinct complex states:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "330"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(IP3R[...])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that, since the default ordering is `NoOrdering`, this is much lower than the $8^4=4096$ states that could be expected if `StrongOrdering` was used instead.\n",
    "\n",
    "The next step is to declare all the reactions involving the IP3R channel. Most of them correspond to IP3 and Ca2+ binding / unbinding that changes the states of subunits. In addition, we also need to write a reaction that will account for the Ca2+ flux from the endoplasmic reticulum (ER) through open IP3R channels. In the following section, we will see how to declare all these reactions.\n",
    "\n",
    "## Reactions involving complex states\n",
    "\n",
    "The simplest way to declare a reaction involving a complex consists in simply using a complex state as a reactant in a normal reaction. For example, if we wanted to only allow Ca2+ through the IP3R channel when the four subunits are in the open state, we would write:\n",
    "\n",
    "```python\n",
    "with mdl, ssys:\n",
    "    IP3R[R110, R110, R110, R110].s + Ca.i <r[1]> IP3R[R110, R110, R110, R110].s + Ca.o\n",
    "    r[1].K = kip3, kip3\n",
    "```\n",
    "\n",
    "Both left hand side and right hand side of the reaction contain the `IP3R` complex in a fully specified state. In this case, no changes are made to the complex but there are a lot of cases in which changes to the complex are required. For example, the unbinding of IP3 from one of the subunits of a fully open receptor could be written as:\n",
    "\n",
    "```python\n",
    "with mdl, ssys:\n",
    "    IP3R[R110, R110, R110, R110].s >r[1]> IP3R[R110, R110, R110, R010].s + Ca.o\n",
    "    r[1].K = rate\n",
    "```\n",
    "\n",
    "Note that the specific position of the subunit that is changed does not matter since we declared the complex using the detault `NoOrdering` setting. Complex states are thus used in reactions as if they were `Species`; this is convenient when only a single state of the complex can undergo a specific reaction but it quickly becomes unpractical when several complex states can undergo the same reaction.\n",
    "\n",
    "If, as is the case in the original De Young Keizer model, the IP3R channel opens when at least 3 subunits are in state `R110`, we would need to declare 8 reactions involving fully specified complex states:\n",
    "\n",
    "```python\n",
    "with mdl, ssys:\n",
    "    IP3R[R110, R110, R110, R000].s + Ca.i <r[1]> IP3R[R110, R110, R110, R000].s + Ca.o\n",
    "    IP3R[R110, R110, R110, R001].s + Ca.i <r[2]> IP3R[R110, R110, R110, R001].s + Ca.o\n",
    "    ...\n",
    "    IP3R[R110, R110, R110, R110].s + Ca.i <r[7]> IP3R[R110, R110, R110, R110].s + Ca.o\n",
    "    IP3R[R110, R110, R110, R111].s + Ca.i <r[8]> IP3R[R110, R110, R110, R111].s + Ca.o\n",
    "    r[1].K = kip3, kip3\n",
    "    ...\n",
    "    r[8].K = kip3, kip3\n",
    "```\n",
    "\n",
    "This case needs to be tackled using complex selectors instead.\n",
    "\n",
    "## Reactions involving complex selectors\n",
    "\n",
    "In order to group all these reactions in a single one, we could use the complex selector `IP3R[R110, R110, R110, :]` that encompasses all of the above 8 states. We would intuitively try to declare the reaction like so:\n",
    "\n",
    "```python\n",
    "with mdl, ssys:\n",
    "    IP3R[R110, R110, R110, :].s + Ca.i <r[1]> IP3R[R110, R110, R110, :].s + Ca.o\n",
    "    r[1].K = kip3, kip3\n",
    "```\n",
    "<div class=\"warning alert alert-block alert-danger\">\n",
    "\n",
    "<b>This raises the following exception</b>: <code>Complex selector IP3R[R110, R110, R110, :] is used in the right hand side of a reaction but is not matching anything in the left hand side and is not fully defined. The reaction is ambiguous.</code>\n",
    "</div>\n",
    "\n",
    "When trying to declare the reaction in this way, STEPS throws an exception. This is due to the fact that, in general, STEPS does not know whether the two result selectors refer to the same specific complex or to distinct ones. It is important here to make the distinction between the complex selectors during reaction declaration and the specific complexes that will exist during a simulation. Specific complexes in a simulation are always fully defined while complex selectors are only partially specified. In an actual simulation, specific complexes thus need to be matched to these partially specified objects. \n",
    "\n",
    "Although it might not seem very important in the reaction we tried to declare above, it becomes critical when expressing reactions between 2 complexes of the same type. Consider the following reaction using the `CC` complex declared in the first part of this chapter:\n",
    "\n",
    "```python\n",
    "CC[:, :, T0] + CC[:, :, T1] >r[1]> CC[:, :, T1] + CC[:, :, T2]\n",
    "r[1].K = 1\n",
    "```\n",
    "\n",
    "This reaction would also result in the same exception being thrown. This reaction happens when two complexes of the same `CC` type meet and when one has its `T` subunit in state `T0` and the other in state `T1`, ignoring the states of the `S` subunits. The intuitive way to read this reaction is that the `T0` complex is changed to `T1` and the `T1` complex is changed to `T2`. It could however be read in a different way: maybe the `T0` complex should be changed to `T2` while the `T1` should remain in `T1`. Imagine for example the specific reaction in which the left hand side is `CC[S0, S0, T0] + CC[S1, S1, T1]`, should the right hand side be `CC[S0, S0, T1] + CC[S1, S1, T2]` or `CC[S0, S0, T2] + CC[S1, S1, T1]`?\n",
    "\n",
    "In order to make it explicit, STEPS thus requires the user to use identified complexes in reactions involving complex selectors. To get an identified complex in the same example, we would write:\n",
    "\n",
    "```python\n",
    "CC_1 = CC.get()\n",
    "CC_2 = CC.get()\n",
    "CC_1[:, :, T0] + CC_2[:, :, T1] >r[1]> CC_1[:, :, T1] + CC_2[:, :, T2]\n",
    "r[1].K = 1\n",
    "```\n",
    "\n",
    "Calling the `get()` method on the complex returns an object that behaves like a `Complex` but keeps a specific identity so that, if it appears several times in a reaction, STEPS knows that it refers to the same specific complex. The reaction is now unambiguous and no exceptions are thrown. Coming back to our IP3R channel example, we can now declare the reaction associated to the Ca2+ flux through open IP3R channels with:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "with mdl, ssys:\n",
    "    # Ca2+ passing through open IP3R channel\n",
    "    IP3R_1 = IP3R.get()\n",
    "    IP3R_1[R110, R110, R110, :].s + Ca.i <r['caflx']> IP3R_1[R110, R110, R110, :].s + Ca.o\n",
    "    r['caflx'].K = kip3, kip3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "The next step is to somehow declare reactions that are associated to IP3 and Ca2+ binding / unbinding to IP3R subunits, as described in the figure.\n",
    "\n",
    "Let us first consider all reactions linked to IP3 binding to IP3R subunits and let us specifically focus on IP3 binding to IP3R subunits in the `R000` state, the rate of these reactions will depend on the number of subunits in this state. We can tackle this by writing a complex selector that controls the number of subunits in this state. For example, `IP3R[R000, ~R000, ~R000, ~R000]` corresponds to all states in which only one subunit is in the `R000` state. We could thus write all IP3 binding reactions to `R000` with:\n",
    "\n",
    "```python\n",
    "with mdl, ssys:\n",
    "    IP3R_1 = IP3R.get()\n",
    "    IP3R_1[R000, ~R000, ~R000, ~R000].s + IP3.o >r[1]> IP3R_1[R100, ~R000, ~R000, ~R000].s\n",
    "    IP3R_1[R000,  R000, ~R000, ~R000].s + IP3.o >r[2]> IP3R_1[R100,  R000, ~R000, ~R000].s\n",
    "    IP3R_1[R000,  R000,  R000, ~R000].s + IP3.o >r[3]> IP3R_1[R100,  R000,  R000, ~R000].s\n",
    "    IP3R_1[R000,  R000,  R000,  R000].s + IP3.o >r[4]> IP3R_1[R100,  R000,  R000,  R000].s\n",
    "    r[1].K = 1 * a1\n",
    "    r[2].K = 2 * a1\n",
    "    r[3].K = 3 * a1\n",
    "    r[4].K = 4 * a1\n",
    "```\n",
    "\n",
    "There are 4 reactions, corresponding to the cases in which the IP3R complex has 1, 2, 3 and 4 subunits in state `R000`. Since there are 4 ways to bind IP3 to an `R000` subunit in a `IP3R[R000, R000, R000, R000]` complex state, the rate of the reaction should be 4 times the elementary rate $a_1$.\n",
    "\n",
    "Expressing the unbinding reactions is however not trivial using these reactions. Let us consider the first of these 4 reactions, making it reversible would be equivalent to adding the following reaction:\n",
    "\n",
    "```python\n",
    "IP3R_1[R100, ~R000, ~R000, ~R000].s >r[1]> IP3R_1[R000, ~R000, ~R000, ~R000].s + IP3.o\n",
    "```\n",
    "\n",
    "In contrast with the binding reactions, it is not clear which rate should be used for this reaction, we know that, in the left hand side, at least one subunit is in state `R100` but the other subunits might also be in the same state, it is not prevented by the `~R000` subunit selector. In order to be sure that e.g. only one subunit is in state `R100` we would instead need to write:\n",
    "\n",
    "```python\n",
    "IP3R_1[R100, ~R100, ~R100, ~R100].s >r[1]> IP3R_1[R000, ~R100, ~R100, ~R100].s + IP3.o\n",
    "r[1].K = b1\n",
    "```\n",
    "\n",
    "The following tentative solution using a single reversible reaction will **not** work:\n",
    "\n",
    "```python\n",
    "IP3R_1[R000, ~R000, ~R000, ~R000].s + IP3.o <r[1]> IP3R_1[R100, ~(R000 | R100), ~(R000 | R100), ~(R000 | R100)].s\n",
    "r[1].K = a1, b1\n",
    "```\n",
    "This reaction is invalid because the right hand side is more restrictive than the left hand side. The left hand side matches e.g. `IP3R[R000, R100, R100, R100]` but the right hand side cannot match it. As a side note, the only way for a right hand side complex selector to be more restrictive is to constrain the subunits to a single state. In this case, there is no ambiguity and the reaction is valid.\n",
    "\n",
    "We could try to fix this validity issue by using the same subunit selectors on the left hand side:\n",
    "```python\n",
    "IP3R_1[R000, ~(R000 | R100), ~(R000 | R100), ~(R000 | R100)].s + IP3.o <r[1]> IP3R_1[R100, ~(R000 | R100), ~(R000 | R100), ~(R000 | R100)].s\n",
    "r[1].K = a1, b1\n",
    "```\n",
    "This is a valid reaction but it does not cover all cases of IP3 binding to an IP3R in which only one subunit is in state `R000`. For example, `IP3R[R000, R100, R111, R111]` would not be taken into account because its second subunit is `R100`, which does not match with the subunit selector `~(R000 | R100)`.\n",
    "\n",
    "From all these examples, it becomes clear that complex selectors are not well suited to declaring reactions that involve single subunits instead of full complexes. These reactions should instead be declared with their dedicated syntax.\n",
    "\n",
    "## Reactions involving subunits\n",
    "\n",
    "In order to express reactions that involve subunits instead of full complexes, we can simply use subunit states as reactants. The IP3 binding reaction to `R000` can thus be declared with:\n",
    "\n",
    "```python\n",
    "with mdl, ssys:\n",
    "    with IP3R[...]:\n",
    "        R000.s + IP3.o <r[1]> R100.s\n",
    "        r[1].K = a1, b1\n",
    "```\n",
    "The reaction itself corresponds exactly to the reaction being represented on the previous figure. The main difference with the full complex reactions we saw before is that the reaction declaration needs to be done inside a `with` block that uses a complex selector. This specifies the complex on which the reaction applies as well as the states that the complex needs to be in for the reaction to apply. In our case, the reaction applies to IP3R complexes in any state. We do not need to specify that at least one subunit should be in state `R000` since it is already implicitely required by the presence of `R000.s` in the left hand side of the reaction.\n",
    "\n",
    "Note that, in addition to being much simpler than our previous attempts using complex selectors, this syntax makes it very easy to declare the unbinding reaction ; we just need to make the reaction reversible.\n",
    "\n",
    "The rates are the per-subunit rates, as in the figure. STEPS will automatically compute the coefficients such that a complex with 2 subunits in state `R000` will undergo the change of one of its subunits with rate $2a_1$. FInally, the position of the complex is indicated by adding the position indicator `.s` to the subunit state itself.\n",
    "\n",
    "The following figure represents the full complex reactions that are equivalent to 2 examples of subunits reactions:\n",
    "\n",
    "<img src=\"images/complex_reactions.png\"/>\n",
    "\n",
    "Note that in both cases, only a very low number of possible reactions are represented. In each case, the required coefficient is applied to the rate that was used in the subunit reaction. For example, the first complex reaction of the left column can happen in four different ways since all four subunits are in the `R000` state; since all these ways result in the same equivalent state `IP3R[R100, R000, R000, R000]`, the subunit reaction rate is multiplied by 4 to get the complex reaction rate. Note that if we used the `StrongOrdering` ordering function, `IP3R[R100, R000, R000, R000]` would be different from e.g. `IP3R[R000, R100, R000, R000]` so four distinct complex reactions with rate $a_1$ would be declared.\n",
    "\n",
    "### Expressing cooperativity with complex selectors\n",
    "\n",
    "In our example, subunits bind IP3 and Ca2+ independently ; a simple way to express cooperativity would be to use several `with` blocks with different complex selectors. For example, if the binding rate of IP3 to a `R000` subunit depended on the number of subunits in the `R100` state we could write:\n",
    "\n",
    "```python\n",
    "with mdl, ssys:\n",
    "    # Binding\n",
    "    with IP3R[~R100, ~R100, ~R100, ~R100]:\n",
    "        R000.s + IP3.o >r[1]> R100.s\n",
    "        r[1].K = a1_0\n",
    "    with IP3R[ R100, ~R100, ~R100, ~R100]:\n",
    "        R000.s + IP3.o >r[1]> R100.s\n",
    "        r[1].K = a1_1\n",
    "    with IP3R[ R100,  R100, ~R100, ~R100]:\n",
    "        R000.s + IP3.o >r[1]> R100.s\n",
    "        r[1].K = a1_2\n",
    "    with IP3R[ R100,  R100,  R100, ~R100]:\n",
    "        R000.s + IP3.o >r[1]> R100.s\n",
    "        r[1].K = a1_3\n",
    "    # Unbinding\n",
    "    with IP3R[...]:\n",
    "        R100.s >r[1]> R000.s + IP3.o\n",
    "        r[1].K = b1\n",
    "```\n",
    "With `a1_0` the IP3 binding rate to `R000` when no subunits are in the `R100` state, `a1_1` when one subunit is in this state, etc. Note that the unbinding reaction now needs to be declared separately because, for the `with IP3R[~R100, ~R100, ~R100, ~R100]:` block, the complex selector would be incompatible with the `R100.s` right hand side.\n",
    "\n",
    "### Expressing cooperativity with complex-dependent reaction rates\n",
    "\n",
    "There is however a simpler way to express cooperativity by using complex-dependent reaction rate. The following example declares the same reactions as the previous one:\n",
    "\n",
    "```python\n",
    "rates = [a1_0, a1_1, a1_2, a1_3]\n",
    "a1 = CompDepRate(lambda state: rates[state.Count(R100)], [IP3R])\n",
    "\n",
    "with mdl, ssys:\n",
    "    with IP3R[...]:\n",
    "        R000.s + IP3.o <r[1]> R100.s\n",
    "        r[1].K = a1, b1\n",
    "```\n",
    "\n",
    "We first declare a list to hold all our `a1_x` rates ; we then declare the `a1` rate as a `CompDepRate` object. Its constructor (see [documentation](API_model.rst#steps.API_2.model.CompDepRate)) takes two parameters: the first one is a function that takes one or several complex states as parameter and returns a reaction rate ; the second is the list of complexes whose states influence the rate. In our case, the rate only depends on the state of the `IP3R` complex. Since it is possible to declare reactions between two complexes, corresponding rate can be declared with `CompDepRate(lambda state1, state2: ..., [Comp1, Comp2])`. Note that the lambda function now takes two parameters, corresponding to the states of the two complexes. They are given in the same order as in the `[Comp1, Comp2]` list.\n",
    "\n",
    "Note that the lambda function in the `CompDepRate` constructor makes uses of the `Count` method (see [documentation](API_model.rst#steps.API_2.model.ComplexState.Count)) from the `ComplexState` class. This method takes a `SubUnitState` or a `SubUnitSelector` as a parameter and returns the number of subunits in the state that correspond to the one passed as parameter.\n",
    "\n",
    "The reaction can then be declared inside a `with IP3R[...]` block, meaning it applies to all complexes, no matter their state. The forward rate is then simply set to the `CompDepRate` object we declared.\n",
    "\n",
    "Declaring reactions involving subunits can be done in a lot of different ways. We covered the most common cases in the previous subsections and advanced use cases are treated in [a separate section](#Advanced-use-of-subunit-reactions), as appendix to this chapter.\n",
    "\n",
    "Let us now come back to our main IP3R simulation example and declare the missing reactions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "with mdl, ssys:\n",
    "\n",
    "    # IP3R subunits reaction network\n",
    "    with IP3R[...]:\n",
    "        R000.s + IP3.o <r[1]> R100.s\n",
    "        R000.s + Ca.o <r[2]> R010.s\n",
    "        R000.s + Ca.o <r[3]> R001.s\n",
    "\n",
    "        R100.s + Ca.o <r[4]> R110.s\n",
    "        R100.s + Ca.o <r[5]> R101.s\n",
    "\n",
    "        R010.s + IP3.o <r[6]> R110.s\n",
    "        R010.s + Ca.o <r[7]> R011.s\n",
    "\n",
    "        R001.s + IP3.o <r[8]> R101.s\n",
    "        R001.s + Ca.o <r[9]> R011.s\n",
    "\n",
    "        R110.s + Ca.o <r[10]> R111.s\n",
    "        R101.s + Ca.o <r[11]> R111.s\n",
    "\n",
    "        R011.s + IP3.o <r[12]> R111.s\n",
    "\n",
    "        r[1].K = a1, b1\n",
    "        r[2].K = a5, b5\n",
    "        r[3].K = a4, b4\n",
    "        r[4].K = a5, b5\n",
    "        r[5].K = a2, b2\n",
    "        r[6].K = a1, b1\n",
    "        r[7].K = a4, b4\n",
    "        r[8].K = a3, b3\n",
    "        r[9].K = a5, b5\n",
    "        r[10].K = a2, b2\n",
    "        r[11].K = a5, b5\n",
    "        r[12].K = a3, b3\n",
    "\n",
    "    # Ca2+ leak\n",
    "    Ca.i <r[1]> Ca.o\n",
    "    r[1].K = v2, c1 * v2\n",
    "\n",
    "    2*Ca.o + ERPump.s <r[1]> ERPump2Ca.s >r[2]> 2*Ca.i + ERPump.s\n",
    "    r[1].K = rf, rb\n",
    "    r[2].K = rp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The full subunit reaction network is declared in the `with IP3R[...]:` block. The remaining lines declare the reactions associated to the Ca2+ leak from the endoplasmic reticulum (ER) as well as the Ca2+ pumping into the ER.\n",
    "\n",
    "## Geometry and simulation\n",
    "\n",
    "The well-mixed geometry is declared easily with:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "geom = Geometry()\n",
    "\n",
    "with geom:\n",
    "    cyt, ER = Compartment.Create()\n",
    "    cyt.Vol = cytVol\n",
    "    ER.Vol = ERVol\n",
    "\n",
    "    memb = Patch.Create(ER, cyt, ssys)\n",
    "    memb.Area = 0.4143e-12"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As in other chapters, we then declare the simulation object as well as the data to be saved:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model checking:\n",
      "No errors were found\n"
     ]
    }
   ],
   "source": [
    "rng = RNG('mt19937', 512, 7233)\n",
    "\n",
    "sim = Simulation('Wmdirect', mdl, geom, rng)\n",
    "\n",
    "rs = ResultSelector(sim)\n",
    "\n",
    "cytCa = rs.cyt.Ca.Conc\n",
    "\n",
    "caFlux = rs.SUM(rs.memb.caflx['fwd'].Extent) << rs.SUM(rs.memb.caflx['bkw'].Extent)\n",
    "\n",
    "IP3RStates =   rs.memb.IP3R[~R110, ~R110, ~R110, ~R110].Count\n",
    "IP3RStates <<= rs.memb.IP3R[ R110, ~R110, ~R110, ~R110].Count\n",
    "IP3RStates <<= rs.memb.IP3R[ R110,  R110, ~R110, ~R110].Count\n",
    "IP3RStates <<= rs.memb.IP3R[ R110,  R110,  R110, ~R110].Count\n",
    "IP3RStates <<= rs.memb.IP3R[ R110,  R110,  R110,  R110].Count\n",
    "\n",
    "sim.toSave(cytCa, caFlux, IP3RStates, dt=0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both `cytCa` and `caFlux` result selectors use syntaxes that were already presented in the previous chapters. Note however that we use `rs.SUM()` on `caFlx` paths because `rs.memb.caflx['fwd'].Extent` saves the extents of all reactions that are implied by the `'caflx'` complex reaction. Since we want to look at the overall complex reaction extent, we sum these values with `rs.SUM()`.\n",
    "\n",
    "The data saving relative to complexes themselves is new but relatively easy to understand. In our example, we want to track how receptors are distributed in terms of number of subunits in the `R110` open state. We save 5 values: the number of `IP3R` that have 0 subunits in the `R110` state, the number of `IP3R` that have 1 subunit in this state, etc. Note that the `rs.memb.IP3R.Count` result selector would save the total number of `IP3R` on the ER membrane.\n",
    "\n",
    "In addition to counting numbers of complexes, it is also possible to count numbers of subunits. `rs.memb.IP3R.R110.Count` would save the total number of subunits of `IP3R` that are in state `R110`.\n",
    "\n",
    "Finally, if one wanted to save the separate counts of all states matching some complex selectors, one could use `rs.memb.LIST(*IP3R[R110, R110, ...]).Count`. This uses the `LIST()` function that we saw in previous chapters by feeding it all the states that we want to save.\n",
    "\n",
    "We can then proceed to setting up intial conditions and running the simulation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "ENDT = 10.0\n",
    "\n",
    "sim.newRun()\n",
    "\n",
    "# Initial conditions\n",
    "sim.cyt.Ca.Conc = 3.30657e-8\n",
    "sim.cyt.IP3.Conc = 0.2e-6\n",
    "sim.ER.Ca.Conc = c0/c1\n",
    "sim.memb.ERPump.Count = nbPumps\n",
    "sim.memb.IP3R[R000, R000, R000, R000].Count = nbIP3R\n",
    "\n",
    "sim.run(ENDT)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that injecting `IP3R` complexes requires specifying their states completely.\n",
    "\n",
    "## Plotting the results\n",
    "\n",
    "We then plot the results from the `cytCa` and `caFlux` result selectors first:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "plt.figure(figsize=(10, 7))\n",
    "plt.plot(cytCa.time[0], cytCa.data[0]*1e6)\n",
    "plt.legend(cytCa.labels)\n",
    "plt.xlabel('Time [s]')\n",
    "plt.ylabel('Concentration [μM]')\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(10, 7))\n",
    "plt.plot(caFlux.time[0], caFlux.data[0])\n",
    "plt.legend(caFlux.labels)\n",
    "plt.xlabel('Time [s]')\n",
    "plt.ylabel('Reaction extent')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then plot the data from the `IP3RStates` result selector. In addition to the raw data, we compute a sliding window average to ease visualization:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 20\n",
    "\n",
    "plt.figure(figsize=(10, 7))\n",
    "for i in range(IP3RStates.data[0].shape[1]):\n",
    "    sig = IP3RStates.data[0, :, i]\n",
    "    avg = np.convolve(sig, np.ones(n) / n, 'valid')\n",
    "    tme = IP3RStates.time[0, n//2:-n//2+1]\n",
    "    \n",
    "    plt.plot(tme, avg, color=f'C{i}', label=IP3RStates.labels[i])\n",
    "    plt.plot(IP3RStates.time[0], sig, '--', linewidth=1, color=f'C{i}', alpha=0.4)\n",
    "\n",
    "plt.legend(loc=1)\n",
    "plt.xlabel('Time [s]')\n",
    "plt.ylabel('Count')\n",
    "plt.show()"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "## Advanced use of subunit reactions\n",
    "\n",
    "This subsection presents advanced use of complexes and can thus easily be skipped by a first-time reader.\n",
    "\n",
    "### Reactions between two complexes\n",
    "\n",
    "In our main example, we only ever declared subunit reactions with a single complex. It is however possible to declare reactions that involve two complexes ; let us take complexes `CB` and `CC` (from the first part of this chapter) as examples and let us declare a reaction between subunit state `B1` from `CB` and subunit state `R0` from `CC`:\n",
    "\n",
    "```python\n",
    "with CB[...], CC[...]:\n",
    "    B1 + R0 <r[1]> B2 + R1\n",
    "    r[1].K = r_f, r_b\n",
    "```\n",
    "\n",
    "The only difference with the reactions that we declared in the main example is that the `with` block now contains two complex selectors, one per complex, separated by a comma. As for single complex reactions, the rates `r_f` and `r_b` are multiplied by the required coefficents when necessary. For example the complex state `CB[B1, B1, B1, B1]` reacts with the complex state `CC[C0, R0, C0, R0]` with a rate equal to $4 \\times 2 r_f = 8 r_f$ since there are 8 ways to have a reaction between one `B1` and one `R0`.\n",
    "\n",
    "Since `B1`, `B2`, `R0`, and `R1` are unambiguously associated to their respective complex, nothing more is necessary for this reaction. In some cases, like when a reaction occurs between two complexes of the same type, it is however necessary to specify which subunit state belongs to which complex:\n",
    "\n",
    "```python\n",
    "with CB[...] as C1, CB[...] as C2:\n",
    "    B0[C1] + B1[C2] >r[1]> B1[C1] + B2[C2]\n",
    "    r[1].K = r_f\n",
    "```\n",
    "\n",
    "Here we defined a reaction between two `CB` complexes ; in order to differentiate them, we need to give them different names by using the `as` python notation. Inside the `with` block, one of them will be called `C1` and the other `C2`. Then, when we declare the reaction betweem subunit states, we specify which complex the subunit state is attached to by using square brackets : `B0[C1]` represents a subunit from complex `C1` in state `B0`.\n",
    "\n",
    "As long as only one subunit per complex is involved in the reaction, this allows to lift any ambiguities. We are however faced with an issue when trying to declare a reaction in which several subunits from the same complex are involved:\n",
    "\n",
    "```python\n",
    "with CB[...]:\n",
    "    B0 + B1 >r[1]> B1 + B2\n",
    "    r[1].K = r_f\n",
    "```\n",
    "\n",
    "Like in the case we mentioned in the [complex selectors section](#Reactions-involving-complex-selectors), it is not clear whether `B0` should become `B1` and `B1` should become `B2` or if `B0` should become `B2` and `B1` should stay `B1`.\n",
    "\n",
    "### Subunit state identifiers\n",
    "\n",
    "This ambiguity can be lifted with subunit state identifiers:\n",
    "\n",
    "```python\n",
    "with CB[...]:\n",
    "    B0['a'] + B1['b'] >r[1]> B1['a'] + B2['b']\n",
    "    r[1].K = r_f\n",
    "```\n",
    "\n",
    "We again use the square bracket notation to add information to a subunit state but this time we give a string that identifies the subunit. This reaction is now unambiguous: the `'a'` subunit goes from `B0` to `B1` while the `'b'` subunit goes from `B1` to `B2`.\n",
    "\n",
    "Note that it is possible to combine subunit identifiers with complex identifiers:\n",
    "\n",
    "```python\n",
    "with CB[...] as C1, CB[...] as C2:\n",
    "    B0[C1, 'a'] + B1[C1, 'b'] + B2[C2] >r[1]> B1[C1, 'a'] + B2[C1, 'b'] + B2[C2]\n",
    "    r[1].K = r_f\n",
    "```\n",
    "\n",
    "This declares a reaction between two `CB` complexes identified as `C1` and `C2`. `C1` needs to have one subunit identified as `'a'` in state `B0` and another subunit identified as `'b'` in state `B1` while `C2` contains one subunit in state `B2`. The reaction transforms subunit `'a'` to `B1`, `'b'` to `B2`, and leaves `B2` from complex `C2` unchanged.\n",
    "\n",
    "Note that this reaction involved two subunits from the same specific complex. When `C1` is in state `CB[B0, B0, B1, B1]` and `C2` in state `CB[B2, B2, B2, B2]` the reaction between these two complexes occurs with rate $2 \\times 2 \\times 4 r_f = 16 r_f$ because there are 4 ways to select a pair of `B0`, `B1` subunits from `C1` and 4 ways to select a `B2` from `C2`.\n",
    "\n",
    "As a side note, when the [verbosity](API_utils.rst#steps.API_2.utils.SetVerbosity) is set higher or equal to 3, all the reactions that are implied by a complex reactions are displayed along with their respective rates. Users should check these reactions in order to be sure that they correspond to what they intended.\n",
    "\n",
    "### Subunit selectors\n",
    "\n",
    "As we saw earlier, it is possible to combine subunit states with the `|` or `~` operators to obtain `SubUnitSelector` objects that represent several subunit states from a single subunit. These subunit selectors can also be used in reactions:\n",
    "\n",
    "```python\n",
    "with CB[...]:\n",
    "    ~B0 >r[1]> B0\n",
    "    r[1].K = r_f\n",
    "```\n",
    "\n",
    "Since the subunits from `CB` can only be in states `B0`, `B1`, or `B2`, this reaction is equivalent to the two following reactions:\n",
    "```python\n",
    "with CB[...]:\n",
    "    B1 >r[1]> B0\n",
    "    B2 >r[2]> B0\n",
    "    r[1].K = r_f\n",
    "    r[2].K = r_f\n",
    "```\n",
    "\n",
    "The same reactions can be expressed with:\n",
    "\n",
    "```python\n",
    "with CB[...]:\n",
    "    (B1|B2) >r[1]> B0\n",
    "    r[1].K = r_f\n",
    "```\n",
    "\n",
    "Note that the `B1|B2` subunit selector is wrapped in parentheses. In this specific example it is not really necessary but it is advised to always wrap subunit selectors in parentheses when used in reaction declaration in order to avoid issues with operator precedence. `B1|B2 >r[1]> B0` works but `B1|B2 + B0 >r[1]> 2*B0` would throw an exception because the `+` operator has priority over `|`. It would thus be interpreted by default as `B1 | (B2 + B0) >r[1]> 2*B0` instead of the correct `(B1|B2) + B0 >r[1]> 2*B0`."
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