docs/education/sb2/chapters/sb2_chapter8.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stoichiometric Structure\n",
    "Part I of this book introduced the basics of dynamic simulation. The process for setting up dynamic equations, their simulation, and processing of the output was presented in Chapter 3. Several concepts of dynamic analysis of networks were illustrated through the use of simple examples of chemical reaction mechanisms in Chapters 4 through 6. Most of these examples were conceptual and had limited direct biological relevance. In Chapter 7 we began to estimate the numerical values and ranges for key quantities in dynamic models. With this background, we now begin the process of addressing issues that are important when one builds realistic dynamic models of biological functions. We start by exploring the consequences of reaction bilinearity and that of the stoichiometric structure of a network. In Part III we then extend the material in this chapter to well-known metabolic pathways.  \n",
    "\n",
    "**MASSpy** will be used to demonstrate some of the topics in this chapter.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-25T17:57:15.869214Z",
     "iopub.status.busy": "2021-01-25T17:57:15.868312Z",
     "iopub.status.idle": "2021-01-25T17:57:18.416205Z",
     "shell.execute_reply": "2021-01-25T17:57:18.416678Z"
    }
   },
   "outputs": [],
   "source": [
    "from mass import (\n",
    "    MassModel, MassMetabolite, MassReaction,\n",
    "    Simulation, MassSolution, strip_time)\n",
    "from mass.util.matrix import nullspace, left_nullspace\n",
    "from mass.visualization import plot_time_profile, plot_phase_portrait"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Other useful packages are also imported at this time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-25T17:57:18.424916Z",
     "iopub.status.busy": "2021-01-25T17:57:18.423945Z",
     "iopub.status.idle": "2021-01-25T17:57:18.426269Z",
     "shell.execute_reply": "2021-01-25T17:57:18.426714Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sympy as sym\n",
    "import matplotlib.pyplot as plt\n",
    "XL_FONT = {\"size\": \"x-large\"}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bilinearity in Biochemical Reactions\n",
    "### Bilinear reactions \n",
    "They are of the form:\n",
    "\n",
    "$$\\begin{equation} x + y \\rightarrow z \\tag{8.1} \\end{equation}$$\n",
    "\n",
    "Two molecules come together to form a new molecule through the breaking and forming of covalent bonds, or a complex through the formation of hydrogen bonds. As illustrated with the pool formations in the bilinear examples in Chapter 4, such reactions come with moiety exchanges. \n",
    "\n",
    "### Enzyme classification  \n",
    "Enzyme catalyzed reactions are classified in to seven categories by Enzyme Commission (EC) numbers, see Figure 8.1a.  These categories are: oxidoreductases, transferases, hydrolases, lyases, isomerases, and ligases. All these chemical transformations are bilinear with the exception of isomerases that simply rearrange a molecule without the participation of other reactants. Thus, the vast majority of biochemical reactions are bilinear. An overall pseudo-elementary representation (i.e., without treating the enzyme itself as a reactant, and just representing the un-catalyzed reaction) is bilinear. \n",
    "\n",
    "![Figure-8-1](../images/Ch8/Figure-8-1.png)\n",
    "\n",
    "**Figure 8.1:** The bilinear nature of biochemical reactions. (a) The classification of enzyme catalyzed reactions into seven categories by the enzyme commission (EC) number system. (b) The detailed view of the role of coenzymes and prosthetic groups in enzyme catalyzed reactions. Coenzymes are often referred to as cofactors. Both images from Koolman, 2005 (reprinted with permission).\n",
    "\n",
    "### Coenzymes and prosthetic groups  \n",
    "There are coenzymes and prosthetic groups that are involved in many biochemical reactions. These molecules are involved in group transfer reactions as illustrated in Figure 8.1b. They can transfer various chemical moieties or redox equivalents, see Table 8.1. Coenzymes act like a reactant and product in a reaction.  They can work with many enzymes performing reactions that need them.  Prosthetic groups  associate with a particular enzyme to give it chemical functionalities that the protein itself does not have, Figure 8.1b. The heme group on hemoglobin is perhaps the most familiar example (see Chapter 13) that allows the protein tetramer to acquire a ferrous ion thus enabling the binding of oxygen.  This binding allows the red blood cell to perform its oxygen delivery functions. There are many such capabilities 'grafted' onto proteins in the form of prosthetic groups. Many of the vitamins confer functions on protein complexes. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bilinearity Leads to a Tangle of Cycles\n",
    "### Moiety exchange:  \n",
    "Biochemical reaction networks are primarily made up of bilinear reactions. A fundamental consequence of this characteristic is a deliberate exchange of chemical moieties and properties between molecules. This exchange is illustrated in Figure 8.2. Here, an incoming molecule, $XA$, puts the moiety, $A$, onto a carrier molecule, $C$. The carrier molecule, now in a 'charged' form $(CA)$, can donate the $A$ moiety to another molecule, $Y$, to form $YA$. The terms _coenzyme,_ _cofactor_ or _carrier_ are used to describe the $C$ molecule.\n",
    "\n",
    "![Figure-8-2](../images/Ch8/Figure-8-2.png)\n",
    "\n",
    "**Figure 8.2:** Carrier $(C)$ mediated transfer of chemical moiety $A$ from compound $X$ to compound $Y$.\n",
    "\n",
    "### Formation of cycles:  \n",
    "The ability of bilinear reactions to exchange moieties in this fashion leads to the formation of distribution networks of chemical moieties and other properties of interest through the formation of a deliberate 'supply-chain' network. The structure of such a network must be thermodynamically feasible and conform to environmental constraints. \n",
    "\n",
    "Bilinearization in biochemical reaction networks leads to a 'tangle of cycles,' where different moieties and properties are being moved around the network. While a property of all biochemical networks, this trafficking of chemical and other properties is best known in metabolism. The major chemical properties that are being exchanged in metabolism are summarized in Table 8.1. These properties include energy, redox potential, one-carbon units, two-carbon units, amide groups, amine groups, etc. We now consider some specific cases. \n",
    "\n",
    "**Table 8.1:** Some activated carriers or coenzymes in metabolism, modified from Kurganov, 1983.\n",
    "\n",
    "![Table-8-1](../images/Ch8/Table-8-1.png)\n",
    "\n",
    "#### Example: Redox and energy trafficking in the core _E. coli_ metabolic pathways  \n",
    "Energy metabolism revolves around the generation of redox potential and chemical energy in the form of high-energy phosphate bonds. The degradation of substrates through a series of chemical reactions culminates in the storage of these properties on key carrier molecules; see Table 8.1. \n",
    "\n",
    "The core metabolic pathways in _E. coli_ illustrate this feature, Figure 8.3. The transmission of redox equivalents through this core set of pathways is shown in Figure 8.3a. Each pathway is coupled to a redox carrier in a particular way. This pathway map can be drawn to show the cofactors rather than the primary metabolites and the main pathways (Figure 8.3b). This figure clearly shows how the cofactors interact and how the bilinear property of the stoichiometry of the core set of pathways leads to a tangle of cycles among the redox carriers. \n",
    "\n",
    "![Figure-8-3](../images/Ch8/Figure-8-3.png)\n",
    "\n",
    "**Figure 8.3:** The tangle of cycles in trafficking of redox potential (R) in _E. coli_ core metabolic pathways. (a) A map organized around the core pathways. (b) The tangle of cycles seen by viewing the cofactors and how they are coupled. Prepared by Jeff Orth.\n",
    "\n",
    "#### Example: Protein trafficking in signaling pathways  \n",
    "Although the considerations above are illustrated using well-known metabolic pathways, these same features are also observed in signaling pathways. Incoming molecules (ligands) trigger a well-defined series of charging and discharging of the protein that make up a signaling network, most often with a phosphate group. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Trafficking of High-Energy Phosphate Bonds\n",
    "Given the bilinear nature of biochemical reaction networks and the key role that cofactors play, we begin the process of building biologically meaningful simulation models by studying the use and formation of high-energy phosphate bonds. Cellular energy is stored in high-energy phosphate bonds in ATP. The dynamic balance of the rates of use and formation of ATP is thus a common denominator in all cellular processes, and thus foundational to the living process. We study the dynamic properties of this system in a bottom-up fashion by starting with its simple elements and making the description progressively more complicated. Throughout the text we make explicit use of the basic methods in **MASSpy**.\n",
    "\n",
    "![Figure-8-4](../images/Ch8/Figure-8-4.png)\n",
    "\n",
    "**Figure 8.4:** Representation of the exchange of high energy phosphate bonds among the adenosine phosphates. (a) The chemical reactions. (b) The molecules with open circles showing the \"vacant\" places for high energy bonds. The capacity to carry high-energy phosphate bonds, the occupancy of high-energy bonds, and the energy charge are shown. (c) The reaction schema of (a) in pictorial form. The solid squares represent AMP and the solid circles the high energy phosphate bonds. (d) The same concepts as in (b) represented in pictorial form.\n",
    "\n",
    "### Distribution of high-energy phosphate groups: adenylate kinase (EC 2.7.4.3)\n",
    "The Adenylate Kinase is an important part in intracellular energy homeostasis. Adenylate Kinase is a phosphotransferase enzyme and it is the enzyme responsible for the redistribution of the phosphate groups among the adenosine phosphates. The redistribution reaction the Adenylate Kinase catalyzes is seen in Figure 8.4a.\n",
    "\n",
    "#### The mass balance:  adenylate kinase\n",
    "The redistribution of the phosphate groups among the adenosine phosphates by the adenylate kinase is given by the following kinetic equations:\n",
    "\n",
    "$$\\begin{equation} \\frac{d\\text{ATP}}{dt} = v_{\\mathrm{distr}}, \\ \\frac{d\\text{ADP}}{dt} = -2\\ v_{\\mathrm{distr}}, \\ \\frac{d\\text{AMP}}{dt} = v_{\\mathrm{distr}} \\tag{8.2} \\end{equation}$$\n",
    "\n",
    "#### The reaction rates: adenylate kinase\n",
    "The mass action form of these basic reaction rates are \n",
    "\n",
    "$$\\begin{equation} v_{\\mathrm{distr}} = k_{\\mathrm{distr}}^\\rightarrow\\text{ADP}^2 - k_{\\mathrm{distr}}^\\leftarrow\\text{ATP}*\\text{AMP} \\tag{8.3} \\end{equation}$$\n",
    "\n",
    "#### Numerical values: adenylate kinase\n",
    "The approximate numerical values of the parameters in this system can be estimated. In metabolically active tissues, the ATP concentration is about 1.6 mM, the ADP concentration is about 0.4 mM, and the AMP concentration is about 0.1 mM.  Total adenosine phosphates are thus about 2.1 mM. Because this reaction is considerably faster compared to other metabolic processes, we set $k_{\\mathrm{distr}}^\\rightarrow$ to 1000/Min. $K_{\\mathrm{distr}}$ for the distribution reaction is approximately unity. We then construct a model of the redistribution of phosphate groups among the adenosine phosphates by adenylate kinase using the above constraints.  This is simple reversible reaction that equilibrates quickly.\n",
    "\n",
    "![Figure-8-5](../images/Ch8/Figure-8-5.png)\n",
    "\n",
    "**Figure 8.5:** The redistribution of phosphate groups among the adenosine phosphates by adenylate kinase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-25T17:57:18.451939Z",
     "iopub.status.busy": "2021-01-25T17:57:18.431985Z",
     "iopub.status.idle": "2021-01-25T17:57:18.457495Z",
     "shell.execute_reply": "2021-01-25T17:57:18.460335Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Set parameter Username\n"
     ]
    }
   ],
   "source": [
    "phos_traffic = MassModel(\"Phosphate_Trafficking\", array_type=\"DataFrame\",\n",
    "                         dtype=np.int64)\n",
    "# Define metabolites\n",
    "atp = MassMetabolite(\"atp\")\n",
    "adp = MassMetabolite(\"adp\")\n",
    "amp = MassMetabolite(\"amp\")\n",
    "# Define reactions\n",
    "v_distr = MassReaction(\"distr\")\n",
    "v_distr.add_metabolites({adp: -2, amp: 1, atp:1})\n",
    "\n",
    "# Add reactions to model\n",
    "phos_traffic.add_reactions([v_distr])\n",
    "\n",
    "# Define initial conditions and parameters\n",
    "atp.ic = 1.6\n",
    "adp.ic = 0.4\n",
    "amp.ic = 0.1\n",
    "\n",
    "v_distr.kf = 1000\n",
    "v_distr.Keq = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Null spaces: adenylate kinase\n",
    "The stoichiometric matrix is basically a column vector."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-25T17:57:18.506845Z",
     "iopub.status.busy": "2021-01-25T17:57:18.500501Z",
     "iopub.status.idle": "2021-01-25T17:57:18.513013Z",
     "shell.execute_reply": "2021-01-25T17:57:18.513602Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>distr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>adp</th>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amp</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>atp</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     distr\n",
       "adp     -2\n",
       "amp      1\n",
       "atp      1"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phos_traffic.S"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It has an empty null space; i.e. zero dimensional."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-25T17:57:18.533875Z",
     "iopub.status.busy": "2021-01-25T17:57:18.526935Z",
     "iopub.status.idle": "2021-01-25T17:57:18.542675Z",
     "shell.execute_reply": "2021-01-25T17:57:18.543192Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([], shape=(1, 0), dtype=float64)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nullspace(phos_traffic.S, rtol=1e-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, the left null space has two dimensions and it thus has two conservation pools. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-25T17:57:18.555675Z",
     "iopub.status.busy": "2021-01-25T17:57:18.551906Z",
     "iopub.status.idle": "2021-01-25T17:57:18.571084Z",
     "shell.execute_reply": "2021-01-25T17:57:18.573869Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>adp</th>\n",
       "      <th>amp</th>\n",
       "      <th>atp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Vacancy</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Occupancy</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           adp  amp  atp\n",
       "Vacancy      1    2    0\n",
       "Occupancy    1    0    2"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Obtain left nullspace\n",
    "lns = left_nullspace(phos_traffic.S, rtol=1e-1)\n",
    "# Iterate through left nullspace, \n",
    "# dividing by the smallest value in each row. \n",
    "for i, row in enumerate(lns):\n",
    "    minval = np.min(abs(row[np.nonzero(row)]))\n",
    "    new_row = np.array(row/minval)\n",
    "    # Round to ensure the left nullspace is composed of only integers\n",
    "    lns[i] = np.array([round(value) for value in new_row])\n",
    "\n",
    "# Ensure positive stoichiometric coefficients if all are negative\n",
    "for i, space in enumerate(lns):\n",
    "    lns[i] = np.negative(space) if all([num <= 0 for num in space]) else space\n",
    "\n",
    "# Create a pandas.DataFrame to represent the left nullspace\n",
    "pd.DataFrame(lns, index=[\"Vacancy\", \"Occupancy\"], \n",
    "             columns=phos_traffic.metabolites, dtype=np.int64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The interpretation of these pools is remarkably interesting:  the first one counts the number of high energy phosphate bonds in the system, while the second counts the number of vacant spots where high energy phosphate bonds can be added.  The left null space is spanned by these two vectors that we can think of as a conjugate pair.  Furthermore, the summation of the two is the total amount of the 'A' nucleotide in the system times two; ie the total number of possible high-energy phosphate bonds that the system can carry.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-25T17:57:18.586224Z",
     "iopub.status.busy": "2021-01-25T17:57:18.585085Z",
     "iopub.status.idle": "2021-01-25T17:57:18.590309Z",
     "shell.execute_reply": "2021-01-25T17:57:18.590955Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>adp</th>\n",
       "      <th>amp</th>\n",
       "      <th>atp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Capacity</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          adp  amp  atp\n",
       "Capacity    2    2    2"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sum the elements of each row to obtain the capacity pool\n",
    "capacity = np.array([np.sum(lns, axis=0)])\n",
    "pd.DataFrame(capacity, index=[\"Capacity\"],\n",
    "             columns=phos_traffic.metabolites, dtype=np.int64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that any activity of this reaction does not change the sizes of these two pools as the left null space is orthogonal to the reaction vector (or the column vector of $(\\textbf{S})$, that represents the direction of motion.\n",
    "\n",
    "### Using and generating high-energy phosphate groups  \n",
    "We now introduce the 'use' and 'formation' reactions for ATP into the above system.  These represent aggregate processes in the cell using and forming high energy bonds.\n",
    "\n",
    "#### The mass balances: trafficking high-energy phosphate bonds\n",
    "$$\\begin{align} \\frac{d\\text{ATP}}{dt} &= -v_{\\mathrm{use}} + v_{\\mathrm{form}} + v_{\\mathrm{distr}} \\tag{8.4} \\\\ \\frac{d\\text{ADP}}{dt} &= v_{\\mathrm{use}} - v_{\\mathrm{form}} - 2\\ v_{\\mathrm{distr}} \\tag{8.5} \\\\ \\frac{d\\text{AMP}}{dt} &= v_{\\mathrm{distr}} \\tag{8.6} \\end{align}$$\n",
    "\n",
    "where $v_{\\mathrm{use}}$ is the rate of use of ATP, $v_{\\mathrm{form}}$ is the rate of formation of ATP, and, as above, $v_{\\mathrm{distr}}$ is the redistribution of the phosphate group among the adenosine phosphates by adenylate kinase. \n",
    "\n",
    "#### The reaction rates: trafficking high-energy phosphate bonds\n",
    "Elementary mass action form for the two additional rate equations are \n",
    "\n",
    "$$\\begin{equation} v_{\\mathrm{use}} = k_{\\mathrm{use}}^\\rightarrow \\text{ATP},\\ v_{\\mathrm{form}} = k_{\\mathrm{form}}^\\rightarrow\\text{ADP}\\tag{8.7} \\end{equation}$$ \n",
    "\n",
    "#### Numerical values: trafficking high-energy phosphate bonds\n",
    "We use the equilibrium concentrations from the distribution model and estimate in the numerical values for the rate constants of ATP use and formation based on the fact that typical use and formation rates of ATP are about 10 mM/min. Using the steady state concentrations, we can calculate $k_{\\mathrm{use}}^\\rightarrow$ and $k_{\\mathrm{form}}^\\rightarrow$, resulting in $k_{\\mathrm{use}}^\\rightarrow=6.25\\ min^{-1}$ and $k_{\\mathrm{form}}^\\rightarrow=25\\ min^{-1}$. These constants are known as Pseudo-Elementary Rate Constants (PERCs). They are a ratio between the flux through a reaction and the concentrations of the involved species, and the simplify the network dynamic analysis. However they are condition dependent and result in a condition dependent kinetic model. What comprises the PERCs is explored further in the later chapters.  \n",
    "\n",
    "We update the distribution model with the additional reactions and parameters. \n",
    "\n",
    "![Figure-8-6](../images/Ch8/Figure-8-6.png)\n",
    "\n",
    "**Figure 8.6:** The trafficking of high-energy phosphate bonds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-25T17:57:18.609285Z",
     "iopub.status.busy": "2021-01-25T17:57:18.608517Z",
     "iopub.status.idle": "2021-01-25T17:57:18.611876Z",
     "shell.execute_reply": "2021-01-25T17:57:18.612392Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kf_distr*(adp(t)**2 - amp(t)*atp(t)/Keq_distr)\n"
     ]
    }
   ],
   "source": [
    "# Create utilization reaction\n",
    "v_use = MassReaction(\"use\", reversible=False)\n",
    "v_use.add_metabolites({atp: -1, adp: 1})\n",
    "v_use.kf = 6.25\n",
    "\n",
    "# Create formation reaction\n",
    "v_form = MassReaction(\"form\", reversible=False)\n",
    "v_form.add_metabolites({adp: -1, atp: 1})\n",
    "v_form.kf = 25\n",
    "\n",
    "# Add reactions to model\n",
    "phos_traffic.add_reactions([v_use, v_form])\n",
    "\n",
    "# View rate of distribution reaction\n",
    "print(v_distr.rate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the model we also see that the net rate for the redistribution of high-energy bonds is\n",
    "\n",
    "$$\\begin{align} v_{\\mathrm{distr}} &= k_{\\mathrm{distr}}^\\rightarrow\\ \\text{ADP}^2 - k_{\\mathrm{distr}}^\\leftarrow\\text{ATP}*\\text{AMP} &\\tag{8.8a} \\\\ &= k_{\\mathrm{distr}}^\\rightarrow( \\text{ADP}^2 - \\text{ATP}*\\text{AMP}/K_{\\mathrm{distr}}) &\\tag{8.8b} \\end{align}$$\n",
    "\n",
    "#### Null spaces: trafficking high-energy phosphate bonds\n",
    "Now the stoichiometric matrix three columns. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-25T17:57:18.640662Z",
     "iopub.status.busy": "2021-01-25T17:57:18.639448Z",
     "iopub.status.idle": "2021-01-25T17:57:18.648104Z",
     "shell.execute_reply": "2021-01-25T17:57:18.649318Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>distr</th>\n",
       "      <th>use</th>\n",
       "      <th>form</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>adp</th>\n",
       "      <td>-2</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amp</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>atp</th>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     distr  use  form\n",
       "adp     -2    1    -1\n",
       "amp      1    0     0\n",
       "atp      1   -1     1"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phos_traffic.S"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It has a one-dimensional null space, that represents an internal loop as the use and formation reactions are the exact opposites of each other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-25T17:57:18.667414Z",
     "iopub.status.busy": "2021-01-25T17:57:18.666771Z",
     "iopub.status.idle": "2021-01-25T17:57:18.690627Z",
     "shell.execute_reply": "2021-01-25T17:57:18.695018Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Path 1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>distr</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>use</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>form</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Path 1\n",
       "distr       0\n",
       "use         1\n",
       "form        1"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Obtain nullspace\n",
    "ns = nullspace(phos_traffic.S, rtol=1e-1)\n",
    "# Transpose and iterate through nullspace, \n",
    "# dividing by the smallest value in each row.\n",
    "ns = ns.T\n",
    "for i, row in enumerate(ns):\n",
    "    minval = np.min(abs(row[np.nonzero(row)]))\n",
    "    new_row = np.array(row/minval)\n",
    "    # Round to ensure the nullspace is composed of only integers\n",
    "    ns[i] = np.array([round(value) for value in new_row])\n",
    "\n",
    "    \n",
    "# Ensure positive stoichiometric coefficients if all are negative\n",
    "for i, space in enumerate(ns):\n",
    "    ns[i] = np.negative(space) if all([num <= 0 for num in space]) else space\n",
    "\n",
    "# Revert transpose\n",
    "ns = ns.T\n",
    "# Create a pandas.DataFrame to represent the nullspace\n",
    "pd.DataFrame(ns, index=[rxn.id for rxn in phos_traffic.reactions],\n",
    "             columns=[\"Path 1\"], dtype=np.int64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The left null space is now one-dimensional;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-25T17:57:18.708886Z",
     "iopub.status.busy": "2021-01-25T17:57:18.706771Z",
     "iopub.status.idle": "2021-01-25T17:57:18.732336Z",
     "shell.execute_reply": "2021-01-25T17:57:18.736123Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>adp</th>\n",
       "      <th>amp</th>\n",
       "      <th>atp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Total AxP</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           adp  amp  atp\n",
       "Total AxP    1    1    1"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Obtain left nullspace\n",
    "lns = left_nullspace(phos_traffic.S, rtol=1e-1)\n",
    "# Iterate through left nullspace, \n",
    "# dividing by the smallest value in each row. \n",
    "for i, row in enumerate(lns):\n",
    "    minval = np.min(abs(row[np.nonzero(row)]))\n",
    "    new_row = np.array(row/minval)\n",
    "    # Round to ensure the left nullspace is composed of only integers\n",
    "    lns[i] = np.array([round(value) for value in new_row])\n",
    "\n",
    "# Ensure positive stoichiometric coefficients if all are negative\n",
    "for i, space in enumerate(lns):\n",
    "    lns[i] = np.negative(space) if all([num <= 0 for num in space]) else space\n",
    "\n",
    "# Create a pandas.DataFrame to represent the left nullspace\n",
    "pd.DataFrame(lns, index=[\"Total AxP\"], \n",
    "             columns=phos_traffic.metabolites, dtype=np.int64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dynamic simulations: trafficking high-energy phosphate bonds \n",
    "The system is steady at the initial conditions given"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-25T17:57:18.748198Z",
     "iopub.status.busy": "2021-01-25T17:57:18.744232Z",
     "iopub.status.idle": "2021-01-25T17:57:19.000538Z",
     "shell.execute_reply": "2021-01-25T17:57:19.001352Z"
    },
    "tags": [
     "remove_cell"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[93mWARNING:\u001b[0m \u001b[93mNo compartments found in model. Therefore creating compartment 'compartment' for entire model.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully loaded MassModel 'Phosphate_Trafficking' into RoadRunner.\n",
      "Getting time points\n",
      "Setting output selections\n",
      "Setting simulation values for 'Phosphate_Trafficking'\n",
      "Simulating 'Phosphate_Trafficking'\n",
      "Simulation for 'Phosphate_Trafficking' successful\n",
      "Adding 'Phosphate_Trafficking' simulation solutions to output\n",
      "Updating stored solutions\n"
     ]
    }
   ],
   "source": [
    "t0, tf = (0, 1e3)\n",
    "sim = Simulation(phos_traffic, verbose=True)\n",
    "conc_sol, flux_sol = sim.simulate(\n",
    "    phos_traffic, time=(t0, tf), interpolate=True,\n",
    "    verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-25T17:57:19.047034Z",
     "iopub.status.busy": "2021-01-25T17:57:19.008171Z",
     "iopub.status.idle": "2021-01-25T17:57:20.518551Z",
     "shell.execute_reply": "2021-01-25T17:57:20.520347Z"
    },
    "tags": [
     "remove_cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=2, ncols=1, figsize=(8, 4),\n",
    "                         )\n",
    "(ax1, ax2) = axes.flatten()\n",
    "\n",
    "plot_time_profile(\n",
    "    conc_sol, ax=ax1,\n",
    "    legend=\"right outside\",plot_function=\"semilogx\", \n",
    "    xlabel=\"Time [min]\", ylabel=\"Concentrations [mM]\", \n",
    "    title=(\"Concentration Profile\", XL_FONT));\n",
    "\n",
    "plot_time_profile(\n",
    "    flux_sol, ax=ax2,\n",
    "    legend=\"right outside\", plot_function=\"semilogx\", \n",
    "    xlabel=\"Time [min]\", ylabel=\"Fluxes [mM/min]\", \n",
    "    title=(\"Flux Profile\", XL_FONT));\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can induce motion in the system by taking 0.2 mM of ADP and splitting it into 0.1 mM addition to AMP and ATP, and set the initial conditions as ATP is 1.7 mM, ADP is 0.2 mM, and AMP is 0.2 mM and simulate the dynamic response.  We graph the concentration profiles, as well as the two pools and the disequilibrium variable: $\\text{ADP}^2 - \\text{ATP}*\\text{AMP}$ that is zero at the equilibrium"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-25T17:57:20.567530Z",
     "iopub.status.busy": "2021-01-25T17:57:20.566875Z",
     "iopub.status.idle": "2021-01-25T17:57:22.803050Z",
     "shell.execute_reply": "2021-01-25T17:57:22.800210Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 800x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define pools and perturbations\n",
    "pools = {\"Occupancy\": \"adp + 2*atp\", \n",
    "         \"Vacancy\": \"adp + 2*amp\", \n",
    "         \"Disequilibrium\": \"adp**2 - atp*amp\"}\n",
    "\n",
    "# Simulate with disturbance\n",
    "conc_sol, flux_sol = sim.simulate(\n",
    "    phos_traffic, time=(t0, tf),\n",
    "    perturbations={\"atp\": 1.7, \"adp\": 0.2, \"amp\": 0.2})\n",
    "\n",
    "# Determine pools\n",
    "for pool_id, equation_str in pools.items():\n",
    "    conc_sol.make_aggregate_solution(\n",
    "        pool_id, equation=equation_str, update=True)\n",
    "\n",
    "# Visualize solutions\n",
    "fig_8_7, axes = plt.subplots(nrows=3, ncols=1, figsize=(8, 8),)\n",
    "(ax1, ax2, ax3) = axes.flatten()\n",
    "\n",
    "plot_time_profile(\n",
    "    conc_sol, ax=ax1, observable=phos_traffic.metabolites,\n",
    "    legend=\"right outside\", plot_function=\"semilogx\", ylim=(0, 1.8),\n",
    "    xlabel=\"Time [min]\", ylabel=\"Concentration [mM]\",\n",
    "    title=(\"(a) Concentration Profile\", XL_FONT));\n",
    "\n",
    "plot_time_profile(\n",
    "    conc_sol, observable=[\"Occupancy\", \"Vacancy\"], ax=ax2, \n",
    "    legend=\"right outside\", plot_function=\"semilogx\", ylim=(0., 4.),\n",
    "    xlabel=\"Time [min]\", ylabel=\"Concentration [mM]\",\n",
    "    title=(\"(b) Occupancy and Vacancy Pools\", XL_FONT));\n",
    "\n",
    "plot_time_profile(\n",
    "    conc_sol, observable=[\"Disequilibrium\"], ax=ax3,\n",
    "    legend=\"right outside\", plot_function=\"semilogx\", ylim=(-.4, 0.1),\n",
    "    xlabel=\"Time [min]\", ylabel=\"Concentration [mM]\",\n",
    "    title=(\"(c) Disequilibrium Variable\", XL_FONT));\n",
    "fig_8_7.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Figure 8.7:** The time response of the adenylate kinase reaction (\"distr\") and with the addition of ATP use and formation to a change in the initial conditions. (a) The concentrations. (b) The occupancy and capacity pools. (c) The disequilibrium variable.\n",
    "\n",
    "#### Towards a realistic simulation of a dynamic response\n",
    "Next, we simulate the response of this system to a more realistic perturbation: a 50% increase in the rate of ATP use. This would represent a sudden increase in energy use by a cell.  At time zero, we have the network in a steady state and we change $k_{\\mathrm{use}}^\\rightarrow$ from $6.25/min$ to $1.5*6.25=9.375/min$, and the rate of ATP use instantly becomes 15 mM/min. \n",
    "\n",
    "The response of the system is perhaps best visualized by showing the phase portrait of the rate of ATP use versus ATP formation. Prior to the increased load, the system is on the 45 degree line, where the rate of ATP formation and use balances.  Then at time zero it is instantly imbalanced by changing $k_{\\mathrm{use}}^\\rightarrow$ above or below its initial value. If $k_{\\mathrm{use}}^\\rightarrow$ is increased then the initial point moved into the region where more ATP is used than formed. From this initial perturbation the response of the system is to move directly towards the 45 degree line to regain balance between ATP use and formation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-25T17:57:22.813957Z",
     "iopub.status.busy": "2021-01-25T17:57:22.813125Z",
     "iopub.status.idle": "2021-01-25T17:57:22.894248Z",
     "shell.execute_reply": "2021-01-25T17:57:22.894782Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Getting time points\n",
      "Parsing perturbations\n",
      "Setting output selections\n",
      "Setting simulation values for 'Phosphate_Trafficking'\n",
      "Simulating 'Phosphate_Trafficking'\n",
      "Simulation for 'Phosphate_Trafficking' successful\n",
      "Adding 'Phosphate_Trafficking' simulation solutions to output\n",
      "Updating stored solutions\n"
     ]
    }
   ],
   "source": [
    "t0, tf = (0, 1e3)\n",
    "# Simulate with disturbance\n",
    "conc_sol, flux_sol = sim.simulate(\n",
    "    phos_traffic, time=(t0, tf), \n",
    "    perturbations={\"kf_use\": \"kf_use * 1.5\"},\n",
    "    verbose=True)\n",
    "\n",
    "# Determine pools\n",
    "for pool_id, equation_str in pools.items():\n",
    "    conc_sol.make_aggregate_solution(\n",
    "        pool_id, equation=equation_str, update=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-25T17:57:22.917630Z",
     "iopub.status.busy": "2021-01-25T17:57:22.905287Z",
     "iopub.status.idle": "2021-01-25T17:57:24.857290Z",
     "shell.execute_reply": "2021-01-25T17:57:24.857730Z"
    },
    "tags": [
     "remove_cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_8_8 = plt.figure(figsize=(15, 5))\n",
    "gs = fig_8_8.add_gridspec(nrows=3, ncols=2, width_ratios=[1, 1.5])\n",
    "\n",
    "ax1 = fig_8_8.add_subplot(gs[:, 0])\n",
    "ax2 = fig_8_8.add_subplot(gs[0, 1])\n",
    "ax3 = fig_8_8.add_subplot(gs[1, 1])\n",
    "ax4 = fig_8_8.add_subplot(gs[2, 1])\n",
    "\n",
    "label = \"{0} [mM/min]\"\n",
    "plot_phase_portrait(\n",
    "    flux_sol, x=v_use, y=v_form, ax=ax1,\n",
    "    time_vector=np.linspace(t0, 1, int(1e4)),\n",
    "    xlabel=label.format(v_use.id), ylabel=label.format(v_form.id), \n",
    "    xlim=(4, 21), ylim=(4, 21), \n",
    "    title=(\"(a) Phase Portrait of ATP use vs. formation\", XL_FONT),\n",
    "    annotate_time_points=\"endpoints\",\n",
    "    annotate_time_points_labels=True);\n",
    "\n",
    "line_data = [i for i in range(0, 22)]\n",
    "ax1.plot(line_data, line_data, ls=\"--\", color=\"black\")\n",
    "ax1.annotate(\"use < form\", xy=(6, 15))\n",
    "ax1.annotate(\"use > form\", xy=(15, 6))\n",
    "ax1.annotate(\"Steady-state line:\\n     use=form\", xy=(15, 19))\n",
    "ax1.annotate(\"initial perturbation\", xy=(9.5, 9), xycoords=\"data\")\n",
    "ax1.annotate(\"\", xy=(flux_sol[v_use.id][0], flux_sol[v_form.id][0]),\n",
    "             xytext=(10, 10), textcoords=\"data\",\n",
    "             arrowprops=dict(arrowstyle=\"->\",connectionstyle=\"arc3\"))\n",
    "\n",
    "plot_time_profile(\n",
    "    conc_sol, observable=phos_traffic.metabolites,\n",
    "    ax=ax2, legend=\"right outside\",\n",
    "    time_vector=np.linspace(t0, 1, int(1e5)),\n",
    "    xlim=(t0, 1), ylim=(0, 2),\n",
    "    xlabel=\"Time [min]\", ylabel=\"Concentration [mM]\",\n",
    "    title=(\"(b) Concentration Profiles\", XL_FONT));\n",
    "\n",
    "plot_time_profile(\n",
    "    flux_sol, observable=[v_use],\n",
    "    ax=ax3, legend=\"right outside\",\n",
    "    time_vector=np.linspace(t0, 1, int(1e5)),\n",
    "    xlim=(t0, 1), ylim=(12, 16),\n",
    "    xlabel=\"Time [min]\", ylabel=\"Flux [mM/min]\",\n",
    "    title=(\"(c) Net ATP use\", XL_FONT));\n",
    "\n",
    "plot_time_profile(\n",
    "    conc_sol, observable=\"Disequilibrium\",\n",
    "    ax=ax4, legend=\"right outside\",\n",
    "    time_vector=np.linspace(t0, 1, int(1e5)), plot_function=\"semilogx\", \n",
    "    xlabel=\"Time [min]\", ylabel=\"Concentration [mM]\",\n",
    "    xlim=(1e-6, 1), ylim=(-.0001, 0.0015),\n",
    "    title=(\"(d) Disequilibrium\", XL_FONT));\n",
    "fig_8_8.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Figure 8.8:** Dynamic responses for Eqs (8.4 - 8.8). (a) The phase portrait for the rates of use and formation of ATP. (b) The concentrations of ATP, ADP, and AMP. (c) Net ATP use (d) The disequilibrium variable for Adenylate kinase.\n",
    "\n",
    "#### Pooling and interpretation: trafficking high-energy phosphate bonds\n",
    "Since AMP is not being synthesized and degraded, the sum of $\\text{ATP} + \\text{ADP} +\\text{AMP}$, or the capacity to carry high-energy phosphate bonds, is a constant. The Atkinson's energy charge \n",
    "\n",
    "$$\\begin{equation} \\text{E.C.} = \\frac{2\\ \\text{ATP} + \\text{ADP}}{2\\ \\text{ATP}+\\text{ADP}+\\text{AMP}} = \\frac{\\text{occupancy}}{\\text{capacity}} \\tag{8.9} \\end{equation}$$\n",
    "\n",
    "shows a monotonic decay to a lower state in response to the increased load  (see Figure 8.9). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-25T17:57:24.864647Z",
     "iopub.status.busy": "2021-01-25T17:57:24.863963Z",
     "iopub.status.idle": "2021-01-25T17:57:24.878326Z",
     "shell.execute_reply": "2021-01-25T17:57:24.879205Z"
    }
   },
   "outputs": [],
   "source": [
    "pools.update({\"EC\": \"(2*atp + adp) / (2*(atp + adp + amp))\"})\n",
    "# Determine pools\n",
    "for pool_id, equation_str in pools.items():\n",
    "    conc_sol.make_aggregate_solution(\n",
    "        pool_id, equation=equation_str, update=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-25T17:57:24.916409Z",
     "iopub.status.busy": "2021-01-25T17:57:24.899309Z",
     "iopub.status.idle": "2021-01-25T17:57:26.254093Z",
     "shell.execute_reply": "2021-01-25T17:57:26.254546Z"
    },
    "tags": [
     "remove_cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABKYAAAGGCAYAAABBiol3AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8o6BhiAAAACXBIWXMAAA9hAAAPYQGoP6dpAACFuUlEQVR4nOzdeVhUZfsH8O/MwAz7ALIjm7ijgqIguCuJSya2qZm4l6WmYa+JuWT5RmWalSa2qKWZaZn9XitNySUVdzH3FcWFRUBAQAaYOb8/iMmRQRkcOAN8P9d1LpjnPOc59zmM3OPNOc+RCIIggIiIiIiIiIiIqJZJxQ6AiIiIiIiIiIgaJhamiIiIiIiIiIhIFCxMERERERERERGRKFiYIiIiIiIiIiIiUbAwRUREREREREREomBhioiIiIiIiIiIRMHCFBERERERERERiYKFKSIiIiIiIiIiEgULU0REREREREREJAoWpogAjBo1Ci4uLigoKKj2GIWFhXBzc8OLL75oxMjI2CQSCXr27Cl2GEREZGT6cvmuXbsgkUjw9ttvV3mc1157DQ4ODsjMzKyBKCtavXo1JBIJVq9eXSv7o0fjZwUiotrFwhQ1eIcPH8aaNWswc+ZMWFtbV3scKysrxMbGYt26dTh8+LBB244ePRoSieShy+jRo6sdW312/fp1zJw5E8HBwXBwcIC5uTlcXFwQERGBTz75BLm5uWKHSERENcxYuRwAZs2aBZVKZVAx60Hbt2/HiBEj4OfnBysrK1haWqJp06YYOXIkfv/998eKrz7S9znI2toabdq0wcyZM3Hnzh2xQyQiohpkJnYARGJ76623YGdnh1deeeWxx3r55Zcxf/58vPXWW/jjjz8M3n7w4MEICgrSu66y9obsq6++wuTJk6FSqRAYGIjhw4fDwcEBWVlZ2Lt3L6ZNm4Z333231v7qTURE4jBmLndzc8Po0aOxYsUKzJgxA97e3lXe9u7du4iOjsbmzZthYWGB3r174+mnn4a5uTmSk5Px22+/Ye3atZg+fTo++uijx461vrn/c1BaWhr+97//4YMPPsCPP/6IQ4cOwdHRUdwAiYioRrAwRQ3ahQsXsGPHDowfPx6WlpaPPZ6FhQWGDh2KFStW4OLFi2jWrJlB20dFRfHKqCr67rvvMGHCBDg4OOCnn37CwIEDK/TZt28fJk2aJEJ0RERUW4ydy4Gy2wKXL1+OL774AgsWLKjSNhqNBs899xy2bduGXr16Ye3atfDw8NDpo1KpEB8fjwsXLhglzvrmwc9BH330EUJDQ3HmzBl89tlnmDdvnnjBERFRjeGtfNSgrVy5EoIgYOjQoRXWFRcXY+nSpRgwYAB8fHygUCjg6OiIiIiIh16GP2zYMAiCgJUrV9ZY3FevXtXe3nf16lUMGzYMTk5OsLCwQMeOHbFly5ZKt/3+++/Rq1cv2Nvbw8LCAq1atcKCBQugUqkq9C2fYyEtLQ3jx4+Hp6cnZDKZzjwY3333HTp06ABLS0u4uLhg5MiRuHXrFnr27AmJRKLtt23bNkgkEowZM0ZvXCqVCk5OTnByctIby/3u3r2L1157DQCwfv16vUUpAOjSpQsOHjyod11mZiZeeukluLu7Q6FQICAgAKtWrarQrzrvA19fX/j6+iIvLw8xMTHw9fWFubm5zm0h27ZtQ5cuXWBtbQ1HR0dERUXh3Llz2tsZrl69WmHcgwcP4tlnn4Wbmxvkcjm8vLzw8ssv49atWw89X0RE9dnDcvn9EhMTERERAaVSCVtbW0RGRuLIkSN6+4aGhsLX11c7dlV8//332LZtG5o2bYr//e9/FYpSAKBQKDB16lQsXrxY7xg7d+5Ez549YWtrCzs7OwwcOBBnz56t0O/ChQuYOXMmOnbsCGdnZygUCvj4+OCll17CjRs3KvS/f66tQ4cOYeDAgXB0dNTJN+W3LzZp0gQKhQJ+fn6YPXs2VCpVpXMulZaW4vPPP0fnzp1hZ2cHKysrtG/fHkuXLoVGo6nSeXsYGxsbjBo1CgBw6NAhbXtubi5iY2PRokULWFhYwMHBAZGRkdixY4fecTQaDeLj49GpUyfY2NjA2toanTp1wvLly6sc5927d/Huu++iTZs2sLOzg62tLfz9/TF06FAcPXr0sY+ViKghY2GKGrQdO3ZAJpOhc+fOFdZlZ2dj6tSpuHv3Lp544gnExMTgqaeewvHjxzFgwAB89dVXescMCQmBubk5tm/fXtPh49q1awgJCcHVq1cxcuRIDB06FKdOncLgwYOxc+fOCv3Hjh2LF154AZcuXcIzzzyDSZMmwdHREXPmzEG/fv1QWlpaYZvs7Gx07twZBw4cwNNPP43JkyfD1dUVAPDhhx/ixRdfxNWrVzFq1CiMGTMGp0+fRpcuXZCTk6MzTt++feHv748NGzbonffpp59+QlZWFkaPHg2FQvHQ4/7xxx+1cfXt2/ehffWNlZOTgy5duiAxMRHPPvssRo0ahVu3bmHs2LH45ptvKhx/dd4HxcXF6N27NzZv3oy+ffti6tSp8PPzA1BWTOvfvz+OHz+O5557Di+//DLu3LmDsLAwvQUpoOw/Xl26dMHvv/+OXr16Ydq0aejYsSO++uordOzYESkpKQ89D0RE9dXDcnm5gwcPomfPnlAoFJg0aRL69++PhIQEdOvWDX/99Zfebbp06YLU1FScPn26SnF88cUXAIA33njjkfNc6ctNW7ZsQd++fWFnZ4eJEyeiW7du+O2339CjR48Kt6Rv2rQJ8fHx8PLywvDhwzFlyhS0bt0aX331FTp16oSbN2/q3W9iYiK6deuGoqIijB07FqNGjYJcLocgCHjmmWcwf/58mJmZYfLkyRg0aBBWr16NYcOG6R2rpKQETz75JCZNmoScnBy88MILeOmll6DRaDBlyhRtQelxlRcGy//YlZOTg/DwcLz//vtQKpWYNm0annnmGSQmJqJv375YsWJFhTFGjhyJV155Benp6Rg/fjxeeukl3L59G6+++ipGjhxZpRj69euHuXPnws7ODuPHj8crr7yC0NBQ7NmzB4mJiUY5ViKiBksgaqDy8/MFmUwmtGnTRu/6oqIi4fr16xXac3JyhICAAMHBwUEoLCzUu21QUJAglUqFvLy8KsUyatQoAYAwePBgYd68eXqXs2fPavsnJycLAAQAwttvv60z1tatWwUAQv/+/XXaV61aJQAQhgwZUiHuefPmCQCEJUuW6LSX72PkyJFCSUmJzrrLly8LZmZmgpOTk5CSkqJt12g0wrBhw7Tb3m/hwoUCAOGzzz6rcA569OghABDOnz//yPM1duxYAYDw1ltvPbLvg8rjGjdunFBaWqptP336tCCTyYRWrVrp9K/O+8DHx0cAIPTp00fIz8/XWZeXlyfY29sLcrlcSEpK0ln35ptvauNLTk7Wtp8/f14wNzcX/P39hRs3buhss2PHDkEqlQpRUVEGnQciovrgUbl8586d2t+rD+aezZs3CwCEpk2bCmq1usK2S5YsEQAIy5Yte2QcJSUlglwuFwAIFy9eNOgYyvOzTCYTduzYobNu5syZAgDhgw8+0Gm/ceOGUFRUVGGsbdu2CVKpVJg4caJO+/3nIT4+vsJ23377rQBA6Natm6BSqbTtd+7cEVq0aCEAEHr06KGzTflnh8mTJ+vk09LSUm2e3rx5c5XOQfnnoFWrVum03717V2jVqpUAQHjnnXcEQRCEl156SQAgvPTSS4JGo9H2vXDhgmBnZyfI5XKdHLpu3ToBgNC+fXvh7t272vb8/HwhODhYACB89913Ovt98Hj//vtvAYDeXKtWq4Xs7OwqHScREenHwhQ1WOfPnxcACE888YTB2y5atEgAIOzevVvv+n79+gkAdIpJD1P+gexhy88//6ztX16Y8vHx0fkwWM7b21to1KiRTltQUJBgZmYm3Llzp0L/0tJSoVGjRkKnTp102gEIcrlcSE9Pr7DNu+++KwAQ5s+fX2Hd1atXBZlMVqEwlZmZKVhYWFT4D8S5c+cEAEKvXr0qjKVP//79BQDC8uXLq9T/fgAEKysrITc3t8K67t27CwB0Prg+TGXvg/LC1IOFJ0EQhDVr1ggAhDFjxlRYd/fuXcHe3r5CYWratGkCAGHLli1644iKihJkMlmVC6FERPXFo3J5eUGmsuJT+R9Fdu3aVWHd+vXrBQDCm2+++cg40tPTtfn63r17Bh1DeWFqxIgRFdZduXJFACA888wzVR6vbdu2gp+fn05b+XkICgrSu02fPn0q/Vyzdu3aCoUatVotODo6Cm5ubhX+cCUIZQUtiUQiPPfcc1WKWd8f6CZOnCh4eHgIAAR/f38hOztbUKlUgpWVlWBjYyNkZWVVGGf27NkVPptEREQIAIRt27ZV6L9jxw69nz8qK0wNHz68SsdDRESG4eTn1GBlZWUBABwcHCrtc/r0aSxcuBB79uxBamoqioqKdNZXdql8+VNjDH0a3KpVqwya/DwoKAgymaxCu5eXl85l5YWFhThx4gScnJywZMkSvWMpFAq981j4+vrCxcWlQvvx48cBAF27dq2wzsfHB15eXhVuS2vUqBGef/55fPvtt9i/fz/Cw8MB/Hv7w8SJE/UfqJE1a9YMdnZ2Fdq9vLwAAHfu3IGNjY22vTrvAwsLC7Rr165C+8POm42NDYKCgrBr1y6d9vKf5e7du3H48OEK22VkZECtVuPChQsIDg6usJ6IqL6qSi4HgG7dukEqrTiDRc+ePbF7924cP34cPXr00FlX3VxeXR07dqzQdn9eup8gCPjuu++wevVqnDhxAnfu3IFardaul8vlevcREhKit/348eOQSqXavHw/ffnqwoULyM7ORrNmzSqdHN7S0lLv54qH+eWXX/DLL79ot/f19cWIESMwc+ZMODg44OTJkygsLESXLl30PqGvd+/eWLBggTbXAsCxY8cglUr1zpHVo0cPyGQynf76tG7dGkFBQfj+++9x7do1DB48GF27dkXHjh0rPddERFR1LExRg1X+5J4HiwzlDhw4gN69e6O0tBR9+vTBU089BTs7O0ilUiQlJeGXX36pdJLue/fu6eyjptjb2+ttNzMz05nM886dOxAEAbdv38b8+fMN2oebm5ve9vJ5osrnm3qQq6ur3vmSXn31VXz77bdYsWIFwsPDoVKp8M0338DFxQVDhgypUkzu7u4AKi8MPsrDzhsAnQ/31X0fuLi46Ez+Xq4q5+1B5f/xWrhw4UOPKz8//6HriYjqm0fl8nKV/c4tz3H65j40JJc7OjpCLpejuLgYN2/ehL+//yO3eZC+3KQvLwFATEwMlixZAnd3d0RGRsLT01Mb5+rVq3Ht2jW9+3hYTnd0dNTu734Py0sXL1586OcKQ/PSo/5AV/5zKv8c8KDy9vvnuSw/Nn0FJDMzMzg5OSEjI+OhcclkMvz5559455138OOPP+LNN98EANja2mLUqFGIi4vT+YMWEREZhoUparDKrwIq/3D1oAULFuDevXvaJ+TcLy4uTvsXPX3Kx9R3pZEYlEolAKB9+/Y4duyYQdvqK64A0F5xlJ6ejoCAgArr09PT9W4XGhqK9u3bY8OGDViyZAl+//13ZGVl4c0334S5uXmVYuratStWrlyJhIQEvPvuu1U8kuqp7vugKudNH33t5T+/3NxcvVd6ERE1VI/K5eUq+52blpYG4N/fs/czJJebmZmhc+fO2LNnDxISEqpVmKqqjIwMfPrpp2jTpg32798PW1tbnfXff/99pds+LDdlZ2ejtLS0QnHqYXlpyJAh2LRpk6GHUG3l+y3/uT0oNTVVp1/599nZ2SgpKanwOaO0tBSZmZlVyq0ODg74+OOP8fHHH+PSpUvYvXs3VqxYgaVLlyInJwdr1qyp7mERETV4fCofNVju7u5wdnbG+fPn9a6/dOkSHB0d9V76vXv37oeOff78eTRq1AiNGzc2RqiPzcbGBgEBATh9+jSys7ONMmb79u0BAHv37q2w7tq1a7h+/Xql27766qsoKirCt99+iy+++AISiQQvvfRSlff97LPPwtHREYmJiZU+GrpcZVe1VdXjvA/0edh5y8/PR1JSUoX28idNVfbkKCKihupRubzc3r17da4kLld+63T57+b7nTt3DkDZbfNVUZ7HPvroIxQWFj607+PkpitXrkCj0aBv374VilI3btzAlStXDB6zffv20Gg02L9/f4V1+vJVy5YtYW9vjwMHDqCkpMTg/VVXixYtYGVlhRMnTlR4+i8A7ROJO3TooG0rP7Y9e/ZU6L9nzx6o1Wqd/lXRtGlTjBs3Drt374aNjc1D/1hJRESPxsIUNVgSiQTdu3dHZmYmLl26VGG9r68vsrOz8ffff+u0f/3119i2bVul4yYnJyM9PR09e/as9C+TYoiJiUFxcTHGjh2r98PcnTt3DLqa6oUXXoCZmRk+++wznSKUIAiIjY2tcNvBg9sqlUp8+OGH2L17N5544gk0adKkyvu2tbXFp59+CgAYOnRopT+PAwcOICwsrMrj6lPd90FlBg8eDKVSie+++w4nTpzQWbdgwQK9P5vJkyfD3Nwcr7/+Oi5cuFBhfXFxMYtWRNQgPSqXl7t48SI+//xznbZffvkFu3fvRtOmTdGtW7cK2xw4cAAymQzdu3evUizDhw9HZGQkLl68iMGDB2uv3rlfcXExli1bhunTp1dpTH18fX0BlBWM7s+1+fn5mDBhAkpLSw0eMzo6GgAwe/ZsFBcXa9tzc3P1XplsZmaGKVOmIDU1Fa+99pr2tsf7paam4syZMwbH8jByuRwjRozA3bt3MWfOHJ11ly9fxqeffgpzc3OMHDlS2z527FgAQGxsrE7BsLCwEDNnzgQAjBs37qH7TU5O1lvwu3PnDlQqVY1P3UBEVN/xVj5q0J555hn89NNP2LZtG5o2baqzbtq0adi2bRu6du2K559/HkqlEkeOHMHevXvx7LPP4scff9Q75h9//KEd21CbN2/WOy8TUPZB1JCJ0R80duxYHD16FJ9//jn8/f0RGRkJb29vZGdnIzk5GXv27MGYMWMQHx9fpfH8/f3xzjvvYNasWQgMDMTQoUOhVCqxfft2ZGdnIzAwsEIxp5yVlRVGjRqlLS69/PLLBh/PiBEjcO/ePUyePBn9+vVDUFAQwsPD4eDggKysLCQmJmonfH8c1X0fVMbOzg7Lli3DyJEjER4ejueffx7u7u7Yv38/Tpw4gR49emD37t06k/S2bNkSK1euxNixYxEQEIB+/fqhefPmKCkpQUpKCv766y84Oztr/7pPRNSQPCyXl+vXrx+mT5+O33//HYGBgbh06RI2bdoECwsLrFy5ssLE6Lm5uTh06BD69Omj9zY/faRSKTZu3IiRI0fil19+QZMmTdCnTx+0atUKMpkMV69exZ9//onbt2/jjTfeqPbxurm5YdiwYVi/fj2CgoLQt29f5ObmYvv27bCwsEBQUJDeq28fJjo6GuvXr8fWrVvRpk0bPPXUUygpKcFPP/2ETp064fz58xXO0Zw5c3DixAnEx8fjf//7H3r37g1PT09kZGTg4sWL2LdvH/773/+idevW1T5Wfd5//3389ddfWLp0KQ4fPoxevXohMzMTGzZswN27d7F06VL4+flp+7/wwgv45ZdfsGHDBgQEBCAqKgoSiQSbN29GcnIyhg4dihEjRjx0nydOnMDTTz+NTp06oVWrVvDw8MDt27fxyy+/oKSkRDvnFBERVZPITwUkEpVKpRJcXFyEkJAQvev/97//CaGhoYKNjY2gVCqFJ554Qti9e7f20c6rVq2qsE1YWJjg7OwsqFSqKsdR/pjkhy33P7Y4OTlZACCMGjVK73jlj7+u7JgGDhwoODs7C+bm5oKrq6vQqVMn4a233hLOnj2r0/fB/erz7bffCkFBQYJCoRCcnJyEESNGCDdv3hQCAgIEpVJZ6XZJSUkCAMHd3V3vo6arKiUlRZgxY4bQvn17QalUCmZmZoKTk5PQs2dP4eOPPxZyc3OrfEzlP4fk5GSddkPfBz4+PoKPj89D4/7tt9+EsLAwwdLSUrC3txeeeuop4ezZs8LAgQMFAMKdO3cqbPP3338Lo0aNEry9vQW5XC44ODgIAQEBwksvvSQkJCQ84kwREdVPD8vlO3fuFAAI8+bNE/bv3y/06dNHsLW1FWxsbIQnnnhCOHTokN4xV6xYIQAQfv7552rFtG3bNmH48OGCr6+vYGFhISgUCsHPz08YPny48Pvvv+v0fdhnCkHQn7cKCgqEWbNmCf7+/oJCoRAaN24svPrqq0JmZqbezwD3n4fK3Lt3T5gzZ47g6+sryOVywcfHR5g1a5Zw48YNAYAwePDgCttoNBrh22+/FXr37i04ODgI5ubmgoeHh9ClSxfhv//9r5CSklKV06XNv5WdgwfduXNHmDFjhtC0aVNBLpcLSqVSiIiIELZt26a3v1qtFpYtWyYEBwcLlpaWgqWlpdChQwdh6dKlglqtrtD/wXN+/fp1ITY2VggPDxdcXV0FuVwueHp6Cv369RN+++23KsVMRESVkwiCINRWEYzIFMXFxWHWrFk4duyY3jkmDPH3338jMDAQ7777LmbPnm2kCOuevLw8uLq6IigoCImJiXr7rF69GmPGjMHs2bNrfALzukKtVqNJkyYoLi7WewsIERHpZ8xcDgAdO3ZEfn4+Tp8+DZlMZoQI667t27ejb9++mDlzJuLi4sQOh4iI6iHOMUUN3uuvvw5vb2/MnTv3sceaO3cuvLy8HmvuiLrk9u3bFSY9LS0txfTp01FUVIQhQ4bo3a60tBSLFy+GmZlZtW7jq+tycnIqTIwrCAIWLFiAlJSUSs8bERHpZ8xcvnnzZhw9ehQfffRRgypK3bp1q0JbVlaWdh4m5iYiIqopnGOKGjwLCwusWbMGO3fuREFBAaytras1TmFhIdq3b49p06Y1mEkwf/rpJ8ydOxcRERHw8vJCdnY29uzZgwsXLiAoKAhTpkzR6b93717s3r0bu3btwsmTJzF58mSTeXJhbTpw4ACGDh2Kvn37wtfXF/n5+Thw4ACSkpLg5eWFt99+W+wQiYjqFGPlcgC4d+8ePv74Yzz55JNGjND0xcTE4MSJEwgPD4ezszNu3LiB33//HdnZ2Xj55ZcREhIidohERFRP8VY+Iqq248eP491338WhQ4eQlZUFAPDz88PTTz+NN998s8JjrN9++23Mnz8fjo6OeOaZZ/DJJ580mCLe/ZKTkzF79mzs27cPt2/fRmlpKRo3bownn3wSs2bNgqurq9ghEhFRA7NhwwYsX74cp0+fRk5ODiwsLBAQEIBx48Zh3LhxJvWkYSIiql9ELUzt2bMHCxcuxNGjR5Gamoqff/4ZUVFRD91m165diImJwenTp+Hl5YXZs2dXeFLZsmXLsHDhQqSlpSEwMBCfffYZ/8pDRERUw95//33ExsZi6tSpWLJkSaX9Nm7ciDlz5uDq1ato1qwZPvjgAwwYMKD2AiUiIiIikyHqHFMFBQUIDAzEsmXLqtQ/OTkZAwcORK9evZCUlIRp06Zh/Pjx2LZtm7bPDz/8gJiYGMybNw/Hjh1DYGAgIiMjkZGRUVOHQURE1OAdPnwYK1asQLt27R7ab//+/Rg+fDjGjRuH48ePIyoqClFRUTh16lQtRUpEREREpsRkbuWTSCSPvGLqzTffxK+//qrz4XXYsGHIycnB1q1bAQChoaHo1KkTli5dCgDQaDTw8vLClClTtJM3EhERkfHk5+ejQ4cO+Pzzz7FgwQIEBQVVesXU0KFDUVBQgC1btmjbOnfujKCgIMTHx9dSxERERERkKurU5OeJiYmIiIjQaYuMjMS0adMAAMXFxTh69ChiY2O166VSKSIiIip9ZD0AqFQqqFQq7WuNRoPs7Gw0atSI99MTEZFJEAQBd+/ehYeHB6RS03qo7qRJkzBw4EBERERgwYIFD+2bmJiImJgYnbbIyEhs3rxZb3/maCIiqgtMOU8Tmbo6VZhKS0urMCmwq6sr8vLycO/ePdy5cwdqtVpvn3PnzlU6blxcHObPn18jMRMRERnT9evXTeppluvXr8exY8dw+PDhKvWvLJenpaXp7c8cTUREdYmp5WmiuqBOFaZqSmxsrM5fb3Nzc+Ht7Y3r16/Dzs5OxMiIiIjK5OXlwcvLq8LTLsV0/fp1TJ06Fdu3b4eFhUWN7IM5moiI6gJTzNNEdUWdKky5ubkhPT1dpy09PR12dnawtLSETCaDTCbT28fNza3ScRUKBRQKRYV2Ozs7fuglIiKTYkq3rx09ehQZGRno0KGDtk2tVmPPnj1YunQpVCoVZDKZzjaV5fLK8jRzNBER1SWmlKeJ6oo6dfNrWFgYEhISdNq2b9+OsLAwAIBcLkdwcLBOH41Gg4SEBG0fIiIiMo4+ffrg5MmTSEpK0i4dO3bEiBEjkJSUVKEoBTw6lxMRERFRwyLqFVP5+fm4dOmS9nVycjKSkpLg6OgIb29vxMbG4ubNm/j2228BABMnTsTSpUsxY8YMjB07Fn/++Sc2bNiAX3/9VTtGTEwMRo0ahY4dOyIkJARLlixBQUEBxowZU+vHR0REVJ/Z2tqiTZs2Om3W1tZo1KiRtj06Ohqenp6Ii4sDAEydOhU9evTAokWLMHDgQKxfvx5HjhzBF198UevxExEREZH4RC1MHTlyBL169dK+Lp9DYtSoUVi9ejVSU1ORkpKiXe/n54dff/0Vr7/+Oj755BM0btwYX331FSIjI7V9hg4ditu3b2Pu3LlIS0tDUFAQtm7dWmGiVSIiIqp5KSkpOk8nCg8Px7p16zB79mzMmjULzZo1w+bNmysUuIiIiIioYZAIgiCIHYSpycvLg1KpRG5uLuevICKqJWq1GiUlJWKHIRpzc3O9t76VY24qw/NARESmiPmJqPrq1OTnRERU/wiCgLS0NOTk5Igdiujs7e3h5ubGiVOJiIiIqMFgYYqIiERVXpRycXGBlZVVgyzKCIKAwsJCZGRkAADc3d1FjoiIiIiIqHawMEVERKJRq9XaolSjRo3EDkdUlpaWAICMjAy4uLg89LY+IiIiIqL6QvroLkRERDWjfE4pKysrkSMxDeXnoSHPtUVEREREDQsLU0REJLqGePuePjwPRERERNTQsDBFRERERERERESiYGGKiIiIiIiIiIhEwcIUERFRNYwePRoSiaTC0q9fP22f48eP47nnnoOrqyssLCzQrFkzTJgwARcuXBAxciIiIiIi08HCFBERUTX169cPqampOsv3338PANiyZQs6d+4MlUqF7777DmfPnsXatWuhVCoxZ84ckSMnIiIiIjINZmIHQEREVFcpFAq4ublVaC8sLMSYMWMwYMAA/Pzzz9p2Pz8/hIaGIicnpxajJCIiIiIyXSxMERGRyRAEAfdK1KLs29JcZrSn4m3btg2ZmZmYMWOG3vX29vZG2Q8RERERUV3HwhQREZmMeyVqtJ67TZR9n3knElZyw9Lili1bYGNjo9M2a9YsmJmVjdOyZUujxUdEREREVB+xMEVERFRNvXr1wvLly3XaHB0d8eWXX4oUERERERFR3cLCFBERmQxLcxnOvBMp2r4NZW1tjaZNm1Zob968OQDg3LlzCAsLe+zYiIiIiIjqKxamiIjIZEgkEoNvpzNFffv2hZOTEz788EOdyc/L5eTkcJ4pIiIiIiKwMEVERFRtKpUKaWlpOm1mZmZwcnLCV199heeeew5PPfUUXnvtNTRt2hSZmZnYsGEDUlJSsH79epGiJiIiIiIyHVKxAyAiIqqrtm7dCnd3d52la9euAIDBgwdj//79MDc3xwsvvICWLVti+PDhyM3NxYIFC0SOnIiIiIjINPCKKSIiompYvXo1Vq9e/dA+HTt2xE8//VQ7ARERERER1UG8YoqIiIiIiIiIiETBwhQREREREREREYmChSkiIiIiIiIiIhIFC1NERERERERERCQKTn5OREREdc7YVYdhbmldo/uQSGp0+NrfD2p+RxIJIJFIIJMAUomk7Htp2fdSqaTsqwSQPbBOp98/y4PrZFIp5DIJzGXSssXsgdcyKeRmD7yWSWFuJoFcJoWV3AyWchms5DKYy/i3WSIiIlPBwhQREYlOo9GIHYJJ4HmoukNXsyFVFIkdBtVRcplUW6Qq/2plXla4srEwg9LSHPaW5lD+s9hbmcPun+8dreVwslGwuEVERGQkLEwREZFo5HI5pFIpbt26BWdnZ8jlckhq6/IREyIIAoqLi3H79m1IpVLI5XKxQzJ5C59tBysb2xobX6ixkR/Yj1Bbe6odGkGARvPPV0GARgDUGgHCfd/rW6f+ZxtBEKD+Z53mn77l60o1GpSqBRSrNShRCygp1aBErfnndVlbqVqDYrXwz2sNSkrLXqtK1CgsUUOtKTvfxWoNiu9pkHuvpNrH6mgth4utAs73LW52FmjsYAUvR0t4OVjBWsGP2kRERI/CbElERKKRSqXw8/NDamoqbt26JXY4orOysoK3tzekUl6J8Sj927rDzs5O7DCoDhGEsqLWvWI1CovVKCwu/eerWqftblEpcu+VVFhyCouRe68UdwqLodYIyC4oRnZBMc6l3a10n47Wcng5WMLL0Qr+zjZo6lK2+DlZw8JcVotHT0REZLpYmCIiIlHJ5XJ4e3ujtLQUarVa7HBEI5PJYGZmVqeuGFu+fDmWL1+Oq1evAgACAgIwd+5c9O/fX2//1atXY8yYMTptCoUCRUW8JY9qnkQigcJMBoWZDPZW1R9HoxFwp7AYt/NVyMhT4fZdFTLuln1Nzb2H63cKcT37HnLvlWiLVydu5OqMIZUAfk7WaOupRNvG9mjrqUSAhx2vsCIiogaJ2Y+IiEQnkUhgbm4Oc3NzsUMhAzRu3Bjvv/8+mjVrBkEQ8M0332Dw4ME4fvw4AgIC9G5jZ2eH8+fPa1/XpUIcEQBIpRI0slGgkY0CLd0q75dXVIIb2WWFqmtZBbiUkY/Lt8u+5t4rweXbBbh8uwCbk8quFpVIgCZO1mjX2B4dfR0Q6ucIf2cb/hshIqJ6j4UpIiIiqpZBgwbpvP7vf/+L5cuX48CBA5UWpiQSCdzcHvK/eaJ6ws7CHK09zNHaQ/eWU0EQcDtfhdO38nDqRi7+vpmLUzdzkZpbpC1W/Xz8JgDAyUaOED9HdGnqhN4tXeCutBTjUIiIiGoUC1NERET02NRqNTZu3IiCggKEhYVV2i8/Px8+Pj7QaDTo0KED3nvvvUqLWET1kUQigYutBVxaWKBXCxdt++27Kpy6mYvj13NwKDkLx1NykJlfjN9OpuG3k2kAgNbudujTygW9W7ogsLE9pFJeTUVERHUfC1NERERUbSdPnkRYWBiKiopgY2ODn3/+Ga1bt9bbt0WLFli5ciXatWuH3NxcfPTRRwgPD8fp06fRuHFjvduoVCqoVCrt67y8vBo5DiKxOdsq0KulC3q1LCtWqUrV+PtGLg5czsLO8xk4fj0HZ1LzcCY1D5/9eQlONnL0bOGCPi1d0LOFCyzlnEydiIjqJolQ355TbAR5eXlQKpXIzc3lE3+IiMgkmGpuKi4uRkpKCnJzc/Hjjz/iq6++wu7duystTt2vpKQErVq1wvDhw/Huu+/q7fP2229j/vz5FdpN7TwQ1bSsfBV2X7iNhHMZ2HP+Nu6qSrXrrOUyRAa44akgD3Rt6gQzGZ/sSVTbTDVPE9UFLEzpwV8qRERkaupKboqIiIC/vz9WrFhRpf7PPfcczMzM8P333+tdr++KKS8vL5M/D0Q1qUStweGr2dh5LgO/n0rDjTv3tOsaWcsxsJ07hrT3RJCXPSdPJ6oldSVPE5ki3spHRERERqPRaHQKSQ+jVqtx8uRJDBgwoNI+CoUCCoXCWOER1QvmMinC/Z0Q7u+EWQNa4VhKDv4v6Sa2/J2KrIJifJt4Dd8mXkNLN1sM6+SFIe0bQ2nFp54SEZFpYmGKiIiIqiU2Nhb9+/eHt7c37t69i3Xr1mHXrl3Ytm0bACA6Ohqenp6Ii4sDALzzzjvo3LkzmjZtipycHCxcuBDXrl3D+PHjxTwMojpNIpEg2McBwT4OmPNka+y7nIXNx2/it5OpOJd2F2//7wzifj+HAW3dMTzEG518HXgVFRERmRQWpoiIiKhaMjIyEB0djdTUVCiVSrRr1w7btm3DE088AQBISUmBVPrvXDd37tzBhAkTkJaWBgcHBwQHB2P//v1Vmo+KiB7NTCZFj+bO6NHcGW8PCsDmpJv4/lAKzqXdxc/Hb+Ln4zfh72yNYZ288UxwYzhay8UOmYiIiHNM6cP7g4mIyNQwN5XheSAyjCAIOHEjF+sPpeD/TtxCYbEaACCXSdGvjRteCPVGqJ8jr6IiekzMT0TVx8KUHvylQkREpoa5qQzPA1H15atK8b8Tt/D9oRT8fSNX2+7vbI3hId54Nrgx7K14FRVRdTA/EVUfC1N68JcKERGZGuamMjwPRMZx6mYuvjuYgl+Sbv57FZWZFE+2dccLod4I9uFcVESGYH4iqj7po7vUrGXLlsHX1xcWFhYIDQ3FoUOHKu1bUlKCd955B/7+/rCwsEBgYCC2bt2q0+ftt9+GRCLRWVq2bFnTh0FEREREVGe08VQi7um2ODirDxZEtUFrdzsUl2qw6fhNPBufiH5L/sLqfcnIvVcidqhERFTPiVqY+uGHHxATE4N58+bh2LFjCAwMRGRkJDIyMvT2nz17NlasWIHPPvsMZ86cwcSJEzFkyBAcP35cp19AQABSU1O1y969e2vjcIiIiIiI6hRbC3O82NkHv77WFZsndcHzHRvDwlyK8+llT/QLfW8Hpq4/jh1n0lFcqhE7XCIiqodEvZUvNDQUnTp1wtKlSwEAGo0GXl5emDJlCmbOnFmhv4eHB9566y1MmjRJ2/bMM8/A0tISa9euBVB2xdTmzZuRlJRU7bh4GSYREZka5qYyPA9ENS/3Xgk2H7+JdQdTcD79rrZdaWmO/m3cMCjQA52bNIJMylv9iMoxPxFVn5lYOy4uLsbRo0cRGxurbZNKpYiIiEBiYqLebVQqFSwsLHTaLC0tK1wRdfHiRXh4eMDCwgJhYWGIi4uDt7d3pbGoVCqoVCrt67y8vOocEhERERFRnae0NMeocF9Eh/kg6XoO/nciFVv+voWMuyqsP3wd6w9fh4OVObo1c0aP5s7o3twZzrYKscMmIqI6SrTCVGZmJtRqNVxdXXXaXV1dce7cOb3bREZGYvHixejevTv8/f2RkJCATZs2Qa1Wa/uEhoZi9erVaNGiBVJTUzF//nx069YNp06dgq2trd5x4+LiMH/+fOMdHBERERFRHSeRSNDe2wHtvR3w1sBWOJichf+dSMXvp1Jxp7AE/3fiFv7vxC0AQDMXG3TwdkAHH3u093ZAEydrmMlEn86WiIjqANFu5bt16xY8PT2xf/9+hIWFadtnzJiB3bt34+DBgxW2uX37NiZMmID//e9/kEgk8Pf3R0REBFauXIl79+7p3U9OTg58fHywePFijBs3Tm8ffVdMeXl58TJMIiIyGbxFoAzPA5H4StUaHL+eg13nM7D7wm2culnxbgNzmQS+jazR1MUGTZyt4aa0hJudBVztFHC2VcBGYQZruRmkvB2Q6gnmJ6LqE+2KKScnJ8hkMqSnp+u0p6enw83NTe82zs7O2Lx5M4qKipCVlQUPDw/MnDkTTZo0qXQ/9vb2aN68OS5dulRpH4VCAYWClx8TERERET2KmUyKTr6O6OTriP9EtkRmvgrHrt3BsZQcHEu5g5M3cnGvRI2LGfm4mJH/0LGs5TLYWJjBSm4GmVQCM6lE+1V632sJ9BewJA+pa1W2rjpjUf01rJM3BrZzFzsMogZNtMKUXC5HcHAwEhISEBUVBaBs8vOEhARMnjz5odtaWFjA09MTJSUl+Omnn/D8889X2jc/Px+XL1/GyJEjjRk+EREREREBcLJRoG+AG/oGlP1xWaMRcDPnHi7fzseljHxczSpAep4K6XlFSM8rQlZ+MUo1ZTdtFBSrUVCsBqB6yB6Iak63Zk5ih0DU4IlWmAKAmJgYjBo1Ch07dkRISAiWLFmCgoICjBkzBgAQHR0NT09PxMXFAQAOHjyImzdvIigoCDdv3sTbb78NjUaDGTNmaMd84403MGjQIPj4+ODWrVuYN28eZDIZhg8fLsoxEhERERE1JFKpBF6OVvBytELPFi4V1guCAFWpBvmqUuQXlSJfVYp7JWqUqgWoNQJKNRqoNYJ2KdE8fOYRY8xMIt5zyklsAR687Y5IbKIWpoYOHYrbt29j7ty5SEtLQ1BQELZu3aqdED0lJQVS6b+TJhYVFWH27Nm4cuUKbGxsMGDAAKxZswb29vbaPjdu3MDw4cORlZUFZ2dndO3aFQcOHICzs3NtHx4RERERET1AIpHAwlwGC3MZnGw4nQYRUUMn2uTnpowT1xERkalhbirD80BERKaI+Ymo+vgMVyIiIiIiIiIiEgULU0REREREREREJAoWpoiIiIiIiIiISBQsTBERERERERERkShYmCIiIiIiIiIiIlGwMEVERERERERERKJgYYqIiIiIiIiIiETBwhQREREREREREYmChSkiIiIiIiIiIhIFC1NERERERERERCQKFqaIiIiIiIiIiEgULEwREREREREREZEoWJgiIiIiIiIiIiJRsDBFRERERERERESiYGGKiIiIqmX58uVo164d7OzsYGdnh7CwMPz+++8P3Wbjxo1o2bIlLCws0LZtW/z222+1FC0RERERmSIWpoiIiKhaGjdujPfffx9Hjx7FkSNH0Lt3bwwePBinT5/W23///v0YPnw4xo0bh+PHjyMqKgpRUVE4depULUdORERERKZCIgiCIHYQpiYvLw9KpRK5ubmws7MTOxwiIqI6k5scHR2xcOFCjBs3rsK6oUOHoqCgAFu2bNG2de7cGUFBQYiPj6/S+HXlPBARUcPC/ERUfbxiioiIiB6bWq3G+vXrUVBQgLCwML19EhMTERERodMWGRmJxMTESsdVqVTIy8vTWYiIiIio/mBhioiIiKrt5MmTsLGxgUKhwMSJE/Hzzz+jdevWevumpaXB1dVVp83V1RVpaWmVjh8XFwelUqldvLy8jBo/EREREYmLhSkiIiKqthYtWiApKQkHDx7EK6+8glGjRuHMmTNGGz82Nha5ubna5fr160Ybm4iIiIjEZyZ2AERERFR3yeVyNG3aFAAQHByMw4cP45NPPsGKFSsq9HVzc0N6erpOW3p6Otzc3CodX6FQQKFQGDdoIiIiIjIZvGKKiIiIjEaj0UClUuldFxYWhoSEBJ227du3VzonFRERERHVf7xiioiIiKolNjYW/fv3h7e3N+7evYt169Zh165d2LZtGwAgOjoanp6eiIuLAwBMnToVPXr0wKJFizBw4ECsX78eR44cwRdffCHmYRARERGRiFiYIiIiomrJyMhAdHQ0UlNToVQq0a5dO2zbtg1PPPEEACAlJQVS6b8XZ4eHh2PdunWYPXs2Zs2ahWbNmmHz5s1o06aNWIdARERERCKTCIIgPKpTdR7NbGdnV62ATEFeXh6USiVyc3Pr9HEQEVH98Ti5qT7lceZoIiIyRcxPRNVXpSum7O3tIZFIqjyoRCLBhQsX0KRJk2oHRkRERMbBPE5EREREpqrKt/L9+OOPcHR0fGQ/QRAwYMCAxwqKiIiIjIt5nIiIiIhMUZUKUz4+PujevTsaNWpUpUGbNGkCc3PzxwqMiIiIjIN5nIiIiIhMVZUKU8nJyQYNeurUqWoFQ0RERMbHPE5EREREpkr66C5ERERERERERETGV+U5pr799tsq9YuOjq52MERERFQzmMeJiIiIyBRJBEEQqtJRKpXCxsYGZmZmqGwTiUSC7OxsowYoBj7qk4iITM3j5qb6kseZo4mIyBQxPxFVX5WvmGrVqhXS09Px4osvYuzYsWjXrl1NxkVERERGxDxORERERKaoynNMnT59Gr/++ivu3buH7t27o2PHjli+fDny8vJqMj4iIiIyAuZxIiIiIjJFBk1+HhoaihUrViA1NRWvvfYaNmzYAHd3d4wYMQIqlaqmYiQiIiIjYB4nIiIiIlNTrafyWVpaIjo6GvPnz0dISAjWr1+PwsJCY8dGRERENYB5nIiIiIhMhcGFqZs3b+K9995Ds2bNMGzYMHTq1AmnT5+Gg4NDTcRHRERERsQ8TkRERESmpMqTn2/YsAGrVq3C7t27ERkZiUWLFmHgwIGQyWQ1GR8REREZAfM4EREREZkiiVDZM6MfIJVK4e3tjREjRsDV1bXSfq+99ppBASxbtgwLFy5EWloaAgMD8dlnnyEkJERv35KSEsTFxeGbb77BzZs30aJFC3zwwQfo169ftcfUh4/6JCIiU/O4uamm8nhtY44mIiJTxPxEVH1VLkz5+vpCIpE8fDCJBFeuXKnyzn/44QdER0cjPj4eoaGhWLJkCTZu3Ijz58/DxcWlQv8333wTa9euxZdffomWLVti27ZtiImJwf79+9G+fftqjakPf6kQEZGpedzcVBN5XAzM0UREZIqYn4iqr8qFqZoQGhqKTp06YenSpQAAjUYDLy8vTJkyBTNnzqzQ38PDA2+99RYmTZqkbXvmmWdgaWmJtWvXVmtMffhLhYiITA1zUxmeByIiMkXMT0TVV62n8hlDcXExjh49ioiIiH+DkUoRERGBxMREvduoVCpYWFjotFlaWmLv3r3VHpOIiIiIiIiIiMRR5cnP73f48GHs3LkTGRkZ0Gg0OusWL15cpTEyMzOhVqsrzHPh6uqKc+fO6d0mMjISixcvRvfu3eHv74+EhARs2rQJarW62mMCZQUvlUqlfZ2Xl1elYyAiIqqLjJHHiYiIiIiMweDC1HvvvYfZs2ejRYsWcHV11Zmv4lFzVzyuTz75BBMmTEDLli0hkUjg7++PMWPGYOXKlY81blxcHObPn2+kKImIiEyXmHmciIiIiOhBBhemPvnkE6xcuRKjR49+rB07OTlBJpMhPT1dpz09PR1ubm56t3F2dsbmzZtRVFSErKwseHh4YObMmWjSpEm1xwSA2NhYxMTEaF/n5eXBy8uruodGRERksoyVx4mIiIiIjMHgOaakUim6dOny2DuWy+UIDg5GQkKCtk2j0SAhIQFhYWEP3dbCwgKenp4oLS3FTz/9hMGDBz/WmAqFAnZ2djoLERFRfWSsPE5EREREZAwGF6Zef/11LFu2zCg7j4mJwZdffolvvvkGZ8+exSuvvIKCggKMGTMGABAdHY3Y2Fht/4MHD2LTpk24cuUK/vrrL/Tr1w8ajQYzZsyo8phEREQNmTHzOBERERHR4zL4Vr433ngDAwcOhL+/P1q3bg1zc3Od9Zs2baryWEOHDsXt27cxd+5cpKWlISgoCFu3btVOXp6SkgKp9N/aWVFREWbPno0rV67AxsYGAwYMwJo1a2Bvb1/lMYmIiBoyY+ZxIiIiIqLHJREEQTBkg8mTJ+Orr75Cr169KkyaCgCrVq0yaoBiyMvLg1KpRG5uLm/rIyIik2Cs3FTX8zhzNBERmSLmJ6LqM/iKqW+++QY//fQTBg4cWBPxEBERUQ1iHiciIiIiU2LwHFOOjo7w9/eviViIiIiohjGPExEREZEpMbgw9fbbb2PevHkoLCysiXiIiIioBhkzj8fFxaFTp06wtbWFi4sLoqKicP78+Ydus3r1akgkEp3FwsLisWMhIiIiorrJ4Fv5Pv30U1y+fBmurq7w9fWtMGnqsWPHjBYcERERGZcx8/ju3bsxadIkdOrUCaWlpZg1axb69u2LM2fOwNrautLt7OzsdApYD85zRUREREQNh8GFqaioqBoIg4iIiGqDMfP41q1bdV6vXr0aLi4uOHr0KLp3717pdhKJBG5ubkaLg4iIiIjqLoMLU/PmzauJOIiIiKgW1GQez83NBVA2j9XD5Ofnw8fHBxqNBh06dMB7772HgIAAvX1VKhVUKpX2dV5envECJiIiIiLRGTzHFBEREdGDNBoNpk2bhi5duqBNmzaV9mvRogVWrlyJX375BWvXroVGo0F4eDhu3Liht39cXByUSqV28fLyqqlDICIiIiIRVKkw5ejoiMzMzCoP6u3tjWvXrlU7KCIiIjKe2sjjkyZNwqlTp7B+/fqH9gsLC0N0dDSCgoLQo0cPbNq0Cc7OzlixYoXe/rGxscjNzdUu169fNyguIiIiIjJtVbqVLycnB7///juUSmWVBs3KyoJarX6swIiIiMg4ajqPT548GVu2bMGePXvQuHFjg2IzNzdH+/btcenSJb3rFQoFFAqFQWMSERERUd1R5TmmRo0aVZNxEBERUQ2qiTwuCAKmTJmCn3/+Gbt27YKfn5/BY6jVapw8eRIDBgwwenxEREREZPqqVJjSaDQ1HQcRERHVkJrK45MmTcK6devwyy+/wNbWFmlpaQAApVIJS0tLAEB0dDQ8PT0RFxcHAHjnnXfQuXNnNG3aFDk5OVi4cCGuXbuG8ePH10iMRERERGTaDH4qHxEREREALF++HADQs2dPnfZVq1Zh9OjRAICUlBRIpf9OaXnnzh1MmDABaWlpcHBwQHBwMPbv34/WrVvXVthEREREZEIkgiAIYgdhavLy8qBUKpGbmws7OzuxwyEiImJu+gfPAxERmSLmJ6Lqq9JT+YiIiIiIiIiIiIyNhSkiIiIiIiIiIhIFC1NERERERERERCSKak1+rtFocOnSJWRkZFR40k/37t2NEhgRERHVDOZxIiIiIjIVBhemDhw4gBdeeAHXrl3Dg/OmSyQSqNVqowVHRERExsU8TkRERESmxODC1MSJE9GxY0f8+uuvcHd3h0QiqYm4iIiIqAYwjxMRERGRKTG4MHXx4kX8+OOPaNq0aU3EQ0RERDWIeZyIiIiITInBhanQ0FBcunSJH2iJiIjqIOZxIiIiqm2CIKC0tJRTBjQQMpkMZmZmVb4y3+DC1JQpUzB9+nSkpaWhbdu2MDc311nfrl07Q4ckIiKiWsI8TkRERLWpuLgYqampKCwsFDsUqkVWVlZwd3eHXC5/ZF+J8ODMp48glUorDiKRQBCEejNpal5eHpRKJXJzc2FnZyd2OEREREbLTXU9jzNHExGRKWJ+0k+j0eDixYuQyWRwdnaGXC7n/Jb1nCAIKC4uxu3bt6FWq9GsWTO9nz/vZ/AVU8nJydUOkIiIiMTFPE5ERES1pbi4GBqNBl5eXrCyshI7HKollpaWMDc3x7Vr11BcXAwLC4uH9je4MOXj41Pt4IiIiEhczONERERU2x51xQzVP4b8zA0uTAHA5cuXsWTJEpw9exYA0Lp1a0ydOhX+/v7VGY6IiIhqEfM4EREREZkKg8uW27ZtQ+vWrXHo0CG0a9cO7dq1w8GDBxEQEIDt27fXRIxERERkJMzjRERERGRKDL5iaubMmXj99dfx/vvvV2h/88038cQTTxgtOCIiIjIu5nEiIiKiqrl+/TrmzZuHrVu3IjMzE+7u7oiKisLcuXPRqFEjscOrNwy+Yurs2bMYN25chfaxY8fizJkzRgmKiIiIagbzOBEREdGjXblyBR07dsTFixfx/fff49KlS4iPj0dCQgLCwsKQnZ0tdoj1hsGFKWdnZyQlJVVoT0pKgouLizFiIiIiohrCPE5ERET0aJMmTYJcLscff/yBHj16wNvbG/3798eOHTtw8+ZNvPXWWwAAlUqFN998E15eXlAoFGjatCm+/vpr7TinT5/Gk08+CTs7O9ja2qJbt264fPkyAKBnz56YNm2azn6joqIwevRo7WtfX1+8++67GD58OKytreHp6Ylly5bpbLN48WK0bdsW1tbW8PLywquvvor8/Hzt+tWrV8Pe3h7btm1Dq1atYGNjg379+iE1NVVnnJUrVyIgIAAKhQLu7u6YPHkygLI/YD755JM6fUtKSuDi4qJzrNVl8K18EyZMwEsvvYQrV64gPDwcALBv3z588MEHiImJeeyAiIiIqOYwjxMREZGYBEHAvRJ1re/X0lwGiURSpb7Z2dnYtm0b/vvf/8LS0lJnnZubG0aMGIEffvgBn3/+OaKjo5GYmIhPP/0UgYGBSE5ORmZmJgDg5s2b6N69O3r27Ik///wTdnZ22LdvH0pLSw2KfeHChZg1axbmz5+Pbdu2YerUqWjevLl2CgapVIpPP/0Ufn5+uHLlCl599VXMmDEDn3/+uXaMwsJCfPTRR1izZg2kUilefPFFvPHGG/juu+8AAMuXL0dMTAzef/999O/fH7m5udi3bx8AYPz48ejevTtSU1Ph7u4OANiyZQsKCwsxdOhQg45FH4MLU3PmzIGtrS0WLVqE2NhYAICHhwfefvttvPbaa48dEBEREdUc5nEiIiIS070SNVrP3Vbr+z3zTiSs5FUrgVy8eBGCIKBVq1Z617dq1Qp37tzB4cOHsWHDBmzfvh0REREAgCZNmmj7LVu2DEqlEuvXr4e5uTkAoHnz5gbH3qVLF8ycOVO7/b59+/Dxxx9rC1P3X3Xl6+uLBQsWYOLEiTqFqZKSEsTHx2ufwjx58mS888472vULFizA9OnTMXXqVG1bp06dAADh4eFo0aIF1qxZgxkzZgAAVq1aheeeew42NjYGH8+DDL6VTyKR4PXXX8eNGzeQm5uL3Nxc3LhxA1OnTq1y9ZGIiIjEwTxOREREVDWCIDx0/dWrVyGTydCjRw+965OSktCtWzdtUaq6wsLCKrw+e/as9vWOHTvQp08feHp6wtbWFiNHjkRWVhYKCwu1faysrLRFKQBwd3dHRkYGACAjIwO3bt1Cnz59Ko1h/PjxWLVqFQAgPT0dv//+O8aOHftYx1XO4Cum7mdra2uUIIiIiKj2MY8TERFRbbM0l+HMO5Gi7LeqmjZtColEgrNnz2LIkCEV1p89exYODg4VbvOrsM9HrJdKpRWKXyUlJVWOEygrjj355JN45ZVX8N///heOjo7Yu3cvxo0bh+LiYlhZWQFAheKYRCLR7vtRcQJAdHQ0Zs6cicTEROzfvx9+fn7o1q2bQbFWpkqFqQ4dOiAhIQEODg5o3779Q/+ieuzYMaMERkRERMbBPE5ERESmQiKRVPmWOrE0atQITzzxBD7//HO8/vrrOoWbtLQ0fPfdd4iOjkbbtm2h0Wiwe/du7a1892vXrh2++eYblJSU6L1qytnZWWcCcrVajVOnTqFXr146/Q4cOFDhdflthkePHoVGo8GiRYsglZbdFLdhwwaDjtfW1ha+vr5ISEiosO9yjRo1QlRUFFatWoXExESMGTPGoH08TJXeDYMHD4ZCodB+31Au9f/52A1Y2VT8a3Jlx1/ZWansdFXaXslI1TntRovVwJgkAKRSCWQSCaRSQCqRQCaVQCqR3Pf9fX3+6fdgH5lEAomkrF0m/ef78u2lEshlUshlUkilDeM9SURUHQ01jxMRERFV19KlSxEeHo7IyEgsWLAAfn5+OH36NP7zn//A09NTe3XSqFGjMHbsWO3k59euXUNGRgaef/55TJ48GZ999hmGDRuG2NhYKJVKHDhwACEhIWjRogV69+6NmJgY/Prrr/D398fixYuRk5NTIZZ9+/bhww8/RFRUFLZv346NGzfi119/BVB2dVdJSQk+++wzDBo0CPv27UN8fLzBx/v2229j4sSJcHFxQf/+/XH37l3s27cPU6ZM0fYZP348nnzySajVaowaNara5/ZBVSpMzZs3TyfYhmLOL6chVViJHQZVgZlUArmZFOYyKeRmZcUqxf2v/2kzN5PCwkwKa4UZrOQy2CjMYCU3g7VCBmuFGawVZrBRyGAlN4PNP68drMxhZ2HO4hcR1VkNNY8TERERVVezZs1w5MgRzJs3D88//zyys7Ph5uaGqKgozJs3D46OjgDKnmY3a9YsvPrqq8jKyoK3tzdmzZoFoOwqoz///BP/+c9/0KNHD8hkMgQFBaFLly4AgLFjx+LEiROIjo6GmZkZXn/9db1XLE2fPh1HjhzB/PnzYWdnh8WLFyMysux2yMDAQCxevBgffPABYmNj0b17d8TFxSE6Otqg4x01ahSKiorw8ccf44033oCTkxOeffZZnT4RERFwd3dHQEAAPDw8DD6nlZEIj5rN6wFNmjTB4cOH0ahRI532nJwcdOjQAVeuXDFacGLJy8uDUqnEC8sSILfUnWG+spNV2VmsvL9Bp73S8cv2oX9lpTFVGquB41QeEgRBgFojQCMAmvu/1whQCwI0gvDv95r7+5T1U2vK1muEB/oIwkPPRU2RSgAHKzkcrOVwtJLDwdocjtZyOFjJ4aa0gJudBdyVlnBTWqCRtZxFLCIyuvLclJubCzs7u2qPU9fzuLHOAxERkTExP+lXVFSE5ORk+Pn5wcLCQuxw6iRfX19MmzZN58l7YsnPz4enpydWrVqFp59++qF9DfnZG3xj59WrV6FWqyu0q1Qq3Lhxw9DhTNryFzvyl4oJEv4pXpVqNChRCygu1fy7qB/4WqpBiVoD1X1tqlI1ClVq5KtKUVhcinyVGoXFpShQlaJApUZBcWnZun/65KtKoRGArIJiZBUUPzI+c5kEbkoL+DayRhMna/g5WcPP2QZNnKzhYW8JGYtWRCSihpTHiYiIiOjxaTQaZGZmYtGiRbC3t8dTTz1l1PGrXJj6v//7P+3327Ztg1Kp1L5Wq9VISEiAn5+fwQEsW7YMCxcuRFpaGgIDA/HZZ58hJCSk0v5LlizB8uXLkZKSor20LC4uTluBe/vttzF//nydbVq0aIFz584ZHBuZJolEApkEkEllUJgBUNTs/opLNcgpLEZ2YTGyC4pxp6AE2YXFuFNQjKx8FdLyipCWW4S0vCJk3FWhRC3gevY9XM++h78uZuqMJTeTwreRFVq726GNpxIBHkoEeNrBzuLxHh9KRPQoNZHH4+LisGnTJpw7dw6WlpYIDw/HBx98gBYtWjx0u40bN2LOnDm4evUqmjVrhg8++AADBgww7ICIiIiIqFakpKTAz88PjRs3xurVq2FmZtzJ66s8WlRUFICyosCDk1yZm5vD19cXixYtMmjnP/zwA2JiYhAfH4/Q0FAsWbIEkZGROH/+PFxcXCr0X7duHWbOnImVK1ciPDwcFy5cwOjRoyGRSLB48WJtv4CAAOzYsePfgzTySaOGRW4mhYudBVzsHn3paYlag9t3Vbhx5x6uZhbgSmYBrtzOR3JmAa5lFaK4VIML6fm4kJ6PzUm3tNs1dbFBqJ8jQps0Qmc/xyrti4jIEDWRx3fv3o1JkyahU6dOKC0txaxZs9C3b1+cOXMG1tbWerfZv38/hg8fjri4ODz55JNYt24doqKicOzYMbRp06Zax0ZERERUX129elXsEODr62vwdESGMHiOKT8/Pxw+fBhOTk6PvfPQ0FB06tQJS5cuBVB2eZiXlxemTJmCmTNnVug/efJknD17FgkJCdq26dOn4+DBg9i7dy+AsiumNm/ejKSkpGrHxfuDqSaoNQJu5dzDxYy7OH0zD6du5eLUzTzczLlXoa+fkzVC/RzRs4UzujZzho2CxVWihs5YucmYefxBt2/fhouLC3bv3o3u3bvr7TN06FAUFBRgy5Yt2rbOnTsjKCioSk+QYY4mIiJTxPykH+eYarhqdI6p5OTkagd2v+LiYhw9ehSxsbHaNqlUioiICCQmJurdJjw8HGvXrsWhQ4cQEhKCK1eu4LfffsPIkSN1+l28eBEeHh6wsLBAWFgY4uLi4O3tbZS4iapLJpXAy9EKXo5W6N3SVdueXVCMw1ezcfBKNg4mZ+FMah6SMwuQnFmA9YevQ2EmRZ9WLniynQd6t3SBhblMxKMgorrOWHlcn9zcXADQPqVGn8TERMTExOi0RUZGYvPmzTUWFxERERGZrmpdhlFQUIDdu3cjJSUFxcW6k0G/9tprVRojMzMTarUarq6uOu2urq6Vzgf1wgsvIDMzE127doUgCCgtLcXEiRO1j2IEyq7CWr16NVq0aIHU1FTMnz8f3bp1w6lTp2Bra6t3XJVKBZVKpX2dl5dXpWMgMgZHazkiA9wQGeAGAMi9V4IjV7Ox91Imdp7LwNWsQvx2Mg2/nUyDtVyGJ1q7YlCgB7o1c4bcTCpy9ERUFxkjjz9Io9Fg2rRp6NKly0NvyUtLS9Ob+9PS0vT2Z44mIiIiqt8MLkwdP34cAwYMQGFhIQoKCuDo6IjMzExYWVnBxcWl2h9oq2LXrl1477338PnnnyM0NBSXLl3C1KlT8e6772LOnDkAgP79+2v7t2vXDqGhofDx8cGGDRswbtw4vePGxcVVmDCdSCxKS3P0aeWKPq1cMffJ1jh9Kw//+/sWtpxIxc2ce9icdAubk26hkbUcw0O8MaKzN9yVlmKHTUR1RE3l8UmTJuHUqVPaW+uNhTmaiIiIqH4z+HKL119/HYMGDcKdO3dgaWmJAwcO4Nq1awgODsZHH31U5XGcnJwgk8mQnp6u056eng43Nze928yZMwcjR47E+PHj0bZtWwwZMgTvvfce4uLioNFo9G5jb2+P5s2b49KlS5XGEhsbi9zcXO1y/fr1Kh8HUU2SSCRo46lEbP9W2PtmL/z0SjjGdPGFs60CWQXFWLrzErp+sBOTvjuGo9eyxQ6XiOoAY+Xx+02ePBlbtmzBzp070bhx44f2dXNzMyj3M0cTERER1W8GF6aSkpIwffp0SKVSyGQyqFQqeHl54cMPP9S5pe5R5HI5goODdSYy12g0SEhIQFhYmN5tCgsLIZXqhiyTlc23U9kc7vn5+bh8+TLc3d0rjUWhUMDOzk5nITI1EokEwT4OmDcoAIkze+PzER0Q4ucItUbArydT8czyRAxdkYgDV7LEDpWITJix8jhQlnsnT56Mn3/+GX/++Sf8/PweuU1YWJhO7geA7du3V5r7maOJiIiI6jeDC1Pm5uba4pCLiwtSUlIAAEql0uC/YsbExODLL7/EN998g7Nnz+KVV15BQUEBxowZAwCIjo7WmRx90KBBWL58OdavX4/k5GRs374dc+bMwaBBg7QFqjfeeAO7d+/G1atXsX//fgwZMgQymQzDhw839FCJTJaZTIoBbd2x4eUw/PZaNzwX3BjmMgkOJmdj2BcHEL3yEE7eyBU7TCIyQcbM45MmTcLatWuxbt062NraIi0tDWlpabh379+njT6Yy6dOnYqtW7di0aJFOHfuHN5++20cOXIEkydPNsLREREREVFdY/AcU+3bt8fhw4fRrFkz9OjRA3PnzkVmZibWrFnz0MlO9Rk6dChu376NuXPnIi0tDUFBQdi6dat2UtSUlBSdK6Rmz54NiUSC2bNn4+bNm3B2dsagQYPw3//+V9vnxo0bGD58OLKysuDs7IyuXbviwIEDcHZ2NvRQieqE1h52WPhcIF5/ojk+33UJ6w9dx54Lt7Hnwm08F9wYsQNawdFaLnaYRGQijJnHly9fDgDo2bOnTvuqVaswevRoABVzeXh4ONatW4fZs2dj1qxZaNasGTZv3mzwvomIiIhqyqBBg1BSUoKtW7dWWPfXX3+he/fuOHHiBNq1aydCdPWPRKjsHrhKHDlyBHfv3kWvXr2QkZGB6Oho7N+/H82aNcPKlSsRGBhYU7HWmry8PCiVSuTm5vKWAapzrmUV4OPtF7A56RYAwN7KHDP7tcTzHb0glUpEjo6IqstYuamu53HmaCIiMkXMT/oVFRUhOTkZfn5+sLCwEDucKtu8eTOeeeYZXLt2rcL8mWPHjsXJkydx+PBhkaKrGwz52Rt0K58gCHBxcdHOA+Hi4oKtW7ciLy8PR48eNfkPs0QNgU8jaywZ1h4/vRKGlm62yCkswcxNJ/Fs/H6cucXHrBM1ZMzjRERERI/25JNPwtnZGatXr9Zpz8/Px8aNGxEVFYXhw4fD09MTVlZWaNu2Lb7//nudvhqNBh9++CGaNm0KhUIBb29vnbu93nzzTTRv3hxWVlZo0qQJ5syZg5KSEu36t99+G0FBQVizZg18fX2hVCoxbNgw3L17t0r76N27d4WpEm7fvg25XF5hvk+xGVyYatq0KZ+IQ1QHBPs4YsuUrpg9sBWs5TIcS8nBU0v3Yvmuy9BoDLpQkojqCeZxIiIiEp0gAMUFtb8YcLOYmZkZoqOjsXr1ap0HrW3cuBFqtRovvvgigoOD8euvv+LUqVN46aWXMHLkSBw6dEjbNzY2Fu+//z7mzJmDM2fOYN26ddppiwDA1tYWq1evxpkzZ/DJJ5/gyy+/xMcff6wTx+XLl7F582Zs2bIFW7Zswe7du/H+++9XaR/jx4/HunXroFKptP3Xrl0LT09P9O7du+o/r1pg8K18AQEB+Prrr9G5c+eaikl0vAyT6pvU3HuY98tp/HGm7BHt4f6NsPj5ILgp687ltEQNnbFyU13P48zRRERkipif9NN7O1dxAfCeR+0HM+sWILeucvdz586hVatW2Llzp3Y+ze7du8PHxwdr1qyp0P/JJ59Ey5Yt8dFHH+Hu3btwdnbG0qVLMX78+Crt76OPPsL69etx5MgRAGVXTC1cuBBpaWmwtbUFAMyYMQN79uzBgQMHHrmPoqIieHh4ID4+Hs8//zwAIDAwEE8//TTmzZtX5fNQXTV2Kx8AvP/++/jPf/6DU6dOVTtAIqpd7kpLrBgZjA+eaQtLcxn2X85Cv0/24I/TaWKHRkS1jHmciIiI6NFatmyJ8PBwrFy5EgBw6dIl/PXXXxg3bhzUajXeffddtG3bFo6OjrCxscG2bdu0Tzs+e/YsVCoV+vTpU+n4P/zwA7p06QI3NzfY2Nhg9uzZ2u3L+fr6aotSAODu7o6MjIwq7cPCwgIjR47Uxn/s2DGcOnVK+4AaU2LwU/mio6NRWFiIwMBAyOVyWFpa6qzPzs42WnBEZDwSiQRDO3mjk68jpq5PwsmbuXhpzVFM6d0Ur0c058ToRA0E8zgRERGJytyq7OolMfZroHHjxmHKlClYtmwZVq1aBX9/f/To0QMffPABPvnkEyxZsgRt27aFtbU1pk2bhuLiYgCo8PnqQYmJiRgxYgTmz5+PyMhIKJVKrF+/HosWLdIN2dxc57VEIoFGo6nSPoCy2/mCgoJw48YNrFq1Cr1794aPj48hp6BWGFyY+vjjjyGR8D+wRHVVE2cb/PRKON7//RxW7kvGZ39ewulbefhkWBBsLcwfPQAR1WnM40RERCQqicSgW+rE9Pzzz2Pq1KlYt24dvv32W7zyyiuQSCTYt28fBg8ejBdffBFA2STkFy5cQOvWrQEAzZo1g6WlJRISEvTeZrd//374+Pjgrbfe0rZdu3bNoNgetQ8AaNu2LTp27Igvv/wS69atw9KlSw3aR20xuDBlipd9EZFh5GZSzB3UGm087RC76ST+PJeB51ccwDdjOsHFjvNOEdVnzONEREREVWNjY4OhQ4ciNjYWeXl52s9RzZo1w48//oj9+/fDwcEBixcvRnp6urYwZWFhgTfffBMzZsyAXC5Hly5dcPv2bZw+fRrjxo1Ds2bNkJKSgvXr16NTp0749ddf8fPPPxsU26P2UW78+PGYPHkyrK2tMWTIEKOdG2MyeI4pmUymvafxfllZWZDJZEYJiohqx9MdGmPjxDA42chxNjUPQz7fj8u388UOi4hqEPM4ERERUdWNGzcOd+7cQWRkJDw8yiZtnz17Njp06IDIyEj07NkTbm5uiIqK0tluzpw5mD59OubOnYtWrVph6NCh2s9gTz31FF5//XVMnjwZQUFB2L9/P+bMmWNwbA/bR7nhw4fDzMwMw4cPf+Qk5GIx+Kl8UqkUaWlpcHFx0Wm/desW/P39ce/ePaMGKAY+UYEampSsQkSvPIirWYWwtzLH16M6IdjHQeywiOg+xspNdT2PM0cTEZEpYn7Sz5Ans1HNuHr1Kvz9/XH48GF06NCh1vZryM++yrfyffrppwDKJtv66quvYGNjo12nVquxZ88etGzZspohE5GYvBtZ4adXwjH2myM4cT0HL3x5APEvBqNXS5dHb0xEdQLzOBEREVHDUVJSgqysLMyePRudO3eu1aKUoapcmPr4448BAIIgID4+Xudyf7lcDl9fX8THxxs/QiKqFY1sFPh+QigmrzuOP89l4OU1R7FiJItTRPUF8zgRERFRw7Fv3z706tULzZs3x48//ih2OA9V5cJUcnIyAKBXr17YtGkTHBx4mw9RfWMlN8OKkcF47fvj+P1UGotTRPUI8zgRERFRw9GzZ08YOHOTaAye/Hznzp38MEtUj5nLpPh0eHsMaOuGYrUGL685ij/PpYsdFhEZCfM4EREREZmSKl8xVU6tVmP16tVISEhARkYGNBqNzvo///zTaMERkTjMZVJ8Mqw9gOP47WQaJq49hm/GhCDMv5HYoRHRY2IeJyIiIiJTYnBhaurUqVi9ejUGDhyINm3aQCKR1ERcRCSy8uJUqfoY/jiTjgnfHsH6lzqjjadS7NCI6DEwjxMREVFtqyu3lJHxGPIzN7gwtX79emzYsAEDBgwwdFMiqmPKb+sbs+owEq9kYdTKQ9g4MQxNnG0evTERmSTmcSIiIqot5ubmAIDCwkJYWlqKHA3VpsLCQgD/vgcexuDClFwuR9OmTQ2PiojqJAtzGb6IDsbwLw/g1M08RK88hM2TusDJRiF2aERUDczjREREVFtkMhns7e2RkZEBALCysuLV2vWcIAgoLCxERkYG7O3tdZ4EXRmJYOA1dYsWLcKVK1ewdOnSevuGysvLg1KpRG5uLuzs7MQOh8gkZOar8Ozy/biaVYgO3vZYN6EzLMwf/UuGiIzDWLmprudx5mgiIjJFzE+VEwQBaWlpyMnJETsUqkX29vZwc3Or0udNgwtTQ4YMwc6dO+Ho6IiAgIAKl2Vt2rTJsGhNEH+pEOl3+XY+hizbh7yiUjwV6IFPhgXVyf/YEtVFxspNdT2PM0cTEZEpYn56NLVajZKSErHDoFpgbm5epSulyhl8K5+9vT2GDBli6GZEVA/4O9sg/sVgRK88hP87cQt+TtZ4/YnmYodFRAZgHiciIiIxyGQyg4oV1HAYfMVUQ8BqN9HDrT+UgpmbTgIAPhkWhMFBniJHRFT/MTeV4XkgIiJTxPxEVH3S6mxUWlqKHTt2YMWKFbh79y4A4NatW8jPzzdqcERkmoaFeOOl7k0AAP/Z+DeOXssWOSIiMgTzOBERERGZCoNv5bt27Rr69euHlJQUqFQqPPHEE7C1tcUHH3wAlUqF+Pj4moiTiEzMm/1aIjmzANvPpOPlNcfw62td4WpnIXZYRPQIzONEREREZEoMvmJq6tSp6NixI+7cuQNLS0tt+5AhQ5CQkGDU4IjIdMmkEnwyLAgtXG2Rma/Cq98dQ3GpRuywiOgRmMeJiIiIyJQYXJj666+/MHv2bMjlcp12X19f3Lx502iBEZHps5KbIX5kMGwtzHD02h3899czYodERI/APE5EREREpsTgwpRGo4Fara7QfuPGDdja2holKCKqO/ycrLFkaBAA4JvEa/j5+A1xAyKih2IeJyIiIiJTYnBhqm/fvliyZIn2tUQiQX5+PubNm4cBAwYYMzYiqiP6tHLFa72bAgBiN53EmVt5IkdERJVhHiciIiIiU2JwYWrRokXYt28fWrdujaKiIrzwwgvay/8/+OCDmoiRiOqAqRHN0aO5M4pKNJi49ihyC0vEDomI9DBmHt+zZw8GDRoEDw8PSCQSbN68+aH9d+3aBYlEUmFJS0t7jCMiIiIiorrM4KfyNW7cGCdOnMAPP/yAEydOID8/H+PGjcOIESN0JlElooalfDL0QUv3IiW7EFN/OI6VozpBKpWIHRoR3ceYebygoACBgYEYO3Ysnn766Spvd/78edjZ2Wlfu7i4GLRfIiIiIqo/JIIgCGIHYWry8vKgVCqRm5ur88GZiB7t9K1cPP35fqhKNZgW0QzTIpqLHRJRvWDquUkikeDnn39GVFRUpX127dqFXr164c6dO7C3t6/Wfkz9PBARUcPE/ERUfQbfyhcXF4eVK1dWaF+5ciVv5SMiBHgo8d6QtgCATxIuYtf5DJEjIqL7mUIeDwoKgru7O5544gns27fvoX1VKhXy8vJ0FiIiIiKqPwwuTK1YsQItW7as0B4QEID4+HijBEVEddszwY0xItQbggBM+yEJN+4Uih0SEf1DzDzu7u6O+Ph4/PTTT/jpp5/g5eWFnj174tixY5VuExcXB6VSqV28vLxqNEYiIiIiql0GF6bS0tLg7u5eod3Z2RmpqalGCYqI6r65g1ojsLESOYUlePW7Yygqqfh4eiKqfWLm8RYtWuDll19GcHAwwsPDsXLlSoSHh+Pjjz+udJvY2Fjk5uZql+vXr9dojERERERUuwwuTHl5eem97H7fvn3w8PAwSlBEVPcpzGT4/MVgOFiZ4+8buXhnyxmxQyIimF4eDwkJwaVLlypdr1AoYGdnp7MQERERUf1h8FP5JkyYgGnTpqGkpAS9e/cGACQkJGDGjBmYPn260QMkorrL094SS4a1x+hVh7DuYAo6eDvg2eDGYodF1KCZWh5PSkrSewUXERERETUMBhem/vOf/yArKwuvvvoqiouLAQAWFhZ48803ERsba/QAiahu69HcGdP6NMfHOy7grZ9PorW7HVp78IoHIrEYM4/n5+frXO2UnJyMpKQkODo6wtvbG7Gxsbh58ya+/fZbAMCSJUvg5+eHgIAAFBUV4auvvsKff/6JP/74w3gHSERERER1ikQQBKE6G+bn5+Ps2bOwtLREs2bNoFAojB2baPioTyLj0mgEjP3mMHadvw2fRlb4v8ldobQ0FzssojrF2LnJGHl8165d6NWrV4X2UaNGYfXq1Rg9ejSuXr2KXbt2AQA+/PBDfPHFF7h58yasrKzQrl07zJ07V+8YlWGOJiIiU8T8RFR91S5M1Wf8pUJkfDmFxRj46V7czLmHiFau+GJkMKRSidhhEdUZzE1leB6IiMgUMT8RVZ/Bt/IVFBTg/fffR0JCAjIyMqDRaHTWX7lyxWjBEVH9YW8lR/yLwXhm+X7sOJuO+D2X8WrPpmKHRdTgMI8TERERkSkxuDA1fvx47N69GyNHjoS7uzskkse74mHZsmVYuHAh0tLSEBgYiM8++wwhISGV9l+yZAmWL1+OlJQUODk54dlnn0VcXBwsLCyqPSYR1Y62jZWYPzgAsZtO4qNt5xHU2B7hTZ3EDouoQTF2HiciIiIiehwGF6Z+//13/Prrr+jSpctj7/yHH35ATEwM4uPjERoaiiVLliAyMhLnz5+Hi4tLhf7r1q3DzJkzsXLlSoSHh+PChQsYPXo0JBIJFi9eXK0xiah2DevkhWPX7mDj0RuY8v1xbHmtK9yVlmKHRdRgGDOPExERERE9LqmhGzg4OMDR0dEoO1+8eDEmTJiAMWPGoHXr1oiPj4eVlRVWrlypt//+/fvRpUsXvPDCC/D19UXfvn0xfPhwHDp0qNpjElHtkkgkeDeqDVq52yGroBgT1xxFUYla7LCIGgxj5nEiIiIiosdlcGHq3Xffxdy5c1FYWPhYOy4uLsbRo0cRERHxbzBSKSIiIpCYmKh3m/DwcBw9elRbiLpy5Qp+++03DBgwoNpjElHtszCXYcWLwbC3MseJG7mYvvEENBo+h4GoNhgrjxMRERERGYPBt/ItWrQIly9fhqurK3x9fWFurvvI92PHjlVpnMzMTKjVari6uuq0u7q64ty5c3q3eeGFF5CZmYmuXbtCEASUlpZi4sSJmDVrVrXHBACVSgWVSqV9nZeXV6VjIKLq825khfgXgzHy64P49e9U+DvbIOaJ5mKHRVTvGSuPExEREREZg8GFqaioqBoIo2p27dqF9957D59//jlCQ0Nx6dIlTJ06Fe+++y7mzJlT7XHj4uIwf/58I0ZKRFXRuUkj/DeqLWb89Dc+TbiIJk7WiGrvKXZYRPWamHmciIiIiOhBBhem5s2bZ5QdOzk5QSaTIT09Xac9PT0dbm5uereZM2cORo4cifHjxwMA2rZti4KCArz00kt46623qjUmAMTGxiImJkb7Oi8vD15eXtU9NCIywPOdvHDpdj6+2HMF//nxBByt5eje3FnssIjqLWPlcSIiIiIiYzB4jqlyR48exdq1a7F27VocP37c4O3lcjmCg4ORkJCgbdNoNEhISEBYWJjebQoLCyGV6oYsk8kAAIIgVGtMAFAoFLCzs9NZiKj2zOzXEk+2c0eJWsDEtUeRdD1H7JCI6r3HzeNERERERMZg8BVTGRkZGDZsGHbt2gV7e3sAQE5ODnr16oX169fD2bnqVzrExMRg1KhR6NixI0JCQrBkyRIUFBRgzJgxAIDo6Gh4enoiLi4OADBo0CAsXrwY7du3197KN2fOHAwaNEhboHrUmERkeqRSCRY/H4TceyX462Imxqw6hPUvhaGFm63YoRHVO8bM40REREREj8vgK6amTJmCu3fv4vTp08jOzkZ2djZOnTqFvLw8vPbaawaNNXToUHz00UeYO3cugoKCkJSUhK1bt2onL09JSUFqaqq2/+zZszF9+nTMnj0brVu3xrhx4xAZGYkVK1ZUeUwiMk1yMyniXwxGYGMl7hSWYPiXB3A2lQ8iIDI2Y+ZxIiIiIqLHJREEwaBntCuVSuzYsQOdOnXSaT906BD69u2LnJwcY8Yniry8PCiVSuTm5vK2PqJalltYgpErD+LvG7mwtzLH2nGhaOOpFDssItEZKzfV9TzOHE1ERKaI+Ymo+gy+Ykqj0VR4tDQAmJubQ6PRGCUoImq4lFbmWDMuFEFe9sgpLMELXx7A/suZYodFVG8wjxMRERGRKTG4MNW7d29MnToVt27d0rbdvHkTr7/+Ovr06WPU4IioYVJammPNuBB08nVAXlEpRq08hB+P3hA7LKJ6gXmciIiIiEyJwYWppUuXIi8vD76+vvD394e/vz/8/PyQl5eHzz77rCZiJKIGyNai7Mqpgf88re+NjScw75dTKCpRix0aUZ3GPE5EREREpsTgOaYAQBAE7NixA+fOnQMAtGrVChEREUYPTiy8P5jIdGg0AhZvv4ClOy8BAFq72+GzF9rD39lG5MiIapcxc1NdzuPM0UREZIqYn4iqr1qFqfqOv1SITM/O8xmYvuEEsguKITeTYlLPpni5RxNYmMvEDo2oVjA3leF5ICIiU8T8RFR9Vb6V788//0Tr1q2Rl1fx8e25ubkICAjAX3/9ZdTgiIjK9Wrhgt+ndkO3Zk4oLtXg4x0X0PfjPdhw5DpK1JywmehRmMeJiIiIyBRVuTC1ZMkSTJgwQW/1V6lU4uWXX8bixYuNGhwR0f1c7Szw7dgQLH2hPVxsFUjJLsSMH/9Gz4W78MWey8i4WyR2iEQmi3mciIiIiExRlQtTJ06cQL9+/Spd37dvXxw9etQoQRERVUYikeDJdh7Y+UZPzBrQEk42CtzMuYf3fjuHsLg/MeyLRCzbeQl/38hBcSmvpCIqxzxORERERKbIrKod09PTYW5uXvlAZma4ffu2UYIiInoUa4UZXuruj+gwX/x8/CZ+PHoDR6/dwYEr2ThwJRsLt52HmVSCpi428HexgbudBdyUFnC2VcBabgYrhQzWcjNYymWQSSWQSiSQSSSQSKB9LZUCEkjEPtRaIWkYhymaRtZySEQ+yczjRERERGSKqlyY8vT0xKlTp9C0aVO96//++2+4u7sbLTAioqqwMJdheIg3hod441pWAfZcuI3dFzJxKDkLeUWlOJd2F+fS7oodJjVwyXEDxA6BeZyIiIiITFKVC1MDBgzAnDlz0K9fP1hYWOisu3fvHubNm4cnn3zS6AESEVWVTyNrjAyzxsgwXwiCgNTcIpxNzUNyZgHS84qQlqdCVr4KBcVqFKpKUVisRmFxKTQCoNEIUAsCNIIAjQbQCGWvG4IGcpgNHvM4EREREZkiiSBU7b8k6enp6NChA2QyGSZPnowWLVoAAM6dO4dly5ZBrVbj2LFjcHV1rdGAawMf9UlERKbmcXNTfcnjzNFERGSKmJ+Iqq/KV0y5urpi//79eOWVVxAbG4vyepZEIkFkZCSWLVtm8h9miYiIGirmcSIiIiIyRVUuTAGAj48PfvvtN9y5cweXLl2CIAho1qwZHBwcaio+IiIiMhLmcSIiIiIyNdLqbOTg4IBOnTohJCSEH2aJiIjqGGPl8T179mDQoEHw8PCARCLB5s2bH7nNrl270KFDBygUCjRt2hSrV6+u9v6JiIiIqO6rVmGKiIiIqKCgAIGBgVi2bFmV+icnJ2PgwIHo1asXkpKSMG3aNIwfPx7btm2r4UiJiIiIyFQZdCsfERERUbn+/fujf//+Ve4fHx8PPz8/LFq0CADQqlUr7N27Fx9//DEiIyNrKkwiIiIiMmG8YoqIiIhqRWJiIiIiInTaIiMjkZiYWOk2KpUKeXl5OgsRERER1R8sTBEREVGtSEtLq/DkP1dXV+Tl5eHevXt6t4mLi4NSqdQuXl5etREqEREREdUSFqaIiIjIZMXGxiI3N1e7XL9+XeyQiIiIiMiIOMcUERER1Qo3Nzekp6frtKWnp8POzg6WlpZ6t1EoFFAoFLURHhERERGJgFdMERERUa0ICwtDQkKCTtv27dsRFhYmUkREREREJDYWpoiIiKha8vPzkZSUhKSkJABAcnIykpKSkJKSAqDsNrzo6Ght/4kTJ+LKlSuYMWMGzp07h88//xwbNmzA66+/Lkb4RERERGQCWJgiIiKiajly5Ajat2+P9u3bAwBiYmLQvn17zJ07FwCQmpqqLVIBgJ+fH3799Vds374dgYGBWLRoEb766itERkaKEj8RERERiU8iCIIgdhCmJi8vD0qlErm5ubCzsxM7HCIiIuamf/A8ENUxggDcuwPcTQXyUsu+FtwGivMB1d1/l5JCQFMKaNT/fC3//p/XeMh/War03xn+l4cqEf4a0GHkYw/D/ERUfZz8nIiIiIiIHl/JPeDWceDmMeD2OSDzAnD7PFCUI3ZkRJW7d0fsCIgaPBamiIiIiIjIcMWFwNW9wOU/gesHgLST/1zdpIelI2DnAdi6AdYugIUdoLAF5DZlX82tAJk5IJUBUrOyRSL757UMkFQ2A4mk8vgkla17yDbU8Dj4iB0BUYPHwhQREREREVXN3XTgzC/Aha1lRSm1Sne9jRvQuCPgGgA4NQecWwCNmgLmluLES0REJo+FKSIiIiIiqlxhNnD2/4BTP5UVowTNv+uUXkDTCMC3K+AVUva60iuViIiIKmJhioiIiIiIdKlLy66KOr4GuLRD9xa9xp2AVk8BzfqWXRHFQhQRET0GFqaIiIiIiKhMznXg2LdlBam7qf+2u7YF2jxdtjj4ihYeERHVPyxMERERERE1ZBo1cHE7cHQVcPGPf2/Vs2oEBI0oW1xaihsjERHVWyxMERERERE1RHm3gGNryq6Qyrvxb7tvNyB4NNBqEGCmEC08IiJqGFiYIiIiIiJqKDRq4PLOsqujzv8OCOqydkuHsiujgkcDTs1EDZGIiBoWFqaIiIiIiOq73JtA0ndlV0flXv+33TsMCB4DtB4MmFuIFx8RETVYLEwREREREdVH6tKyOaOOfaM7d5SFEmg3DOg4BnBpJW6MRETU4LEwRURERERUn9y5ChxfW7bc/2Q9ny5Ah1FA66cAc0vRwiMiIrofC1NERERERHVdQSZw+mfg5I/A9QP/tls1AoJeKCtIce4oIiIyQSxMERERERHVRUW5wIVtwN8bgMt//juROSRAk55A8CigxUDATC5mlERERA8lFTsAAFi2bBl8fX1hYWGB0NBQHDp0qNK+PXv2hEQiqbAMHDhQ22f06NEV1vfr1682DoWIiIiIqOZkXgL2LwVWPwl82ATYNAG4tL2sKOXRHoh8D4g5C0RvBgKGsChFREQmT/Qrpn744QfExMQgPj4eoaGhWLJkCSIjI3H+/Hm4uLhU6L9p0yYUFxdrX2dlZSEwMBDPPfecTr9+/fph1apV2tcKhaLmDoKIiIiIqCaUFgMp+8uujLqwDci+rLveqQXQ5mmgzbOAU1NxYiQiInoMohemFi9ejAkTJmDMmDEAgPj4ePz6669YuXIlZs6cWaG/o6Ojzuv169fDysqqQmFKoVDAzc2t5gInIiIiIjK2UhVw8xhwbV/Zcv0QUJz/73qpOeDbFWjeD2jeF3BsIl6sRERERiBqYaq4uBhHjx5FbGystk0qlSIiIgKJiYlVGuPrr7/GsGHDYG1trdO+a9cuuLi4wMHBAb1798aCBQvQqFEjvWOoVCqoVCrt67y8vGocDRERERGRAdSlQOZ54FYScOs4kJoEpJ0ESot0+1m7AM36As0jAf9egMJWjGiJiIhqhKiFqczMTKjVari6uuq0u7q64ty5c4/c/tChQzh16hS+/vprnfZ+/frh6aefhp+fHy5fvoxZs2ahf//+SExMhEwmqzBOXFwc5s+f/3gHQ0RERESkjyAAd9OAzAtly+1zQOrf/xSh7lXsb+UE+ISXXRnlEw64BABSk5galoiIyOhEv5XvcXz99ddo27YtQkJCdNqHDRum/b5t27Zo164d/P39sWvXLvTp06fCOLGxsYiJidG+zsvLg5eXV80FTkRERET1iyofyLsJ3LkG5PyzlH+ffRVQ5erfTm4DuAeWTVzuHgR4BAGNmgISSS0GT0REJB5RC1NOTk6QyWRIT0/XaU9PT3/k/FAFBQVYv3493nnnnUfup0mTJnBycsKlS5f0FqYUCgUnRyciIiKiMoIAqPKAezlAUU7Z13t3yr4vyATyM4D89H+Xu+lAScHDx5RIAQffssnKnZsDrm3KilGO/rwaioiIGjRRC1NyuRzBwcFISEhAVFQUAECj0SAhIQGTJ09+6LYbN26ESqXCiy+++Mj93LhxA1lZWXB3dzdG2ERERERUUwQB0KgBQfPP8s/3mlJAXQKoi/9ZHvX9fW0lRWWFo+JCoKQQKC4oW8q/Lyn8Z10BUJQHFOWW7ddQclvAwQew96n4tZE/YMY/hBIRET1I9Fv5YmJiMGrUKHTs2BEhISFYsmQJCgoKtE/pi46OhqenJ+Li4nS2+/rrrxEVFVVhQvP8/HzMnz8fzzzzDNzc3HD58mXMmDEDTZs2RWRkZK0dFxEREdWgL3oDljX5MUaowbHv300t7ac2j0e4r6ikeaC4JGj+KTrd1+/BIpQpkSkAS3vA0gGwsP/ne0fA1hWwcfvn632LwkbkgImIiOoe0QtTQ4cOxe3btzF37lykpaUhKCgIW7du1U6InpKSAukDlzefP38ee/fuxR9//FFhPJlMhr///hvffPMNcnJy4OHhgb59++Ldd9/l7XpERET1RdYFQME5eBokmQKQyQGZ+T9f7/9eX5scMJMD5taA3Aowtyqb10n7vfU/X63K+ihsywpRlvaAuaXYR0tERFTvSQSh1v5UV2fk5eVBqVQiNzcXdnZ2YodDRETE3PQP7Xk48RvsbK1reG+1VPiqtUmua2k/UhkgkZXNqSSR/PNael+b9J82iZ628n73b/dPm1QGSM04KTgRmSTmaaLqE/2KKSIiIiKD+XYB+MGfiIiIqM7jI0CIiIjosSxbtgy+vr6wsLBAaGgoDh06VGnf1atXQyKR6CwWFha1GC0RERERmRIWpoiIiKjafvjhB8TExGDevHk4duwYAgMDERkZiYyMjEq3sbOzQ2pqqna5du1aLUZMRERERKaEhSkiIiKqtsWLF2PChAkYM2YMWrdujfj4eFhZWWHlypWVbiORSODm5qZdyh94QkREREQNDwtTREREVC3FxcU4evQoIiIitG1SqRQRERFITEysdLv8/Hz4+PjAy8sLgwcPxunTp2sjXCIiIiIyQSxMERERUbVkZmZCrVZXuOLJ1dUVaWlperdp0aIFVq5ciV9++QVr166FRqNBeHg4bty4obe/SqVCXl6ezkJERERE9QcLU0RERFRrwsLCEB0djaCgIPTo0QObNm2Cs7MzVqxYobd/XFwclEqldvHy8qrliImIiIioJrEwRURERNXi5OQEmUyG9PR0nfb09HS4ublVaQxzc3O0b98ely5d0rs+NjYWubm52uX69euPHTcRERERmQ4WpoiIiKha5HI5goODkZCQoG3TaDRISEhAWFhYlcZQq9U4efIk3N3d9a5XKBSws7PTWYiIiIio/jATOwAiIiKqu2JiYjBq1Ch07NgRISEhWLJkCQoKCjBmzBgAQHR0NDw9PREXFwcAeOedd9C5c2c0bdoUOTk5WLhwIa5du4bx48eLeRhEREREJBIWpoiIiKjahg4ditu3b2Pu3LlIS0tDUFAQtm7dqp0QPSUlBVLpvxdo37lzBxMmTEBaWhocHBwQHByM/fv3o3Xr1mIdAhERERGJSCIIgiB2EKYmLy8PSqUSubm5vGWAiIhMAnNTGZ4HIiIyRcxPRNXHOaaIiIiIiIiIiEgULEwREREREREREZEoWJgiIiIiIiIiIiJRsDBFRERERERERESiYGGKiIiIiIiIiIhEwcIUERERERERERGJgoUpIiIiIiIiIiISBQtTREREREREREQkChamiIiIiIiIiIhIFCxMERERERERERGRKFiYIiIiIiIiIiIiUbAwRUREREREREREomBhioiIiIiIiIiIRMHCFBERERERERERiYKFKSIiIiIiIiIiEgULU0REREREREREJAoWpoiIiIiIiIiISBQsTBERERERERERkShYmCIiIiIiIiIiIlGwMEVERERERERERKJgYYqIiIiIiIiIiETBwhQREREREREREYmChSkiIiIiIiIiIhIFC1NERERERERERCQKFqaIiIiIiIiIiEgULEwREREREREREZEoWJgiIiIiIiIiIiJRmERhatmyZfD19YWFhQVCQ0Nx6NChSvv27NkTEomkwjJw4EBtH0EQMHfuXLi7u8PS0hIRERG4ePFibRwKERFRg2NIHgeAjRs3omXLlrCwsEDbtm3x22+/1VKkRERERGRqRC9M/fDDD4iJicG8efNw7NgxBAYGIjIyEhkZGXr7b9q0Campqdrl1KlTkMlkeO6557R9PvzwQ3z66aeIj4/HwYMHYW1tjcjISBQVFdXWYRERETUIhubx/fv3Y/jw4Rg3bhyOHz+OqKgoREVF4dSpU7UcORERERGZAokgCIKYAYSGhqJTp05YunQpAECj0cDLywtTpkzBzJkzH7n9kiVLMHfuXKSmpsLa2hqCIMDDwwPTp0/HG2+8AQDIzc2Fq6srVq9ejWHDhj1yzLy8PCiVSuTm5sLOzu7xDpCIiMgITDU3GZrHhw4dioKCAmzZskXb1rlzZwQFBSE+Pv6R+zPV80BERA0b8xNR9ZmJufPi4mIcPXoUsbGx2japVIqIiAgkJiZWaYyvv/4aw4YNg7W1NQAgOTkZaWlpiIiI0PZRKpUIDQ1FYmKi3sKUSqWCSqXSvs7NzQVQ9suFiIjIFJTnJJH/nqSjOnk8MTERMTExOm2RkZHYvHmz3v7M0UREVBeYYp4mqitELUxlZmZCrVbD1dVVp93V1RXnzp175PaHDh3CqVOn8PXXX2vb0tLStGM8OGb5ugfFxcVh/vz5Fdq9vLweGQMREVFtunv3LpRKpdhhAKheHk9LS2OOJiKiesuU8jRRXSFqYepxff3112jbti1CQkIea5zY2Fidv95qNBpkZ2ejUaNGkEgkjxtmg5aXlwcvLy9cv36dl7TWAp7v2sNzXbt4vsv+Anv37l14eHiIHUqtYo6uOfx3Vbt4vmsXz3ft4vluuHmayBhELUw5OTlBJpMhPT1dpz09PR1ubm4P3bagoADr16/HO++8o9Nevl16ejrc3d11xgwKCtI7lkKhgEKh0Gmzt7ev4lFQVdjZ2TXYJCUGnu/aw3Nduxr6+Ta1v8BWJ4+7ubkZ1J85uuY19H9XtY3nu3bxfNeuhn6+TS1PE9UVoj6VTy6XIzg4GAkJCdo2jUaDhIQEhIWFPXTbjRs3QqVS4cUXX9Rp9/Pzg5ubm86YeXl5OHjw4CPHJCIioqqrTh4PCwvT6Q8A27dvZ44mIiIiaqBEv5UvJiYGo0aNQseOHRESEoIlS5agoKAAY8aMAQBER0fD09MTcXFxOtt9/fXXiIqKQqNGjXTaJRIJpk2bhgULFqBZs2bw8/PDnDlz4OHhgaioqNo6LCIiogbB0Dw+depU9OjRA4sWLcLAgQOxfv16HDlyBF988YWYh0FEREREIhG9MDV06FDcvn0bc+fORVpaGoKCgrB161btxKgpKSmQSnUv7Dp//jz27t2LP/74Q++YM2bMQEFBAV566SXk5OSga9eu2Lp1KywsLGr8eEiXQqHAvHnzKtyGQTWD57v28FzXLp5v02VoHg8PD8e6deswe/ZszJo1C82aNcPmzZvRpk0bsQ6hweK/q9rF8127eL5rF883ET0OicDnWRIRERERERERkQhEnWOKiIiIiIiIiIgaLhamiIiIiIiIiIhIFCxMERERERERERGRKFiYIiIiIiIiIiIiUbAwRSalsLAQPj4+eOONN8QOpV67fv06evbsidatW6Ndu3bYuHGj2CHVO1u2bEGLFi3QrFkzfPXVV2KHU6/x/UxUO5ijawd/p9U85ujaw/czEVUFn8pHJuWtt97CpUuX4OXlhY8++kjscOqt1NRUpKenIygoCGlpaQgODsaFCxdgbW0tdmj1QmlpKVq3bo2dO3dCqVQiODgY+/fvR6NGjcQOrV7i+5modjBH1w7+TqtZzNG1i+9nIqoKXjFFJuPixYs4d+4c+vfvL3Yo9Z67uzuCgoIAAG5ubnByckJ2dra4QdUjhw4dQkBAADw9PWFjY4P+/fvjjz/+EDuseovvZ6Kaxxxde/g7rWYxR9cuvp+JqCpYmKIq2bNnDwYNGgQPDw9IJBJs3ry5Qp9ly5bB19cXFhYWCA0NxaFDhwzaxxtvvIG4uDgjRVy31cb5Lnf06FGo1Wp4eXk9ZtT1x+Oe/1u3bsHT01P72tPTEzdv3qyN0OskY77f+X6mhog5unYxR4uLObp2MUcTUW1gYYqqpKCgAIGBgVi2bJne9T/88ANiYmIwb948HDt2DIGBgYiMjERGRoa2T1BQENq0aVNhuXXrFn755Rc0b94czZs3r61DMmk1fb7LZWdnIzo6Gl988UWNH1NdYozzT1VnrPPN9zM1VMzRtYs5WlzM0bWLOZqIaoVAZCAAws8//6zTFhISIkyaNEn7Wq1WCx4eHkJcXFyVxpw5c6bQuHFjwcfHR2jUqJFgZ2cnzJ8/35hh11k1cb4FQRCKioqEbt26Cd9++62xQq2XqnP+9+3bJ0RFRWnXT506Vfjuu+9qJd66rrrvd76ficowR9cu5mhxMUfXLuZoIqopvGKKHltxcTGOHj2KiIgIbZtUKkVERAQSExOrNEZcXByuX7+Oq1ev4qOPPsKECRMwd+7cmgq5TjPG+RYEAaNHj0bv3r0xcuTImgq1XqrK+Q8JCcGpU6dw8+ZN5Ofn4/fff0dkZKRYIddpVTnffD8TVY45unYxR4uLObp2MUcTkbGwMEWPLTMzE2q1Gq6urjrtrq6uSEtLEymq+ssY53vfvn344YcfsHnzZgQFBSEoKAgnT56siXDrnaqcfzMzMyxatAi9evVCUFAQpk+fzqf9VFNVzjffz0SVY46uXczR4mKOrl3M0URkLGZiB0D0oNGjR4sdQr3XtWtXaDQascOo15566ik89dRTYofRIPD9TFR7mKNrHn+n1Tzm6NrD9zMRVQWvmKLH5uTkBJlMhvT0dJ329PR0uLm5iRRV/cXzLS6e/9rF8030ePhvqHbxfIuL57928XwTkbGwMEWPTS6XIzg4GAkJCdo2jUaDhIQEhIWFiRhZ/cTzLS6e/9rF8030ePhvqHbxfIuL57928XwTkbHwVj6qkvz8fFy6dEn7Ojk5GUlJSXB0dIS3tzdiYmIwatQodOzYESEhIViyZAkKCgowZswYEaOuu3i+xcXzX7t4vokeD/8N1S6eb3Hx/Ncunm8iqhXiPhSQ6oqdO3cKACoso0aN0vb57LPPBG9vb0EulwshISHCgQMHxAu4juP5FhfPf+3i+SZ6PPw3VLt4vsXF81+7eL6JqDZIBEEQaq7sRUREREREREREpB/nmCIiIiIiIiIiIlGwMEVERET/397dhES5hXEAf+ZWkPjaGAlqmwZKaZGWQZsiiBZCRuTGPqARXURFCLOTIKxFhK1CaBeE0apFJCFUi5AWbUqMIggD16F9YFrttLu4NNC96UW919eP3w8GZl495zm4mGf4j+c9AACQCsEUAAAAAKkQTAEAAACQCsEUAAAAAKkQTAEAAACQCsEUAAAAAKkQTAEAAACQCsEUAAAAAKkQTMEq09bWFs3NzYtet7e3NzKZTGQymSgUCgueq7y8fE5j2traivX7+voWVB8A/g96tB4NsBqtTXsBwH8nk8nM+vNLly5FT09P/PjxY5FW9KsNGzbE8PBwlJaWLmie48ePR1NT05zG9PT0RHd3d1RXVy+oNgDMhx49Mz0aYHUTTMEK8v79++Lzu3fvRldXVwwPDxevJUkSSZKksbSI+OtDeVVV1YLnKSkpiZKSkjmNyWazkc1mF1wbAOZDj56ZHg2wutnKBytIVVVV8ZHNZosfMn8+kiT5xzaBAwcOREdHRxQKhdi4cWNUVlbGzZs349u3b9He3h5lZWWxbdu2ePjw4S+13rx5E4cOHYokSaKysjLy+Xx8/PhxzmvO5XJx5cqVaG1tjSRJYsuWLfHgwYP48OFDHD16NJIkifr6+hgcHCyO+fs2gcuXL8euXbvizp07kcvlIpvNxokTJ2JycnLO6wGA/4MerUcD8HuCKSBu374dFRUV8fz58+jo6Ihz585FS0tL7N27N4aGhqKxsTHy+Xx8//49IiLGx8fj4MGD0dDQEIODg/Ho0aMYHR2NY8eOzav+9evXY9++ffHy5cs4fPhw5PP5aG1tjVOnTsXQ0FBs3bo1WltbZ93eMDIyEn19fdHf3x/9/f3x9OnT6O7untd6AGCp0KMBWOkEU0Ds3LkzLl68GDU1NXHhwoVYv359VFRUxOnTp6Ompia6urri06dP8fr164iIuHHjRjQ0NMTVq1dj+/bt0dDQELdu3YqBgYF49+7dnOs3NTXFmTNnirUmJiZiz5490dLSErW1tdHZ2Rlv376N0dHRGeeYnp6O3t7e2LFjR+zfvz/y+Xw8efJk3n8TAFgK9GgAVjr3mAKivr6++HzNmjWxadOmqKurK16rrKyMiIixsbGIiHj16lUMDAz89l4YIyMjUVtbO+/6P2vNVH+m+1/kcrkoKysrvq6uri6uFwCWKz0agJVOMAXEunXrfnmdyWR+ufbzJKHp6emIiPj69WscOXIkrl279o+55nOizu9qzVb/3+b4OWa23weA5UCPBmClE0wBc7Z79+64d+9e5HK5WLvW2wgALBV6NADLjXtMAXN2/vz5+Pz5c5w8eTJevHgRIyMj8fjx42hvb4+pqam0lwcAq5YeDcByI5gC5mzz5s3x7NmzmJqaisbGxqirq4tCoRDl5eXxxx/eVgAgLXo0AMtN5sdsZ7sC/Ed6e3ujUCjE+Ph4quvIZDJx//79aG5uTnUdALBU6NEApMnXJsCi+fLlSyRJEp2dnYte++zZs789oQgA0KMBSI//mAIWxeTkZIyOjkZERHl5eVRUVCxq/bGxsZiYmIiIv04lKi0tXdT6ALBU6dEApEkwBQAAAEAqbOUDAAAAIBWCKQAAAABSIZgCAAAAIBWCKQAAAABSIZgCAAAAIBWCKQAAAABSIZgCAAAAIBWCKQAAAABSIZgCAAAAIBV/AusmEnGScW6KAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_8_9, axes = plt.subplots(nrows=1, ncols=2, figsize=(12, 4))\n",
    "(ax1, ax2) = axes.flatten()\n",
    "\n",
    "plot_time_profile(\n",
    "    conc_sol, observable=[\"EC\"], ax=ax1, legend=\"best\",\n",
    "    plot_function=\"semilogx\", ylim= (.7, 1),\n",
    "    xlabel=\"Time [min]\", ylabel=\"Concentration [mM]\",\n",
    "    title=(\"(a) Energy Charge\", XL_FONT));\n",
    "\n",
    "plot_time_profile(\n",
    "    conc_sol, observable=[\"Occupancy\", \"Vacancy\"], ax=ax2, \n",
    "    legend=\"right outside\", plot_function=\"semilogx\",\n",
    "    ylim=(0., 4.), xlabel=\"Time [min]\", ylabel=\"Concentration [mM]\",\n",
    "    title=(\"(b) Charge Pools\", XL_FONT));\n",
    "fig_8_9.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Figure 8.9:** (a) The Atkinson's energy charge (Eq. 8.9). (b) The occupancy and vacancy pools move in the opposite directions. Increasing the load drops the occupancy pool and increases the vacancy pool as the system becomes discharged. Reduced loads have the opposite reaction.\n",
    "\n",
    "![Figure-8-10](../images/Ch8/Figure-8-10.png)\n",
    "\n",
    "**Figure 8.10:** Graphical representation of the energy charge (x-direction) versus the capacity (y-direction). The drop in the charge is indicated by the arrow. The capacity is a constant in this case.\n",
    "\n",
    "### Buffering the energy charge  \n",
    "#### Reaction mechanism:  E.C. buffering\n",
    "In many situations, there is a buffering effect on the energy charge by a coupled carrier of high energy bonds. This exchange is: \n",
    "\n",
    "$$\\begin{equation} \\text{ATP}\\ + \\text{B} \\leftrightharpoons \\text{ADP}\\ + \\text{BP} \\tag{8.10} \\end{equation}$$\n",
    "\n",
    "where the buffering molecule, $\\text{B}$, picks up the high-energy phosphate group through a fast equilibrating reaction. \n",
    "\n",
    "![Figure-8-11](../images/Ch8/Figure-8-11.png)\n",
    "\n",
    "**Figure 8.11:** The trafficking of high-energy phosphate bonds with the buffer molecule exchange reaction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-25T17:57:26.261086Z",
     "iopub.status.busy": "2021-01-25T17:57:26.260440Z",
     "iopub.status.idle": "2021-01-25T17:57:26.276159Z",
     "shell.execute_reply": "2021-01-25T17:57:26.276643Z"
    }
   },
   "outputs": [],
   "source": [
    "# Copy the model to create a new, yet identical model instance \n",
    "phos_buffered = phos_traffic.copy()\n",
    "phos_buffered.id += \"_Buffered\"\n",
    "\n",
    "# Create the buffer metabolites\n",
    "b = MassMetabolite(\"b\")\n",
    "bp = MassMetabolite(\"bp\")\n",
    "\n",
    "# Create the buffer reaction and add the metaolites\n",
    "v_buff = MassReaction(\"buff\")\n",
    "v_buff.add_metabolites({atp:-1, b:-1, adp:1, bp:1})\n",
    "\n",
    "# Update model\n",
    "phos_buffered.add_reactions(v_buff)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The rate equation of the buffering reaction is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-25T17:57:26.420800Z",
     "iopub.status.busy": "2021-01-25T17:57:26.419861Z",
     "iopub.status.idle": "2021-01-25T17:57:26.423264Z",
     "shell.execute_reply": "2021-01-25T17:57:26.423870Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kf_buff*(atp*b - adp*bp/Keq_buff)\n"
     ]
    }
   ],
   "source": [
    "print(strip_time(phos_buffered.rates[v_buff]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Examples of buffer molecules  \n",
    "In Eq. (8.10), $\\text{B}$ represents a phosphagen, which is a compound containing a high-energy phosphate bond that is used as energy storage to buffer the ATP/ADP ratio. The most well-known phosphagen is creatine, which is found in the muscles of mammals. Marine organisms have other phosphagens (arginine, taurocyamine, glycocyamine), while earthworms use lombricine (Nguyen, 1960). \n",
    "\n",
    "#### Buffering:  \n",
    "When the reaction in Eq. 8.10 is at equilibrium we have \n",
    "\n",
    "$$\\begin{equation} k_{\\mathrm{buff}}^\\rightarrow\\text{ATP}*\\text{B} = k_{\\mathrm{buff}}^\\leftarrow \\text{ADP}*\\text{BP} \\tag{8.11} \\end{equation}$$\n",
    "\n",
    "This equation can be rearranged as \n",
    "\n",
    "$$\\begin{equation} 4 K_{\\mathrm{buff}} = \\text{BP}/\\text{B} \\tag{8.12} \\end{equation}$$\n",
    "\n",
    "where $\\text{ATP}/\\text{ADP}=1.6/0.4=4$ in the steady state, and $K_{\\mathrm{buff}} = k_{\\mathrm{buff}}/k_{-buff}$. If the buffering molecule is present in a constant amount, then \n",
    "\n",
    "$$\\begin{equation} \\text{B}_{\\mathrm{tot}} = \\text{B} + \\text{BP} \\tag{8.13} \\end{equation}$$\n",
    "\n",
    "We can rearrange equations  (8.12) and (8.13) as:\n",
    "\n",
    "$$\\begin{equation} \\frac{\\text{BP}}{\\text{B}_{\\mathrm{tot}}} = \\frac{4 K_{\\mathrm{buff}}}{4 K_{\\mathrm{buff}} + 1} \\tag{8.14} \\end{equation}$$\n",
    "\n",
    "In this equation, $\\text{B}_{\\mathrm{tot}}$ is the capacity of the buffer to carry the high energy phosphate bond whereas $\\text{BP}/\\text{B}_{\\mathrm{tot}}$ is the energy charge of the buffer. \n",
    "\n",
    "We note that the value of $K_{\\mathrm{buff}}$ is a key variable. If $K_{\\mathrm{buff}} = 1/4$ then the buffer is half charged at equilibrium, whereas if $K_{\\mathrm{buff}}=1$ then the buffer is 80% charged. Thus, this numerical value (a thermodynamic quantity) is key and will specify the relative charge on the buffer and the adenosine phosphates. The effect of $K_{\\mathrm{buff}}$ can be determined through simulation. \n",
    "\n",
    "#### Updating the model with the buffering reaction\n",
    "It is assumed that the buffering reaction is at equilibrium and that the amount of buffering molecules is constant:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-25T17:57:26.454919Z",
     "iopub.status.busy": "2021-01-25T17:57:26.453993Z",
     "iopub.status.idle": "2021-01-25T17:57:26.457155Z",
     "shell.execute_reply": "2021-01-25T17:57:26.457710Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            ⎛         adp⋅bp ⎞\n",
      "0 = kf_buff⋅⎜atp⋅b - ────────⎟\n",
      "            ⎝        Keq_buff⎠\n",
      "b + bp = 10\n"
     ]
    }
   ],
   "source": [
    "# Use sympy to set up a symbolic equation for the buffer equilibrium\n",
    "buff_equilibrium = sym.Eq(\n",
    "    sym.S.Zero, strip_time(phos_buffered.rates[v_buff]))\n",
    "\n",
    "# Set amount of buffer molecules\n",
    "btot = 10\n",
    "\n",
    "# Use sympy to set up a symbolic equation for the buffer pool\n",
    "b_sym = sym.Symbol(b.id)\n",
    "bp_sym = sym.Symbol(bp.id)\n",
    "buff_pool = sym.Eq(b_sym + bp_sym, btot)\n",
    "\n",
    "# Pretty print the equations\n",
    "sym.pprint(buff_equilibrium)\n",
    "sym.pprint(buff_pool)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Solve the equilibrium system:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-25T17:57:26.508980Z",
     "iopub.status.busy": "2021-01-25T17:57:26.501021Z",
     "iopub.status.idle": "2021-01-25T17:57:26.511788Z",
     "shell.execute_reply": "2021-01-25T17:57:26.511259Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{b: 10.0/(4.0*Keq_buff + 1.0), bp: 40.0*Keq_buff/(4.0*Keq_buff + 1.0)}\n"
     ]
    }
   ],
   "source": [
    "# Obtain a dict of ic values for substitution into the sympy expressions\n",
    "ic_dict = {sym.Symbol(met.id): ic \n",
    "          for met, ic in phos_buffered.initial_conditions.items()}\n",
    "# Substitute known concentrations\n",
    "buff_equilibrium = buff_equilibrium.subs(ic_dict)\n",
    "\n",
    "# Obtain solutions for B and BP\n",
    "buff_sol = sym.solve([buff_equilibrium, buff_pool], [b_sym, bp_sym])\n",
    "# Pretty print the equation\n",
    "print(buff_sol)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set $K_{\\mathrm{buff}}$ and $k_{\\mathrm{buff}}^\\rightarrow$:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-25T17:57:26.520100Z",
     "iopub.status.busy": "2021-01-25T17:57:26.519299Z",
     "iopub.status.idle": "2021-01-25T17:57:26.522064Z",
     "shell.execute_reply": "2021-01-25T17:57:26.522508Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "adp: 0.4 mM\n",
      "amp: 0.1 mM\n",
      "atp: 1.6 mM\n",
      "b: 2.0 mM\n",
      "bp: 8.0 mM\n"
     ]
    }
   ],
   "source": [
    "v_buff.kf = 1000\n",
    "v_buff.Keq = 1\n",
    "\n",
    "# Obtain a dict of parameter values for substitution into the sympy expressions\n",
    "param_dict = {\n",
    "    sym.Symbol(parameter): value \n",
    "    for parameter, value in v_buff.parameters.items()}\n",
    "\n",
    "buffer_ics = {\n",
    "    phos_buffered.metabolites.get_by_id(str(met)): float(expr.subs(param_dict))\n",
    "    for met, expr in buff_sol.items()}\n",
    "\n",
    "# Update initial conditions with buffer molecule concentrations\n",
    "phos_buffered.update_initial_conditions(buffer_ics)\n",
    "for met, ic in phos_buffered.initial_conditions.items():\n",
    "    print(\"{0}: {1} mM\".format(met, ic))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Null spaces: E.C. buffering\n",
    "With the addition of the buffer, stoichiometric matrix four columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-25T17:57:26.530804Z",
     "iopub.status.busy": "2021-01-25T17:57:26.530037Z",
     "iopub.status.idle": "2021-01-25T17:57:26.532860Z",
     "shell.execute_reply": "2021-01-25T17:57:26.533383Z"
    },
    "tags": [
     "remove_cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>distr</th>\n",
       "      <th>use</th>\n",
       "      <th>form</th>\n",
       "      <th>buff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>adp</th>\n",
       "      <td>-2</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amp</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>atp</th>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bp</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     distr  use  form  buff\n",
       "adp     -2    1    -1     1\n",
       "amp      1    0     0     0\n",
       "atp      1   -1     1    -1\n",
       "b        0    0     0    -1\n",
       "bp       0    0     0     1"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phos_buffered.S"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It has still has a one-dimensional null space, that represents and internal loop as the use and formation reactions are the exact opposites of each other. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-25T17:57:26.543690Z",
     "iopub.status.busy": "2021-01-25T17:57:26.542952Z",
     "iopub.status.idle": "2021-01-25T17:57:26.545807Z",
     "shell.execute_reply": "2021-01-25T17:57:26.546330Z"
    },
    "tags": [
     "remove_cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Path 1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>distr</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>use</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>form</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>buff</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Path 1\n",
       "distr       0\n",
       "use         1\n",
       "form        1\n",
       "buff        0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Obtain nullspace\n",
    "ns = nullspace(phos_buffered.S, rtol=1e-1)\n",
    "# Transpose and iterate through nullspace, \n",
    "# dividing by the smallest value in each row.\n",
    "ns = ns.T\n",
    "for i, row in enumerate(ns):\n",
    "    minval = np.min(abs(row[np.nonzero(row)]))\n",
    "    new_row = np.array(row/minval)\n",
    "    # Round to ensure the nullspace is composed of only integers\n",
    "    ns[i] = np.array([round(value) for value in new_row])\n",
    "\n",
    "# Ensure positive stoichiometric coefficients if all are negative\n",
    "for i, space in enumerate(ns):\n",
    "    ns[i] = np.negative(space) if all([num <= 0 for num in space]) else space\n",
    "\n",
    "# Revert transpose\n",
    "ns = ns.T\n",
    "# Create a pandas.DataFrame to represent the nullspace\n",
    "pd.DataFrame(ns, index=[rxn.id for rxn in phos_buffered.reactions],\n",
    "             columns=[\"Path 1\"], dtype=np.int64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The left null space is  two-dimensional.  It represents conservation of the nucleotide and the buffer molecule. Neither AxP or B is produced or destroyed in the model;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-25T17:57:26.558304Z",
     "iopub.status.busy": "2021-01-25T17:57:26.557445Z",
     "iopub.status.idle": "2021-01-25T17:57:26.560552Z",
     "shell.execute_reply": "2021-01-25T17:57:26.561027Z"
    },
    "tags": [
     "remove_cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>adp</th>\n",
       "      <th>amp</th>\n",
       "      <th>atp</th>\n",
       "      <th>b</th>\n",
       "      <th>bp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Total AxP</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total B</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           adp  amp  atp  b  bp\n",
       "Total AxP    1    1    1  0   0\n",
       "Total B      0    0    0  1   1"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Obtain left nullspace\n",
    "lns = left_nullspace(phos_buffered.S, rtol=1e-1)\n",
    "# Iterate through left nullspace, \n",
    "# dividing by the smallest value in each row. \n",
    "for i, row in enumerate(lns):\n",
    "    minval = np.min(abs(row[np.nonzero(row)]))\n",
    "    new_row = np.array(row/minval)\n",
    "    # Round to ensure the left nullspace is composed of only integers\n",
    "    lns[i] = np.array([round(value) for value in new_row])\n",
    "\n",
    "# Ensure positive stoichiometric coefficients if all are negative\n",
    "for i, space in enumerate(lns):\n",
    "    lns[i] = np.negative(space) if all([num <= 0 for num in space]) else space\n",
    "\n",
    "# Create a pandas.DataFrame to represent the left nullspace\n",
    "pd.DataFrame(lns, index=[\"Total AxP\", \"Total B\"], \n",
    "             columns=phos_buffered.metabolites, dtype=np.int64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dynamic simulation: E.C. buffering\n",
    "The model is initially in steady state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-25T17:57:26.566196Z",
     "iopub.status.busy": "2021-01-25T17:57:26.565541Z",
     "iopub.status.idle": "2021-01-25T17:57:26.995920Z",
     "shell.execute_reply": "2021-01-25T17:57:26.996926Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[93mWARNING:\u001b[0m \u001b[93mNo compartments found in model. Therefore creating compartment 'compartment' for entire model.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully loaded MassModel 'Phosphate_Trafficking_Buffered' into RoadRunner.\n",
      "Getting time points\n",
      "Setting output selections\n",
      "Setting simulation values for 'Phosphate_Trafficking_Buffered'\n",
      "Simulating 'Phosphate_Trafficking_Buffered'\n",
      "Simulation for 'Phosphate_Trafficking_Buffered' successful\n",
      "Adding 'Phosphate_Trafficking_Buffered' simulation solutions to output\n",
      "Updating stored solutions\n"
     ]
    }
   ],
   "source": [
    "t0, tf = (0, 1e3)\n",
    "sim = Simulation(phos_buffered, verbose=True)\n",
    "conc_sol, flux_sol = sim.simulate(phos_buffered, time=(t0, tf), verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-25T17:57:27.014275Z",
     "iopub.status.busy": "2021-01-25T17:57:27.012753Z",
     "iopub.status.idle": "2021-01-25T17:57:28.399960Z",
     "shell.execute_reply": "2021-01-25T17:57:28.400717Z"
    },
    "tags": [
     "remove_cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=2, ncols=1, figsize=(8, 4),\n",
    "                         )\n",
    "(ax1, ax2) = axes.flatten()\n",
    "\n",
    "plot_time_profile(\n",
    "    conc_sol, ax=ax1, legend=\"right outside\", \n",
    "    plot_function=\"semilogx\", \n",
    "    xlabel=\"Time [min]\", ylabel=\"Concentrations [mM]\", \n",
    "    title=(\"Concentration Profile\", XL_FONT));\n",
    "\n",
    "plot_time_profile(\n",
    "    flux_sol, ax=ax2, legend=\"right outside\",\n",
    "    plot_function=\"semilogx\", \n",
    "    xlabel=\"Time [min]\", ylabel=\"Fluxes [mM/min]\", \n",
    "    title=(\"Flux Profile\", XL_FONT));\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can compare the flux dynamics of the buffered vs. unbuffered system.  The buffered system has a much longer response time.  Once again, we consider a simulation where we increase the ATP use rate by a 'multiplier' in this figure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-25T17:57:28.415118Z",
     "iopub.status.busy": "2021-01-25T17:57:28.414069Z",
     "iopub.status.idle": "2021-01-25T17:57:29.078746Z",
     "shell.execute_reply": "2021-01-25T17:57:29.079279Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[93mWARNING:\u001b[0m \u001b[93mNo compartments found in model. Therefore creating compartment 'compartment' for entire model.\u001b[0m\n",
      "\u001b[93mWARNING:\u001b[0m \u001b[93mNo compartments found in model. Therefore creating compartment 'compartment' for entire model.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_8_12, ax = plt.subplots(nrows=1, ncols=1, figsize=(8, 4),\n",
    "                       )\n",
    "\n",
    "buff_strs = [\"unbuffered\", \"buffered\"]\n",
    "linestyles = [\"--\", \"-\"]\n",
    "\n",
    "t0, tf = (0, 1e3)\n",
    "# Simulate both models with the disturbance\n",
    "for i, model in enumerate([phos_traffic, phos_buffered]):\n",
    "    sim = Simulation(model)\n",
    "    conc_sol, flux_sol = sim.simulate(\n",
    "        model, time=(t0, tf),\n",
    "        perturbations={\"kf_use\": \"kf_use * 1.5\"})\n",
    "\n",
    "    plot_time_profile(\n",
    "        flux_sol, observable=[\"use\", \"form\"], ax=ax, \n",
    "        legend=([\"use \" + buff_strs[i], \"form \" + buff_strs[i]],\n",
    "                \"right outside\"),\n",
    "        plot_function=\"semilogx\",\n",
    "        xlabel=\"Time [min]\", ylabel=\"Fluxes [mM/min]\", \n",
    "        color=[\"red\", \"blue\"], linestyle=linestyles[i])\n",
    "fig_8_12.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Figure 8.12:** The fluxes of ATP use and formation respond more slowly when the ATP buffer is present.\n",
    "\n",
    "The response of the adenosine phosphate system can be simulated in the presence of a buffer. We choose the parameters as $\\text{B}_{\\mathrm{tot}}=10\\ mM$, $K_{\\mathrm{buff}}=1$, and $k_{\\mathrm{buff}}=1000/min$ and all other conditions as in Figure 8.8. The results of the simulation are shown in Figure 8.13. The time response of the energy charge is shown, along with the buffer charge $\\text{BP}/\\text{B}_{\\mathrm{tot}}$. We see that the fast response in the energy charge is now slower as the initial reaction is buffered by release of the high energy bonds that are bound to the buffer. The overall change in the energy charge is the same: it goes from 0.86 to 0.78. The charge of the buffer drops from 0.80 to 0.73 at the same time. \n",
    "\n",
    "![Figure-8-13](../images/Ch8/Figure-8-13.png)\n",
    "\n",
    "**Figure 8.13:** Pictorial representation of the phosphate exchange among the adenosine phosphates and a buffering molecule. (a) The reaction schema. (b) A pictorial representation of the molecules, their charged states, and the definition of pooled variables*\n",
    "\n",
    "#### Pooling and interpretation: E.C. buffering\n",
    "\n",
    "A pictorial representation of the phosphate buffering is given in Figure 8.13. Here, a generalized definition of the overall phosphate charge is: \n",
    "\n",
    "$$\\begin{equation} \\text{overall charge} =  \\frac{\\text{overall occupancy}}{\\text{overall capacity}} = \\frac{2\\ \\text{ATP}+\\text{ADP}+\\text{BP}}{2\\ (\\text{ATP}+\\text{ADP}+\\text{AMP})+\\text{BP} + \\text{B}} \\tag{8.15} \\end{equation}$$\n",
    "\n",
    "This combined charge system can be represented similarly to the representation in Figure 8.10.  Figure 8.14 shows a stacking of the buffer and adenosine phosphate capacity versus their charge. The total capacity to carry high-energy bonds is now 14.2 mM. The overall charge is 0.82 (or 11.64 mM concentration of high-energy bonds) in the system before the perturbation. The increased load brings the overall charge down to 0.74. \n",
    "\n",
    "![Figure-8-14](../images/Ch8/Figure-8-14.png)\n",
    "\n",
    "**Figure 8.14:**  The representation of the energy and buffer charge versus the capacity (in mM on y-axis). The lumping of the two quantities into ‘overall’ quantities is illustrated. The case considered corresponds to the simulation in Figure 8.15.\n",
    "\n",
    "To understand this effect, we first define more pools:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-25T17:57:29.083464Z",
     "iopub.status.busy": "2021-01-25T17:57:29.082798Z",
     "iopub.status.idle": "2021-01-25T17:57:29.087096Z",
     "shell.execute_reply": "2021-01-25T17:57:29.086356Z"
    }
   },
   "outputs": [],
   "source": [
    "pools.update({\n",
    "    \"BC\": \"bp / (bp + b)\",\n",
    "    \"Overall_Charge\": \"(2*atp + adp + bp) / (2*(atp + adp + amp) + bp + b)\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and then plot the dynamic responses of the pools:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-25T17:57:29.126425Z",
     "iopub.status.busy": "2021-01-25T17:57:29.124651Z",
     "iopub.status.idle": "2021-01-25T17:57:29.673297Z",
     "shell.execute_reply": "2021-01-25T17:57:29.673778Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[93mWARNING:\u001b[0m \u001b[93mNo compartments found in model. Therefore creating compartment 'compartment' for entire model.\u001b[0m\n",
      "\u001b[93mWARNING:\u001b[0m \u001b[93mNo compartments found in model. Therefore creating compartment 'compartment' for entire model.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_8_15, axes = plt.subplots(nrows=2, ncols=1, figsize=(8, 6),)\n",
    "(ax1, ax2) = axes.flatten()\n",
    "legend_labels = [\"E.C. Unbuffered\", \"E.C. Buffered\"]\n",
    "for i, model in enumerate([phos_traffic, phos_buffered]):\n",
    "    sim = Simulation(model)\n",
    "    conc_sol, flux_sol = sim.simulate(\n",
    "        model, time=(t0, tf),\n",
    "        perturbations={\"kf_use\": \"kf_use * 1.5\"})\n",
    "\n",
    "    # Determine pools\n",
    "    for pool_id, equation_str in pools.items():\n",
    "        # Skip buffered charge for model with no buffer\n",
    "        if i == 0 and pool_id in [\"BC\", \"Overall_Charge\"]:\n",
    "            continue\n",
    "        conc_sol.make_aggregate_solution(\n",
    "            pool_id, equation=equation_str, update=True)\n",
    "\n",
    "    if i == 1:\n",
    "        # Plot the charge pools for the buffered solution\n",
    "        plot_time_profile(\n",
    "            conc_sol, observable=[\"EC\", \"BC\", \"Overall_Charge\"], ax=ax1,\n",
    "            legend=([\"E.C.\", \"B.C.\", \"Overall Charge\"], \"right outside\"),\n",
    "            xlabel=\"Time [min]\", ylabel=\"Charge\", \n",
    "            xlim=(t0, 1), ylim=(.7, .9),\n",
    "            title=(\"(a) Charge Pools of Buffered Model\", XL_FONT));\n",
    "\n",
    "    # Compare the buffered and unbuffered solutions\n",
    "    plot_time_profile(\n",
    "        conc_sol, observable=[\"EC\"], ax=ax2,\n",
    "        legend=(legend_labels[i], \"right outside\"),\n",
    "        xlabel=\"Time [min]\", ylabel=\"Charge\", \n",
    "        xlim=(t0, 1), ylim=(.7, .9),\n",
    "        title=(\"(b) E.C. Unbuffered Vs Buffered\", XL_FONT));\n",
    "fig_8_15.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Figure 8.15:** Dynamic responses for Eqs.(8.4 - 8.8) with the buffering effect (Eq. (8.10)). (a) The Atkinson's energy charge (Eq. (8.9)) the buffer charge (Eq. (8.14)), and the overall charge (Eq. (8.15) are shown as a function of time. (b) Comparison of the buffered and unbuffered energy charge. $B_{\\mathrm{tot}}=10 mM$, $K_{\\mathrm{buff}}=1$ and $k_{\\mathrm{buff}}=1000$. All other conditions are as in Figure 8.8; i.e., we simulate the response to a 'multiplier' increase in $k_{\\mathrm{use}}$. Note the slower response of the E.C. in panel (b) when the system is buffered.\n",
    "\n",
    "### Open system: long term adjustment of the capacity  \n",
    "#### Inputs and outputs:  \n",
    "Although the rates of formation and degradation of AMP are low, their effects can be significant. These fluxes will determine the total amount of the adenosine phosphates and thus their capacity to carry high energy bonds. The additional elementary rate laws needed to account for the rate of AMP formation and drain are: \n",
    "\n",
    "$$\\begin{equation} v_{\\mathrm{form,\\ AMP}} = b_{1}, \\ v_{\\mathrm{drain}} = k_{\\mathrm{drain}} * \\text{AMP} \\tag{8.16} \\end{equation}$$\n",
    "\n",
    "where $b_1$ is the net synthesis rate of AMP. The numerical values used are $b_{1}=0.03\\ mM/min$ and $k_{\\mathrm{drain}} = (0.03\\ mM/min)/(0.1\\ mM) = 0.3\\ mM/min$. \n",
    "\n",
    "#### Updating the model for long term capacity adjustment\n",
    "Define the AMP exchange reaction:\n",
    "\n",
    "![Figure-8-16](../images/Ch8/Figure-8-16.png)\n",
    "\n",
    "**Figure 8.16:** The trafficking of high-energy phosphate bonds with the buffer molecule and AMP exchange reactions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-25T17:57:29.681953Z",
     "iopub.status.busy": "2021-01-25T17:57:29.681248Z",
     "iopub.status.idle": "2021-01-25T17:57:29.717229Z",
     "shell.execute_reply": "2021-01-25T17:57:29.717766Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-amp*kf_drain + kf_b1\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "# Copy the model to create a new, yet identical model instance \n",
    "phos_open = phos_buffered.copy()\n",
    "phos_open.id += \"_Open\"\n",
    "\n",
    "# Get MassMetabolite amp assoicated with the new copied model\n",
    "amp = phos_open.metabolites.amp\n",
    "\n",
    "# Define AMP formation\n",
    "b1 = MassReaction(\"b1\", reversible=False)\n",
    "b1.add_metabolites({amp:1})\n",
    "b1.kf = 0.03\n",
    "\n",
    "# Define AMP drain\n",
    "drain = MassReaction(\"drain\", reversible=False)\n",
    "drain.add_metabolites({amp:-1})\n",
    "drain.kf = 0.3\n",
    "# Add reactions to the model\n",
    "phos_open.add_reactions([b1, drain])\n",
    "# Set custom rate for formation of AMP\n",
    "phos_open.add_custom_rate(b1, custom_rate=b1.kf_str)\n",
    "\n",
    "# Display the net rate for AMP synthesis and draining\n",
    "rate = strip_time(phos_open.rates[b1] - phos_open.rates[drain])\n",
    "print(rate)\n",
    "# Substitute values to check if steady state\n",
    "print(rate.subs({\n",
    "    sym.Symbol('amp'): amp.ic, # AMP concentration at steady state\n",
    "    sym.Symbol('kf_drain'): drain.kf, # forward rate constant for drain reaction\n",
    "    sym.Symbol('kf_b1'): b1.kf})) # Synthesis rate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the specified parameters and initial conditions, the system is in a steady state, i.e. no net exchange of AMP."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Null spaces:  long term capacity adjustment\n",
    "With the addition of the AMP exchanges, stoichiometric matrix six columns. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-25T17:57:29.726810Z",
     "iopub.status.busy": "2021-01-25T17:57:29.726090Z",
     "iopub.status.idle": "2021-01-25T17:57:29.729162Z",
     "shell.execute_reply": "2021-01-25T17:57:29.729618Z"
    },
    "tags": [
     "remove_cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>distr</th>\n",
       "      <th>use</th>\n",
       "      <th>form</th>\n",
       "      <th>buff</th>\n",
       "      <th>b1</th>\n",
       "      <th>drain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>adp</th>\n",
       "      <td>-2</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amp</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>atp</th>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bp</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     distr  use  form  buff  b1  drain\n",
       "adp     -2    1    -1     1   0      0\n",
       "amp      1    0     0     0   1     -1\n",
       "atp      1   -1     1    -1   0      0\n",
       "b        0    0     0    -1   0      0\n",
       "bp       0    0     0     1   0      0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phos_open.S"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It has still has a two-dimensional null space, that 1) represents and internal loop as the use and formation reactions are the exact opposites of each other, as before, and 2) an exchange pathways of AMP coming into the system and leaving the system. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-25T17:57:29.740553Z",
     "iopub.status.busy": "2021-01-25T17:57:29.739718Z",
     "iopub.status.idle": "2021-01-25T17:57:29.742912Z",
     "shell.execute_reply": "2021-01-25T17:57:29.743362Z"
    },
    "tags": [
     "remove_cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Path 1</th>\n",
       "      <th>Path 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>distr</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>use</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>form</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>buff</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drain</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Path 1  Path 2\n",
       "distr       0       0\n",
       "use         1       0\n",
       "form        1       0\n",
       "buff        0       0\n",
       "b1          0       1\n",
       "drain       0       1"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Obtain nullspace\n",
    "ns = nullspace(phos_open.S, rtol=1e-1)\n",
    "# Transpose and iterate through nullspace, \n",
    "# dividing by the smallest value in each row.\n",
    "ns = ns.T\n",
    "for i, row in enumerate(ns):\n",
    "    minval = np.min(abs(row[np.nonzero(row)]))\n",
    "    new_row = np.array(row/minval)\n",
    "    # Round to ensure the nullspace is composed of only integers\n",
    "    ns[i] = np.array([round(value) for value in new_row])\n",
    "\n",
    "# Ensure positive stoichiometric coefficients if all are negative\n",
    "for i, space in enumerate(ns):\n",
    "    ns[i] = np.negative(space) if all([num <= 0 for num in space]) else space\n",
    "\n",
    "# Revert transpose\n",
    "ns = ns.T\n",
    "# Create a pandas.DataFrame to represent the nullspace\n",
    "pd.DataFrame(ns, index=[r.id for r in phos_open.reactions],\n",
    "             columns=[\"Path 1\", \"Path 2\"], dtype=np.int64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The left null space becomes one-dimensional.  The total amount of A is no longer conserved as AMP can now enter or leave the system, i.e. pathway 2) can have a net flux.  The buffer molecule, B, on the other hand is always contained within the system"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-25T17:57:29.754772Z",
     "iopub.status.busy": "2021-01-25T17:57:29.753893Z",
     "iopub.status.idle": "2021-01-25T17:57:29.757182Z",
     "shell.execute_reply": "2021-01-25T17:57:29.757709Z"
    },
    "tags": [
     "remove_cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>adp</th>\n",
       "      <th>amp</th>\n",
       "      <th>atp</th>\n",
       "      <th>b</th>\n",
       "      <th>bp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Total B</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         adp  amp  atp  b  bp\n",
       "Total B    0    0    0  1   1"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Obtain left nullspace\n",
    "lns = left_nullspace(phos_open.S, rtol=1e-1)\n",
    "# Iterate through left nullspace, \n",
    "# dividing by the smallest value in each row. \n",
    "for i, row in enumerate(lns):\n",
    "    minval = np.min(abs(row[np.nonzero(row)]))\n",
    "    new_row = np.array(row/minval)\n",
    "    # Round to ensure the left nullspace is composed of only integers\n",
    "    lns[i] = np.array([round(value) for value in new_row])\n",
    "\n",
    "# Ensure positive stoichiometric coefficients if all are negative\n",
    "for i, space in enumerate(lns):\n",
    "    lns[i] = np.negative(space) if all([num <= 0 for num in space]) else space\n",
    "\n",
    "# Create a pandas.DataFrame to represent the left nullspace\n",
    "pd.DataFrame(lns, index=[\"Total B\"], \n",
    "             columns=phos_open.metabolites, dtype=np.int64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dynamic simulations: long term capacity adjustment\n",
    "Initially, the open system is in a steady-state. Once again, we consider a simulation where we increase the ATP use rate by a 'multiplier'. This system has a bi-phasic response for the values of the kinetic constants. We can start the system in a steady state at $t=0^-$ and simulate the response for increasing the ATP load by shifting the value of $k_{\\mathrm{use}}^\\rightarrow$ by a 'multiplier' at $t=0$, as before. The initial rapid response is similar to what is shown in Figure 8.8a, where the concentration of ATP drops in response to the load and the concentrations of ADP and AMP rise. This initial response is followed by a much slower response where all three concentrations drop. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-25T17:57:29.773439Z",
     "iopub.status.busy": "2021-01-25T17:57:29.772378Z",
     "iopub.status.idle": "2021-01-25T17:57:30.250586Z",
     "shell.execute_reply": "2021-01-25T17:57:30.251397Z"
    },
    "tags": [
     "remove_cell"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[93mWARNING:\u001b[0m \u001b[93mNo compartments found in model. Therefore creating compartment 'compartment' for entire model.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully loaded MassModel 'Phosphate_Trafficking_Buffered_Open' into RoadRunner.\n"
     ]
    }
   ],
   "source": [
    "t0, tf = (0, 1e3)\n",
    "sim = Simulation(phos_open, verbose=True)\n",
    "sim.find_steady_state(models=phos_open, strategy=\"simulate\")\n",
    "conc_sol, flux_sol = sim.simulate(\n",
    "    phos_open, time=(t0, tf), \n",
    "    perturbations={\"kf_use\": \"kf_use * 1.5\"})\n",
    "pools.update({\"Capacity\": \"2*(atp + adp + amp)\"})\n",
    "# Determine pools\n",
    "for pool_id, equation_str in pools.items():\n",
    "    # Skip buffered charge for model with no buffer\n",
    "    conc_sol.make_aggregate_solution(\n",
    "        pool_id, equation=equation_str, update=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-25T17:57:30.273234Z",
     "iopub.status.busy": "2021-01-25T17:57:30.272027Z",
     "iopub.status.idle": "2021-01-25T17:57:31.375257Z",
     "shell.execute_reply": "2021-01-25T17:57:31.383565Z"
    },
    "tags": [
     "remove_cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=2, ncols=1, figsize=(8, 6),\n",
    "                         )\n",
    "(ax1, ax2) = axes.flatten()\n",
    "plot_time_profile(\n",
    "    conc_sol, ax=ax1, observable=phos_open.metabolites,\n",
    "    legend=\"right outside\",\n",
    "    plot_function=\"semilogx\", \n",
    "    xlabel=\"Time [min]\", ylabel=\"Concentrations [mM]\", \n",
    "    title=(\"Concentration Profile\", XL_FONT));\n",
    "\n",
    "plot_time_profile(\n",
    "    flux_sol, ax=ax2, observable=phos_open.reactions,\n",
    "    legend=\"right outside\",\n",
    "    plot_function=\"semilogx\", \n",
    "    xlabel=\"Time [min]\", ylabel=\"Fluxes [mM/min]\", \n",
    "    title=(\"Flux Profile\", XL_FONT));\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Interpretation of the bi-phasic response\n",
    "\n",
    "This bi-phasic response can be examined further by looking at dynamic phase portraits of key fluxes (Figure 8.17) and key pools (Figure 8.18). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-25T17:57:31.419911Z",
     "iopub.status.busy": "2021-01-25T17:57:31.407224Z",
     "iopub.status.idle": "2021-01-25T17:57:31.912100Z",
     "shell.execute_reply": "2021-01-25T17:57:31.912558Z"
    },
    "tags": [
     "remove_cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_8_17, axes = plt.subplots(nrows=1, ncols=2, figsize=(10, 5))\n",
    "(ax1, ax2) = axes.flatten()\n",
    "\n",
    "label = \"{0} [mM/min]\"\n",
    "plot_phase_portrait(\n",
    "    flux_sol, x=\"use\", y=\"form\", ax=ax1,\n",
    "    xlim=(4, 21), ylim=(4, 21), \n",
    "    xlabel=label.format(\"use\"), ylabel=label.format(\"form\"), \n",
    "    title=(\"(a) Phase Portrait of ATP use vs. formation\", XL_FONT),\n",
    "    annotate_time_points=[0, 1e-1, 1e0, 25, 150],\n",
    "    annotate_time_points_color=[\"red\"],\n",
    "    annotate_time_points_labels=True);\n",
    "\n",
    "# Annotate plot\n",
    "line_data = [i for i in range(0, 22)]\n",
    "ax1.plot(line_data, line_data, ls=\"--\", color=\"black\");\n",
    "ax1.annotate(\"use < form\", xy=(6, 15));\n",
    "ax1.annotate(\"use > form\", xy=(15, 6));\n",
    "ax1.annotate(\"Steady-state line:\\n     use=form\", xy=(15, 19));\n",
    "ax1.annotate(\"initial perturbation\", xy=(9.5, 9), xycoords=\"data\");\n",
    "ax1.annotate(\"\", xy=(flux_sol[\"use\"][0], flux_sol[\"form\"][0]),\n",
    "             xytext=(10, 10), textcoords=\"data\",\n",
    "             arrowprops=dict(arrowstyle=\"->\",connectionstyle=\"arc3\"));\n",
    "\n",
    "plot_phase_portrait(\n",
    "    flux_sol, x=\"use\", y=\"drain\", ax=ax2,\n",
    "    xlim=(0, 21), ylim=(0, 0.1), \n",
    "    xlabel=label.format(\"use\"), ylabel=label.format(\"drain\"),\n",
    "    title=(\"(b) Phase Portrait of use vs. drain\", XL_FONT),\n",
    "    annotate_time_points=[0, 1e-1, 1e0, 25, 150],\n",
    "    annotate_time_points_color=[\"red\"],\n",
    "    annotate_time_points_labels=True);\n",
    "\n",
    "# Annotate plot\n",
    "ax2.plot(line_data, [0.03]*22, ls=\"--\", color=\"black\");\n",
    "ax2.annotate(\"net AMP\\ngain\", xy=(1.5, 0.02));\n",
    "ax2.annotate(\"net AMP\\ndrain\", xy=(1.5, 0.04));\n",
    "fig_8_17.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Figure 8.17:** Dynamic phase portraits of fluxes for the simulation of the adenosine phosphate system with formation and drain of AMP (Eq. (8.16)). (a) the ATP use $(v_{\\mathrm{use}})$ versus the ATP formation rate $(v_{\\mathrm{form}})$. (b) the ATP use $(v_{\\mathrm{use}})$ versus the AMP drain $(v_{\\mathrm{drain}})$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-25T17:57:31.941247Z",
     "iopub.status.busy": "2021-01-25T17:57:31.940528Z",
     "iopub.status.idle": "2021-01-25T17:57:36.354130Z",
     "shell.execute_reply": "2021-01-25T17:57:36.354600Z"
    },
    "tags": [
     "remove_cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_8_18 = plt.figure(figsize=(12, 4))\n",
    "gs = fig_8_18.add_gridspec(nrows=1, ncols=2, width_ratios=[1, 1.5])\n",
    "\n",
    "ax1 = fig_8_18.add_subplot(gs[0, 0])\n",
    "ax2 = fig_8_18.add_subplot(gs[0, 1])\n",
    "\n",
    "plot_phase_portrait(\n",
    "    conc_sol, x=\"Occupancy\", y=\"Capacity\", ax=ax1,\n",
    "    time_vector=np.linspace(t0, 10, int(1e6)),\n",
    "    xlim=(2.7, 4.3), ylim=(2.7, 4.3),\n",
    "    xlabel=\"Occupancy\", ylabel=\"Capacity\",\n",
    "    title=(\"(a) Occupancy vs. Capacity\", XL_FONT),\n",
    "    annotate_time_points=\"endpoints\",\n",
    "    annotate_time_points_labels=True);\n",
    "\n",
    "plot_time_profile(\n",
    "    conc_sol, observable=[\"EC\", \"BC\", \"Overall_Charge\"], ax=ax2,\n",
    "    legend=([\"E.C.\", \"B.C.\", \"Overall Charge\"], \"right outside\"),\n",
    "    time_vector=np.linspace(t0, 10, int(1e6)),\n",
    "    xlabel=\"Time [min]\", ylabel=\"Charge\",\n",
    "    xlim=(t0, 10), ylim=(0.65, 1),\n",
    "    title=(\"(b) Charge Responses\", XL_FONT));\n",
    "fig_8_18.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Figure 8.18:** The Energy Charge response. (a) Dynamic phase portrait of 2ATP+ADP versus 2(ATP+ADP+AMP). (b) The response of E.C., B.C., and overall charge.\n",
    "\n",
    "* First, we examine how the system balances the use of ATP $(v_{\\mathrm{use}})$ with its rate of formation $(v_{\\mathrm{form}})$, see Figure 8.17. At $t=0$ the system is at rest at $v_{\\mathrm{use}}=v_{\\mathrm{form}}=10.0\\ mM/min$. Then the system is perturbed by moving the ATP drain, $v_{\\mathrm{use}}$, to 15.0 mM/min, as before. The initial response is to increase the formation rate of ATP to about 13 mM/min with the simultaneous drop in the use rate to about the same number, due to a net drop in the concentration of ATP during this period. The rate of ATP use and formation is approximately the same at this point in time. Then, during the slower response time, the use and formation rates of ATP are similar and the system moves along the 45 degree line to a new steady state point at 6.67 mM/min. \n",
    "\n",
    "\n",
    "* The slow dynamics are associated with the inventory of the adenosine phosphates (ATP + ADP + AMP). The AMP drain can be graphed versus the ATP use, see Figure 8.17b. Initially, the AMP drain increases rapidly as the increased ATP use leads to ADP buildup that gets converted into AMP by adenylate kinase $(v_{\\mathrm{distr}})$. The AMP drain then drops and sets at the same rate to balance the formation rate, set at 0.03 mM/min. \n",
    "\n",
    "\n",
    "* We can graph the occupancy against the capacity (Figure 8.18a). During the initial response, the occupancy moves while the capacity is a constant. Then, during the slower phase, the two move at a constant ratio. This gives a bi-phasic response of the energy charge (Figure 8.18b). In about a minute, the energy charge changes from 0.86 to about 0.77 and then stays a constant. The energy charge is roughly a constant even though all the other concentrations are changing. \n",
    "\n",
    "\n",
    "This feature of keeping the energy charge a constant while the capacity is changing has a role in a variety of physiological responses, from blood storage to the ischemic response in the heart. Note that this property is a stoichiometric one; no regulation is required to produce this effect. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Charging Substrates and Recovery of High-Energy Bonds\n",
    "### Reaction mechanism: \n",
    "As discussed in Section 8.2, most catabolic pathways generate energy (and other metabolic resources) in the form of activated (or charged) carrier molecules. Before energy can be extracted from a compound, it is typically activated by the use of metabolic resources (a biological equivalent of \"it takes money to make money\"). This basic structure shown in Figure 2.5 is redrawn in Figure 8.19a where one ATP molecule is used to 'charge' a substrate $(x_1)$ with one high-energy bond to form an intermediate $(x_2)$. This intermediate is then degraded through a process wherein two ATP molecules are synthesized and an inorganic phosphate is incorporated. The net gain of ATP is 1 for every $(x_2)$ metabolized, and this ATP molecule can then be used to drive a process $v_{\\mathrm{load}}$ that uses an ATP molecule. The trafficking of high-energy phosphate bonds is shown pictorially in Figure 8.19b. \n",
    "\n",
    "![Figure-8-19](../images/Ch8/Figure-8-19.png)\n",
    "\n",
    "**Figure 8.19:** Coupling of the adenosine phosphates with a skeleton metabolic pathway. (a) The reaction map. (b) A pictorial view of the molecules emphasizing the exchange of the high-energy phosphate group (solid circle). The blue square is AMP. The rate laws used are: $b_1 = 0.03\\ mM/min.$; $b_2 = 5\\ mM/min.$; $k_{\\mathrm{drain}}=b_1/0.1$; $k_{\\mathrm{load}}=5/1.6$; $k_1=5/0.4$. The flux of $b_2$ was set to 5 mM/min, as the ATP production rate is double that number, thus the steady state value for ATP production is 10 mM/min, to match what is discussed in section 8.3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-25T17:57:36.498981Z",
     "iopub.status.busy": "2021-01-25T17:57:36.497266Z",
     "iopub.status.idle": "2021-01-25T17:57:36.540176Z",
     "shell.execute_reply": "2021-01-25T17:57:36.540865Z"
    }
   },
   "outputs": [],
   "source": [
    "# Create model\n",
    "phos_recovery = MassModel(\"Phosphate_Recovery\", array_type=\"dense\",\n",
    "                          dtype=np.int64)\n",
    "# Define metabolites\n",
    "atp = MassMetabolite(\"atp\")\n",
    "adp = MassMetabolite(\"adp\")\n",
    "amp = MassMetabolite(\"amp\")\n",
    "phos = MassMetabolite(\"phos\")\n",
    "x1 = MassMetabolite(\"x1\")\n",
    "x2 = MassMetabolite(\"x2\")\n",
    "x3 = MassMetabolite(\"x3\")\n",
    "# Define reactions\n",
    "b1 = MassReaction(\"b1\", reversible=False)\n",
    "b1.add_metabolites({amp:1})\n",
    "\n",
    "distr = MassReaction(\"distr\")\n",
    "distr.add_metabolites({adp: -2, amp: 1, atp:1})\n",
    "\n",
    "load = MassReaction(\"load\", reversible=False)\n",
    "load.add_metabolites({atp: -1, adp: 1, phos: 1})\n",
    "\n",
    "drain = MassReaction(\"drain\", reversible=False)\n",
    "drain.add_metabolites({amp:-1})\n",
    "\n",
    "b2 = MassReaction(\"b2\", reversible=False)\n",
    "b2.add_metabolites({x1: 1})\n",
    "\n",
    "v1 = MassReaction(\"v1\", reversible=False)\n",
    "v1.add_metabolites({atp: -1, x1: -1, adp: 1, x2: 1})\n",
    "\n",
    "v2 = MassReaction(\"v2\", reversible=False)\n",
    "v2.add_metabolites({adp: -2, phos: -1, x2: -1, atp: 2, x3: 1})\n",
    "\n",
    "v3 = MassReaction(\"v3\", reversible=False)\n",
    "v3.add_metabolites({x3: -1})\n",
    "\n",
    "# Add reactions to model\n",
    "phos_recovery.add_reactions([b1, distr, load, drain, b2, v1, v2, v3])\n",
    "\n",
    "# Define initial conditions and parameters\n",
    "atp.ic = 1.6\n",
    "adp.ic = 0.4\n",
    "amp.ic = 0.1\n",
    "phos.ic = 2.5\n",
    "x1.ic = 1\n",
    "x2.ic = 1\n",
    "x3.ic = 1\n",
    "\n",
    "b1.kf = 0.03\n",
    "distr.kf = 1000\n",
    "distr.Keq = 1\n",
    "load.kf = 5/1.6\n",
    "drain.kf = 0.3\n",
    "b2.kf = 5\n",
    "v1.kf = 5/1.6\n",
    "v2.kf = 5/0.4\n",
    "v3.kf = 5\n",
    "\n",
    "# Set custom rate for source reactions \n",
    "phos_recovery.add_custom_rate(b1, custom_rate=b1.kf_str)\n",
    "phos_recovery.add_custom_rate(b2, custom_rate=b2.kf_str)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The dynamic mass balances:  \n",
    "The dynamic mass balance equations that describe this process are: \n",
    "\n",
    "$$\\begin{align} \\frac{dx_1}{dt} &= b_2 - v_1 &\\tag{8.17a} \\\\ \\frac{dx_2}{dt} &= v_1 - v_2 &\\tag{8.17b} \\\\ \\frac{d\\text{ATP}}{dt} &= -(v_1 + v_{\\mathrm{load}}) + 2v_2 + v_{\\mathrm{distr}} &\\tag{8.17c} \\\\ \\frac{d\\text{ADP}}{dt} &= (v_1 + v_{\\mathrm{load}}) - 2v_2 - 2v_{\\mathrm{distr}} &\\tag{8.17d} \\\\ \\frac{d\\text{AMP}}{dt} &= b_1 - v_{\\mathrm{drain}} + v_{\\mathrm{distr}} &\\tag{8.17e} \\\\ \\end{align}$$\n",
    "\n",
    "To integrate the reaction schema in Figure 8.13a with this skeleton pathway, we have replaced the use rate of ATP $(v_{\\mathrm{use}})$ with $v_1 + v_{\\mathrm{load}}$ and the formation rate of ATP $(v_{\\mathrm{form}})$ with $2v_2$. \n",
    "\n",
    "#### Dynamic simulation:  \n",
    "The flow of substrate into the cell, given by $b_2$, will be set to 5 mM/min in the simulation to follow to set the gross ATP production at 10 mM/min. The response of this system can be simulated to a change in the ATP load parameter, as in previous examples. The difference from the previous examples here is that the net ATP production rate is 5 mM/min. \n",
    "\n",
    "The time response of the concentrations and fluxes are shown in Figure 8.20, the flux phase portraits in Figure 8.21, and the pools and ratios in Figure 8.22."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-25T17:57:36.569035Z",
     "iopub.status.busy": "2021-01-25T17:57:36.568280Z",
     "iopub.status.idle": "2021-01-25T17:57:37.357077Z",
     "shell.execute_reply": "2021-01-25T17:57:37.357665Z"
    },
    "scrolled": false,
    "tags": [
     "remove_cell"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[93mWARNING:\u001b[0m \u001b[93mNo compartments found in model. Therefore creating compartment 'compartment' for entire model.\u001b[0m\n",
      "\u001b[93mWARNING:\u001b[0m \u001b[93mNo compartments found in model. Therefore creating compartment 'compartment' for entire model.\u001b[0m\n",
      "\u001b[91mERROR:\u001b[0m \u001b[91mSomething unexpected occurred and the model could not be loaded into the current RoadRunner instance. Therefore initializing a new RoadRunner instance for the Simulation.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully loaded MassModel 'Phosphate_Recovery' into RoadRunner.\n"
     ]
    }
   ],
   "source": [
    "t0, tf = (0, 100)\n",
    "sim = Simulation(phos_recovery, verbose=True)\n",
    "conc_sol, flux_sol = sim.simulate(\n",
    "    phos_recovery, time=(t0, tf), \n",
    "    perturbations={\"kf_load\": \"kf_load * 1.5\"},\n",
    "    interpolate=True)\n",
    "\n",
    "pools = {\"Occupancy\": \"adp + 2*atp\", \n",
    "         \"Capacity\": \"2*(atp + adp + amp)\",\n",
    "         \"EC\": \"(2*atp + adp) / (2*(atp + adp + amp))\"}\n",
    "\n",
    "for pool_id, equation_str in pools.items():\n",
    "    conc_sol.make_aggregate_solution(\n",
    "        pool_id, equation=equation_str, update=True)\n",
    "\n",
    "netfluxes = {\n",
    "    \"load_total\": \"v1 + load\", \n",
    "    \"generation\": \"2*v2\", \n",
    "    \"drain_total\": \"drain\"}\n",
    "for flux_id, equation_str in netfluxes.items():\n",
    "    # Skip buffered charge for model with no buffer\n",
    "    flux_sol.make_aggregate_solution(\n",
    "        flux_id, equation=equation_str, update=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-25T17:57:37.426250Z",
     "iopub.status.busy": "2021-01-25T17:57:37.413539Z",
     "iopub.status.idle": "2021-01-25T17:57:38.191855Z",
     "shell.execute_reply": "2021-01-25T17:57:38.192377Z"
    },
    "tags": [
     "remove_cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 800x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_8_20, axes = plt.subplots(nrows=3, ncols=1, figsize=(8, 6))\n",
    "(ax1, ax2, ax3) = axes.flatten()\n",
    "\n",
    "plot_time_profile(\n",
    "    conc_sol, observable=phos_recovery.metabolites,\n",
    "    ax=ax1, legend=\"right outside\", \n",
    "    xlim=(t0, 25), ylim=(0, 2.0),\n",
    "    xlabel=\"Time [min]\", ylabel=\"Concentration [mM]\",\n",
    "    title=(\"(a) Concentrations\", XL_FONT));\n",
    "\n",
    "plot_time_profile(\n",
    "    flux_sol, observable=[\"v1\", \"v2\", \"load\"], \n",
    "    ax=ax2, legend=\"right outside\", \n",
    "    xlim=(t0, 25), ylim=(4, 8),\n",
    "    xlabel=\"Time [min]\", ylabel=\"Fluxes [mM/min]\",\n",
    "    title=(\"(b) High-Flux Reactions\", XL_FONT));\n",
    "\n",
    "plot_time_profile(\n",
    "    flux_sol, observable=[\"distr\", \"drain\"], \n",
    "    ax=ax3, legend=\"right outside\", \n",
    "    xlim=(t0, 25), ylim=(0, .4),\n",
    "    xlabel=\"Time [min]\", ylabel=\"Fluxes [mM/min]\",\n",
    "    title=(\"(c) Low-Flux Reactions\", XL_FONT));\n",
    "fig_8_20.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Figure 8.20:** The response of the systems shown in Figure 8.19 to a 50% increase in the ATP load rate constant. (a) Dynamic response of the concentrations on a fast and slow time scale. (b) Dynamic response of the main fluxes on a fast and slow time scale. (c) Dynamic response of the AMP determining fluxes on a fast and slow time scale. Parameter values are the same as in Figure 8.19."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-25T17:57:38.227594Z",
     "iopub.status.busy": "2021-01-25T17:57:38.224774Z",
     "iopub.status.idle": "2021-01-25T17:57:38.726339Z",
     "shell.execute_reply": "2021-01-25T17:57:38.726755Z"
    },
    "tags": [
     "remove_cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_8_21, axes = plt.subplots(nrows=1, ncols=2, figsize=(10, 5))\n",
    "(ax1, ax2) = axes.flatten()\n",
    "\n",
    "plot_phase_portrait(\n",
    "    flux_sol, x=\"load_total\", y=\"generation\", ax=ax1, \n",
    "    xlabel=\"ATP load total\", ylabel=\"ATP Synthesis\", \n",
    "    xlim=(9, 13.5), ylim=(9, 13.5), \n",
    "    title=(\"(a) ATP Load vs. Synthesis\", XL_FONT),\n",
    "    annotate_time_points=\"endpoints\",\n",
    "    annotate_time_points_labels=True);\n",
    "\n",
    "# Annotate plot\n",
    "line_data = [i for i in range(8, 15)]\n",
    "ax1.plot(line_data, line_data, ls=\"--\", color=\"black\");\n",
    "ax1.annotate(\n",
    "    \"\", xy=(flux_sol[\"load_total\"](0), flux_sol[\"generation\"](0)),\n",
    "    xytext=(10, 10), textcoords=\"data\",\n",
    "    arrowprops=dict(arrowstyle=\"->\",connectionstyle=\"arc3\"));\n",
    "ax1.annotate(\"initial perturbation\", xy=(\n",
    "    flux_sol[\"load_total\"](0) - 1.7, \n",
    "    flux_sol[\"generation\"](0) - 0.2));\n",
    "\n",
    "plot_phase_portrait(\n",
    "    flux_sol, x=\"load_total\", y=\"drain_total\", ax=ax2, \n",
    "    xlabel=\"ATP load total\", ylabel=\"AMP drain\", \n",
    "    xlim=(8, 13.5), ylim=(0, 0.125),\n",
    "    title=(\"(a) ATP Load vs. Drain\", XL_FONT),\n",
    "    annotate_time_points=\"endpoints\",\n",
    "    annotate_time_points_labels=True);\n",
    "\n",
    "ax2.plot(line_data, [0.03] * 7, ls=\"--\", color=\"black\");\n",
    "fig_8_21.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Figure 8.21:** The response of the system shown in Figure 8.19 to a change in the ATP load rate constant. (a) ATP load versus ATP synthesis rate. (b) ATP load versus AMP drainage rate. You can compare this response to Figure 8.17."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-25T17:57:38.756031Z",
     "iopub.status.busy": "2021-01-25T17:57:38.755300Z",
     "iopub.status.idle": "2021-01-25T17:57:39.050127Z",
     "shell.execute_reply": "2021-01-25T17:57:39.050572Z"
    },
    "tags": [
     "remove_cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_8_22 = plt.figure(figsize=(10, 4))\n",
    "gs = fig_8_22.add_gridspec(nrows=1, ncols=2, width_ratios=[1, 1.5])\n",
    "\n",
    "ax1 = fig_8_22.add_subplot(gs[0, 0])\n",
    "ax2 = fig_8_22.add_subplot(gs[0, 1])\n",
    "\n",
    "plot_phase_portrait(\n",
    "    conc_sol, x=\"Occupancy\", y=\"Capacity\", ax=ax1,\n",
    "    xlim=(2.3, 4.3), ylim=(2.3, 4.3),\n",
    "    xlabel=\"Occupancy\", ylabel=\"Capacity\",\n",
    "    title=(\"(a) Occupancy vs. Capacity\", XL_FONT),\n",
    "    annotate_time_points=[t0, 1e0, 50],\n",
    "    annotate_time_points_color=[\"red\"],\n",
    "    annotate_time_points_labels=True);\n",
    "# Annotate plot\n",
    "ax1.annotate(\" fast\\nmotion\\n\", xy=(conc_sol[\"Occupancy\"](0.3) - .25, \n",
    "                                   conc_sol[\"Capacity\"](0.3) - .35))\n",
    "\n",
    "plot_time_profile(\n",
    "    conc_sol, observable=[\"EC\"], ax=ax2, legend=\"best\",\n",
    "    xlim=(t0, 50), ylim=(0.65, 1),\n",
    "    xlabel=\"Time [min]\", ylabel=\"Energy Charge\",\n",
    "    title=(\"(b) Stoichiometric Disturbance Rejection Property\", XL_FONT));\n",
    "fig_8_22.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Figure 8.22:** The response of the system shown in Figure 8.19 to a change in the ATP load rate constant. (a) Dynamic phase portrait of the pools 2ATP+ADP versus 2(ATP+ADP+AMP).  (b) Energy charge ratio as a function of time. You can compare this response to Figure 8.18.\n",
    "\n",
    "### Interpretation:  \n",
    "We can make the following observations from this dynamic response: \n",
    "\n",
    "* The concentrations move on two principal time scales (Figure 8.20): a fast time scale that is about three to five minutes, and a slower time scale that is about 50 min. ATP and $x_1$ move primarily on the fast time scale, whereas ADP, AMP, and $x_2$ move on the slower time scale. You can see this clearly by changing time in Figure 8.20.\n",
    "\n",
    "* Initially $v_{\\mathrm{load}}$ increases sharply, and $v_2$ increases and $v_1$ decreases to meet the increased load. The three high flux reactions $v_1$, $v_2$, and $v_{\\mathrm{load}}$ restabilize at about 5 mM/min after about a three to five minute time frame, after which they are closely, but not fully, balanced (Figure 8.20). \n",
    "\n",
    "* The dynamic phase portrait, Figure 8.21a, shows that the overall ATP use $(v_1 + v_{\\mathrm{load}})$ quickly moves to about 12.5 mM/min while the production rate $(2v_2)$ is about 10 mM/min. Following this initial response, the ATP use drops and the ATP synthesis rate increases to move towards the 45 degree line. The 45 degree line is not reached. After 0.1 min, $v_2$ starts to drop and the system moves somewhat parallel to the 45 degree line until 1.5 min have passed. At this time the ATP concentration has dropped to about 1.06 mM, which makes the ATP use and production rate approximately balanced. Following this point, both the use and production rate increase slowly and return the system back to the initial point where both have a value of 10 mM/min. Since the input rate of $x_1$ is a constant, the system has to return to the initial state. \n",
    "\n",
    "* AMP initially increases leading to a net drain of AMP from the system. This drain unfolds on a long time scale leading to a net flux through the adenylate kinase that decays on the slower time scale. The effects of AMP drainage can be seen in the flux phase portrait in Figure 8.21b. Initially the AMP drain increases as the ATP usage drops close to its eventual steady state. Then the vertical motion in the phase portrait shows that there is a slower motion in which the ATP usage does not change much but the AMP drainage rate drops to match its input rate at 0.03 mM/hr. \n",
    "\n",
    "* The dynamic response of the energy charge (Figure 8.22b) shows that it drops on the faster time scale from an initial value of 0.86 to reach a minimum of about 0.67 at about 1.5 min. This initial response results from the increase in the ATP load parameter of 50%. After this initial response, the energy charge increases on the slower time scale to an eventual value of about 0.82. \n",
    "\n",
    "* Notice that this secondary response is not a result of a regulatory mechanism, but is a property that is built into the stoichiometric structure and the values of the rate constants that lead to the time scale separation. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary   \n",
    "\n",
    "* Most biochemical reactions are bilinear. Six of the seven categories of enzymes catalyze bilinear reactions. \n",
    "\n",
    "* The bilinear properties of biochemical reactions lead to complex patterns of exchange of key chemical moieties and properties. Many such simultaneous exchange processes lead to a 'tangle of cycles' in biochemical reaction networks. \n",
    "\n",
    "* Skeleton (or scaffold) dynamic models of biochemical processes can be carried out using dynamic mass balances based on elementary reaction representations and mass action kinetics. \n",
    "\n",
    "* Complex kinetic models are built in a bottom-up fashion, adding more details in a step-wise fashion, making sure that every new feature is consistently integrated. This chapter demonstrated a four-step analysis of the ATP cofactor sub-network and then its integration to a skeleton ATP generating pathway. \n",
    "\n",
    "* Once dynamic network models are formulated, the perturbations to which we simulate their responses are in fluxes, typically the exchange and demand fluxes. \n",
    "\n",
    "* A recurring theme is the formation of pools and the state of those pools in terms of how their total concentration is distributed among its constituent members. \n",
    "\n",
    "* Some dynamic properties are a result of the stoichiometric structure and do not result from intricate regulatory mechanisms or complex kinetic expressions. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$\\tiny{\\text{© B. Ø. Palsson 2011;}\\ \\text{This publication is in copyright.}\\\\ \\text{Subject to statutory exception and to the provisions of relevant collective licensing agreements,}\\\\ \\text{no reproduction of any part may take place without the written permission of Cambridge University Press.}}$"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}