enzinvivo.ipynb

{
 "cells": [
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   "attachments": {},
   "cell_type": "markdown",
   "id": "9608990b-2ac8-478a-9256-21ce22de7488",
   "metadata": {},
   "source": [
    "# Analysis of *in vivo* enzymatic data"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "ddd82e8e-faa3-4273-bb4e-078dfd19db16",
   "metadata": {},
   "source": [
    "This notebook is part of Supporting information of the following publication:\n",
    "> Combining systems and synthetic biology for in vivo enzymology. Castaño-Cerezo et al., 2024, bioRxiv preprint, doi: 10.1101/2024.02.02.578620\n",
    "\n",
    "### Content\n",
    "\n",
    "- Processing functions\n",
    "- Simulation results\n",
    "- *In vivo* analyses of phytoene synthase (CrtB)\n",
    "- *In vivo* analyses of phytoene desaturases (CrtI)\n",
    "     - CrtI from *Xanthophyllomyces dendrorhous*\n",
    "     - CrtI from *Pantoea ananatis*\n",
    "     - CrtI from *Blakeslea trispora*"
   ]
  },
  {
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   "cell_type": "markdown",
   "id": "72a0ccd8-2fb5-4555-af06-30e78fb46fe5",
   "metadata": {},
   "source": [
    "## Processing functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1eda4b54-af5f-42ed-83b9-e346316b1328",
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   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import lmfit\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import scipy\n",
    "\n",
    "sns.set_style(\"white\")\n",
    "\n",
    "def simulate(x, p):\n",
    "    \"\"\"Simulate michaelian curve.\n",
    "\n",
    "    Args:\n",
    "        x: substrate concentrations\n",
    "        p: enzymatic parameters (vmax and km)\n",
    "    \"\"\"\n",
    "    \n",
    "    vmax = p['vmax'].value\n",
    "    km = p['km'].value\n",
    "    \n",
    "    return vmax * x / (km + x)\n",
    "\n",
    "def residual(p, x, data):\n",
    "    \"\"\"Cost function used to calculate the residuals between \n",
    "    experimental and simulated data.\n",
    "\n",
    "    Args:\n",
    "        x: measured substrate concentrations\n",
    "        p: enzymatic parameters (vmax and km)\n",
    "        data: measured fluxes\n",
    "    \"\"\"\n",
    "    \n",
    "    return (data - simulate(x, p))\n",
    "\n",
    "def fit_data(x, y):\n",
    "    \"\"\"Estimate michaelien parameters by fitting a Michaelis-Menten \n",
    "    rate law to experimental data (fluxes measured at different \n",
    "    substrate concentrations).\n",
    "\n",
    "    Args:\n",
    "        x: measured substrate concentrations\n",
    "        y: measured fluxes\n",
    "    \"\"\"\n",
    "\n",
    "    # initialize parameters (vmax and km)\n",
    "    p = lmfit.Parameters()\n",
    "    p.add('vmax', value=1., min=0.)\n",
    "    p.add('km', value=1., min=0.)\n",
    "    \n",
    "    # create Minimizer\n",
    "    mini = lmfit.Minimizer(residual, params=p, fcn_args=(x, y))\n",
    "    \n",
    "    # solve with Levenberg-Marquardt algorithm\n",
    "    out = mini.minimize(method='leastsq')\n",
    "\n",
    "    # fit experimental data\n",
    "    mini.minimize()\n",
    "\n",
    "    return out, mini\n",
    "\n",
    "def monte_carlo(x, y, n_iter=100, xlim=[0]):\n",
    "    \"\"\"Sensitivity analysis to determine the precision on the \n",
    "    estimated michaelien parameters.\n",
    "\n",
    "    Args:\n",
    "        x: measured substrate concentrations\n",
    "        y: measured fluxes\n",
    "        n_iter: number of Monte-Carlo simulations\n",
    "    \"\"\"\n",
    "\n",
    "    # get parameters from the best fit\n",
    "    out, _ = fit_data(x, y)\n",
    "    # simulate data from the estimated parameters for\n",
    "    # the measured substrate concentrations\n",
    "    out_sim = simulate(x, out.params)\n",
    "\n",
    "    # generate high-resolution simulated data for \n",
    "    # many (200) concentrations, used to estimate \n",
    "    # the dispersion of simulated data and for plot\n",
    "    x_sim = np.arange(0, np.max([np.max(x), np.max(xlim)]), np.max([np.max(x), np.max(xlim)])/200)\n",
    "    y_sim = simulate(x_sim, out.params)\n",
    "\n",
    "    # estimate mean error on the fit\n",
    "    noise = np.std(out.residual)\n",
    "\n",
    "    # arrays used to store simulation results and parameters\n",
    "    res_mc_sim = np.empty([n_iter, y_sim.size])\n",
    "    res_mc_par = np.empty([n_iter, 2])\n",
    "\n",
    "    # perform monte carlo iterations:\n",
    "    for i in range(n_iter):\n",
    "        # generate noisy dataset\n",
    "        data_noise = np.random.normal(out_sim, [noise]*y.size)\n",
    "        data_noise[data_noise < 0] = 0\n",
    "        # estimate parameters\n",
    "        out_mc, _ = fit_data(x, data_noise)\n",
    "        res_mc_par[i,] = [out_mc.params['vmax'].value, out_mc.params['km'].value]\n",
    "        # simulate data for the best fit\n",
    "        res_mc_sim[i,] = simulate(x_sim, out_mc.params)\n",
    "    \n",
    "    # estimate 95% confidence interval on the fit \n",
    "    ci_sim = np.empty([2, y_sim.size])\n",
    "    ci_sim[0,] = np.percentile(res_mc_sim, 2.5, axis=0)\n",
    "    ci_sim[1,] = np.percentile(res_mc_sim, 97.5, axis=0)\n",
    "\n",
    "    # calculate statistics on estimated parameters (mean, median, sd, rsd, CI_0.025, CI_0.975) \n",
    "    ci_par = {'best fit': [out.params['vmax'].value, out.params['km'].value],\n",
    "              'mean': np.mean(res_mc_par, axis=0),\n",
    "              'median': np.median(res_mc_par, axis=0),\n",
    "              'sd': np.std(res_mc_par, axis=0),\n",
    "              'rsd': np.std(res_mc_par, axis=0)/np.mean(res_mc_par, axis=0),\n",
    "              'lower bound (95% ci)': np.percentile(res_mc_par, 2.5, axis=0), \n",
    "              'upper bound (95% ci)': np.percentile(res_mc_par, 97.5, axis=0)} \n",
    "    ci_par = pd.DataFrame(ci_par, index=['vmax', 'km']) \n",
    "    \n",
    "    return ci_sim, ci_par\n",
    "\n",
    "def plot_results(x, y, mini, out, results_mc, save=None, plot=dict(), plot_exp=dict(), plot_sim=dict(), plot_resid=dict(), xlab=\"\", ylab=\"\", leglab=\"\", xlim=[0]):\n",
    "    \"\"\"Plot results.\n",
    "\n",
    "    Args:\n",
    "        x: measured substrate concentrations\n",
    "        y: measured fluxes\n",
    "        mini: minimizer object used for parameter estimation\n",
    "        out: parameter estimation results for the best fit\n",
    "        results_mc: results of Monte-Carlo analysis\n",
    "        save: filename(s) to save the plot (if not None)\n",
    "        plot: additional arguments passed to plt.subplots()\n",
    "        plot_exp: additional arguments used to plot experimental data\n",
    "        plot_sim: additional arguments used to plot simulated data\n",
    "        plot_resid: additional arguments used to plot the distribution of residuals\n",
    "    \"\"\"\n",
    "    \n",
    "    # initialize plot\n",
    "    fig, axes = plt.subplots(1, 3, **plot)\n",
    "\n",
    "    # simulate data for the best fit\n",
    "    x_sim = np.arange(0, np.max([np.max(x), np.max(xlim)]), np.max([np.max(x), np.max(xlim)])/200)\n",
    "    y_sim = simulate(x_sim, out.params)\n",
    "\n",
    "    # plot simulated and experimental data\n",
    "    axes[0].fill_between(x_sim, results_mc[0,], results_mc[1,], color=\"#EF553B\", alpha=0.3)\n",
    "    if leglab == \"\":\n",
    "        axes[0].plot(x, y, 'o', markersize=5, label='data', **plot_exp)\n",
    "    else:\n",
    "        axes[0].plot(x, y, 'o', markersize=5, label=leglab, **plot_exp)\n",
    "    axes[0].plot(x_sim, y_sim, label='best fit (95% CI)', **plot_sim)\n",
    "    axes[0].tick_params(bottom=True, left=True)\n",
    "    if xlab == \"\":\n",
    "        axes[0].set_xlabel('concentration')\n",
    "    else:\n",
    "        axes[0].set_xlabel(xlab)\n",
    "    if ylab == \"\":\n",
    "        axes[0].set_ylabel('flux')\n",
    "    else:\n",
    "        axes[0].set_ylabel(ylab)\n",
    "    axes[0].legend(frameon=False)\n",
    "    \n",
    "    # plot confidence intervals (vmax vs km)\n",
    "    try:\n",
    "        cx, cy, grid = lmfit.conf_interval2d(mini, out, 'vmax', 'km', 50, 50)\n",
    "        ctp = axes[1].contourf(cx, cy, grid, np.linspace(0, 1, 11))\n",
    "        fig.colorbar(ctp, ax=axes[1])\n",
    "        axes[1].tick_params(bottom=True, left=True)\n",
    "        axes[1].set_xlabel('vmax')\n",
    "        axes[1].set_ylabel('km')\n",
    "    except:\n",
    "        pass\n",
    "\n",
    "    # plot distribution of residuals\n",
    "    sns.histplot(ax=axes[2], data=out.residual, **plot_resid)\n",
    "    axes[2].tick_params(bottom=True, left=True)\n",
    "    axes[2].set(xlabel='residual')\n",
    "\n",
    "    # adjust plot layout\n",
    "    plt.tight_layout()\n",
    "\n",
    "    # save figure in file(s)\n",
    "    if save is not None:\n",
    "        if isinstance(save, str):\n",
    "            plt.savefig(save)\n",
    "        elif isinstance(save, list):\n",
    "            for i in save:\n",
    "                plt.savefig(i)\n",
    "\n",
    "    # display plot\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57908ec3-73b7-4fff-87fd-29d3b8d67409",
   "metadata": {},
   "source": [
    "# Model simulations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3450fd7f-f816-4aa1-8dcb-7f07ccd8f509",
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   "source": [
    "low_GGPP = np.float64([1, 1.07809, 1.16583, 1.26506, 1.37811, 1.50799, 1.65865, 1.83537, 2.04541, 2.29898, 2.61093, 3.00374, 3.51316, 4.19959, 5.1739, 6.66394, 9.22425, 14.6484, 33.8091])\n",
    "low_flux = np.float64([0.01, 0.0103758, 0.0107657, 0.0111702, 0.01159, 0.0120255, 0.0124774, 0.0129463, 0.0134327, 0.0139375, 0.0144613, 0.0150047, 0.0155685, 0.0161535, 0.0167606, 0.0173904, 0.0180439, 0.0187219, 0.0194254])\n",
    "\n",
    "medium_GGPP = np.float64([0.025641, 0.0266302, 0.0276586, 0.0287278, 0.0298395, 0.0309956, 0.0321978, 0.0334482, 0.0347488, 0.0361017, 0.0375092, 0.0389737, 0.0404975, 0.0420833, 0.0437339, 0.045452, 0.0472407, 0.049103, 0.0510424, 0.0530622, 0.0551662, 0.0573581, 0.059642, 0.0620222, 0.0645032, 0.0670897, 0.0697867, 0.0725995, 0.0755337, 0.0785952, 0.0817902, 0.0851253, 0.0886075, 0.0922443, 0.0960436, 0.100014, 0.104163, 0.108502, 0.11304, 0.117789, 0.122758, 0.127961, 0.133411, 0.139122, 0.145109, 0.151387, 0.157974, 0.164889, 0.172152, 0.179784, 0.187809, 0.196252, 0.205139, 0.214501, 0.22437, 0.234781, 0.245772, 0.257384, 0.269665, 0.282662, 0.296433, 0.311037, 0.326541, 0.343021, 0.360558, 0.379246, 0.399186, 0.420494, 0.4433, 0.46775, 0.49401, 0.522269, 0.552742, 0.585678, 0.621361, 0.660124, 0.702352, 0.7485, 0.799103, 0.8548, 0.916356, 0.984698, 1.06096, 1.14654, 1.24318, 1.35311, 1.47917, 1.62508, 1.79581, 1.99813, 2.24152, 2.53967, 2.91312, 3.39414, 4.03651, 4.93706, 6.28942, 8.54539, 13.0604, 26.6116])\n",
    "medium_flux = np.float64([0.01, 0.0103758, 0.0107657, 0.0111702, 0.01159, 0.0120255, 0.0124774, 0.0129463, 0.0134327, 0.0139375, 0.0144613, 0.0150047, 0.0155685, 0.0161535, 0.0167606, 0.0173904, 0.0180439, 0.0187219, 0.0194254, 0.0201554, 0.0209128, 0.0216986, 0.022514, 0.0233601, 0.0242379, 0.0251487, 0.0260937, 0.0270742, 0.0280916, 0.0291472, 0.0302425, 0.031379, 0.0325581, 0.0337816, 0.035051, 0.0363681, 0.0377348, 0.0391527, 0.040624, 0.0421506, 0.0437345, 0.0453779, 0.0470831, 0.0488524, 0.0506881, 0.0525929, 0.0545692, 0.0566198, 0.0587474, 0.060955, 0.0632456, 0.0656222, 0.0680881, 0.0706467, 0.0733014, 0.0760559, 0.0789139, 0.0818793, 0.0849561, 0.0881486, 0.091461, 0.0948979, 0.0984639, 0.102164, 0.106003, 0.109986, 0.114119, 0.118408, 0.122857, 0.127474, 0.132264, 0.137234, 0.142391, 0.147742, 0.153294, 0.159054, 0.165031, 0.171232, 0.177667, 0.184343, 0.19127, 0.198458, 0.205916, 0.213653, 0.221682, 0.230012, 0.238656, 0.247624, 0.256929, 0.266584, 0.276601, 0.286995, 0.29778, 0.30897, 0.32058, 0.332627, 0.345126, 0.358095, 0.371551, 0.385513])\n",
    "\n",
    "high_GGPP = np.float64([0.0144928, 0.0150456, 0.0156198, 0.0162162, 0.0168359, 0.0174796, 0.0181483, 0.0188431, 0.0195651, 0.0203152, 0.0210947, 0.0219048, 0.0227466, 0.0236216, 0.024531, 0.0254763, 0.026459, 0.0274806, 0.0285427, 0.0296471, 0.0307954, 0.0319897, 0.0332317, 0.0345236, 0.0358675, 0.0372655, 0.0387201, 0.0402336, 0.0418087, 0.043448, 0.0451544, 0.0469309, 0.0487804, 0.0507064, 0.0527123, 0.0548017, 0.0569783, 0.0592463, 0.0616098, 0.0640733, 0.0666414, 0.0693193, 0.072112, 0.0750251, 0.0780644, 0.0812362, 0.0845469, 0.0880036, 0.0916135, 0.0953845, 0.0993249, 0.103443, 0.107749, 0.112253, 0.116964, 0.121895, 0.127058, 0.132465, 0.13813, 0.144069, 0.150296, 0.15683, 0.163688, 0.17089, 0.178457, 0.186413, 0.194783, 0.203592, 0.212872, 0.222652, 0.232968, 0.243857, 0.25536, 0.267523, 0.280395, 0.29403, 0.308487, 0.323833, 0.340141, 0.357492, 0.375977, 0.395696, 0.416762, 0.439303, 0.463461, 0.489401, 0.517305, 0.547384, 0.579882, 0.615075, 0.653287, 0.694895, 0.74034, 0.790142, 0.844921, 0.905418, 0.972531, 1.04735, 1.13123, 1.22585, 1.33333])\n",
    "high_flux = np.float64([0.01, 0.0103758, 0.0107657, 0.0111702, 0.01159, 0.0120255, 0.0124774, 0.0129463, 0.0134327, 0.0139375, 0.0144613, 0.0150047, 0.0155685, 0.0161535, 0.0167606, 0.0173904, 0.0180439, 0.0187219, 0.0194254, 0.0201554, 0.0209128, 0.0216986, 0.022514, 0.0233601, 0.0242379, 0.0251487, 0.0260937, 0.0270742, 0.0280916, 0.0291472, 0.0302425, 0.031379, 0.0325581, 0.0337816, 0.035051, 0.0363681, 0.0377348, 0.0391527, 0.040624, 0.0421506, 0.0437345, 0.0453779, 0.0470831, 0.0488524, 0.0506881, 0.0525929, 0.0545692, 0.0566198, 0.0587474, 0.060955, 0.0632456, 0.0656222, 0.0680881, 0.0706467, 0.0733014, 0.0760559, 0.0789139, 0.0818793, 0.0849561, 0.0881486, 0.091461, 0.0948979, 0.0984639, 0.102164, 0.106003, 0.109986, 0.114119, 0.118408, 0.122857, 0.127474, 0.132264, 0.137234, 0.142391, 0.147742, 0.153294, 0.159054, 0.165031, 0.171232, 0.177667, 0.184343, 0.19127, 0.198458, 0.205916, 0.213653, 0.221682, 0.230012, 0.238656, 0.247624, 0.256929, 0.266584, 0.276601, 0.286995, 0.29778, 0.30897, 0.32058, 0.332627, 0.345126, 0.358095, 0.371551, 0.385513, 0.4])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dc8937ad-ac8a-4b12-afd9-9b1f8ffd3643",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 400x350 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# initialize plot\n",
    "fig, axes = plt.subplots(1, 1, figsize=(4, 3.5))\n",
    "\n",
    "# plot simulated and experimental data\n",
    "axes.plot(low_GGPP, low_flux, label='low', color=\"#FFD967\")\n",
    "axes.plot(medium_GGPP, medium_flux, label='medium', color=\"#FFA500\")\n",
    "axes.plot(high_GGPP, high_flux, label='high', color=\"#CD5C5C\")\n",
    "\n",
    "# plot simulated and experimental data\n",
    "axes.tick_params(bottom=True, left=True)\n",
    "axes.set_xlabel('[Substrate] (A.U.)', fontname='Calibri', fontsize=\"12\")\n",
    "axes.set_ylabel('Flux (A.U.)', fontname='Calibri', fontsize=\"12\")\n",
    "axes.legend(fontsize=\"9\", frameon=False)\n",
    "\n",
    "axes.xaxis.set_ticks([0,10,20,30,40])\n",
    "# adjust plot layout\n",
    "plt.tight_layout()\n",
    "\n",
    "# save figure in file(s)\n",
    "plt.savefig(\"./figures/simulations.svg\")\n",
    "plt.savefig(\"./figures/simulations.png\")\n",
    "plt.savefig(\"./figures/simulations.pdf\")\n",
    "\n",
    "# display plot\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b92967d4-6f15-4936-a505-c4e4ff916e20",
   "metadata": {},
   "source": [
    "# *In vivo* analyses of phytoene synthase (CrtB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d0cdf682-ccdd-4b2d-9a0a-08ebb29e4e99",
   "metadata": {},
   "outputs": [],
   "source": [
    "# PGIp\n",
    "CrtB_pg1p_GGPP = np.float64([0.384, 0.588, 0.542, 2.390, 2.850, 3.241, 7.469, 9.538, 7.217, 7.722, 7.401, 9.722, 18.409, 15.651, 16.984, 20.363, 21.190, 0.230, 0.230, 0.230, 9.193, 8.504, 6.435, 0.460, 0.230, 0.230, 8.734, 7.125, 5.056, 0.414, 0.437, 0.460, 0.115, 0.299, 0.299, 0.345, 0.276, 0.207, 0.276, 0.184, 0.345, 0.161, 0.069, 0.414, 0.437, 0.460, 0.115, 0.299, 0.299, 0.345, 0.276, 0.207, 0.276, 0.184, 0.345, 0.161, 0.069])\n",
    "CrtB_pg1p_phytoene_flux = np.float64([3.765, 4.344, 4.429, 84.003, 55.159, 80.050, 252.813, 247.067, 232.588, 289.586, 306.363, 286.828, 384.275, 424.036, 432.080, 418.290, 433.689, 1.790, 2.167, 3.298, 250.974, 231.668, 222.016, 9.140, 7.633, 7.161, 250.974, 270.280, 164.098, 22.202, 19.788, 25.741, 1.531, 17.375, 14.847, 19.214, 14.341, 8.504, 14.456, 17.007, 16.548, 1.407, 1.903, 22.202, 19.788, 25.741, 1.531, 17.375, 14.847, 19.214, 14.341, 8.504, 14.456, 17.007, 16.548, 1.407, 1.903])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e49c67c4-407f-4cc8-a261-e50f4b713768",
   "metadata": {},
   "source": [
    "## PGIp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "85d5c118-9c25-4ce7-9534-64829ba4f4d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fit data\n",
    "results, mini = fit_data(CrtB_pg1p_GGPP, CrtB_pg1p_phytoene_flux)\n",
    "\n",
    "# calculate confidence intervals on the fit\n",
    "ci_sim, ci_par = monte_carlo(CrtB_pg1p_GGPP, CrtB_pg1p_phytoene_flux, n_iter=1000, xlim=[0,25])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "697a71d4-ac12-4446-9ad4-1195c58d4eb9",
   "metadata": {},
   "source": [
    "Show results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f70c7403-c291-41e7-aaa1-3a7dde5425ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h2>Fit Result</h2> <table class=\"jp-toc-ignore\"><caption class=\"jp-toc-ignore\">Fit Statistics</caption><tr><td style='text-align:left'>fitting method</td><td style='text-align:right'>leastsq</td></tr><tr><td style='text-align:left'># function evals</td><td style='text-align:right'>22</td></tr><tr><td style='text-align:left'># data points</td><td style='text-align:right'>57</td></tr><tr><td style='text-align:left'># variables</td><td style='text-align:right'>2</td></tr><tr><td style='text-align:left'>chi-square</td><td style='text-align:right'> 23016.2776</td></tr><tr><td style='text-align:left'>reduced chi-square</td><td style='text-align:right'> 418.477775</td></tr><tr><td style='text-align:left'>Akaike info crit.</td><td style='text-align:right'> 346.051625</td></tr><tr><td style='text-align:left'>Bayesian info crit.</td><td style='text-align:right'> 350.137727</td></tr></table><table class=\"jp-toc-ignore\"><caption>Parameters</caption><tr><th style='text-align:left'>name</th><th style='text-align:left'>value</th><th style='text-align:left'>standard error</th><th style='text-align:left'>relative error</th><th style='text-align:left'>initial value</th><th style='text-align:left'>min</th><th style='text-align:left'>max</th><th style='text-align:right'>vary</th></tr><tr><td style='text-align:left'>vmax</td><td style='text-align:left'> 848.654369</td><td style='text-align:left'> 74.6404290</td><td style='text-align:left'>(8.80%)</td><td style='text-align:left'>1.0</td><td style='text-align:left'> 0.00000000</td><td style='text-align:left'>        inf</td><td style='text-align:right'>True</td></tr><tr><td style='text-align:left'>km</td><td style='text-align:left'> 19.1202877</td><td style='text-align:left'> 2.73876520</td><td style='text-align:left'>(14.32%)</td><td style='text-align:left'>1.0</td><td style='text-align:left'> 0.00000000</td><td style='text-align:left'>        inf</td><td style='text-align:right'>True</td></tr></table><table class=\"jp-toc-ignore\"><caption>Correlations (unreported values are < 0.100)</caption><tr><th style='text-align:left'>Parameter1</th><th style='text-align:left'>Parameter 2</th><th style='text-align:right'>Correlation</th></tr><tr><td style='text-align:left'>vmax</td><td style='text-align:left'>km</td><td style='text-align:right'>+0.9834</td></tr></table>"
      ],
      "text/plain": [
       "<lmfit.minimizer.MinimizerResult at 0x25123614710>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "87fb1250-5470-4e41-86a8-a2ca4bc42f4f",
   "metadata": {},
   "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>best fit</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>sd</th>\n",
       "      <th>rsd</th>\n",
       "      <th>lower bound (95% ci)</th>\n",
       "      <th>upper bound (95% ci)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>vmax</th>\n",
       "      <td>848.654369</td>\n",
       "      <td>843.378092</td>\n",
       "      <td>834.758195</td>\n",
       "      <td>76.662517</td>\n",
       "      <td>0.090899</td>\n",
       "      <td>715.909705</td>\n",
       "      <td>1017.520922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>km</th>\n",
       "      <td>19.120288</td>\n",
       "      <td>18.930314</td>\n",
       "      <td>18.594202</td>\n",
       "      <td>2.837296</td>\n",
       "      <td>0.149881</td>\n",
       "      <td>14.200018</td>\n",
       "      <td>25.437542</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        best fit        mean      median         sd       rsd  \\\n",
       "vmax  848.654369  843.378092  834.758195  76.662517  0.090899   \n",
       "km     19.120288   18.930314   18.594202   2.837296  0.149881   \n",
       "\n",
       "      lower bound (95% ci)  upper bound (95% ci)  \n",
       "vmax            715.909705           1017.520922  \n",
       "km               14.200018             25.437542  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ci_par"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "eb69178d-6200-40dd-9a03-ad7b34d187ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x350 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_results(CrtB_pg1p_GGPP, CrtB_pg1p_phytoene_flux, mini, results, ci_sim,\n",
    "             save=[\"./figures/CrtB_pg1p.svg\", \"./figures/CrtB_pg1p.png\", \"./figures/CrtB_pg1p.pdf\"],\n",
    "             plot=dict(figsize=(11, 3.5)),\n",
    "             plot_exp=dict(markerfacecolor=\"#FEB886\", markeredgewidth=1, markeredgecolor=\"#81624C\"),\n",
    "             plot_sim=dict(color=\"#EF553B\"),\n",
    "             plot_resid=dict(kde=True, stat=\"density\", color=\"#FEB886\", kde_kws=dict(cut=3)), xlim=[0,25],\n",
    "             xlab=\"[GGPP] (µM)\", ylab=\"Phytoene flux (µM/h)\", leglab=\"PGIp_CrtB\") "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d77b9c1-6313-4b4b-b4f9-3962a526c9ba",
   "metadata": {},
   "source": [
    "# Comparison of the results obtained for the three expression levels of CrtB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b495e212-1d28-4472-b4a0-fd5bb2095c22",
   "metadata": {},
   "outputs": [],
   "source": [
    "# PDC1p\n",
    "CrtB_pdc1p_GGPP = np.float64([1.39, 1.07, 1.05, 1.49, 1.53, 2.11, 1.53, 2.48, 3.37, 1.19, 1.87, 2.25, 1.11, 2.19, 2.61, 3.18, 4.76, 5.77])\n",
    "CrtB_pdc1p_phytoene_flux = np.float64([8.56, 4.01, 3.04, 198.38, 176.03, 385.86, 343.24, 360.35, 382.24, 397.10, 184.47, 398.29, 600.24, 629.38, 580.09, 589.83, 551.69, 606.44])\n",
    "\n",
    "# TEF1mut2p\n",
    "CrtB_tef1mu2p_GGPP = np.float64([0.82, 0.61, 0.58, 3.42, 3.36, 3.26, 11.91, 8.66, 9.31, 16.41, 8.99, 9.56, 20.04, 23.21, 35.16, 24.36, 30.11, 0.23, 0.23, 21.83, 11.72, 20.91])\n",
    "CrtB_tef1mu2p_phytoene_flux = np.float64([3.28, 1.54, 1.51, 9.88, 11.63, 11.88, 25.69, 25.37, 24.71, 44.47, 47.53, 54.35, 17.03, 31.46, 22.34, 22.66, 22.34, 1.79, 2.17, 53.09, 58.88, 55.99])\n",
    "\n",
    "# PGIp\n",
    "CrtB_pg1p_GGPP = np.float64([0.384, 0.588, 0.542, 2.390, 2.850, 3.241, 7.469, 9.538, 7.217, 7.722, 7.401, 9.722, 18.409, 15.651, 16.984, 20.363, 21.190, 0.230, 0.230, 0.230, 9.193, 8.504, 6.435, 0.460, 0.230, 0.230, 8.734, 7.125, 5.056, 0.414, 0.437, 0.460, 0.115, 0.299, 0.299, 0.345, 0.276, 0.207, 0.276, 0.184, 0.345, 0.161, 0.069, 0.414, 0.437, 0.460, 0.115, 0.299, 0.299, 0.345, 0.276, 0.207, 0.276, 0.184, 0.345, 0.161, 0.069])\n",
    "CrtB_pg1p_phytoene_flux = np.float64([3.765, 4.344, 4.429, 84.003, 55.159, 80.050, 252.813, 247.067, 232.588, 289.586, 306.363, 286.828, 384.275, 424.036, 432.080, 418.290, 433.689, 1.790, 2.167, 3.298, 250.974, 231.668, 222.016, 9.140, 7.633, 7.161, 250.974, 270.280, 164.098, 22.202, 19.788, 25.741, 1.531, 17.375, 14.847, 19.214, 14.341, 8.504, 14.456, 17.007, 16.548, 1.407, 1.903, 22.202, 19.788, 25.741, 1.531, 17.375, 14.847, 19.214, 14.341, 8.504, 14.456, 17.007, 16.548, 1.407, 1.903])\n",
    "\n",
    "# fit data\n",
    "results, mini = fit_data(CrtB_pg1p_GGPP, CrtB_pg1p_phytoene_flux)\n",
    "\n",
    "# calculate confidence intervals on the fit\n",
    "ci_sim, ci_par = monte_carlo(CrtB_pg1p_GGPP, CrtB_pg1p_phytoene_flux, n_iter=1000, xlim=[0,40])\n",
    "\n",
    "# fit data\n",
    "results2, mini2 = fit_data(CrtB_tef1mu2p_GGPP, CrtB_tef1mu2p_phytoene_flux)\n",
    "\n",
    "# calculate confidence intervals on the fit\n",
    "ci_sim2, ci_par2 = monte_carlo(CrtB_tef1mu2p_GGPP, CrtB_tef1mu2p_phytoene_flux, n_iter=1000, xlim=[0,40])\n",
    "\n",
    "# fit data\n",
    "results3, mini3 = fit_data(CrtB_pdc1p_GGPP, CrtB_pdc1p_phytoene_flux)\n",
    "\n",
    "# calculate confidence intervals on the fit\n",
    "ci_sim3, ci_par3 = monte_carlo(CrtB_pdc1p_GGPP, CrtB_pdc1p_phytoene_flux, n_iter=1000, xlim=[0,40])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "183d7383-3914-44f1-a7e0-998aa6ad725e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>best fit</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>sd</th>\n",
       "      <th>rsd</th>\n",
       "      <th>lower bound (95% ci)</th>\n",
       "      <th>upper bound (95% ci)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>vmax</th>\n",
       "      <td>848.654369</td>\n",
       "      <td>844.190576</td>\n",
       "      <td>837.064713</td>\n",
       "      <td>71.941012</td>\n",
       "      <td>0.085219</td>\n",
       "      <td>721.979286</td>\n",
       "      <td>1000.337168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>km</th>\n",
       "      <td>19.120288</td>\n",
       "      <td>18.975989</td>\n",
       "      <td>18.733434</td>\n",
       "      <td>2.640061</td>\n",
       "      <td>0.139126</td>\n",
       "      <td>14.703249</td>\n",
       "      <td>24.728792</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        best fit        mean      median         sd       rsd  \\\n",
       "vmax  848.654369  844.190576  837.064713  71.941012  0.085219   \n",
       "km     19.120288   18.975989   18.733434   2.640061  0.139126   \n",
       "\n",
       "      lower bound (95% ci)  upper bound (95% ci)  \n",
       "vmax            721.979286           1000.337168  \n",
       "km               14.703249             24.728792  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# PGIp\n",
    "\n",
    "results\n",
    "ci_par"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "652aea75-848c-4efe-8759-8126ed8f5362",
   "metadata": {},
   "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>best fit</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>sd</th>\n",
       "      <th>rsd</th>\n",
       "      <th>lower bound (95% ci)</th>\n",
       "      <th>upper bound (95% ci)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>vmax</th>\n",
       "      <td>42.744860</td>\n",
       "      <td>323729.273174</td>\n",
       "      <td>42.002535</td>\n",
       "      <td>6.153772e+06</td>\n",
       "      <td>19.009008</td>\n",
       "      <td>30.360454</td>\n",
       "      <td>75.143411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>km</th>\n",
       "      <td>3.783669</td>\n",
       "      <td>192242.092864</td>\n",
       "      <td>3.098715</td>\n",
       "      <td>3.764882e+06</td>\n",
       "      <td>19.584068</td>\n",
       "      <td>0.568741</td>\n",
       "      <td>20.160325</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       best fit           mean     median            sd        rsd  \\\n",
       "vmax  42.744860  323729.273174  42.002535  6.153772e+06  19.009008   \n",
       "km     3.783669  192242.092864   3.098715  3.764882e+06  19.584068   \n",
       "\n",
       "      lower bound (95% ci)  upper bound (95% ci)  \n",
       "vmax             30.360454             75.143411  \n",
       "km                0.568741             20.160325  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TEF1mut2p\n",
    "\n",
    "results2\n",
    "ci_par2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6fbef1a8-a436-4081-80b9-c09e1146c5f3",
   "metadata": {},
   "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>best fit</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>sd</th>\n",
       "      <th>rsd</th>\n",
       "      <th>lower bound (95% ci)</th>\n",
       "      <th>upper bound (95% ci)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>vmax</th>\n",
       "      <td>1172.566651</td>\n",
       "      <td>9.257816e+07</td>\n",
       "      <td>1130.020156</td>\n",
       "      <td>8.567562e+08</td>\n",
       "      <td>9.254410</td>\n",
       "      <td>516.625835</td>\n",
       "      <td>12626.771421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>km</th>\n",
       "      <td>4.740208</td>\n",
       "      <td>6.408258e+05</td>\n",
       "      <td>4.352376</td>\n",
       "      <td>5.961192e+06</td>\n",
       "      <td>9.302358</td>\n",
       "      <td>0.829327</td>\n",
       "      <td>78.537816</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         best fit          mean       median            sd       rsd  \\\n",
       "vmax  1172.566651  9.257816e+07  1130.020156  8.567562e+08  9.254410   \n",
       "km       4.740208  6.408258e+05     4.352376  5.961192e+06  9.302358   \n",
       "\n",
       "      lower bound (95% ci)  upper bound (95% ci)  \n",
       "vmax            516.625835          12626.771421  \n",
       "km                0.829327             78.537816  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# PDC1\n",
    "\n",
    "results3\n",
    "ci_par3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5ff03fe8-0ee0-4a28-b123-17308fdcb524",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x350 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# initialize plot\n",
    "fig, axes = plt.subplots(1, 1, figsize=(4, 3.5))\n",
    "\n",
    "# simulate data for the best fit\n",
    "x_sim = np.arange(0, 40, 40/200)\n",
    "y_sim = simulate(x_sim, results2.params)\n",
    "\n",
    "# simulate data for the best fit\n",
    "x_sim2 = np.arange(0, 40, 40/200)\n",
    "y_sim2 = simulate(x_sim2, results.params)\n",
    "\n",
    "# simulate data for the best fit\n",
    "x_sim3 = np.arange(0, 40, 40/200)\n",
    "y_sim3 = simulate(x_sim3, results3.params)\n",
    "\n",
    "# plot simulated and experimental data\n",
    "axes.plot(CrtB_tef1mu2p_GGPP, CrtB_tef1mu2p_phytoene_flux, 'o', markersize=5, label='TEF1pmut2-CrtB', markerfacecolor=\"#FFD967\", markeredgewidth=1, markeredgecolor=\"#FFBF00\")\n",
    "axes.plot(CrtB_pg1p_GGPP, CrtB_pg1p_phytoene_flux, 'o', markersize=5, label='PGIp-CrtB', markerfacecolor=\"#FFA500\", markeredgewidth=1, markeredgecolor=\"#FF7F00\")\n",
    "axes.plot(x_sim2, y_sim2, color=\"#EF553B\")\n",
    "axes.fill_between(x_sim2, ci_sim[0,], ci_sim[1,], color=\"#EF553B\", alpha=0.3)\n",
    "axes.plot(CrtB_pdc1p_GGPP, CrtB_pdc1p_phytoene_flux, 'o', markersize=5, label='PDC1p-CrtB', markerfacecolor=\"#CD5C5C\", markeredgewidth=1, markeredgecolor=\"#7C0A02\")\n",
    "axes.plot(CrtB_pg1p_GGPP, CrtB_pg1p_phytoene_flux, 'o', markersize=5, markerfacecolor=\"#FFA500\", markeredgewidth=1, markeredgecolor=\"#FF7F00\")\n",
    "axes.plot(x_sim2, y_sim2, color=\"#EF553B\")\n",
    "\n",
    "# plot simulated and experimental data\n",
    "axes.tick_params(bottom=True, left=True)\n",
    "axes.set_xlabel('[GGPP] (µM)', fontname='Calibri', fontsize=\"12\")\n",
    "axes.set_ylabel('Phytoene flux (µM/h)', fontname='Calibri', fontsize=\"12\")\n",
    "axes.legend(fontsize=\"9\", frameon=False)\n",
    "\n",
    "axes.xaxis.set_ticks([0,10,20,30,40])\n",
    "# adjust plot layout\n",
    "plt.tight_layout()\n",
    "\n",
    "# save figure in file(s)\n",
    "plt.savefig(\"./figures/CrtB.svg\")\n",
    "plt.savefig(\"./figures/CrtB.png\")\n",
    "plt.savefig(\"./figures/CrtB.pdf\")\n",
    "\n",
    "# display plot\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e99b646b-7c4a-41b7-bc90-33b3062d1d6a",
   "metadata": {},
   "source": [
    "# *In vivo* analysis of phytoene desaturases (CrtI)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "1d94ba51-3cd1-40e5-9851-c427843df307",
   "metadata": {},
   "source": [
    "## CrtI from *Xanthophyllomyces dendrorhous*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3c5167f1-7b37-44e9-bd2c-8344d0b3d92d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# phytoene concentration\n",
    "XdCrtI_phytoene = np.float64([944.7255309, 943.038521, 0.421752469, 1.265257407, 2046.34298, 1749.850995, 1582.415264, 1486.255701, 261.9082833, 302.3965204, 949.7865605, 892.8499772, 0, 0, 0, 985.2137679, 951.0518179, 857.4227698, 1767.564598, 1795.400261, 1735.933163, 911.8288383, 1726.232856, 1609.407422, 208.7674722, 203.7064426, 203.2846901, 285.1046691, 292.2744611, 315.8925994, 49.34503889, 50.18854383, 48.07978148, 266.969313, 237.4466401, 233.2291154, 1.687009877, 2.952267284, 0, 1603.92464, 1679.840085, 1741.837698, 1290.562556, 1308.697912, 1159.397538, 384.6382519, 419.6437068, 740.1755834, 698.0003364, 782.3508303, 202.4411852, 202.0194327, 204.1281951, 296.4919858, 254.7384914, 250.0992142, 46.81452408, 44.28400926, 45.1275142, 233.6508679, 260.6430259, 237.0248877])\n",
    "# lycopene flux\n",
    "XdCrtI_lycopene_flux = np.float64([124.7965487, 120.7719585, 1.258754814, 1.198814109, 178.452043, 167.7740345, 135.4831202, 105.4271379, 102.8668135, 116.2079019, 138.6428517, 114.4524955, 2.012295111, 1.695465668, 1.507080594, 122.2447872, 139.1480662, 128.0333468, 136.3222901, 126.0981183, 139.7217844, 123.2894681, 172.0298246, 128.5728132, 54.33196799, 40.99944251, 48.16663829, 84.27663184, 93.17354511, 85.1586165, 16.01701279, 24.75122986, 15.2420651, 55.78338936, 58.76329872, 61.65329702, 2.106487648, 3.159731472, 2.63310956, 105.3243824, 109.5373577, 114.2769549, 139.379266, 123.0539868, 120.9474991, 76.00909597, 85.48829038, 102.1646509, 100.0581633, 107.0797888, 46.34272826, 46.51826889, 46.86935017, 73.90260832, 73.20044577, 76.53571788, 22.46920158, 20.53825457, 21.94257967, 52.31110993, 58.80611351, 53.01327247])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "48afd88f-6bf8-4d88-bcbb-997ca2a7d521",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fit data\n",
    "results, mini = fit_data(XdCrtI_phytoene, XdCrtI_lycopene_flux)\n",
    "\n",
    "# calculate confidence intervals on the fit\n",
    "ci_sim, ci_par = monte_carlo(XdCrtI_phytoene, XdCrtI_lycopene_flux, n_iter=1000, xlim=[0,2500])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "69c09b20-03f2-47b1-874a-9a5df43a61cc",
   "metadata": {},
   "source": [
    "Show results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "92700953-b324-4064-8aa5-30772f04a8d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h2>Fit Result</h2> <table class=\"jp-toc-ignore\"><caption class=\"jp-toc-ignore\">Fit Statistics</caption><tr><td style='text-align:left'>fitting method</td><td style='text-align:right'>leastsq</td></tr><tr><td style='text-align:left'># function evals</td><td style='text-align:right'>16</td></tr><tr><td style='text-align:left'># data points</td><td style='text-align:right'>62</td></tr><tr><td style='text-align:left'># variables</td><td style='text-align:right'>2</td></tr><tr><td style='text-align:left'>chi-square</td><td style='text-align:right'> 13287.5353</td></tr><tr><td style='text-align:left'>reduced chi-square</td><td style='text-align:right'> 221.458922</td></tr><tr><td style='text-align:left'>Akaike info crit.</td><td style='text-align:right'> 336.781732</td></tr><tr><td style='text-align:left'>Bayesian info crit.</td><td style='text-align:right'> 341.036001</td></tr></table><table class=\"jp-toc-ignore\"><caption>Parameters</caption><tr><th style='text-align:left'>name</th><th style='text-align:left'>value</th><th style='text-align:left'>standard error</th><th style='text-align:left'>relative error</th><th style='text-align:left'>initial value</th><th style='text-align:left'>min</th><th style='text-align:left'>max</th><th style='text-align:right'>vary</th></tr><tr><td style='text-align:left'>vmax</td><td style='text-align:left'> 168.412320</td><td style='text-align:left'> 7.49366580</td><td style='text-align:left'>(4.45%)</td><td style='text-align:left'>1.0</td><td style='text-align:left'> 0.00000000</td><td style='text-align:left'>        inf</td><td style='text-align:right'>True</td></tr><tr><td style='text-align:left'>km</td><td style='text-align:left'> 379.863544</td><td style='text-align:left'> 49.8766737</td><td style='text-align:left'>(13.13%)</td><td style='text-align:left'>1.0</td><td style='text-align:left'> 0.00000000</td><td style='text-align:left'>        inf</td><td style='text-align:right'>True</td></tr></table><table class=\"jp-toc-ignore\"><caption>Correlations (unreported values are < 0.100)</caption><tr><th style='text-align:left'>Parameter1</th><th style='text-align:left'>Parameter 2</th><th style='text-align:right'>Correlation</th></tr><tr><td style='text-align:left'>vmax</td><td style='text-align:left'>km</td><td style='text-align:right'>+0.8892</td></tr></table>"
      ],
      "text/plain": [
       "<lmfit.minimizer.MinimizerResult at 0x25129439d90>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "806e23fa-80d0-4627-8c20-af7c893ef32f",
   "metadata": {},
   "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>best fit</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>sd</th>\n",
       "      <th>rsd</th>\n",
       "      <th>lower bound (95% ci)</th>\n",
       "      <th>upper bound (95% ci)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>vmax</th>\n",
       "      <td>168.412320</td>\n",
       "      <td>168.634939</td>\n",
       "      <td>168.376409</td>\n",
       "      <td>7.208753</td>\n",
       "      <td>0.042748</td>\n",
       "      <td>155.572124</td>\n",
       "      <td>183.867239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>km</th>\n",
       "      <td>379.863544</td>\n",
       "      <td>380.577989</td>\n",
       "      <td>377.869375</td>\n",
       "      <td>48.201341</td>\n",
       "      <td>0.126653</td>\n",
       "      <td>296.491907</td>\n",
       "      <td>481.400828</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        best fit        mean      median         sd       rsd  \\\n",
       "vmax  168.412320  168.634939  168.376409   7.208753  0.042748   \n",
       "km    379.863544  380.577989  377.869375  48.201341  0.126653   \n",
       "\n",
       "      lower bound (95% ci)  upper bound (95% ci)  \n",
       "vmax            155.572124            183.867239  \n",
       "km              296.491907            481.400828  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ci_par"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5c27f9d3-6f0c-4d08-be59-e54cc9f3feeb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x350 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_results(XdCrtI_phytoene, XdCrtI_lycopene_flux, mini, results, ci_sim,\n",
    "             save=[\"./figures/XdCrtI.svg\", \"./figures/XdCrtI.png\", \"./figures/XdCrtI.pdf\"],\n",
    "             plot=dict(figsize=(11, 3.5)),\n",
    "             plot_exp=dict(markerfacecolor=\"#D8BFD8\", markeredgewidth=1, markeredgecolor=\"#702963\"),\n",
    "             plot_sim=dict(color=\"#EF553B\"),\n",
    "             plot_resid=dict(kde=True, stat=\"density\", color=\"#702963\", kde_kws=dict(cut=3)),\n",
    "             xlim=[0,2500],\n",
    "             xlab=\"[Phytoene] (µM)\", ylab=\"Lycopene flux (µM/h)\", leglab=\"XdCrtI\") "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "4039fafa-cbc3-4575-bad7-0ad0333d288d",
   "metadata": {},
   "source": [
    "## CrtI from *Pantoea ananatis*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4de569d4-fce5-4859-9f24-6d1554513028",
   "metadata": {},
   "outputs": [],
   "source": [
    "# phytoene concentration\n",
    "PaCrtI_phytoene = np.float64([940.0862537, 1005.457886, 4.639277161, 4.217524692, 1449.563236, 1490.473226, 1414.557782, 1200.307527, 338.2454803, 376.2032025, 944.3037784, 829.5871068, 5.904534568, 7.591544445, 5.482782099, 1080.108073, 1053.95942, 1045.102619, 2150.937593, 2016.820307, 1961.148982, 1889.029309, 1835.044993, 1789.495727, 6.748039506, 5.06102963, 7.169791976, 1790.339232, 1681.527095, 1814.379122, 482.9065772, 517.9120321, 499.7766759, 0, 924.0596599, 1058.598698])\n",
    "# lycopene flux\n",
    "PaCrtI_lycopene_flux = np.float64([4.795256435, 4.564056571, 0, 0, 6.182455617, 6.679107177, 4.795256435, 3.930397685, 2.115050606, 2.63310956, 4.212975296, 3.510812747, 0.184103595, 0, 0.141288806, 7.278514231, 7.762321353, 8.091995233, 12.66461476, 10.93061578, 15.64452412, 10.94346022, 11.09759346, 8.151935939, 0.175540637, 0.175540637, 0.175540637, 16.67636055, 16.14973863, 17.02744182, 10.18135697, 10.3568976, 11.23460079, 0, 7.723788043, 8.601491229])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c0646e8c-aad2-4c66-9fde-d637ca106f84",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fit data\n",
    "results, mini = fit_data(PaCrtI_phytoene, PaCrtI_lycopene_flux)\n",
    "\n",
    "# calculate confidence intervals on the fit\n",
    "ci_sim, ci_par = monte_carlo(PaCrtI_phytoene, PaCrtI_lycopene_flux, n_iter=10000, xlim=[0,2500])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "046f0257-29f7-4d38-a8a3-dbc8b60d4bfa",
   "metadata": {},
   "source": [
    "Show results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1af19867-a810-4875-b030-5c4d7643e7d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h2>Fit Result</h2> <table class=\"jp-toc-ignore\"><caption class=\"jp-toc-ignore\">Fit Statistics</caption><tr><td style='text-align:left'>fitting method</td><td style='text-align:right'>leastsq</td></tr><tr><td style='text-align:left'># function evals</td><td style='text-align:right'>46</td></tr><tr><td style='text-align:left'># data points</td><td style='text-align:right'>36</td></tr><tr><td style='text-align:left'># variables</td><td style='text-align:right'>2</td></tr><tr><td style='text-align:left'>chi-square</td><td style='text-align:right'> 339.246870</td></tr><tr><td style='text-align:left'>reduced chi-square</td><td style='text-align:right'> 9.97784912</td></tr><tr><td style='text-align:left'>Akaike info crit.</td><td style='text-align:right'> 84.7555288</td></tr><tr><td style='text-align:left'>Bayesian info crit.</td><td style='text-align:right'> 87.9225667</td></tr></table><table class=\"jp-toc-ignore\"><caption>Parameters</caption><tr><th style='text-align:left'>name</th><th style='text-align:left'>value</th><th style='text-align:left'>standard error</th><th style='text-align:left'>relative error</th><th style='text-align:left'>initial value</th><th style='text-align:left'>min</th><th style='text-align:left'>max</th><th style='text-align:right'>vary</th></tr><tr><td style='text-align:left'>vmax</td><td style='text-align:left'> 40.5384398</td><td style='text-align:left'> 36.3004876</td><td style='text-align:left'>(89.55%)</td><td style='text-align:left'>1.0</td><td style='text-align:left'> 0.00000000</td><td style='text-align:left'>        inf</td><td style='text-align:right'>True</td></tr><tr><td style='text-align:left'>km</td><td style='text-align:left'> 4525.96291</td><td style='text-align:left'> 5416.60190</td><td style='text-align:left'>(119.68%)</td><td style='text-align:left'>1.0</td><td style='text-align:left'> 0.00000000</td><td style='text-align:left'>        inf</td><td style='text-align:right'>True</td></tr></table><table class=\"jp-toc-ignore\"><caption>Correlations (unreported values are < 0.100)</caption><tr><th style='text-align:left'>Parameter1</th><th style='text-align:left'>Parameter 2</th><th style='text-align:right'>Correlation</th></tr><tr><td style='text-align:left'>vmax</td><td style='text-align:left'>km</td><td style='text-align:right'>+0.9971</td></tr></table>"
      ],
      "text/plain": [
       "<lmfit.minimizer.MinimizerResult at 0x251293e9760>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3cea288b-1269-4893-92a2-bbad96187365",
   "metadata": {},
   "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>best fit</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>sd</th>\n",
       "      <th>rsd</th>\n",
       "      <th>lower bound (95% ci)</th>\n",
       "      <th>upper bound (95% ci)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>vmax</th>\n",
       "      <td>40.538440</td>\n",
       "      <td>5.510245e+06</td>\n",
       "      <td>37.038352</td>\n",
       "      <td>2.273765e+07</td>\n",
       "      <td>4.126432</td>\n",
       "      <td>13.268964</td>\n",
       "      <td>7.455862e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>km</th>\n",
       "      <td>4525.962909</td>\n",
       "      <td>8.276806e+08</td>\n",
       "      <td>3988.321309</td>\n",
       "      <td>3.394510e+09</td>\n",
       "      <td>4.101232</td>\n",
       "      <td>597.817992</td>\n",
       "      <td>1.139805e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         best fit          mean       median            sd       rsd  \\\n",
       "vmax    40.538440  5.510245e+06    37.038352  2.273765e+07  4.126432   \n",
       "km    4525.962909  8.276806e+08  3988.321309  3.394510e+09  4.101232   \n",
       "\n",
       "      lower bound (95% ci)  upper bound (95% ci)  \n",
       "vmax             13.268964          7.455862e+07  \n",
       "km              597.817992          1.139805e+10  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ci_par"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8cc20591-3a88-4a78-9080-6dc5dd746dfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x350 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_results(PaCrtI_phytoene, PaCrtI_lycopene_flux, mini, results, ci_sim,\n",
    "             save=[\"./figures/PaCrtI.svg\", \"./figures/PaCrtI.png\", \"./figures/PaCrtI.pdf\"],\n",
    "             plot=dict(figsize=(11, 3.5)),\n",
    "             plot_exp=dict(markerfacecolor=\"#5B9BD5\", markeredgewidth=1, markeredgecolor=\"#225A8C\"),\n",
    "             plot_sim=dict(color=\"#EF553B\"),\n",
    "             plot_resid=dict(kde=True, stat=\"density\", color=\"#225A8C\", kde_kws=dict(cut=3)),\n",
    "             xlim=[0,2500],\n",
    "             xlab=\"[Phytoene] (µM)\", ylab=\"Lycopene flux (µM/h)\", leglab=\"PaCrtI\") "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d47317b3-32ba-4431-ac21-4ff2331b9b83",
   "metadata": {},
   "source": [
    "## CrtI from *Blakeslea trispora*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1c40429c-7427-4826-81e7-e54267e0f7e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# phytoene concentration\n",
    "BtCrtI_phytoene = np.float64([4.5498e-11, 7.16043e-11, 5.83074e-11, 425.8979942, 466.0111087, 450.5714745, 804.3820197, 800.1878115, 843.39871, 25.65958167, 163.2663664, 153.684233, 104.7860927, 100.1267329, 108.3409435, 13.93487416, 24.75926007, 22.54114005, 108.2371067, 68.65009639, 59.4336431])\n",
    "# lycopene flux\n",
    "BtCrtI_lycopene_flux = np.float64([7.111893703, 8.061019082, 7.491143756, 270.2873056, 319.2802888, 285.0495263, 300.8581289, 266.7920683, 307.5923021, 253.2127748, 301.158852, 267.9379311, 227.7921724, 210.9660681, 216.4953912, 65.69072403, 123.3973453, 125.5335503, 189.7000527, 112.9620463, 95.68464831])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "768ef442-8667-4fe0-bec0-2aecef740274",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fit data\n",
    "results, mini = fit_data(BtCrtI_phytoene, BtCrtI_lycopene_flux)\n",
    "\n",
    "# calculate confidence intervals on the fit\n",
    "ci_sim, ci_par = monte_carlo(BtCrtI_phytoene, BtCrtI_lycopene_flux, n_iter=1000, xlim=[0,300])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "6510e010-66bc-46ba-a64e-8c8c52c0c581",
   "metadata": {},
   "source": [
    "Show results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e17b0960-f105-40c5-8623-a04e116ba7b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h2>Fit Result</h2> <table class=\"jp-toc-ignore\"><caption class=\"jp-toc-ignore\">Fit Statistics</caption><tr><td style='text-align:left'>fitting method</td><td style='text-align:right'>leastsq</td></tr><tr><td style='text-align:left'># function evals</td><td style='text-align:right'>35</td></tr><tr><td style='text-align:left'># data points</td><td style='text-align:right'>21</td></tr><tr><td style='text-align:left'># variables</td><td style='text-align:right'>2</td></tr><tr><td style='text-align:left'>chi-square</td><td style='text-align:right'> 39929.6509</td></tr><tr><td style='text-align:left'>reduced chi-square</td><td style='text-align:right'> 2101.56058</td></tr><tr><td style='text-align:left'>Akaike info crit.</td><td style='text-align:right'> 162.557392</td></tr><tr><td style='text-align:left'>Bayesian info crit.</td><td style='text-align:right'> 164.646437</td></tr></table><table class=\"jp-toc-ignore\"><caption>Parameters</caption><tr><th style='text-align:left'>name</th><th style='text-align:left'>value</th><th style='text-align:left'>standard error</th><th style='text-align:left'>relative error</th><th style='text-align:left'>initial value</th><th style='text-align:left'>min</th><th style='text-align:left'>max</th><th style='text-align:right'>vary</th></tr><tr><td style='text-align:left'>vmax</td><td style='text-align:left'> 309.360278</td><td style='text-align:left'> 22.7767456</td><td style='text-align:left'>(7.36%)</td><td style='text-align:left'>1.0</td><td style='text-align:left'> 0.00000000</td><td style='text-align:left'>        inf</td><td style='text-align:right'>True</td></tr><tr><td style='text-align:left'>km</td><td style='text-align:left'> 41.1672958</td><td style='text-align:left'> 12.7408933</td><td style='text-align:left'>(30.95%)</td><td style='text-align:left'>1.0</td><td style='text-align:left'> 0.00000000</td><td style='text-align:left'>        inf</td><td style='text-align:right'>True</td></tr></table><table class=\"jp-toc-ignore\"><caption>Correlations (unreported values are < 0.100)</caption><tr><th style='text-align:left'>Parameter1</th><th style='text-align:left'>Parameter 2</th><th style='text-align:right'>Correlation</th></tr><tr><td style='text-align:left'>vmax</td><td style='text-align:left'>km</td><td style='text-align:right'>+0.7653</td></tr></table>"
      ],
      "text/plain": [
       "<lmfit.minimizer.MinimizerResult at 0x251293ead50>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2568d124-ff01-4236-a812-7071ebc50f48",
   "metadata": {},
   "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>best fit</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>sd</th>\n",
       "      <th>rsd</th>\n",
       "      <th>lower bound (95% ci)</th>\n",
       "      <th>upper bound (95% ci)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>vmax</th>\n",
       "      <td>309.360278</td>\n",
       "      <td>310.943039</td>\n",
       "      <td>309.662246</td>\n",
       "      <td>22.191573</td>\n",
       "      <td>0.071369</td>\n",
       "      <td>271.955234</td>\n",
       "      <td>357.105465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>km</th>\n",
       "      <td>41.167296</td>\n",
       "      <td>42.537911</td>\n",
       "      <td>41.162870</td>\n",
       "      <td>12.242604</td>\n",
       "      <td>0.287805</td>\n",
       "      <td>23.054289</td>\n",
       "      <td>69.831202</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        best fit        mean      median         sd       rsd  \\\n",
       "vmax  309.360278  310.943039  309.662246  22.191573  0.071369   \n",
       "km     41.167296   42.537911   41.162870  12.242604  0.287805   \n",
       "\n",
       "      lower bound (95% ci)  upper bound (95% ci)  \n",
       "vmax            271.955234            357.105465  \n",
       "km               23.054289             69.831202  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ci_par"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9dc5caf1-287a-48a1-b290-d58b6457d479",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x350 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_results(BtCrtI_phytoene, BtCrtI_lycopene_flux, mini, results, ci_sim,\n",
    "             save=[\"./figures/BtCrtI.svg\", \"./figures/BtCrtI.png\", \"./figures/BtCrtI.pdf\"],\n",
    "             plot=dict(figsize=(11, 3.5)),\n",
    "             plot_exp=dict(markerfacecolor=\"#4fA15A\", markeredgewidth=1, markeredgecolor=\"#23612B\"),\n",
    "             plot_sim=dict(color=\"#EF553B\"),\n",
    "             plot_resid=dict(kde=True, stat=\"density\", color=\"#23612B\", kde_kws=dict(cut=3)),\n",
    "             xlim=[0,300],\n",
    "             xlab=\"[Phytoene] (µM)\", ylab=\"Lycopene flux (µM/h)\", leglab=\"BtCrtI\") "
   ]
  }
 ],
 "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": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}