{ "cells": [ { "cell_type": "markdown", "id": "66755e2e-daeb-4e9c-bedf-e72787095e54", "metadata": {}, "source": [ "# Origem dos parâmetros utilizados\n", "\n", "## Rebnegger et al. (2016), *Pichia pastoris* em glicose\n", "- **Parâmetros de Pirt**: $Y_{X/S,\\max}=0.584\\ \\text{gX/gS},\\ m_S=0.0031\\ \\text{gS/gX/h}$ vêm diretamente do artigo de **Rebnegger et al. (2016, *Applied and Environmental Microbiology*)**, estimados em retentostat e chemostato. \n", "\n", "- **Crescimento (Monod)**: $\\mu_{\\max}=0.18\\ \\text{h}^{-1},\\ K_S=0.11\\ \\text{g/L}$ foram adotados como **valores plausíveis para leveduras em glicose**, conservadores em relação a máximos (~0.2–0.3 h⁻¹). \n", "\n", "- **Condições iniciais**: $X_0=0.05\\ \\text{g/L},\\ S_0=3.0\\ \\text{g/L}$ simulam uma batelada com glicose abundante, típica de ensaios laboratoriais.\n", " \n", "\n", "🔗 Artigo: [Rebnegger et al., 2016 — *Applied and Environmental Microbiology*](https://doi.org/10.1128/AEM.00638-16)\n", "\n", "---\n", "\n", "**Resumo**: cada conjunto de parâmetros combina **valores reportados nos artigos** com **ajustes plausíveis baseados na literatura**, garantindo perfis de crescimento e consumo biologicamente realistas.\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "25799689-cad4-4442-8ae8-717481ee5606", "metadata": {}, "outputs": [], "source": [ "\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from scipy.integrate import solve_ivp\n", "from IPython.display import display\n", "\n", "# =========================\n", "# Modelos\n", "# =========================\n", "def mu_haldane(S, mu_max, Ks, Ki):\n", " S = np.clip(np.asarray(S), 0, None)\n", " return mu_max * S / (Ks + S + (S**2)/Ki)\n", "\n", "def mu_monod(S, mu_max, Ks):\n", " S = np.clip(np.asarray(S), 0, None)\n", " return mu_max * S / (Ks + S)\n", "\n", "def qS_pirt(mu, Yxs, mS):\n", " return mu / Yxs + mS\n", "\n", "def rhs_batch(t, y, mu_fun, mu_params, Yxs, mS):\n", " X, S = y\n", " S = max(S, 0.0)\n", " mu = mu_fun(S, **mu_params)\n", " dXdt = mu * X\n", " dSdt = -qS_pirt(mu, Yxs, mS) * X\n", " return [dXdt, dSdt]\n", "\n", "# =========================\n", "\n", "t_span = (0.0, 22.0)\n", "t_eval = np.linspace(*t_span, 600)\n", "\n", "reb_params_mu = dict(mu_max=0.18, Ks=0.11) # h^-1, g/L\n", "reb_Yxs = 0.584\n", "reb_mS = 0.0031\n", "X0_reb, S0_reb = 0.05, 3.00 # g/L\n", "\n", "sol_reb = solve_ivp(\n", " rhs_batch, t_span, [X0_reb, S0_reb],\n", " args=(lambda S, **p: mu_monod(S, **p), reb_params_mu, reb_Yxs, reb_mS),\n", " t_eval=t_eval, rtol=1e-7, atol=1e-9\n", ")\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "8981bf23-87ed-4156-ba33-8e473abac249", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Rebnegger 2016 — Monod + Pirttempo_hsubstrato_S_gLcelulas_X_gL
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\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def tabela_por_hora(sol, titulo, t_max=22):\n", " # tempos inteiros dentro do intervalo da simulação\n", " t_horas = np.arange(0, min(t_max, sol.t[-1]) + 1, 2.0)\n", "\n", " # interpolação sobre o grid t_eval já calculado\n", " X_interp = np.interp(t_horas, sol.t, sol.y[0])\n", " S_interp = np.interp(t_horas, sol.t, sol.y[1])\n", "\n", " df = pd.DataFrame({\n", " \"tempo_h\": t_horas,\n", " \"substrato_S_gL\": S_interp,\n", " \"celulas_X_gL\": X_interp,\n", " })\n", " df = df[[\"tempo_h\", \"substrato_S_gL\", \"celulas_X_gL\"]].copy()\n", " df.columns.name = titulo\n", " return df\n", "\n", "df_reb = tabela_por_hora(sol_reb, \"Rebnegger 2016 — Monod + Pirt\", t_max=22)\n", "\n", "display(df_reb.head().style.format({\"substrato_S_gL\": \"{:.4f}\", \"celulas_X_gL\": \"{:.4f}\"}))\n" ] }, { "cell_type": "code", "execution_count": 3, "id": "f46812ea-e972-4906-bf76-8891cf2405e5", "metadata": {}, "outputs": [], "source": [ "# Parâmetros do ruído\n", "mean = 0.0 # média do ruído\n", "std_X = 0.05 # desvio padrão para X\n", "std_S = 0.05 # desvio padrão para S\n", "\n", "# Copiar o DataFrame para não sobrescrever o original\n", "df_noisy = df_reb.copy()\n", "\n", "# Adicionar ruído gaussiano às colunas de interesse\n", "idx = df_reb.index[1:]\n", "\n", "df_noisy.loc[idx, \"celulas_X_gL\"] = df_reb.loc[idx, \"celulas_X_gL\"] + np.random.normal(mean, std_X, size=len(idx))\n", "df_noisy.loc[idx, \"substrato_S_gL\"] = df_reb.loc[idx, \"substrato_S_gL\"] + np.random.normal(mean, std_S, size=len(idx))\n", "\n", "df_noisy = df_noisy.clip(lower=0)\n" ] }, { "cell_type": "code", "execution_count": 4, "id": "311fda7a-5189-4549-8735-730e6609ac32", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(7,5))\n", "plt.scatter(df_noisy[\"tempo_h\"], df_noisy[\"celulas_X_gL\"], label=\"X ruidoso\")\n", "plt.plot(sol_reb.t, sol_reb.y[0], lw=2, label=\"X modelado\")\n", "\n", "plt.scatter(df_noisy[\"tempo_h\"], df_noisy[\"substrato_S_gL\"], label=\"S Ruidoso\")\n", "plt.plot(sol_reb.t, sol_reb.y[1], lw=2, label=\"S modelado\")\n", "\n", "plt.xlabel(\"Tempo (h)\")\n", "plt.ylabel(\"Concentração (g/L)\")\n", "plt.title(\"Curvas modeladas e dados com ruido arificial\")\n", "plt.grid(True); plt.legend(); plt.show()\n" ] }, { "cell_type": "code", "execution_count": 5, "id": "75dbf5b1-b6cd-47f0-8a19-edf03311649d", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Rebnegger 2016 — Monod + Pirttempo_hsubstrato_S_gLcelulas_X_gL
00.03.0000000.050000
12.03.1214290.001534
24.02.9714750.108785
36.02.9046320.186982
48.02.8132560.225855
\n", "
" ], "text/plain": [ "Rebnegger 2016 — Monod + Pirt tempo_h substrato_S_gL celulas_X_gL\n", "0 0.0 3.000000 0.050000\n", "1 2.0 3.121429 0.001534\n", "2 4.0 2.971475 0.108785\n", "3 6.0 2.904632 0.186982\n", "4 8.0 2.813256 0.225855" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_noisy.head()" ] }, { "cell_type": "code", "execution_count": 6, "id": "b9ee85c0-b95d-46e2-bacd-8503e83ab504", "metadata": {}, "outputs": [], "source": [ "df_noisy.to_json(\"dados.json\", orient=\"records\", indent=2)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (SciPy + Matplotlib)", "language": "python", "name": "python3-sci" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.11" } }, "nbformat": 4, "nbformat_minor": 5 }