From 8bc3729f496ab96fade20e3595ba42ccb643a97b Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Cl=C3=A9mence=20R=C3=A9da?= <clemence.reda@inserm.fr>
Date: Thu, 16 May 2024 10:20:43 +0000
Subject: [PATCH] gt bioss notebooks

---
 GTBioss2024/README.md                      |   26 +
 GTBioss2024/synthetic_random_diff3-1.ipynb | 1222 ++++++++++++++++
 GTBioss2024/toy_reprogramming_1.ipynb      |  856 +++++++++++
 GTBioss2024/toy_star_1.ipynb               | 1501 ++++++++++++++++++++
 4 files changed, 3605 insertions(+)
 create mode 100644 GTBioss2024/README.md
 create mode 100644 GTBioss2024/synthetic_random_diff3-1.ipynb
 create mode 100644 GTBioss2024/toy_reprogramming_1.ipynb
 create mode 100644 GTBioss2024/toy_star_1.ipynb

diff --git a/GTBioss2024/README.md b/GTBioss2024/README.md
new file mode 100644
index 0000000..1057814
--- /dev/null
+++ b/GTBioss2024/README.md
@@ -0,0 +1,26 @@
+# NORDic (Boolean network; end-to-end inference)
+
+Note that NORDic relies on [BoneSiS](https://github.com/bioasp/bonesis) for the inference of Boolean networks. In those synthetic examples, we consider the Boolean trajectories, an appropriate prior knowledge network (or the complete undirected unsigned graph if we have no biological information), and then run the inference + model selection pipeline to get a single model. Note that NORDic can only consider an initial and final (steady) states (no intermediary ones) and a single gene perturbation at a time. Then, some (parts) of the Boolean trajectories are actually ignored in these notebooks.
+
+## Installation
+
+```bash
+## create virtual env
+conda create -n nordic_env python=3.8 -y
+conda activate nordic_env
+
+python3 -m pip install nordic jupyter
+
+## clean up (if needed)
+python3 -m pip cache purge
+conda clean -a -y
+```
+
+## Launch notebook(s)
+
+```bash
+conda activate nordic_env
+jupyter notebook toy_star_1.ipynb
+jupyter notebook toy_reprogramming_1.ipynb
+jupyter notebook synthetic_random_diff3-1.ipynb
+```
diff --git a/GTBioss2024/synthetic_random_diff3-1.ipynb b/GTBioss2024/synthetic_random_diff3-1.ipynb
new file mode 100644
index 0000000..f8efb56
--- /dev/null
+++ b/GTBioss2024/synthetic_random_diff3-1.ipynb
@@ -0,0 +1,1222 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "bbf1f33f",
+   "metadata": {},
+   "source": [
+    "# Apply NORDic to the ``synthetic_random_diff3-1`` synthetic dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "2f1b7776",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import NORDic\n",
+    "from IPython.display import Image\n",
+    "import pandas as pd\n",
+    "\n",
+    "dataset_name = \"synthetic_random_diff3-1\"\n",
+    "pert_genes = [\"x14\", \"x1\", \"x2\", \"x7\", \"x9\"]\n",
+    "\n",
+    "file_folder=f\"{dataset_name}/\"\n",
+    "seed_number=123456"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d6daf80d",
+   "metadata": {},
+   "source": [
+    "In this case, we directly have access to binarized (Boolean) trajectories from an initial state to a single steady state (plus intermediary states, that will be ignored) for several synthetic KO experiments.\n",
+    "\n",
+    "Let's convert this into a NORDic-compatible format.\n",
+    "\n",
+    "\n",
+    "## 0. Retrieve the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "26636303",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "wt-boolean-trajectories.csv\n",
+      "                   x0  x1  x10  x11  x12  x13  x14  x15  x16  x17  ...  x27  \\\n",
+      "init                0   0    1    0    0    1    1    0    0    0  ...    1   \n",
+      "init_to_steady1_1   0   0    1    0    0    1    1    0    0    0  ...    1   \n",
+      "init_to_steady1_2   1   0    1    0    0    1    1    0    0    0  ...    1   \n",
+      "init_to_steady1_3   1   0    1    0    0    1    1    0    0    0  ...    1   \n",
+      "init_to_steady1_4   1   0    1    0    0    1    1    0    0    0  ...    1   \n",
+      "\n",
+      "                   x28  x29  x3  x4  x5  x6  x7  x8  x9  \n",
+      "init                 0    1   1   1   1   1   0   0   0  \n",
+      "init_to_steady1_1    0    1   1   0   1   1   0   0   0  \n",
+      "init_to_steady1_2    0    1   1   0   1   1   0   0   0  \n",
+      "init_to_steady1_3    0    1   1   0   1   1   0   0   0  \n",
+      "init_to_steady1_4    0    1   1   0   1   1   0   0   0  \n",
+      "\n",
+      "[5 rows x 30 columns]\n",
+      "x14KO-boolean-trajectories.csv\n",
+      "                   x0  x1  x10  x11  x12  x13  x14  x15  x16  x17  ...  x27  \\\n",
+      "init                0   0    1    0    0    1    0    0    0    0  ...    1   \n",
+      "init_to_steady1_1   0   0    1    0    0    1    0    0    0    0  ...    1   \n",
+      "init_to_steady1_2   1   0    1    0    0    1    0    0    0    0  ...    1   \n",
+      "init_to_steady1_3   1   0    1    0    0    1    0    0    0    0  ...    1   \n",
+      "init_to_steady1_4   1   0    1    0    0    1    0    0    0    0  ...    1   \n",
+      "\n",
+      "                   x28  x29  x3  x4  x5  x6  x7  x8  x9  \n",
+      "init                 0    1   1   1   1   1   0   0   0  \n",
+      "init_to_steady1_1    0    1   1   1   1   1   0   0   0  \n",
+      "init_to_steady1_2    0    1   1   1   1   1   0   0   0  \n",
+      "init_to_steady1_3    0    1   1   1   1   1   0   0   1  \n",
+      "init_to_steady1_4    0    0   1   1   1   1   0   0   1  \n",
+      "\n",
+      "[5 rows x 30 columns]\n",
+      "x1KO-boolean-trajectories.csv\n",
+      "                   x0  x1  x10  x11  x12  x13  x14  x15  x16  x17  ...  x27  \\\n",
+      "init                0   0    1    0    0    1    1    0    0    0  ...    1   \n",
+      "init_to_steady1_1   0   0    1    0    0    1    1    0    0    0  ...    1   \n",
+      "init_to_steady1_2   1   0    1    0    0    1    1    0    0    0  ...    1   \n",
+      "init_to_steady1_3   1   0    1    0    0    1    1    0    0    0  ...    1   \n",
+      "init_to_steady1_4   1   0    1    0    0    1    1    0    0    0  ...    1   \n",
+      "\n",
+      "                   x28  x29  x3  x4  x5  x6  x7  x8  x9  \n",
+      "init                 0    1   1   1   1   1   0   0   0  \n",
+      "init_to_steady1_1    0    1   1   0   1   1   0   0   0  \n",
+      "init_to_steady1_2    0    1   1   0   1   1   0   0   0  \n",
+      "init_to_steady1_3    0    1   1   0   1   1   0   0   0  \n",
+      "init_to_steady1_4    0    1   1   0   1   1   0   0   0  \n",
+      "\n",
+      "[5 rows x 30 columns]\n",
+      "x2KO-boolean-trajectories.csv\n",
+      "                   x0  x1  x10  x11  x12  x13  x14  x15  x16  x17  ...  x27  \\\n",
+      "init                0   0    1    0    0    1    1    0    0    0  ...    1   \n",
+      "init_to_steady1_1   0   0    1    0    0    0    1    0    0    0  ...    1   \n",
+      "init_to_steady1_2   0   0    1    0    0    0    1    0    0    0  ...    1   \n",
+      "init_to_steady1_3   0   0    1    0    0    0    1    0    0    0  ...    1   \n",
+      "init_to_steady1_4   0   0    1    0    0    0    1    0    0    0  ...    1   \n",
+      "\n",
+      "                   x28  x29  x3  x4  x5  x6  x7  x8  x9  \n",
+      "init                 0    1   1   1   1   1   0   0   0  \n",
+      "init_to_steady1_1    0    1   1   1   1   1   0   0   0  \n",
+      "init_to_steady1_2    0    1   1   1   1   1   0   0   0  \n",
+      "init_to_steady1_3    0    1   1   1   1   1   0   0   0  \n",
+      "init_to_steady1_4    0    1   1   1   1   1   0   0   0  \n",
+      "\n",
+      "[5 rows x 30 columns]\n",
+      "x7KO-boolean-trajectories.csv\n",
+      "                   x0  x1  x10  x11  x12  x13  x14  x15  x16  x17  ...  x27  \\\n",
+      "init                0   0    1    0    0    1    1    0    0    0  ...    1   \n",
+      "init_to_steady1_1   0   0    1    0    0    1    1    0    0    0  ...    1   \n",
+      "init_to_steady1_2   1   0    1    0    0    1    1    0    0    0  ...    1   \n",
+      "init_to_steady1_3   1   0    1    0    0    1    1    0    0    0  ...    1   \n",
+      "init_to_steady1_4   1   0    1    0    0    1    1    0    0    0  ...    1   \n",
+      "\n",
+      "                   x28  x29  x3  x4  x5  x6  x7  x8  x9  \n",
+      "init                 0    1   1   1   1   1   0   0   0  \n",
+      "init_to_steady1_1    0    1   1   0   1   1   0   0   0  \n",
+      "init_to_steady1_2    0    1   1   0   1   1   0   0   0  \n",
+      "init_to_steady1_3    0    1   1   0   1   1   0   0   0  \n",
+      "init_to_steady1_4    0    1   1   0   1   1   0   0   0  \n",
+      "\n",
+      "[5 rows x 30 columns]\n",
+      "x9KO-boolean-trajectories.csv\n",
+      "                   x0  x1  x10  x11  x12  x13  x14  x15  x16  x17  ...  x27  \\\n",
+      "init                0   0    1    0    0    1    1    0    0    0  ...    1   \n",
+      "init_to_steady1_1   0   0    1    0    0    1    1    0    1    0  ...    1   \n",
+      "init_to_steady1_2   0   0    1    0    0    1    1    0    1    0  ...    0   \n",
+      "init_to_steady1_3   0   0    1    0    0    1    1    0    1    0  ...    0   \n",
+      "init_to_steady1_4   0   0    1    0    0    1    1    0    1    0  ...    0   \n",
+      "\n",
+      "                   x28  x29  x3  x4  x5  x6  x7  x8  x9  \n",
+      "init                 0    1   1   1   1   1   0   0   0  \n",
+      "init_to_steady1_1    0    1   1   1   1   1   0   0   0  \n",
+      "init_to_steady1_2    0    1   1   1   1   1   0   0   0  \n",
+      "init_to_steady1_3    0    1   1   1   1   1   0   0   0  \n",
+      "init_to_steady1_4    0    1   0   1   1   1   0   0   0  \n",
+      "\n",
+      "[5 rows x 30 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "from subprocess import Popen\n",
+    "\n",
+    "proc = Popen(f\"mkdir -p {file_folder}\".split(\" \"))\n",
+    "proc.wait()\n",
+    "\n",
+    "data_url=\"https://raw.githubusercontent.com/gt-bioss/benchmark-dynamical-models/main/\"\n",
+    "data_url+=f\"from-boolean-networks/{dataset_name}/ground-truth/\"\n",
+    "\n",
+    "exp_list = [\"wt-boolean-trajectories.csv\"]+[\n",
+    "    \"%sKO-boolean-trajectories.csv\" % g\n",
+    "    for g in pert_genes\n",
+    "]\n",
+    "\n",
+    "for exp in exp_list:\n",
+    "    proc = Popen(f\"wget -O {file_folder}/{exp} -q {data_url}/{exp}\".split(\" \"))\n",
+    "    proc.wait()\n",
+    "    df = pd.read_csv(f\"{file_folder}/{exp}\", sep=\",\", index_col=0)\n",
+    "    print(exp)\n",
+    "    print(df.head())\n",
+    "    gene_lst = list(df.columns)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "97f13857",
+   "metadata": {},
+   "source": [
+    "## 1. Prior Knowledge Network\n",
+    "\n",
+    "The dataset is synthetic, so there is no prior on the structure of the network. We then build a complete graph (to prevent the resort to the STRING database which will be otherwise useless)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "3c89f0ff-d3ef-43b9-a69a-0ce9987f7cbe",
+   "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>preferredName_A</th>\n",
+       "      <th>preferredName_B</th>\n",
+       "      <th>sign</th>\n",
+       "      <th>directed</th>\n",
+       "      <th>score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>x0</td>\n",
+       "      <td>x1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>x0</td>\n",
+       "      <td>x10</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>x0</td>\n",
+       "      <td>x11</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>x0</td>\n",
+       "      <td>x12</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>x0</td>\n",
+       "      <td>x13</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  preferredName_A preferredName_B  sign  directed  score\n",
+       "0              x0              x1     2         0    0.5\n",
+       "1              x0             x10     2         0    0.5\n",
+       "2              x0             x11     2         0    0.5\n",
+       "3              x0             x12     2         0    0.5\n",
+       "4              x0             x13     2         0    0.5"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "NETWORK_fname = f\"{file_folder}/complete_ppi.tsv\"\n",
+    "\n",
+    "## complete graph\n",
+    "all_pairs = [(g,gp) for ig, g in enumerate(gene_lst) for gp in gene_lst[(ig+1):]]\n",
+    "n = len(all_pairs)\n",
+    "PPI = pd.DataFrame([\n",
+    "    [g, gp, 2, 0, 0.5] ## build undirected unsigned edges with score=0.5\n",
+    "    for g, gp in all_pairs\n",
+    "], columns=[\"preferredName_A\", \"preferredName_B\", \"sign\", \"directed\", \"score\"], index=range(n))\n",
+    "PPI.to_csv(NETWORK_fname, sep=\"\\t\", index=None)\n",
+    "\n",
+    "PPI.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "63034035-4929-4389-a211-57488f322518",
+   "metadata": {},
+   "source": [
+    "## 2. Experimental file\n",
+    "\n",
+    "We convert the Boolean trajectories that we retrieved into a format compatible with NORDic. For each retrieved experimental file (corresponding to a single (non)perturbation experiment):\n",
+    "\n",
+    "- There is the Boolean state of the initial condition named ``init``\n",
+    "- We ignore intermediary states named ``init_to_steady1_*``\n",
+    "- There is the final steady state named ``steady1``"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "340a755b-29cf-42a5-b646-6e03622a50f2",
+   "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>x0</th>\n",
+       "      <th>x1</th>\n",
+       "      <th>x10</th>\n",
+       "      <th>x11</th>\n",
+       "      <th>x12</th>\n",
+       "      <th>x13</th>\n",
+       "      <th>x14</th>\n",
+       "      <th>x15</th>\n",
+       "      <th>x16</th>\n",
+       "      <th>x17</th>\n",
+       "      <th>...</th>\n",
+       "      <th>x5</th>\n",
+       "      <th>x6</th>\n",
+       "      <th>x7</th>\n",
+       "      <th>x8</th>\n",
+       "      <th>x9</th>\n",
+       "      <th>annotation</th>\n",
+       "      <th>perturbed</th>\n",
+       "      <th>perturbation</th>\n",
+       "      <th>cell_line</th>\n",
+       "      <th>sigid</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>initial_syntheticrandomdiff3-1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>syntheticrandomdiff3-1</td>\n",
+       "      <td>wt-boolean-trajectories.csv:init</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>x14_KD_syntheticrandomdiff3-1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>x14</td>\n",
+       "      <td>KD</td>\n",
+       "      <td>syntheticrandomdiff3-1</td>\n",
+       "      <td>x14KO-boolean-trajectories.csv:steady</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>x1_KD_syntheticrandomdiff3-1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>x1</td>\n",
+       "      <td>KD</td>\n",
+       "      <td>syntheticrandomdiff3-1</td>\n",
+       "      <td>x1KO-boolean-trajectories.csv:steady</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>x2_KD_syntheticrandomdiff3-1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>x2</td>\n",
+       "      <td>KD</td>\n",
+       "      <td>syntheticrandomdiff3-1</td>\n",
+       "      <td>x2KO-boolean-trajectories.csv:steady</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>x7_KD_syntheticrandomdiff3-1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>x7</td>\n",
+       "      <td>KD</td>\n",
+       "      <td>syntheticrandomdiff3-1</td>\n",
+       "      <td>x7KO-boolean-trajectories.csv:steady</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 35 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                               x0 x1 x10 x11 x12 x13 x14 x15 x16 x17  ... x5  \\\n",
+       "initial_syntheticrandomdiff3-1  0  0   1   0   0   1   1   0   0   0  ...  1   \n",
+       "x14_KD_syntheticrandomdiff3-1   1  0   1   1   0   0   0   0   1   1  ...  1   \n",
+       "x1_KD_syntheticrandomdiff3-1    1  0   1   1   0   0   0   0   1   1  ...  1   \n",
+       "x2_KD_syntheticrandomdiff3-1    0  0   1   1   1   0   1   1   1   0  ...  1   \n",
+       "x7_KD_syntheticrandomdiff3-1    1  0   1   1   0   0   0   0   1   1  ...  1   \n",
+       "\n",
+       "                               x6 x7 x8 x9 annotation perturbed perturbation  \\\n",
+       "initial_syntheticrandomdiff3-1  1  0  0  0          1      None         None   \n",
+       "x14_KD_syntheticrandomdiff3-1   0  0  0  1          2       x14           KD   \n",
+       "x1_KD_syntheticrandomdiff3-1    0  0  0  1          2        x1           KD   \n",
+       "x2_KD_syntheticrandomdiff3-1    0  0  0  0          2        x2           KD   \n",
+       "x7_KD_syntheticrandomdiff3-1    0  0  0  1          2        x7           KD   \n",
+       "\n",
+       "                                             cell_line  \\\n",
+       "initial_syntheticrandomdiff3-1  syntheticrandomdiff3-1   \n",
+       "x14_KD_syntheticrandomdiff3-1   syntheticrandomdiff3-1   \n",
+       "x1_KD_syntheticrandomdiff3-1    syntheticrandomdiff3-1   \n",
+       "x2_KD_syntheticrandomdiff3-1    syntheticrandomdiff3-1   \n",
+       "x7_KD_syntheticrandomdiff3-1    syntheticrandomdiff3-1   \n",
+       "\n",
+       "                                                                sigid  \n",
+       "initial_syntheticrandomdiff3-1       wt-boolean-trajectories.csv:init  \n",
+       "x14_KD_syntheticrandomdiff3-1   x14KO-boolean-trajectories.csv:steady  \n",
+       "x1_KD_syntheticrandomdiff3-1     x1KO-boolean-trajectories.csv:steady  \n",
+       "x2_KD_syntheticrandomdiff3-1     x2KO-boolean-trajectories.csv:steady  \n",
+       "x7_KD_syntheticrandomdiff3-1     x7KO-boolean-trajectories.csv:steady  \n",
+       "\n",
+       "[5 rows x 35 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "EXPERIMENTS_fname = f\"{file_folder}/experiments.csv\"\n",
+    "experiments = pd.DataFrame([], index=gene_lst+[\"annotation\", \"perturbed\", \"perturbation\", \"cell_line\", \"sigid\"])\n",
+    "cell=\"\".join(dataset_name.split(\"_\")) ## no \"_\"\n",
+    "\n",
+    "for ie, exp in enumerate(exp_list):\n",
+    "    df = pd.read_csv(f\"{file_folder}/{exp}\", sep=\",\", index_col=0)\n",
+    "    if (\"wt-\" in exp):\n",
+    "        experiments[f\"initial_{cell}\"] = df.loc[\"init\"][gene_lst].values.flatten().tolist()+[\"1\",\"None\",\"None\",cell,f\"{exp}:init\"]\n",
+    "        continue\n",
+    "    if ((len(exp.split(\"KO\"))>2) or (\"UP\" in exp)):\n",
+    "        continue\n",
+    "    else:\n",
+    "        pert_gene = exp.split(\"KO\")[0]\n",
+    "        pert_dir = \"KD\"\n",
+    "    exp_name = f\"{pert_gene}_KD_\"+cell\n",
+    "    steady_name = \"steady\" if (\"steady\" in df.index) else \"steady1\"\n",
+    "    experiments[exp_name] = df.loc[steady_name][gene_lst].values.flatten().tolist()+[\"2\",pert_gene,pert_dir,cell,f\"{exp}:steady\"]\n",
+    "experiments.to_csv(EXPERIMENTS_fname, sep=\",\")\n",
+    "\n",
+    "experiments.T.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ee59a30f-bc01-4af6-b727-e346609475ba",
+   "metadata": {},
+   "source": [
+    "## 3. Parameters\n",
+    "\n",
+    "Not needed, since both the PKN and the experiments are available from the start."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "be6929c0-d0e3-489a-94bc-63997747f7f1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from multiprocessing import cpu_count\n",
+    "njobs=1\n",
+    "\n",
+    "## Random\n",
+    "taxon_id=1111\n",
+    "disease_cids=[\"Unknown\"] \n",
+    "cell_lines=[cell]\n",
+    "path_to_genes=None\n",
+    "\n",
+    "DisGeNET_credentials = \"\"\n",
+    "STRING_credentials = \"\"\n",
+    "LINCS_credentials = \"\"\n",
+    "DISGENET_args = {\"credentials\": DisGeNET_credentials, \"disease_cids\": disease_cids}\n",
+    "STRING_args = {\"credentials\": STRING_credentials, \"score\": 0}\n",
+    "EDGE_args = {\"tau\": None, \"filter\": False, \"connected\": False}\n",
+    "accept_nonRNA=True\n",
+    "preserve_network_sign=False\n",
+    "LINCS_args = {\"path_to_lincs\": \"\", \n",
+    "              \"credentials\": LINCS_credentials,\n",
+    "              \"cell_lines\": cell_lines, \"thres_iscale\": None}\n",
+    "SIG_args = {\"bin_thres\": 0.5}\n",
+    "force_experiments=False\n",
+    "DESIRABILITY = {\"DS\": 3, \"CL\": 3, \"Centr\": 3, \"GT\": 1}\n",
+    "\n",
+    "## Identification parameters\n",
+    "BONESIS_args = {\"limit\": 100, \"exact\": False, \"max_maxclause\": 1}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "292b0b3b-509e-416c-b08a-90e9f975ca11",
+   "metadata": {},
+   "source": [
+    "## 4. Network identification"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "1cf59895-8f6f-4c37-8c9b-f9293d8bf41f",
+   "metadata": {},
+   "outputs": [
+    {
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+       "                name: f.name\n",
+       "            };\n",
+       "\n",
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+       "function resolve_function(tool_api, funcname) {\n",
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+       "        return window[funcname];\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "function colomoto_extension(Jupyter, ssid, name, menu, toolbar, tool_api) {\n",
+       "\n",
+       "    function insert_snippet_code(snippet) {\n",
+       "        var cell = Jupyter.notebook.get_selected_cell();\n",
+       "        Jupyter.notebook.edit_mode();\n",
+       "        cell.code_mirror.replaceSelection(snippet, 'around');\n",
+       "        //cell.focus_editor();\n",
+       "    }\n",
+       "\n",
+       "    /**\n",
+       "        from https://github.com/moble/jupyter_boilerplate/blob/master/main.js\n",
+       "    */\n",
+       "    function callback_insert_snippet (evt) {\n",
+       "        // this (or event.currentTarget, see below) always refers to the DOM\n",
+       "        // element the listener was attached to - see\n",
+       "        // http://stackoverflow.com/questions/12077859\n",
+       "        insert_snippet_code($(evt.currentTarget).data('snippet-code'));\n",
+       "    }\n",
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+       "\n",
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+       "\n",
+       "        var a = $('<a/>')\n",
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+       "            .appendTo(element);\n",
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+       "            .addClass('snippet');\n",
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+       "            a.attr('href', menu_item_spec['external-link']);\n",
+       "            a.attr({\n",
+       "                'target' : '_blank',\n",
+       "                'title' : 'Opens in a new window',\n",
+       "            });\n",
+       "            $('<i class=\"fa fa-external-link menu-icon pull-right\"/>').appendTo(a);\n",
+       "            $('<span/>').html(menu_item_spec.name).appendTo(a);\n",
+       "        }\n",
+       "\n",
+       "        if (menu_item_spec.hasOwnProperty('sub-menu')) {\n",
+       "            element\n",
+       "                .addClass('dropdown-submenu')\n",
+       "                .toggleClass('dropdown-submenu-left', direction === 'left');\n",
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+       "                .toggleClass('dropdown-menu-compact', menu_item_spec.overlay === true) // For space-saving menus\n",
+       "                .appendTo(element);\n",
+       "\n",
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+       "                var sub_menu_item_spec = build_menu_element(menu_item_spec['sub-menu'][j], new_direction);\n",
+       "                if(sub_menu_item_spec !== null) {\n",
+       "                    sub_menu_item_spec.appendTo(sub_element);\n",
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+       "        }\n",
+       "\n",
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+       "\n",
+       "    function menu_setup (menu_item_specs, sibling, insert_before_sibling) {\n",
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+       "            var menu_element = build_menu_element(menu_item_spec, direction);\n",
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+       "                && oa.data[\"text/html\"].indexOf(' class=\"to-be-removed\"') >= 0) {\n",
+       "                to_remove = i;\n",
+       "                break;\n",
+       "            }\n",
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+       "        } else {\n",
+       "            cell.output_area.outputs.splice(to_remove, 1);\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    function toolbar_setup(actions) {\n",
+       "        var buttons = [];\n",
+       "        for (var i = 0; i < actions.length; ++i) {\n",
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+       "            if (typeof setup.handler == 'string') {\n",
+       "                setup.handler = resolve_function(tool_api, setup.handler);\n",
+       "            }\n",
+       "            buttons.push(Jupyter.actions.register(actions[i].setup,\n",
+       "                actions[i].name, name));\n",
+       "        }\n",
+       "        $(\"#\"+name+\"-toolbar\").remove();\n",
+       "        Jupyter.toolbar.add_buttons_group(buttons, name+\"-toolbar\");\n",
+       "    }\n",
+       "\n",
+       "    function replace_menu_snippets(menu_spec, orig, dest) {\n",
+       "        if (menu_spec.hasOwnProperty(\"snippet\")) {\n",
+       "            var snippet = menu_spec.snippet;\n",
+       "            if (typeof snippet == \"string\" || snippet instanceof String) {\n",
+       "                menu_spec[\"snippet\"] = snippet.replace(orig, dest);\n",
+       "            } else {\n",
+       "                for (var i = 0; i < snippet.length; ++i) {\n",
+       "                    menu_spec[\"snippet\"][i] = snippet[i].replace(orig, dest);\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "        if (menu_spec.hasOwnProperty(\"sub-menu\")) {\n",
+       "            for (var i = 0; i < menu_spec[\"sub-menu\"].length; ++i) {\n",
+       "                replace_menu_snippets(menu_spec[\"sub-menu\"][i], orig, dest);\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    function load_ipython_extension() {\n",
+       "\n",
+       "        var mycellelt = $(\"#\"+ssid).parents('.cell');\n",
+       "        var myidx = Jupyter.notebook.get_cell_elements().index(mycellelt);\n",
+       "        var import_cell = Jupyter.notebook.get_cell(myidx);\n",
+       "\n",
+       "        var alias = detect_import(import_cell, name);\n",
+       "        tool_api.module_alias = alias;\n",
+       "        if (alias && alias != name) {\n",
+       "            var orig = new RegExp(\"\\\\b\"+name+\"\\\\b\", \"g\");\n",
+       "            replace_menu_snippets(menu, orig, alias);\n",
+       "        }\n",
+       "\n",
+       "        if (toolbar) {\n",
+       "            toolbar_setup(toolbar);\n",
+       "        }\n",
+       "\n",
+       "        $(\"#\"+name+\"_menu\").parent().remove();\n",
+       "        if (menu) {\n",
+       "            menu_setup([menu], $(\"#help_menu\").parent(), true);\n",
+       "        }\n",
+       "\n",
+       "        if (tool_api.hasOwnProperty(\"post_install_callback\")) {\n",
+       "            tool_api.post_install_callback();\n",
+       "        }\n",
+       "\n",
+       "        setTimeout(self_cleanup, 5000);\n",
+       "    };\n",
+       "\n",
+       "    load_ipython_extension();\n",
+       "}\n",
+       "\n",
+       "function resolve_toolbar_handlers(tool_api, toolbar_spec) {\n",
+       "    for (var i = 0; i < toolbar_spec.length; ++i) {\n",
+       "        func = resolve_function(tool_api, toolbar_spec[i][\"setup\"][\"handler\"]);\n",
+       "        toolbar_spec[i][\"setup\"][\"handler\"] = func;\n",
+       "    }\n",
+       "    return toolbar_spec\n",
+       "}\n",
+       "\n",
+       "\n",
+       "    var minibn_jsapi = {  };\n",
+       "    colomoto_extension(Jupyter, \"colomoto-setup-765099\", \"minibn\", null, null, minibn_jsapi);\n",
+       "     }</script>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "100 solutions are already generated, and saved at synthetic_random_diff3-1/SOLUTIONS-100_binthres=0.500_thresiscale=None_score=0.00_maxclause=1_{1,...1}.zip\n",
+      "--- SELECTION OF OPTIMAL MODEL ---\n",
+      "A. Select an optimal model based on a topological criteria...  ...100 solutions (100 unique solutions)\n",
+      "... 100 solutions (10/31 constant genes in average)\n",
+      "<MODEL VISUALIZATION> Minimal solution: 8 edges\n",
+      "<MODEL VISUALIZATION> Maximal solution: 35 edges\n",
+      "          1         2        3         4         5         6         7    \\\n",
+      "GTP  0.863681  0.870383  0.85169  0.857943  0.860424  0.854826  0.862331   \n",
+      "\n",
+      "          8         9         10       11        12       13        14   \\\n",
+      "GTP  0.854432  0.850094  0.863363  0.83956  0.867622  0.85591  0.872872   \n",
+      "\n",
+      "          15        16        17      18        19        20        21   \\\n",
+      "GTP  0.876584  0.842547  0.874981  0.8725  0.867887  0.845996  0.854163   \n",
+      "\n",
+      "          22        23        24        25       26        27        28   \\\n",
+      "GTP  0.862455  0.873937  0.861933  0.853648  0.86199  0.836758  0.861784   \n",
+      "\n",
+      "          29        30        31        32        33        34        35   \\\n",
+      "GTP  0.877292  0.858581  0.864791  0.863015  0.836531  0.862513  0.849207   \n",
+      "\n",
+      "          36        37        38        39        40        41        42   \\\n",
+      "GTP  0.838651  0.855295  0.871055  0.873083  0.862898  0.841452  0.853423   \n",
+      "\n",
+      "         43        44        45        46        47        48        49   \\\n",
+      "GTP  0.86478  0.847717  0.851254  0.870482  0.826107  0.871889  0.872306   \n",
+      "\n",
+      "          50        51        52       53        54        55        56   \\\n",
+      "GTP  0.861512  0.852671  0.841539  0.87135  0.857139  0.871978  0.849256   \n",
+      "\n",
+      "          57        58        59        60        61        62        63   \\\n",
+      "GTP  0.860781  0.858428  0.862434  0.860153  0.872826  0.856641  0.861873   \n",
+      "\n",
+      "          64        65        66        67        68        69        70   \\\n",
+      "GTP  0.844687  0.877925  0.874057  0.870195  0.866309  0.840394  0.859524   \n",
+      "\n",
+      "         71        72        73        74        75        76        77   \\\n",
+      "GTP  0.84572  0.867655  0.859167  0.873993  0.859008  0.849639  0.856746   \n",
+      "\n",
+      "          78        79        80        81        82        83        84   \\\n",
+      "GTP  0.874788  0.880147  0.852869  0.859047  0.864834  0.877719  0.887459   \n",
+      "\n",
+      "          85        86       87       88        89       90       91   \\\n",
+      "GTP  0.847165  0.870728  0.85477  0.87264  0.846999  0.85782  0.83298   \n",
+      "\n",
+      "          92        93        94        95        96        97        98   \\\n",
+      "GTP  0.875021  0.850032  0.859421  0.858255  0.861844  0.848344  0.868603   \n",
+      "\n",
+      "          99        100  \n",
+      "GTP  0.870059  0.859242  \n",
+      "<MODEL SELECTION> Saving optimal model in 'synthetic_random_diff3-1//solution.bnet' ...\n",
+      "x0, !x15\n",
+      "x1, x15&x17\n",
+      "x10, 1\n",
+      "x11, !x4\n",
+      "x12, !x9\n",
+      "...\n",
+      "... saved!\n"
+     ]
+    }
+   ],
+   "source": [
+    "from NORDic.NORDic_NI.functions import network_identification\n",
+    "\n",
+    "solution = NORDic.NORDic_NI.functions.network_identification(\n",
+    "    file_folder, taxon_id, path_to_genes=path_to_genes, disgenet_args=DISGENET_args, \n",
+    "    string_args=STRING_args, lincs_args=LINCS_args, edge_args=EDGE_args, sig_args=SIG_args,\n",
+    "    bonesis_args=BONESIS_args, weights=DESIRABILITY, experiments_fname=EXPERIMENTS_fname,\n",
+    "    seed=seed_number, network_fname=NETWORK_fname, njobs=njobs, force_experiments=force_experiments, \n",
+    "    accept_nonRNA=accept_nonRNA, preserve_network_sign=preserve_network_sign)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "9741d5f2-54f2-418c-b8e5-b2c1336d21f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import Image\n",
+    "Image(filename=f'{file_folder}/inferred_max_criterion_solution.png') "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8b76b4d6-6042-4cee-98d1-0d132578f34c",
+   "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>GTP</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>84</th>\n",
+       "      <td>0.887459</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>79</th>\n",
+       "      <td>0.880147</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>65</th>\n",
+       "      <td>0.877925</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>83</th>\n",
+       "      <td>0.877719</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>0.877292</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>0.838651</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>0.836758</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>0.836531</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>91</th>\n",
+       "      <td>0.832980</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>0.826107</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>100 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         GTP\n",
+       "84  0.887459\n",
+       "79  0.880147\n",
+       "65  0.877925\n",
+       "83  0.877719\n",
+       "29  0.877292\n",
+       "..       ...\n",
+       "36  0.838651\n",
+       "27  0.836758\n",
+       "33  0.836531\n",
+       "91  0.832980\n",
+       "47  0.826107\n",
+       "\n",
+       "[100 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.read_csv(f\"{file_folder}/GPT.csv\", index_col=0).T.sort_values(by=\"GTP\", ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f49749d-9624-49f1-9613-01de50db6b21",
+   "metadata": {},
+   "source": [
+    "## 5. Ground truth network"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "db11bcfa-34d3-4621-b829-c752a3fde9a0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "network_fname=\"boolean-network.bnet\"\n",
+    "proc = Popen(f\"wget -O {file_folder}/{network_fname} -q {data_url}/{network_fname}\".split(\" \"))\n",
+    "proc.wait()\n",
+    "\n",
+    "from NORDic.UTILS.utils_grn import solution2influences\n",
+    "from NORDic.UTILS.utils_plot import influences2graph\n",
+    "\n",
+    "with open(f\"{file_folder}/{network_fname}\", \"r\") as f:\n",
+    "    ground_truth = pd.DataFrame({\n",
+    "        \"Solution\": dict([[\"_\".join(g.split(\"-\")) for g in x.split(\", \")] for x in f.read().split(\"\\n\") if (len(x)>0)])\n",
+    "    })\n",
+    "influences_ground_truth = solution2influences(ground_truth[\"Solution\"])\n",
+    "influences2graph(influences_ground_truth, file_folder+\"ground_truth\", optional=False, compile2png=True, engine=[\"sfdp\",\"dot\"][0])\n",
+    "from IPython.display import Image\n",
+    "Image(filename=file_folder+'ground_truth.png') "
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "23949c4a-35de-4683-9690-5d7ccc9f1060",
+   "metadata": {},
+   "source": [
+    "if (os.path.exists(f\"{file_folder}\")):\n",
+    "\tproc = Popen(f\"rm -rf {file_folder}\".split(\" \"))\n",
+    "\tproc.wait()"
+   ]
+  }
+ ],
+ "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": "3.8.19"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/GTBioss2024/toy_reprogramming_1.ipynb b/GTBioss2024/toy_reprogramming_1.ipynb
new file mode 100644
index 0000000..7b6abef
--- /dev/null
+++ b/GTBioss2024/toy_reprogramming_1.ipynb
@@ -0,0 +1,856 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "bbf1f33f",
+   "metadata": {},
+   "source": [
+    "# Apply NORDic to the ``toy_reprogramming_1`` synthetic dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 160,
+   "id": "2f1b7776",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import NORDic\n",
+    "from IPython.display import Image\n",
+    "import pandas as pd\n",
+    "\n",
+    "dataset_name = \"toy_reprogramming_1\"\n",
+    "pert_genes = [\"G8UPG1\", \"TF1\", \"TF1KOTF2\", \"TF2\", \"TF5\", \"TF7\"]\n",
+    "\n",
+    "file_folder=f\"{dataset_name}/\"\n",
+    "seed_number=123456"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d6daf80d",
+   "metadata": {},
+   "source": [
+    "In this case, we directly have access to binarized (Boolean) trajectories from an initial state to a single steady state (plus intermediary states, that will be ignored) for several synthetic KO experiments.\n",
+    "\n",
+    "Let's convert this into a NORDic-compatible format.\n",
+    "\n",
+    "\n",
+    "## 0. Retrieve the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 161,
+   "id": "26636303",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "wt-boolean-trajectories.csv\n",
+      "                   G1  G2  G3  G4  G5  G6  G7  G8  G9  TF1  TF2  TF3  TF4  \\\n",
+      "init                0   0   0   0   0   0   1   1   1    0    0    0    0   \n",
+      "init_to_switch1_1   0   0   0   0   0   0   1   1   1    0    0    0    0   \n",
+      "init_to_switch1_2   0   0   0   0   0   0   0   1   1    0    0    0    0   \n",
+      "init_to_switch1_3   0   0   0   0   0   0   0   0   1    0    0    0    0   \n",
+      "init_to_switch1_4   0   0   0   0   0   0   0   0   0    0    0    0    0   \n",
+      "\n",
+      "                   TF5  TF6  TF7  \n",
+      "init                 0    1    1  \n",
+      "init_to_switch1_1    0    0    1  \n",
+      "init_to_switch1_2    0    0    1  \n",
+      "init_to_switch1_3    0    0    1  \n",
+      "init_to_switch1_4    0    0    1  \n",
+      "G8UPG1KO-boolean-trajectories.csv\n",
+      "                   G1  G2  G3  G4  G5  G6  G7  G8  G9  TF1  TF2  TF3  TF4  \\\n",
+      "init                0   0   0   0   0   0   1   1   1    0    0    0    0   \n",
+      "init_to_switch1_1   0   0   0   0   0   0   1   1   1    0    0    0    0   \n",
+      "init_to_switch1_2   0   0   0   0   0   0   0   1   1    0    0    0    0   \n",
+      "init_to_switch1_3   0   0   0   0   0   0   0   1   0    0    0    0    0   \n",
+      "init_to_switch1_4   0   0   0   0   0   0   0   1   0    0    0    0    0   \n",
+      "\n",
+      "                   TF5  TF6  TF7  \n",
+      "init                 0    1    1  \n",
+      "init_to_switch1_1    0    0    1  \n",
+      "init_to_switch1_2    0    0    1  \n",
+      "init_to_switch1_3    0    0    1  \n",
+      "init_to_switch1_4    1    0    1  \n",
+      "TF1KO-boolean-trajectories.csv\n",
+      "                   G1  G2  G3  G4  G5  G6  G7  G8  G9  TF1  TF2  TF3  TF4  \\\n",
+      "init                0   0   0   0   0   0   1   1   1    0    0    0    0   \n",
+      "init_to_steady1_1   0   0   0   0   0   0   1   1   1    0    0    0    0   \n",
+      "init_to_steady1_2   0   0   0   0   0   0   0   1   1    0    0    0    0   \n",
+      "init_to_steady1_3   0   0   0   0   0   0   0   0   1    0    0    0    0   \n",
+      "init_to_steady1_4   0   0   0   0   0   0   0   0   0    0    0    0    0   \n",
+      "\n",
+      "                   TF5  TF6  TF7  \n",
+      "init                 0    1    1  \n",
+      "init_to_steady1_1    0    0    1  \n",
+      "init_to_steady1_2    0    0    1  \n",
+      "init_to_steady1_3    0    0    1  \n",
+      "init_to_steady1_4    0    0    1  \n",
+      "TF1KOTF2KO-boolean-trajectories.csv\n",
+      "                   G1  G2  G3  G4  G5  G6  G7  G8  G9  TF1  TF2  TF3  TF4  \\\n",
+      "init                0   0   0   0   0   0   1   1   1    0    0    0    0   \n",
+      "init_to_steady1_1   0   0   0   0   0   0   1   1   1    0    0    0    0   \n",
+      "init_to_steady1_2   0   0   0   0   0   0   0   1   1    0    0    0    0   \n",
+      "init_to_steady1_3   0   0   0   0   0   0   0   0   1    0    0    0    0   \n",
+      "init_to_steady1_4   0   0   0   0   0   0   0   0   0    0    0    0    0   \n",
+      "\n",
+      "                   TF5  TF6  TF7  \n",
+      "init                 0    1    1  \n",
+      "init_to_steady1_1    0    0    1  \n",
+      "init_to_steady1_2    0    0    1  \n",
+      "init_to_steady1_3    0    0    1  \n",
+      "init_to_steady1_4    0    0    1  \n",
+      "TF2KO-boolean-trajectories.csv\n",
+      "                   G1  G2  G3  G4  G5  G6  G7  G8  G9  TF1  TF2  TF3  TF4  \\\n",
+      "init                0   0   0   0   0   0   1   1   1    0    0    0    0   \n",
+      "init_to_steady1_1   0   0   0   0   0   0   1   1   1    0    0    0    0   \n",
+      "init_to_steady1_2   0   0   0   0   0   0   0   1   1    0    0    0    0   \n",
+      "init_to_steady1_3   0   0   0   0   0   0   0   0   1    0    0    0    0   \n",
+      "init_to_steady1_4   0   0   0   0   0   0   0   0   0    0    0    0    0   \n",
+      "\n",
+      "                   TF5  TF6  TF7  \n",
+      "init                 0    1    1  \n",
+      "init_to_steady1_1    0    0    1  \n",
+      "init_to_steady1_2    0    0    1  \n",
+      "init_to_steady1_3    0    0    1  \n",
+      "init_to_steady1_4    0    0    1  \n",
+      "TF5KO-boolean-trajectories.csv\n",
+      "                   G1  G2  G3  G4  G5  G6  G7  G8  G9  TF1  TF2  TF3  TF4  \\\n",
+      "init                0   0   0   0   0   0   1   1   1    0    0    0    0   \n",
+      "init_to_steady1_1   0   0   0   0   0   0   1   1   1    0    0    0    0   \n",
+      "init_to_steady1_2   0   0   0   0   0   0   0   1   1    0    0    0    0   \n",
+      "init_to_steady1_3   0   0   0   0   0   0   0   0   1    0    0    0    0   \n",
+      "steady1             0   0   0   0   0   0   0   0   0    0    0    0    0   \n",
+      "\n",
+      "                   TF5  TF6  TF7  \n",
+      "init                 0    1    1  \n",
+      "init_to_steady1_1    0    0    1  \n",
+      "init_to_steady1_2    0    0    1  \n",
+      "init_to_steady1_3    0    0    1  \n",
+      "steady1              0    0    1  \n",
+      "TF7KO-boolean-trajectories.csv\n",
+      "        G1  G2  G3  G4  G5  G6  G7  G8  G9  TF1  TF2  TF3  TF4  TF5  TF6  TF7\n",
+      "steady   0   0   0   0   0   0   1   1   1    0    0    0    0    0    1    0\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "from subprocess import Popen\n",
+    "\n",
+    "proc = Popen(f\"mkdir -p {file_folder}\".split(\" \"))\n",
+    "proc.wait()\n",
+    "\n",
+    "data_url=\"https://raw.githubusercontent.com/gt-bioss/benchmark-dynamical-models/main/\"\n",
+    "data_url+=f\"from-boolean-networks/{dataset_name}/ground-truth/\"\n",
+    "\n",
+    "exp_list = [\"wt-boolean-trajectories.csv\"]+[\n",
+    "    \"%sKO-boolean-trajectories.csv\" % g\n",
+    "    for g in pert_genes\n",
+    "]\n",
+    "\n",
+    "for exp in exp_list:\n",
+    "    proc = Popen(f\"wget -O {file_folder}/{exp} -q {data_url}/{exp}\".split(\" \"))\n",
+    "    proc.wait()\n",
+    "    df = pd.read_csv(f\"{file_folder}/{exp}\", sep=\",\", index_col=0)\n",
+    "    print(exp)\n",
+    "    print(df.head())\n",
+    "    gene_lst = list(df.columns)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "97f13857",
+   "metadata": {},
+   "source": [
+    "## 1. Prior Knowledge Network\n",
+    "\n",
+    "The dataset is synthetic, so there is no prior on the structure of the network. We then build a complete graph (to prevent the resort to the STRING database which will be otherwise useless)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 162,
+   "id": "3c89f0ff-d3ef-43b9-a69a-0ce9987f7cbe",
+   "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>preferredName_A</th>\n",
+       "      <th>preferredName_B</th>\n",
+       "      <th>sign</th>\n",
+       "      <th>directed</th>\n",
+       "      <th>score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>TF1</td>\n",
+       "      <td>TF1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>TF1</td>\n",
+       "      <td>TF2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>TF1</td>\n",
+       "      <td>TF3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>TF1</td>\n",
+       "      <td>TF4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>TF1</td>\n",
+       "      <td>TF5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  preferredName_A preferredName_B  sign  directed  score\n",
+       "0             TF1             TF1     2         0    0.5\n",
+       "1             TF1             TF2     2         0    0.5\n",
+       "2             TF1             TF3     2         0    0.5\n",
+       "3             TF1             TF4     2         0    0.5\n",
+       "4             TF1             TF5     2         0    0.5"
+      ]
+     },
+     "execution_count": 162,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "NETWORK_fname = f\"{file_folder}/complete_ppi.tsv\"\n",
+    "\n",
+    "tf_tf = [(g,gp) for g in gene_lst for gp in gene_lst if (\"TF\" in g and \"TF\" in gp)]\n",
+    "tf_g = [(g,gp) for g in gene_lst for gp in gene_lst if (\"TF\" in g and \"G\" in gp)]\n",
+    "n = len(tf_tf)+len(tf_g)\n",
+    "PPI = pd.DataFrame([\n",
+    "    [g, gp, 2, 0, 0.5] ## build undirected unsigned edges for TF-TF with score=0.5\n",
+    "    for g, gp in tf_tf\n",
+    "]+[\n",
+    "    [g, gp, 1, 1, 0.5] ## build directed positive edges for TF->G with score=0.5\n",
+    "    for g, gp in tf_g\n",
+    "], columns=[\"preferredName_A\", \"preferredName_B\", \"sign\", \"directed\", \"score\"], index=range(n))\n",
+    "PPI.to_csv(NETWORK_fname, sep=\"\\t\", index=None)\n",
+    "\n",
+    "PPI.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "63034035-4929-4389-a211-57488f322518",
+   "metadata": {},
+   "source": [
+    "## 2. Experimental file\n",
+    "\n",
+    "We convert the Boolean trajectories that we retrieved into a format compatible with NORDic. For each retrieved experimental file (corresponding to a single (non)perturbation experiment):\n",
+    "\n",
+    "- There is the Boolean state of the initial condition named ``init``\n",
+    "- We ignore intermediary states named ``init_to_steady1_*``\n",
+    "- There is the final steady state named ``steady1``"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 163,
+   "id": "340a755b-29cf-42a5-b646-6e03622a50f2",
+   "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>G1</th>\n",
+       "      <th>G2</th>\n",
+       "      <th>G3</th>\n",
+       "      <th>G4</th>\n",
+       "      <th>G5</th>\n",
+       "      <th>G6</th>\n",
+       "      <th>G7</th>\n",
+       "      <th>G8</th>\n",
+       "      <th>G9</th>\n",
+       "      <th>TF1</th>\n",
+       "      <th>TF2</th>\n",
+       "      <th>TF3</th>\n",
+       "      <th>TF4</th>\n",
+       "      <th>TF5</th>\n",
+       "      <th>TF6</th>\n",
+       "      <th>TF7</th>\n",
+       "      <th>annotation</th>\n",
+       "      <th>perturbed</th>\n",
+       "      <th>perturbation</th>\n",
+       "      <th>cell_line</th>\n",
+       "      <th>sigid</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>initial_toyreprogramming1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>toyreprogramming1</td>\n",
+       "      <td>wt-boolean-trajectories.csv:init</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TF1_KD_toyreprogramming1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>TF1</td>\n",
+       "      <td>KD</td>\n",
+       "      <td>toyreprogramming1</td>\n",
+       "      <td>TF1KO-boolean-trajectories.csv:steady</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TF2_KD_toyreprogramming1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>TF2</td>\n",
+       "      <td>KD</td>\n",
+       "      <td>toyreprogramming1</td>\n",
+       "      <td>TF2KO-boolean-trajectories.csv:steady</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TF5_KD_toyreprogramming1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>TF5</td>\n",
+       "      <td>KD</td>\n",
+       "      <td>toyreprogramming1</td>\n",
+       "      <td>TF5KO-boolean-trajectories.csv:steady</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TF7_KD_toyreprogramming1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>TF7</td>\n",
+       "      <td>KD</td>\n",
+       "      <td>toyreprogramming1</td>\n",
+       "      <td>TF7KO-boolean-trajectories.csv:steady</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                          G1 G2 G3 G4 G5 G6 G7 G8 G9 TF1 TF2 TF3 TF4 TF5 TF6  \\\n",
+       "initial_toyreprogramming1  0  0  0  0  0  0  1  1  1   0   0   0   0   0   1   \n",
+       "TF1_KD_toyreprogramming1   0  0  0  1  1  1  0  0  0   0   1   1   1   1   0   \n",
+       "TF2_KD_toyreprogramming1   1  1  1  0  0  0  0  0  0   1   0   1   1   1   0   \n",
+       "TF5_KD_toyreprogramming1   0  0  0  0  0  0  0  0  0   0   0   0   0   0   0   \n",
+       "TF7_KD_toyreprogramming1   0  0  0  0  0  0  1  1  1   0   0   0   0   0   1   \n",
+       "\n",
+       "                          TF7 annotation perturbed perturbation  \\\n",
+       "initial_toyreprogramming1   1          1      None         None   \n",
+       "TF1_KD_toyreprogramming1    1          2       TF1           KD   \n",
+       "TF2_KD_toyreprogramming1    1          2       TF2           KD   \n",
+       "TF5_KD_toyreprogramming1    1          2       TF5           KD   \n",
+       "TF7_KD_toyreprogramming1    0          2       TF7           KD   \n",
+       "\n",
+       "                                   cell_line  \\\n",
+       "initial_toyreprogramming1  toyreprogramming1   \n",
+       "TF1_KD_toyreprogramming1   toyreprogramming1   \n",
+       "TF2_KD_toyreprogramming1   toyreprogramming1   \n",
+       "TF5_KD_toyreprogramming1   toyreprogramming1   \n",
+       "TF7_KD_toyreprogramming1   toyreprogramming1   \n",
+       "\n",
+       "                                                           sigid  \n",
+       "initial_toyreprogramming1       wt-boolean-trajectories.csv:init  \n",
+       "TF1_KD_toyreprogramming1   TF1KO-boolean-trajectories.csv:steady  \n",
+       "TF2_KD_toyreprogramming1   TF2KO-boolean-trajectories.csv:steady  \n",
+       "TF5_KD_toyreprogramming1   TF5KO-boolean-trajectories.csv:steady  \n",
+       "TF7_KD_toyreprogramming1   TF7KO-boolean-trajectories.csv:steady  "
+      ]
+     },
+     "execution_count": 163,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "EXPERIMENTS_fname = f\"{file_folder}/experiments.csv\"\n",
+    "experiments = pd.DataFrame([], index=gene_lst+[\"annotation\", \"perturbed\", \"perturbation\", \"cell_line\", \"sigid\"])\n",
+    "cell=\"\".join(dataset_name.split(\"_\")) ## no \"_\"\n",
+    "\n",
+    "for ie, exp in enumerate(exp_list):\n",
+    "    df = pd.read_csv(f\"{file_folder}/{exp}\", sep=\",\", index_col=0)\n",
+    "    if (\"wt-\" in exp):\n",
+    "        experiments[f\"initial_{cell}\"] = df.loc[\"init\"][gene_lst].values.flatten().tolist()+[\"1\",\"None\",\"None\",cell,f\"{exp}:init\"]\n",
+    "        continue\n",
+    "    if ((len(exp.split(\"KO\"))>2) or (\"UP\" in exp)):\n",
+    "        continue\n",
+    "    else:\n",
+    "        pert_gene = exp.split(\"KO\")[0]\n",
+    "        pert_dir = \"KD\"\n",
+    "    exp_name = f\"{pert_gene}_KD_\"+cell\n",
+    "    steady_name = \"steady\" if (\"steady\" in df.index) else \"steady1\"\n",
+    "    experiments[exp_name] = df.loc[steady_name][gene_lst].values.flatten().tolist()+[\"2\",pert_gene,pert_dir,cell,f\"{exp}:steady\"]\n",
+    "experiments.to_csv(EXPERIMENTS_fname, sep=\",\")\n",
+    "\n",
+    "experiments.T.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ee59a30f-bc01-4af6-b727-e346609475ba",
+   "metadata": {},
+   "source": [
+    "## 3. Parameters\n",
+    "\n",
+    "Not needed, since both the PKN and the experiments are available from the start."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "id": "be6929c0-d0e3-489a-94bc-63997747f7f1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from multiprocessing import cpu_count\n",
+    "njobs=1\n",
+    "\n",
+    "## Random\n",
+    "taxon_id=1111\n",
+    "disease_cids=[\"Unknown\"] \n",
+    "cell_lines=[cell]\n",
+    "path_to_genes=None\n",
+    "\n",
+    "DisGeNET_credentials = \"\"\n",
+    "STRING_credentials = \"\"\n",
+    "LINCS_credentials = \"\"\n",
+    "DISGENET_args = {\"credentials\": DisGeNET_credentials, \"disease_cids\": disease_cids}\n",
+    "STRING_args = {\"credentials\": STRING_credentials, \"score\": 0}\n",
+    "EDGE_args = {\"tau\": None, \"filter\": False, \"connected\": False}\n",
+    "accept_nonRNA=True\n",
+    "preserve_network_sign=False\n",
+    "LINCS_args = {\"path_to_lincs\": \"\", \n",
+    "              \"credentials\": LINCS_credentials,\n",
+    "              \"cell_lines\": cell_lines, \"thres_iscale\": None}\n",
+    "SIG_args = {\"bin_thres\": 0.5}\n",
+    "force_experiments=False\n",
+    "DESIRABILITY = {\"DS\": 3, \"CL\": 3, \"Centr\": 3, \"GT\": 1}\n",
+    "\n",
+    "## Identification parameters\n",
+    "BONESIS_args = {\"limit\": 1, \"exact\": False, \"max_maxclause\": 10}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "292b0b3b-509e-416c-b08a-90e9f975ca11",
+   "metadata": {},
+   "source": [
+    "## 4. Network identification"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "id": "1cf59895-8f6f-4c37-8c9b-f9293d8bf41f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- DATA IMPORT ---\n",
+      "1. Import gene set from DisGeNET... ... 16 genes imported.\n",
+      "2. Import network from toy_reprogramming_1//complete_ppi.tsv... ... 112 edges in model (including 63 directed edges) with a total of 16 non-isolated genes\n",
+      "3. Import experiments from toy_reprogramming_1//experiments.csv... \n",
+      "\t1 cell lines are considered (['toyreprogramming1'])\n",
+      "... 16 genes in 5 profiles (5 experiments)\n",
+      "\n",
+      "--- CONSTRAINT BUILDING ---\n",
+      "1. Filtering out edges by minimum set of edges with highest score which preserve connectivity... ... 112 unique edges involving genes both in experiments (16 genes in total)\n",
+      "... score_STRING 0.000000\t16 genes (non isolated in PPI)\t259 edges in PPI\n",
+      "2. Build topological constraints from filtered edges using gene expression data...  ...... 14 negative, 98 positive undirected interactions (112 edges in total), 16 non isolated genes in experiments\n",
+      "3. Build dynamical constraints by binarization of experimental profiles...  ...... 4 experiments on 1 cell lines and 16/16 genes (Frobenius norm signature matrix: 8.944, 0 possibly constant genes: 0.0%, 0 genes with undetermined status\n",
+      "\n",
+      "--- INFER BOOLEAN NETWORK ---\n",
+      "1. Generate solutions from topological & dynamical constraints...  ...<UTILS_GRN> Maximum possible #activators=7\n",
+      "<BONESIS> 112 interactions (maximum # of clauses = 7)\n",
+      "\n",
+      "<UTILS_GRN> 4 experiments\n",
+      "            TF2  G7  G2  TF7  G1  G3  G4  TF5  G9  TF6  TF4  G6  G5  TF3  TF1  \\\n",
+      "Exp1_init     0   1   0    0   0   0   0    0   1    1    0   0   0    0    0   \n",
+      "Exp1_final    0   1   0    0   0   0   0    0   1    1    0   0   0    0    0   \n",
+      "Exp2_init     0   1   0    1   0   0   0    0   1    1    0   0   0    0    0   \n",
+      "Exp2_final    1   0   0    1   0   0   1    1   0    0    1   1   1    1    0   \n",
+      "Exp3_init     0   1   0    1   0   0   0    0   1    1    0   0   0    0    0   \n",
+      "Exp3_final    0   0   1    1   1   1   0    1   0    0    1   0   0    1    1   \n",
+      "Exp4_init     0   1   0    1   0   0   0    0   1    1    0   0   0    0    0   \n",
+      "Exp4_final    0   0   0    1   0   0   0    0   0    0    0   0   0    0    0   \n",
+      "\n",
+      "            G8  \n",
+      "Exp1_init    1  \n",
+      "Exp1_final   1  \n",
+      "Exp2_init    1  \n",
+      "Exp2_final   0  \n",
+      "Exp3_init    1  \n",
+      "Exp3_final   0  \n",
+      "Exp4_init    1  \n",
+      "Exp4_final   0  \n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0%|                                                                                                | 0/1 [00:00<?, ?it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Grounding...done in 0.3s\n",
+      "Found 1 solutions in 0.3s (first in 0.3s; rate 0.3s)"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00,  1.51it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- SELECTION OF OPTIMAL MODEL ---\n",
+      "A. Select an optimal model based on a topological criteria...  ...1 solutions (1 unique solutions)\n",
+      "... 1 solutions (1/17 constant genes in average)\n",
+      "<MODEL VISUALIZATION> Minimal solution: 15 edges\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<MODEL VISUALIZATION> Maximal solution: 15 edges\n",
+      "            1\n",
+      "GTP  0.856166\n",
+      "<MODEL SELECTION> Saving optimal model in 'toy_reprogramming_1//solution.bnet' ...\n",
+      "G1, TF1\n",
+      "G2, TF1\n",
+      "G3, TF1\n",
+      "G4, TF2\n",
+      "G5, TF2\n",
+      "...\n",
+      "... saved!\n"
+     ]
+    }
+   ],
+   "source": [
+    "from NORDic.NORDic_NI.functions import network_identification\n",
+    "\n",
+    "solution = NORDic.NORDic_NI.functions.network_identification(\n",
+    "    file_folder, taxon_id, path_to_genes=path_to_genes, disgenet_args=DISGENET_args, \n",
+    "    string_args=STRING_args, lincs_args=LINCS_args, edge_args=EDGE_args, sig_args=SIG_args,\n",
+    "    bonesis_args=BONESIS_args, weights=DESIRABILITY, experiments_fname=EXPERIMENTS_fname,\n",
+    "    seed=seed_number, network_fname=NETWORK_fname, njobs=njobs, force_experiments=force_experiments, \n",
+    "    accept_nonRNA=accept_nonRNA, preserve_network_sign=preserve_network_sign)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 166,
+   "id": "9741d5f2-54f2-418c-b8e5-b2c1336d21f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 166,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import Image\n",
+    "Image(filename=f'{file_folder}/inferred_max_criterion_solution.png') "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
+   "id": "8b76b4d6-6042-4cee-98d1-0d132578f34c",
+   "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>GTP</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.856166</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        GTP\n",
+       "1  0.856166"
+      ]
+     },
+     "execution_count": 167,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.read_csv(f\"{file_folder}/GPT.csv\", index_col=0).T.sort_values(by=\"GTP\", ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f49749d-9624-49f1-9613-01de50db6b21",
+   "metadata": {},
+   "source": [
+    "## 5. Ground truth network"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "id": "db11bcfa-34d3-4621-b829-c752a3fde9a0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 168,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "network_fname=\"boolean-network.bnet\"\n",
+    "proc = Popen(f\"wget -O {file_folder}/{network_fname} -q {data_url}/{network_fname}\".split(\" \"))\n",
+    "proc.wait()\n",
+    "\n",
+    "from NORDic.UTILS.utils_grn import solution2influences\n",
+    "from NORDic.UTILS.utils_plot import influences2graph\n",
+    "\n",
+    "with open(f\"{file_folder}/{network_fname}\", \"r\") as f:\n",
+    "    ground_truth = pd.DataFrame({\n",
+    "        \"Solution\": dict([[\"_\".join(g.split(\"-\")) for g in x.split(\", \")] for x in f.read().split(\"\\n\") if (len(x)>0)])\n",
+    "    })\n",
+    "influences_ground_truth = solution2influences(ground_truth[\"Solution\"])\n",
+    "influences2graph(influences_ground_truth, file_folder+\"ground_truth\", optional=False, compile2png=True, engine=[\"sfdp\",\"dot\"][0])\n",
+    "from IPython.display import Image\n",
+    "Image(filename=file_folder+'ground_truth.png') "
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "23949c4a-35de-4683-9690-5d7ccc9f1060",
+   "metadata": {},
+   "source": [
+    "if (os.path.exists(f\"{file_folder}\")):\n",
+    "\tproc = Popen(f\"rm -rf {file_folder}\".split(\" \"))\n",
+    "\tproc.wait()"
+   ]
+  }
+ ],
+ "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": "3.8.19"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/GTBioss2024/toy_star_1.ipynb b/GTBioss2024/toy_star_1.ipynb
new file mode 100644
index 0000000..604e669
--- /dev/null
+++ b/GTBioss2024/toy_star_1.ipynb
@@ -0,0 +1,1501 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "bbf1f33f",
+   "metadata": {},
+   "source": [
+    "# Apply NORDic to the ``toy_star_1`` synthetic dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "2f1b7776",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import NORDic\n",
+    "from IPython.display import Image\n",
+    "import pandas as pd\n",
+    "\n",
+    "dataset_name = \"toy_star_1\"\n",
+    "pert_genes = [\"gene%d\" % i for i in range(1,11)]\n",
+    "\n",
+    "file_folder=f\"{dataset_name}/\"\n",
+    "seed_number=123456"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d6daf80d",
+   "metadata": {},
+   "source": [
+    "In this case, we directly have access to binarized (Boolean) trajectories from an initial state to a single steady state (plus intermediary states, that will be ignored) for several synthetic KO experiments.\n",
+    "\n",
+    "Let's convert this into a NORDic-compatible format.\n",
+    "\n",
+    "\n",
+    "## 0. Retrieve the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "26636303",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "wt-boolean-trajectories.csv\n",
+      "                   tf  gene1  gene2  gene3  gene4  gene5  gene6  gene7  gene8  \\\n",
+      "init                1      0      0      0      0      0      0      0      0   \n",
+      "init_to_steady1_1   1      0      1      0      0      0      0      0      0   \n",
+      "init_to_steady1_2   1      0      1      0      0      0      0      0      1   \n",
+      "init_to_steady1_3   1      0      1      1      0      0      0      0      1   \n",
+      "init_to_steady1_4   1      0      1      1      1      0      0      0      1   \n",
+      "\n",
+      "                   gene9  gene10  \n",
+      "init                   0       0  \n",
+      "init_to_steady1_1      0       0  \n",
+      "init_to_steady1_2      0       0  \n",
+      "init_to_steady1_3      0       0  \n",
+      "init_to_steady1_4      0       0  \n",
+      "gene1KO-boolean-trajectories.csv\n",
+      "                   tf  gene1  gene2  gene3  gene4  gene5  gene6  gene7  gene8  \\\n",
+      "init                1      0      0      0      0      0      0      0      0   \n",
+      "init_to_steady1_1   1      0      1      0      0      0      0      0      0   \n",
+      "init_to_steady1_2   1      0      1      0      0      0      0      0      1   \n",
+      "init_to_steady1_3   1      0      1      1      0      0      0      0      1   \n",
+      "init_to_steady1_4   1      0      1      1      1      0      0      0      1   \n",
+      "\n",
+      "                   gene9  gene10  \n",
+      "init                   0       0  \n",
+      "init_to_steady1_1      0       0  \n",
+      "init_to_steady1_2      0       0  \n",
+      "init_to_steady1_3      0       0  \n",
+      "init_to_steady1_4      0       0  \n",
+      "gene2KO-boolean-trajectories.csv\n",
+      "                   tf  gene1  gene2  gene3  gene4  gene5  gene6  gene7  gene8  \\\n",
+      "init                1      0      0      0      0      0      0      0      0   \n",
+      "init_to_steady1_1   1      0      0      0      0      0      0      0      1   \n",
+      "init_to_steady1_2   1      0      0      1      0      0      0      0      1   \n",
+      "init_to_steady1_3   1      0      0      1      1      0      0      0      1   \n",
+      "init_to_steady1_4   1      1      0      1      1      0      0      0      1   \n",
+      "\n",
+      "                   gene9  gene10  \n",
+      "init                   0       0  \n",
+      "init_to_steady1_1      0       0  \n",
+      "init_to_steady1_2      0       0  \n",
+      "init_to_steady1_3      0       0  \n",
+      "init_to_steady1_4      0       0  \n",
+      "gene3KO-boolean-trajectories.csv\n",
+      "                   tf  gene1  gene2  gene3  gene4  gene5  gene6  gene7  gene8  \\\n",
+      "init                1      0      0      0      0      0      0      0      0   \n",
+      "init_to_steady1_1   1      0      1      0      0      0      0      0      0   \n",
+      "init_to_steady1_2   1      0      1      0      0      0      0      0      1   \n",
+      "init_to_steady1_3   1      0      1      0      1      0      0      0      1   \n",
+      "init_to_steady1_4   1      1      1      0      1      0      0      0      1   \n",
+      "\n",
+      "                   gene9  gene10  \n",
+      "init                   0       0  \n",
+      "init_to_steady1_1      0       0  \n",
+      "init_to_steady1_2      0       0  \n",
+      "init_to_steady1_3      0       0  \n",
+      "init_to_steady1_4      0       0  \n",
+      "gene4KO-boolean-trajectories.csv\n",
+      "                   tf  gene1  gene2  gene3  gene4  gene5  gene6  gene7  gene8  \\\n",
+      "init                1      0      0      0      0      0      0      0      0   \n",
+      "init_to_steady1_1   1      0      1      0      0      0      0      0      0   \n",
+      "init_to_steady1_2   1      0      1      0      0      0      0      0      1   \n",
+      "init_to_steady1_3   1      0      1      1      0      0      0      0      1   \n",
+      "init_to_steady1_4   1      1      1      1      0      0      0      0      1   \n",
+      "\n",
+      "                   gene9  gene10  \n",
+      "init                   0       0  \n",
+      "init_to_steady1_1      0       0  \n",
+      "init_to_steady1_2      0       0  \n",
+      "init_to_steady1_3      0       0  \n",
+      "init_to_steady1_4      0       0  \n",
+      "gene5KO-boolean-trajectories.csv\n",
+      "                   tf  gene1  gene2  gene3  gene4  gene5  gene6  gene7  gene8  \\\n",
+      "init                1      0      0      0      0      0      0      0      0   \n",
+      "init_to_steady1_1   1      0      1      0      0      0      0      0      0   \n",
+      "init_to_steady1_2   1      0      1      0      0      0      0      0      1   \n",
+      "init_to_steady1_3   1      0      1      1      0      0      0      0      1   \n",
+      "init_to_steady1_4   1      0      1      1      1      0      0      0      1   \n",
+      "\n",
+      "                   gene9  gene10  \n",
+      "init                   0       0  \n",
+      "init_to_steady1_1      0       0  \n",
+      "init_to_steady1_2      0       0  \n",
+      "init_to_steady1_3      0       0  \n",
+      "init_to_steady1_4      0       0  \n",
+      "gene6KO-boolean-trajectories.csv\n",
+      "                   tf  gene1  gene2  gene3  gene4  gene5  gene6  gene7  gene8  \\\n",
+      "init                1      0      0      0      0      0      0      0      0   \n",
+      "init_to_steady1_1   1      0      1      0      0      0      0      0      0   \n",
+      "init_to_steady1_2   1      0      1      0      0      0      0      0      1   \n",
+      "init_to_steady1_3   1      0      1      1      0      0      0      0      1   \n",
+      "init_to_steady1_4   1      0      1      1      1      0      0      0      1   \n",
+      "\n",
+      "                   gene9  gene10  \n",
+      "init                   0       0  \n",
+      "init_to_steady1_1      0       0  \n",
+      "init_to_steady1_2      0       0  \n",
+      "init_to_steady1_3      0       0  \n",
+      "init_to_steady1_4      0       0  \n",
+      "gene7KO-boolean-trajectories.csv\n",
+      "                   tf  gene1  gene2  gene3  gene4  gene5  gene6  gene7  gene8  \\\n",
+      "init                1      0      0      0      0      0      0      0      0   \n",
+      "init_to_steady1_1   1      0      1      0      0      0      0      0      0   \n",
+      "init_to_steady1_2   1      0      1      0      0      0      0      0      1   \n",
+      "init_to_steady1_3   1      0      1      1      0      0      0      0      1   \n",
+      "init_to_steady1_4   1      0      1      1      1      0      0      0      1   \n",
+      "\n",
+      "                   gene9  gene10  \n",
+      "init                   0       0  \n",
+      "init_to_steady1_1      0       0  \n",
+      "init_to_steady1_2      0       0  \n",
+      "init_to_steady1_3      0       0  \n",
+      "init_to_steady1_4      0       0  \n",
+      "gene8KO-boolean-trajectories.csv\n",
+      "                   tf  gene1  gene2  gene3  gene4  gene5  gene6  gene7  gene8  \\\n",
+      "init                1      0      0      0      0      0      0      0      0   \n",
+      "init_to_steady1_1   1      0      1      0      0      0      0      0      0   \n",
+      "init_to_steady1_2   1      0      1      1      0      0      0      0      0   \n",
+      "init_to_steady1_3   1      0      1      1      1      0      0      0      0   \n",
+      "init_to_steady1_4   1      1      1      1      1      0      0      0      0   \n",
+      "\n",
+      "                   gene9  gene10  \n",
+      "init                   0       0  \n",
+      "init_to_steady1_1      0       0  \n",
+      "init_to_steady1_2      0       0  \n",
+      "init_to_steady1_3      0       0  \n",
+      "init_to_steady1_4      0       0  \n",
+      "gene9KO-boolean-trajectories.csv\n",
+      "                   tf  gene1  gene2  gene3  gene4  gene5  gene6  gene7  gene8  \\\n",
+      "init                1      0      0      0      0      0      0      0      0   \n",
+      "init_to_steady1_1   1      0      1      0      0      0      0      0      0   \n",
+      "init_to_steady1_2   1      0      1      0      0      0      0      0      1   \n",
+      "init_to_steady1_3   1      0      1      1      0      0      0      0      1   \n",
+      "init_to_steady1_4   1      0      1      1      1      0      0      0      1   \n",
+      "\n",
+      "                   gene9  gene10  \n",
+      "init                   0       0  \n",
+      "init_to_steady1_1      0       0  \n",
+      "init_to_steady1_2      0       0  \n",
+      "init_to_steady1_3      0       0  \n",
+      "init_to_steady1_4      0       0  \n",
+      "gene10KO-boolean-trajectories.csv\n",
+      "                   tf  gene1  gene2  gene3  gene4  gene5  gene6  gene7  gene8  \\\n",
+      "init                1      0      0      0      0      0      0      0      0   \n",
+      "init_to_steady1_1   1      0      1      0      0      0      0      0      0   \n",
+      "init_to_steady1_2   1      0      1      0      0      0      0      0      1   \n",
+      "init_to_steady1_3   1      0      1      1      0      0      0      0      1   \n",
+      "init_to_steady1_4   1      0      1      1      1      0      0      0      1   \n",
+      "\n",
+      "                   gene9  gene10  \n",
+      "init                   0       0  \n",
+      "init_to_steady1_1      0       0  \n",
+      "init_to_steady1_2      0       0  \n",
+      "init_to_steady1_3      0       0  \n",
+      "init_to_steady1_4      0       0  \n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "from subprocess import Popen\n",
+    "\n",
+    "proc = Popen(f\"mkdir -p {file_folder}\".split(\" \"))\n",
+    "proc.wait()\n",
+    "\n",
+    "data_url=\"https://raw.githubusercontent.com/gt-bioss/benchmark-dynamical-models/main/\"\n",
+    "data_url+=f\"from-boolean-networks/{dataset_name}/ground-truth/\"\n",
+    "\n",
+    "exp_list = [\"wt-boolean-trajectories.csv\"]+[\n",
+    "    \"%sKO-boolean-trajectories.csv\" % g\n",
+    "    for g in pert_genes\n",
+    "]\n",
+    "\n",
+    "for exp in exp_list:\n",
+    "    proc = Popen(f\"wget -O {file_folder}/{exp} -q {data_url}/{exp}\".split(\" \"))\n",
+    "    proc.wait()\n",
+    "    df = pd.read_csv(f\"{file_folder}/{exp}\", sep=\",\", index_col=0)\n",
+    "    print(exp)\n",
+    "    print(df.head())\n",
+    "    gene_lst = list(df.columns)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "97f13857",
+   "metadata": {},
+   "source": [
+    "## 1. Prior Knowledge Network\n",
+    "\n",
+    "The dataset is synthetic, so there is no prior on the structure of the network. We then build a complete graph (to prevent the resort to the STRING database which will be otherwise useless)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "3c89f0ff-d3ef-43b9-a69a-0ce9987f7cbe",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "55 edges in total (expected 55)\n"
+     ]
+    },
+    {
+     "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>preferredName_A</th>\n",
+       "      <th>preferredName_B</th>\n",
+       "      <th>sign</th>\n",
+       "      <th>directed</th>\n",
+       "      <th>score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>tf</td>\n",
+       "      <td>gene1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>tf</td>\n",
+       "      <td>gene2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>tf</td>\n",
+       "      <td>gene3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>tf</td>\n",
+       "      <td>gene4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>tf</td>\n",
+       "      <td>gene5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  preferredName_A preferredName_B  sign  directed  score\n",
+       "0              tf           gene1     1         0    0.5\n",
+       "1              tf           gene2     1         0    0.5\n",
+       "2              tf           gene3     1         0    0.5\n",
+       "3              tf           gene4     1         0    0.5\n",
+       "4              tf           gene5     1         0    0.5"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "NETWORK_fname = f\"{file_folder}/complete_ppi.tsv\"\n",
+    "\n",
+    "all_pairs = [(g, gp) for ig, g in enumerate(gene_lst) for gp in gene_lst[(ig+1):]] ## all unique distinct pairs\n",
+    "PPI = pd.DataFrame([\n",
+    "    [g, gp, 1, 0, 0.5] ## build undirected edges with score=0.5 and positive\n",
+    "    for g, gp in all_pairs\n",
+    "], columns=[\"preferredName_A\", \"preferredName_B\", \"sign\", \"directed\", \"score\"], index=range(len(all_pairs)))\n",
+    "PPI.to_csv(NETWORK_fname, sep=\"\\t\", index=None)\n",
+    "\n",
+    "n=len(gene_lst)\n",
+    "print(\"%d edges in total (expected %d)\" % (len(all_pairs), n*(n-1)/2))\n",
+    "PPI.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "63034035-4929-4389-a211-57488f322518",
+   "metadata": {},
+   "source": [
+    "## 2. Experimental file\n",
+    "\n",
+    "We convert the Boolean trajectories that we retrieved into a format compatible with NORDic. For each retrieved experimental file (corresponding to a single (non)perturbation experiment):\n",
+    "\n",
+    "- There is the Boolean state of the initial condition named ``init``\n",
+    "- We ignore intermediary states named ``init_to_steady1_*``\n",
+    "- There is the final steady state named ``steady1``"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "340a755b-29cf-42a5-b646-6e03622a50f2",
+   "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>tf</th>\n",
+       "      <th>gene1</th>\n",
+       "      <th>gene2</th>\n",
+       "      <th>gene3</th>\n",
+       "      <th>gene4</th>\n",
+       "      <th>gene5</th>\n",
+       "      <th>gene6</th>\n",
+       "      <th>gene7</th>\n",
+       "      <th>gene8</th>\n",
+       "      <th>gene9</th>\n",
+       "      <th>gene10</th>\n",
+       "      <th>annotation</th>\n",
+       "      <th>perturbed</th>\n",
+       "      <th>perturbation</th>\n",
+       "      <th>cell_line</th>\n",
+       "      <th>sigid</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>initial_toystar1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>toystar1</td>\n",
+       "      <td>wt-boolean-trajectories.csv:init</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gene1_KD_toystar1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>gene1</td>\n",
+       "      <td>KD</td>\n",
+       "      <td>toystar1</td>\n",
+       "      <td>gene1KO-boolean-trajectories.csv:steady</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gene2_KD_toystar1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>gene2</td>\n",
+       "      <td>KD</td>\n",
+       "      <td>toystar1</td>\n",
+       "      <td>gene2KO-boolean-trajectories.csv:steady</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gene3_KD_toystar1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>gene3</td>\n",
+       "      <td>KD</td>\n",
+       "      <td>toystar1</td>\n",
+       "      <td>gene3KO-boolean-trajectories.csv:steady</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gene4_KD_toystar1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>gene4</td>\n",
+       "      <td>KD</td>\n",
+       "      <td>toystar1</td>\n",
+       "      <td>gene4KO-boolean-trajectories.csv:steady</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  tf gene1 gene2 gene3 gene4 gene5 gene6 gene7 gene8 gene9  \\\n",
+       "initial_toystar1   1     0     0     0     0     0     0     0     0     0   \n",
+       "gene1_KD_toystar1  1     0     1     1     1     1     1     1     1     1   \n",
+       "gene2_KD_toystar1  1     1     0     1     1     1     1     1     1     1   \n",
+       "gene3_KD_toystar1  1     1     1     0     1     1     1     1     1     1   \n",
+       "gene4_KD_toystar1  1     1     1     1     0     1     1     1     1     1   \n",
+       "\n",
+       "                  gene10 annotation perturbed perturbation cell_line  \\\n",
+       "initial_toystar1       0          1      None         None  toystar1   \n",
+       "gene1_KD_toystar1      1          2     gene1           KD  toystar1   \n",
+       "gene2_KD_toystar1      1          2     gene2           KD  toystar1   \n",
+       "gene3_KD_toystar1      1          2     gene3           KD  toystar1   \n",
+       "gene4_KD_toystar1      1          2     gene4           KD  toystar1   \n",
+       "\n",
+       "                                                     sigid  \n",
+       "initial_toystar1          wt-boolean-trajectories.csv:init  \n",
+       "gene1_KD_toystar1  gene1KO-boolean-trajectories.csv:steady  \n",
+       "gene2_KD_toystar1  gene2KO-boolean-trajectories.csv:steady  \n",
+       "gene3_KD_toystar1  gene3KO-boolean-trajectories.csv:steady  \n",
+       "gene4_KD_toystar1  gene4KO-boolean-trajectories.csv:steady  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "EXPERIMENTS_fname = f\"{file_folder}/experiments.csv\"\n",
+    "experiments = pd.DataFrame([], index=gene_lst+[\"annotation\", \"perturbed\", \"perturbation\", \"cell_line\", \"sigid\"])\n",
+    "cell=\"\".join(dataset_name.split(\"_\")) ## no \"_\"\n",
+    "\n",
+    "for ie, exp in enumerate(exp_list):\n",
+    "    df = pd.read_csv(f\"{file_folder}/{exp}\", sep=\",\", index_col=0)\n",
+    "    if (\"wt-\" in exp):\n",
+    "        experiments[f\"initial_{cell}\"] = df.loc[\"init\"][gene_lst].values.flatten().tolist()+[\"1\",\"None\",\"None\",cell,f\"{exp}:init\"]\n",
+    "        continue\n",
+    "    pert_gene = exp.split(\"KO\")[0]\n",
+    "    pert_dir = \"KD\"\n",
+    "    exp_name = f\"{pert_gene}_KD_\"+cell\n",
+    "    steady_name = \"steady\"+(\"1\" if (\"steady1\" in df.index) else \"\")\n",
+    "    experiments[exp_name] = df.loc[steady_name][gene_lst].values.flatten().tolist()+[\"2\",pert_gene,pert_dir,cell,f\"{exp}:steady\"]\n",
+    "experiments.to_csv(EXPERIMENTS_fname, sep=\",\")\n",
+    "\n",
+    "experiments.T.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ee59a30f-bc01-4af6-b727-e346609475ba",
+   "metadata": {},
+   "source": [
+    "## 3. Parameters\n",
+    "\n",
+    "Not needed, since both the PKN and the experiments are available from the start."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "be6929c0-d0e3-489a-94bc-63997747f7f1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from multiprocessing import cpu_count\n",
+    "njobs=1\n",
+    "\n",
+    "## Random\n",
+    "taxon_id=1111\n",
+    "disease_cids=[\"Unknown\"] \n",
+    "cell_lines=[cell]\n",
+    "path_to_genes=None\n",
+    "\n",
+    "DisGeNET_credentials = \"\"\n",
+    "STRING_credentials = \"\"\n",
+    "LINCS_credentials = \"\"\n",
+    "DISGENET_args = {\"credentials\": DisGeNET_credentials, \"disease_cids\": disease_cids}\n",
+    "STRING_args = {\"credentials\": STRING_credentials, \"score\": 0}\n",
+    "EDGE_args = {\"tau\": None, \"filter\": False, \"connected\": True}\n",
+    "accept_nonRNA=True\n",
+    "preserve_network_sign=True\n",
+    "LINCS_args = {\"path_to_lincs\": \"\", \n",
+    "              \"credentials\": LINCS_credentials,\n",
+    "              \"cell_lines\": cell_lines, \"thres_iscale\": None}\n",
+    "SIG_args = {\"bin_thres\": 0.5}\n",
+    "force_experiments=False\n",
+    "DESIRABILITY = {\"DS\": 3, \"CL\": 3, \"Centr\": 3, \"GT\": 1}\n",
+    "\n",
+    "## Identification parameters\n",
+    "BONESIS_args = {\"limit\": 500, \"exact\": False, \"max_maxclause\": 1}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "292b0b3b-509e-416c-b08a-90e9f975ca11",
+   "metadata": {},
+   "source": [
+    "## 4. Network identification"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "1cf59895-8f6f-4c37-8c9b-f9293d8bf41f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "\n",
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+       "            };\n",
+       "\n",
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+       "            // hack/workaround:\n",
+       "            // it seems that Jupyter does not like very long lines\n",
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+       "            var chunk_length = 100;\n",
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+       "function resolve_function(tool_api, funcname) {\n",
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+       "}\n",
+       "\n",
+       "function colomoto_extension(Jupyter, ssid, name, menu, toolbar, tool_api) {\n",
+       "\n",
+       "    function insert_snippet_code(snippet) {\n",
+       "        var cell = Jupyter.notebook.get_selected_cell();\n",
+       "        Jupyter.notebook.edit_mode();\n",
+       "        cell.code_mirror.replaceSelection(snippet, 'around');\n",
+       "        //cell.focus_editor();\n",
+       "    }\n",
+       "\n",
+       "    /**\n",
+       "        from https://github.com/moble/jupyter_boilerplate/blob/master/main.js\n",
+       "    */\n",
+       "    function callback_insert_snippet (evt) {\n",
+       "        // this (or event.currentTarget, see below) always refers to the DOM\n",
+       "        // element the listener was attached to - see\n",
+       "        // http://stackoverflow.com/questions/12077859\n",
+       "        insert_snippet_code($(evt.currentTarget).data('snippet-code'));\n",
+       "    }\n",
+       "    function build_menu_element (menu_item_spec, direction) {\n",
+       "        // Create the menu item html element\n",
+       "        var element = $('<li/>');\n",
+       "\n",
+       "        if (typeof menu_item_spec == 'string') {\n",
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+       "                        .addClass('ui-state-disabled')\n",
+       "                        .attr({\"style\": \"padding:2px .4em\"})\n",
+       "                       ;\n",
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+       "            return element.addClass('divider');\n",
+       "        }\n",
+       "\n",
+       "        var a = $('<a/>')\n",
+       "            .attr('href', '#')\n",
+       "            .html(menu_item_spec.name)\n",
+       "            .appendTo(element);\n",
+       "        if (menu_item_spec.hasOwnProperty('snippet')) {\n",
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+       "                snippet = [snippet];\n",
+       "            }\n",
+       "            a.attr({\n",
+       "                'title' : \"\", // Do not remove this, even though it's empty!\n",
+       "                'data-snippet-code' : snippet.join('\\n'),\n",
+       "            })\n",
+       "            .on('click', callback_insert_snippet)\n",
+       "            .addClass('snippet');\n",
+       "        }\n",
+       "        else if (menu_item_spec.hasOwnProperty('internal-link')) {\n",
+       "            a.attr('href', menu_item_spec['internal-link']);\n",
+       "        }\n",
+       "        else if (menu_item_spec.hasOwnProperty('external-link')) {\n",
+       "            a.empty();\n",
+       "            a.attr('href', menu_item_spec['external-link']);\n",
+       "            a.attr({\n",
+       "                'target' : '_blank',\n",
+       "                'title' : 'Opens in a new window',\n",
+       "            });\n",
+       "            $('<i class=\"fa fa-external-link menu-icon pull-right\"/>').appendTo(a);\n",
+       "            $('<span/>').html(menu_item_spec.name).appendTo(a);\n",
+       "        }\n",
+       "\n",
+       "        if (menu_item_spec.hasOwnProperty('sub-menu')) {\n",
+       "            element\n",
+       "                .addClass('dropdown-submenu')\n",
+       "                .toggleClass('dropdown-submenu-left', direction === 'left');\n",
+       "            var sub_element = $('<ul class=\"dropdown-menu\"/>')\n",
+       "                .toggleClass('dropdown-menu-compact', menu_item_spec.overlay === true) // For space-saving menus\n",
+       "                .appendTo(element);\n",
+       "\n",
+       "            var new_direction = (menu_item_spec['sub-menu-direction'] === 'left') ? 'left' : 'right';\n",
+       "            for (var j=0; j<menu_item_spec['sub-menu'].length; ++j) {\n",
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+       "                if(sub_menu_item_spec !== null) {\n",
+       "                    sub_menu_item_spec.appendTo(sub_element);\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "\n",
+       "        return element;\n",
+       "    }\n",
+       "\n",
+       "    function menu_setup (menu_item_specs, sibling, insert_before_sibling) {\n",
+       "        for (var i=0; i<menu_item_specs.length; ++i) {\n",
+       "            var menu_item_spec;\n",
+       "            if (insert_before_sibling) {\n",
+       "                menu_item_spec = menu_item_specs[i];\n",
+       "            } else {\n",
+       "                menu_item_spec = menu_item_specs[menu_item_specs.length-1-i];\n",
+       "            }\n",
+       "            var direction = (menu_item_spec['menu-direction'] == 'left') ? 'left' : 'right';\n",
+       "            var menu_element = build_menu_element(menu_item_spec, direction);\n",
+       "            // We need special properties if this item is in the navbar\n",
+       "            if ($(sibling).parent().is('ul.nav.navbar-nav')) {\n",
+       "                menu_element\n",
+       "                    .addClass('dropdown')\n",
+       "                    .removeClass('dropdown-submenu dropdown-submenu-left');\n",
+       "                menu_element.children('a')\n",
+       "                    .addClass('dropdown-toggle')\n",
+       "                    .attr({\n",
+       "                        'id': name+'_menu',\n",
+       "                        'data-toggle' : 'dropdown',\n",
+       "                        'aria-expanded' : 'false'\n",
+       "                    });\n",
+       "            }\n",
+       "\n",
+       "            // Insert the menu element into DOM\n",
+       "            menu_element[insert_before_sibling ? 'insertBefore': 'insertAfter'](sibling);\n",
+       "        }\n",
+       "    }\n",
+       "    /** end from */\n",
+       "\n",
+       "\n",
+       "    function self_cleanup() {\n",
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+       "        var cell_idx = Jupyter.notebook.get_cell_elements().index(cell_element);\n",
+       "        var cell = Jupyter.notebook.get_cell(cell_idx);\n",
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+       "        for (var i = 0; i < cell.output_area.outputs.length; ++i) {\n",
+       "            var oa = cell.output_area.outputs[i];\n",
+       "            if (oa.output_type == \"display_data\"\n",
+       "                && typeof oa.data[\"text/html\"] != 'undefined'\n",
+       "                && oa.data[\"text/html\"].indexOf(' class=\"to-be-removed\"') >= 0) {\n",
+       "                to_remove = i;\n",
+       "                break;\n",
+       "            }\n",
+       "        }\n",
+       "        if (to_remove == -1) {\n",
+       "            console.log(\"cannot find toberemoved\");\n",
+       "        } else {\n",
+       "            cell.output_area.outputs.splice(to_remove, 1);\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    function toolbar_setup(actions) {\n",
+       "        var buttons = [];\n",
+       "        for (var i = 0; i < actions.length; ++i) {\n",
+       "            var setup = actions[i].setup;\n",
+       "            if (typeof setup.handler == 'string') {\n",
+       "                setup.handler = resolve_function(tool_api, setup.handler);\n",
+       "            }\n",
+       "            buttons.push(Jupyter.actions.register(actions[i].setup,\n",
+       "                actions[i].name, name));\n",
+       "        }\n",
+       "        $(\"#\"+name+\"-toolbar\").remove();\n",
+       "        Jupyter.toolbar.add_buttons_group(buttons, name+\"-toolbar\");\n",
+       "    }\n",
+       "\n",
+       "    function replace_menu_snippets(menu_spec, orig, dest) {\n",
+       "        if (menu_spec.hasOwnProperty(\"snippet\")) {\n",
+       "            var snippet = menu_spec.snippet;\n",
+       "            if (typeof snippet == \"string\" || snippet instanceof String) {\n",
+       "                menu_spec[\"snippet\"] = snippet.replace(orig, dest);\n",
+       "            } else {\n",
+       "                for (var i = 0; i < snippet.length; ++i) {\n",
+       "                    menu_spec[\"snippet\"][i] = snippet[i].replace(orig, dest);\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "        if (menu_spec.hasOwnProperty(\"sub-menu\")) {\n",
+       "            for (var i = 0; i < menu_spec[\"sub-menu\"].length; ++i) {\n",
+       "                replace_menu_snippets(menu_spec[\"sub-menu\"][i], orig, dest);\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    function load_ipython_extension() {\n",
+       "\n",
+       "        var mycellelt = $(\"#\"+ssid).parents('.cell');\n",
+       "        var myidx = Jupyter.notebook.get_cell_elements().index(mycellelt);\n",
+       "        var import_cell = Jupyter.notebook.get_cell(myidx);\n",
+       "\n",
+       "        var alias = detect_import(import_cell, name);\n",
+       "        tool_api.module_alias = alias;\n",
+       "        if (alias && alias != name) {\n",
+       "            var orig = new RegExp(\"\\\\b\"+name+\"\\\\b\", \"g\");\n",
+       "            replace_menu_snippets(menu, orig, alias);\n",
+       "        }\n",
+       "\n",
+       "        if (toolbar) {\n",
+       "            toolbar_setup(toolbar);\n",
+       "        }\n",
+       "\n",
+       "        $(\"#\"+name+\"_menu\").parent().remove();\n",
+       "        if (menu) {\n",
+       "            menu_setup([menu], $(\"#help_menu\").parent(), true);\n",
+       "        }\n",
+       "\n",
+       "        if (tool_api.hasOwnProperty(\"post_install_callback\")) {\n",
+       "            tool_api.post_install_callback();\n",
+       "        }\n",
+       "\n",
+       "        setTimeout(self_cleanup, 5000);\n",
+       "    };\n",
+       "\n",
+       "    load_ipython_extension();\n",
+       "}\n",
+       "\n",
+       "function resolve_toolbar_handlers(tool_api, toolbar_spec) {\n",
+       "    for (var i = 0; i < toolbar_spec.length; ++i) {\n",
+       "        func = resolve_function(tool_api, toolbar_spec[i][\"setup\"][\"handler\"]);\n",
+       "        toolbar_spec[i][\"setup\"][\"handler\"] = func;\n",
+       "    }\n",
+       "    return toolbar_spec\n",
+       "}\n",
+       "\n",
+       "\n",
+       "    var minibn_jsapi = {  };\n",
+       "    colomoto_extension(Jupyter, \"colomoto-setup-5379951\", \"minibn\", null, null, minibn_jsapi);\n",
+       "     }</script>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- DATA IMPORT ---\n",
+      "1. Import gene set from DisGeNET... ... 11 genes imported.\n",
+      "2. Import network from toy_star_1//complete_ppi.tsv... ... 55 edges in model (including 0 directed edges) with a total of 11 non-isolated genes\n",
+      "3. Import experiments from toy_star_1//experiments.csv... \n",
+      "\t1 cell lines are considered (['toystar1'])\n",
+      "... 11 genes in 11 profiles (11 experiments)\n",
+      "\n",
+      "--- CONSTRAINT BUILDING ---\n",
+      "1. Filtering out edges by minimum set of edges with highest score which preserve connectivity... ... 55 unique edges involving genes both in experiments (11 genes in total)\n",
+      "... score_STRING 0.000000\t11 genes (non isolated in PPI)\t110 edges in PPI\n",
+      "2. Build topological constraints from filtered edges using gene expression data...  ...... 0 negative, 110 positive undirected interactions (110 edges in total), 11 non isolated genes in experiments\n",
+      "3. Build dynamical constraints by binarization of experimental profiles...  ...... 10 experiments on 1 cell lines and 11/11 genes (Frobenius norm signature matrix: 11.000, 1 possibly constant genes: 9.1%, 0 genes with undetermined status\n",
+      "\n",
+      "--- INFER BOOLEAN NETWORK ---\n",
+      "1. Generate solutions from topological & dynamical constraints...  ...<UTILS_GRN> Maximum possible #activators=10\n",
+      "<BONESIS> 110 interactions (maximum # of clauses = 1)\n",
+      "\n",
+      "<UTILS_GRN> 10 experiments\n",
+      "             gene5  gene6  gene9  gene10  gene1  gene3  gene8  gene4  gene7  \\\n",
+      "Exp1_init        0      0      0       0      0      0      0      0      0   \n",
+      "Exp1_final       1      1      0       1      1      1      1      1      1   \n",
+      "Exp2_init        0      0      0       0      0      0      0      0      0   \n",
+      "Exp2_final       1      1      1       0      1      1      1      1      1   \n",
+      "Exp3_init        0      0      0       0      0      0      0      0      0   \n",
+      "Exp3_final       1      1      1       1      0      1      1      1      1   \n",
+      "Exp4_init        0      0      0       0      0      0      0      0      0   \n",
+      "Exp4_final       1      1      1       1      1      1      1      1      1   \n",
+      "Exp5_init        0      0      0       0      0      0      0      0      0   \n",
+      "Exp5_final       1      1      1       1      1      0      1      1      1   \n",
+      "Exp6_init        0      0      0       0      0      0      0      0      0   \n",
+      "Exp6_final       1      1      1       1      1      1      1      0      1   \n",
+      "Exp7_init        0      0      0       0      0      0      0      0      0   \n",
+      "Exp7_final       0      1      1       1      1      1      1      1      1   \n",
+      "Exp8_init        0      0      0       0      0      0      0      0      0   \n",
+      "Exp8_final       1      0      1       1      1      1      1      1      1   \n",
+      "Exp9_init        0      0      0       0      0      0      0      0      0   \n",
+      "Exp9_final       1      1      1       1      1      1      1      1      0   \n",
+      "Exp10_init       0      0      0       0      0      0      0      0      0   \n",
+      "Exp10_final      1      1      1       1      1      1      0      1      1   \n",
+      "\n",
+      "             tf  gene2  \n",
+      "Exp1_init     1      0  \n",
+      "Exp1_final    1      1  \n",
+      "Exp2_init     1      0  \n",
+      "Exp2_final    1      1  \n",
+      "Exp3_init     1      0  \n",
+      "Exp3_final    1      1  \n",
+      "Exp4_init     1      0  \n",
+      "Exp4_final    1      0  \n",
+      "Exp5_init     1      0  \n",
+      "Exp5_final    1      1  \n",
+      "Exp6_init     1      0  \n",
+      "Exp6_final    1      1  \n",
+      "Exp7_init     1      0  \n",
+      "Exp7_final    1      1  \n",
+      "Exp8_init     1      0  \n",
+      "Exp8_final    1      1  \n",
+      "Exp9_init     1      0  \n",
+      "Exp9_final    1      1  \n",
+      "Exp10_init    1      0  \n",
+      "Exp10_final   1      1  \n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0%|                                                                                                | 0/1 [00:00<?, ?it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Grounding...done in 0.0s\n",
+      "Found 500 solutions in 7.2s (first in 0.0s; rate 0.0s)"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:07<00:00,  7.19s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- SELECTION OF OPTIMAL MODEL ---\n",
+      "A. Select an optimal model based on a topological criteria...  ..."
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "500 solutions (500 unique solutions)\n",
+      "... 500 solutions (7/12 constant genes in average)\n",
+      "<MODEL VISUALIZATION> Minimal solution: 0 edges\n",
+      "<MODEL VISUALIZATION> Maximal solution: 7 edges\n",
+      "          1       2       3         4       5         6         7         8    \\\n",
+      "GTP  0.829375  0.8524  0.8524  0.868908  0.8524  0.868908  0.797309  0.829375   \n",
+      "\n",
+      "          9         10        11        12      13      14        15   \\\n",
+      "GTP  0.880757  0.829375  0.829375  0.829375  0.8524  0.8524  0.829375   \n",
+      "\n",
+      "          16        17        18        19        20        21        22   \\\n",
+      "GTP  0.868908  0.797309  0.829375  0.829375  0.829375  0.880757  0.868908   \n",
+      "\n",
+      "        23        24        25      26        27      28        29      30   \\\n",
+      "GTP  0.8524  0.797309  0.829375  0.8524  0.829375  0.8524  0.868908  0.8524   \n",
+      "\n",
+      "        31      32        33        34      35        36        37        38   \\\n",
+      "GTP  0.8524  0.8524  0.868908  0.797309  0.8524  0.829375  0.829375  0.868908   \n",
+      "\n",
+      "          39      40        41        42        43      44      45      46   \\\n",
+      "GTP  0.797309  0.8524  0.797309  0.829375  0.829375  0.8524  0.8524  0.8524   \n",
+      "\n",
+      "          47        48      49      50      51      52        53        54   \\\n",
+      "GTP  0.868908  0.829375  0.8524  0.8524  0.8524  0.8524  0.797309  0.829375   \n",
+      "\n",
+      "        55      56      57        58      59      60        61        62   \\\n",
+      "GTP  0.8524  0.8524  0.8524  0.829375  0.8524  0.8524  0.868908  0.868908   \n",
+      "\n",
+      "        63        64        65      66        67      68        69        70   \\\n",
+      "GTP  0.8524  0.868908  0.797309  0.8524  0.829375  0.8524  0.880757  0.829375   \n",
+      "\n",
+      "        71        72      73        74      75      76        77        78   \\\n",
+      "GTP  0.8524  0.797309  0.8524  0.829375  0.8524  0.8524  0.868908  0.868908   \n",
+      "\n",
+      "        79        80        81        82        83        84      85      86   \\\n",
+      "GTP  0.8524  0.752907  0.797309  0.868908  0.868908  0.868908  0.8524  0.8524   \n",
+      "\n",
+      "          87        88        89        90      91        92      93   \\\n",
+      "GTP  0.868908  0.868908  0.829375  0.829375  0.8524  0.868908  0.8524   \n",
+      "\n",
+      "          94        95      96        97        98        99        100  \\\n",
+      "GTP  0.880757  0.797309  0.8524  0.829375  0.880757  0.868908  0.797309   \n",
+      "\n",
+      "        101     102       103       104       105     106       107       108  \\\n",
+      "GTP  0.8524  0.8524  0.868908  0.868908  0.829375  0.8524  0.868908  0.829375   \n",
+      "\n",
+      "        109     110       111       112     113     114     115       116  \\\n",
+      "GTP  0.8524  0.8524  0.880757  0.868908  0.8524  0.8524  0.8524  0.829375   \n",
+      "\n",
+      "          117     118       119     120       121     122       123     124  \\\n",
+      "GTP  0.868908  0.8524  0.829375  0.8524  0.889284  0.8524  0.880757  0.8524   \n",
+      "\n",
+      "          125       126     127     128       129     130     131     132  \\\n",
+      "GTP  0.829375  0.829375  0.8524  0.8524  0.868908  0.8524  0.8524  0.8524   \n",
+      "\n",
+      "          133     134       135     136       137       138       139  \\\n",
+      "GTP  0.797309  0.8524  0.829375  0.8524  0.880757  0.797309  0.829375   \n",
+      "\n",
+      "          140     141     142     143       144       145       146     147  \\\n",
+      "GTP  0.797309  0.8524  0.8524  0.8524  0.829375  0.829375  0.829375  0.8524   \n",
+      "\n",
+      "        148     149       150     151     152       153       154     155  \\\n",
+      "GTP  0.8524  0.8524  0.797309  0.8524  0.8524  0.829375  0.868908  0.8524   \n",
+      "\n",
+      "        156     157     158       159     160       161       162       163  \\\n",
+      "GTP  0.8524  0.8524  0.8524  0.868908  0.8524  0.797309  0.829375  0.868908   \n",
+      "\n",
+      "        164     165       166       167       168       169       170  \\\n",
+      "GTP  0.8524  0.8524  0.797309  0.829375  0.868908  0.868908  0.829375   \n",
+      "\n",
+      "          171       172     173       174       175     176       177     178  \\\n",
+      "GTP  0.868908  0.829375  0.8524  0.868908  0.752907  0.8524  0.829375  0.8524   \n",
+      "\n",
+      "          179       180     181       182     183       184       185     186  \\\n",
+      "GTP  0.868908  0.889284  0.8524  0.880757  0.8524  0.797309  0.797309  0.8524   \n",
+      "\n",
+      "          187       188       189     190     191       192       193     194  \\\n",
+      "GTP  0.868908  0.829375  0.880757  0.8524  0.8524  0.868908  0.880757  0.8524   \n",
+      "\n",
+      "          195       196       197       198       199       200       201  \\\n",
+      "GTP  0.829375  0.797309  0.829375  0.868908  0.868908  0.797309  0.880757   \n",
+      "\n",
+      "          202     203     204       205       206       207     208       209  \\\n",
+      "GTP  0.868908  0.8524  0.8524  0.829375  0.868908  0.868908  0.8524  0.797309   \n",
+      "\n",
+      "        210       211     212       213       214       215       216  \\\n",
+      "GTP  0.8524  0.829375  0.8524  0.868908  0.829375  0.868908  0.829375   \n",
+      "\n",
+      "          217     218       219       220     221       222     223     224  \\\n",
+      "GTP  0.868908  0.8524  0.829375  0.797309  0.8524  0.829375  0.8524  0.8524   \n",
+      "\n",
+      "          225       226       227       228       229       230       231  \\\n",
+      "GTP  0.829375  0.829375  0.797309  0.829375  0.797309  0.868908  0.829375   \n",
+      "\n",
+      "          232       233       234     235       236       237     238     239  \\\n",
+      "GTP  0.829375  0.797309  0.829375  0.8524  0.868908  0.829375  0.8524  0.8524   \n",
+      "\n",
+      "          240       241     242       243     244       245       246  \\\n",
+      "GTP  0.868908  0.829375  0.8524  0.868908  0.8524  0.868908  0.692201   \n",
+      "\n",
+      "          247       248     249     250       251       252       253     254  \\\n",
+      "GTP  0.829375  0.880757  0.8524  0.8524  0.868908  0.868908  0.868908  0.8524   \n",
+      "\n",
+      "        255       256       257       258     259       260       261     262  \\\n",
+      "GTP  0.8524  0.868908  0.797309  0.880757  0.8524  0.829375  0.829375  0.8524   \n",
+      "\n",
+      "          263     264       265       266       267       268     269  \\\n",
+      "GTP  0.829375  0.8524  0.829375  0.829375  0.829375  0.829375  0.8524   \n",
+      "\n",
+      "          270     271       272       273       274       275       276  \\\n",
+      "GTP  0.868908  0.8524  0.880757  0.868908  0.829375  0.868908  0.868908   \n",
+      "\n",
+      "        277     278       279     280     281     282     283       284  \\\n",
+      "GTP  0.8524  0.8524  0.752907  0.8524  0.8524  0.8524  0.8524  0.829375   \n",
+      "\n",
+      "          285       286       287       288       289     290       291  \\\n",
+      "GTP  0.868908  0.829375  0.797309  0.880757  0.829375  0.8524  0.889284   \n",
+      "\n",
+      "        292       293       294       295       296       297       298  \\\n",
+      "GTP  0.8524  0.880757  0.868908  0.868908  0.868908  0.829375  0.829375   \n",
+      "\n",
+      "        299       300     301       302       303     304     305       306  \\\n",
+      "GTP  0.8524  0.829375  0.8524  0.829375  0.868908  0.8524  0.8524  0.868908   \n",
+      "\n",
+      "          307       308       309     310       311       312       313  \\\n",
+      "GTP  0.829375  0.829375  0.752907  0.8524  0.797309  0.797309  0.829375   \n",
+      "\n",
+      "          314       315       316       317       318       319       320  \\\n",
+      "GTP  0.880757  0.868908  0.829375  0.868908  0.829375  0.829375  0.889284   \n",
+      "\n",
+      "          321       322     323       324       325     326       327  \\\n",
+      "GTP  0.829375  0.829375  0.8524  0.868908  0.868908  0.8524  0.829375   \n",
+      "\n",
+      "          328     329     330     331       332     333     334     335  \\\n",
+      "GTP  0.868908  0.8524  0.8524  0.8524  0.797309  0.8524  0.8524  0.8524   \n",
+      "\n",
+      "        336       337     338       339       340       341       342     343  \\\n",
+      "GTP  0.8524  0.829375  0.8524  0.752907  0.868908  0.868908  0.868908  0.8524   \n",
+      "\n",
+      "          344     345       346       347     348       349       350  \\\n",
+      "GTP  0.829375  0.8524  0.829375  0.868908  0.8524  0.868908  0.829375   \n",
+      "\n",
+      "          351     352       353       354       355     356     357       358  \\\n",
+      "GTP  0.868908  0.8524  0.868908  0.797309  0.829375  0.8524  0.8524  0.868908   \n",
+      "\n",
+      "          359       360       361       362       363     364     365  \\\n",
+      "GTP  0.868908  0.797309  0.829375  0.829375  0.829375  0.8524  0.8524   \n",
+      "\n",
+      "          366     367       368       369     370       371       372     373  \\\n",
+      "GTP  0.752907  0.8524  0.868908  0.829375  0.8524  0.880757  0.880757  0.8524   \n",
+      "\n",
+      "          374       375     376       377       378     379       380     381  \\\n",
+      "GTP  0.868908  0.829375  0.8524  0.868908  0.752907  0.8524  0.868908  0.8524   \n",
+      "\n",
+      "          382     383       384     385       386       387       388  \\\n",
+      "GTP  0.868908  0.8524  0.829375  0.8524  0.797309  0.829375  0.797309   \n",
+      "\n",
+      "          389       390     391       392       393     394     395     396  \\\n",
+      "GTP  0.829375  0.829375  0.8524  0.868908  0.868908  0.8524  0.8524  0.8524   \n",
+      "\n",
+      "          397     398       399       400       401       402     403  \\\n",
+      "GTP  0.880757  0.8524  0.752907  0.829375  0.752907  0.829375  0.8524   \n",
+      "\n",
+      "          404       405       406       407       408       409       410  \\\n",
+      "GTP  0.880757  0.868908  0.797309  0.829375  0.829375  0.829375  0.880757   \n",
+      "\n",
+      "          411     412       413     414     415       416       417     418  \\\n",
+      "GTP  0.880757  0.8524  0.829375  0.8524  0.8524  0.868908  0.868908  0.8524   \n",
+      "\n",
+      "        419     420       421       422       423       424       425  \\\n",
+      "GTP  0.8524  0.8524  0.868908  0.868908  0.880757  0.829375  0.868908   \n",
+      "\n",
+      "          426       427       428       429     430     431     432     433  \\\n",
+      "GTP  0.868908  0.797309  0.797309  0.868908  0.8524  0.8524  0.8524  0.8524   \n",
+      "\n",
+      "        434       435       436       437       438     439       440     441  \\\n",
+      "GTP  0.8524  0.829375  0.868908  0.868908  0.797309  0.8524  0.797309  0.8524   \n",
+      "\n",
+      "        442       443       444       445     446       447     448     449  \\\n",
+      "GTP  0.8524  0.829375  0.829375  0.797309  0.8524  0.868908  0.8524  0.8524   \n",
+      "\n",
+      "          450       451     452       453       454       455       456  \\\n",
+      "GTP  0.829375  0.868908  0.8524  0.752907  0.797309  0.829375  0.868908   \n",
+      "\n",
+      "          457       458     459     460       461     462     463       464  \\\n",
+      "GTP  0.868908  0.829375  0.8524  0.8524  0.868908  0.8524  0.8524  0.868908   \n",
+      "\n",
+      "          465       466       467     468       469       470     471     472  \\\n",
+      "GTP  0.829375  0.868908  0.829375  0.8524  0.829375  0.868908  0.8524  0.8524   \n",
+      "\n",
+      "          473       474       475     476       477       478       479  \\\n",
+      "GTP  0.868908  0.880757  0.797309  0.8524  0.829375  0.868908  0.829375   \n",
+      "\n",
+      "          480     481       482       483       484       485     486  \\\n",
+      "GTP  0.829375  0.8524  0.797309  0.868908  0.829375  0.868908  0.8524   \n",
+      "\n",
+      "          487     488     489       490       491       492     493     494  \\\n",
+      "GTP  0.880757  0.8524  0.8524  0.868908  0.868908  0.829375  0.8524  0.8524   \n",
+      "\n",
+      "          495       496       497       498     499       500  \n",
+      "GTP  0.829375  0.829375  0.880757  0.868908  0.8524  0.880757  \n",
+      "<MODEL SELECTION> Saving optimal model in 'toy_star_1//solution.bnet' ...\n",
+      "gene1, tf\n",
+      "gene10, 1\n",
+      "gene2, tf\n",
+      "gene3, 1\n",
+      "gene4, tf\n",
+      "...\n",
+      "... saved!\n"
+     ]
+    }
+   ],
+   "source": [
+    "from NORDic.NORDic_NI.functions import network_identification\n",
+    "\n",
+    "solution = NORDic.NORDic_NI.functions.network_identification(\n",
+    "    file_folder, taxon_id, path_to_genes=path_to_genes, disgenet_args=DISGENET_args, \n",
+    "    string_args=STRING_args, lincs_args=LINCS_args, edge_args=EDGE_args, sig_args=SIG_args,\n",
+    "    bonesis_args=BONESIS_args, weights=DESIRABILITY, experiments_fname=EXPERIMENTS_fname,\n",
+    "    seed=seed_number, network_fname=NETWORK_fname, njobs=njobs, force_experiments=force_experiments, \n",
+    "    accept_nonRNA=accept_nonRNA, preserve_network_sign=preserve_network_sign)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "9741d5f2-54f2-418c-b8e5-b2c1336d21f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import Image\n",
+    "Image(filename=f'{file_folder}/inferred_max_criterion_solution.png') "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8b76b4d6-6042-4cee-98d1-0d132578f34c",
+   "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>GTP</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>320</th>\n",
+       "      <td>0.889284</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>180</th>\n",
+       "      <td>0.889284</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>121</th>\n",
+       "      <td>0.889284</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>291</th>\n",
+       "      <td>0.889284</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>288</th>\n",
+       "      <td>0.880757</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>401</th>\n",
+       "      <td>0.752907</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>399</th>\n",
+       "      <td>0.752907</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>339</th>\n",
+       "      <td>0.752907</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>378</th>\n",
+       "      <td>0.752907</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>246</th>\n",
+       "      <td>0.692201</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>500 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          GTP\n",
+       "320  0.889284\n",
+       "180  0.889284\n",
+       "121  0.889284\n",
+       "291  0.889284\n",
+       "288  0.880757\n",
+       "..        ...\n",
+       "401  0.752907\n",
+       "399  0.752907\n",
+       "339  0.752907\n",
+       "378  0.752907\n",
+       "246  0.692201\n",
+       "\n",
+       "[500 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.read_csv(f\"{file_folder}/GPT.csv\", index_col=0).T.sort_values(by=\"GTP\", ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f49749d-9624-49f1-9613-01de50db6b21",
+   "metadata": {},
+   "source": [
+    "## 5. Ground truth network"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "db11bcfa-34d3-4621-b829-c752a3fde9a0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "network_fname=\"boolean-network.bnet\"\n",
+    "proc = Popen(f\"wget -O {file_folder}/{network_fname} -q {data_url}/{network_fname}\".split(\" \"))\n",
+    "proc.wait()\n",
+    "\n",
+    "from NORDic.UTILS.utils_grn import solution2influences\n",
+    "from NORDic.UTILS.utils_plot import influences2graph\n",
+    "\n",
+    "with open(f\"{file_folder}/{network_fname}\", \"r\") as f:\n",
+    "    ground_truth = pd.DataFrame({\n",
+    "        \"Solution\": dict([[\"_\".join(g.split(\"-\")) for g in x.split(\", \")] for x in f.read().split(\"\\n\") if (len(x)>0)])\n",
+    "    })\n",
+    "influences_ground_truth = solution2influences(ground_truth[\"Solution\"])\n",
+    "influences2graph(influences_ground_truth, file_folder+\"ground_truth\", optional=False, compile2png=True, engine=[\"sfdp\",\"dot\"][0])\n",
+    "from IPython.display import Image\n",
+    "Image(filename=file_folder+'ground_truth.png') "
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "23949c4a-35de-4683-9690-5d7ccc9f1060",
+   "metadata": {},
+   "source": [
+    "if (os.path.exists(f\"{file_folder}\")):\n",
+    "\tproc = Popen(f\"rm -rf {file_folder}\".split(\" \"))\n",
+    "\tproc.wait()"
+   ]
+  }
+ ],
+ "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": "3.8.19"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}