Repository: pacti-org/pacti (language: python)

Issue:
writeContract raising exceptions
Traceback (most recent call last):
  File "/Users/pier/Projects/gear/case_studies/topologies/grammar/contracts.py", line 43, in refines_io_contract
    writeContract(c, f"C_exc_lhs")
  File "/Users/pier/Projects/gear/src/pacti/terms/polyhedra/loaders.py", line 80, in writeContract
    json.dump(c_dict, f)
  File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/json/__init__.py", line 179, in dump
    for chunk in iterable:
  File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/json/encoder.py", line 432, in _iterencode
    yield from _iterencode_dict(o, _current_indent_level)
  File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/json/encoder.py", line 406, in _iterencode_dict
    yield from chunks
  File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/json/encoder.py", line 326, in _iterencode_list
    yield from chunks
  File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/json/encoder.py", line 406, in _iterencode_dict
    yield from chunks
  File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/json/encoder.py", line 439, in _iterencode
    o = _default(o)
        ^^^^^^^^^^^
  File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/json/encoder.py", line 180, in default
    raise TypeError(f'Object of type {o.__class__.__name__} '
TypeError: Object of type Integer is not JSON serializable


To reproduce:
branch: pier
run: case_study/topologies/main.py

Exception caused by line 41 in case_study/topologies/grammar/contract.py






Discussion / hints:

Fix patch:
diff --git a/case_studies/biocircuit_specifications/dCas9_repression.json b/case_studies/biocircuit_specifications/dCas9_repression.json
new file mode 100644
index 00000000..ac3609f3
--- /dev/null
+++ b/case_studies/biocircuit_specifications/dCas9_repression.json
@@ -0,0 +1,4 @@
+{
+"contract0":{"InputVars": ["xRFP", "dCas9"], "OutputVars": ["RFP"], "assumptions": [{"constant": -0.05095790261282662, "coefficients": {"xRFP": -1.0}}, {"constant": 1.3327510926365798, "coefficients": {"xRFP": 1.0}}, {"constant": -0.3165528392857134, "coefficients": {"dCas9": -1.0}}, {"constant": 1.2985033035714197, "coefficients": {"dCas9": 1.0}}], "guarantees": [{"constant": 0.004899999999999904, "coefficients": {"RFP": 1.0}}]},
+"contract1":{"InputVars": ["xRFP", "dCas9"], "OutputVars": ["RFP"], "assumptions": [{"constant": 0.0046999999999999265, "coefficients": {"xRFP": 1.0}}, {"constant": -0.3165528392857134, "coefficients": {"dCas9": -1.0}}, {"constant": 1.2985033035714197, "coefficients": {"dCas9": 1.0}}], "guarantees": [{"constant": -1.3, "coefficients": {"RFP": -1.0}}]}
+}
\ No newline at end of file
diff --git a/case_studies/biocircuit_specifications/main_figure.png b/case_studies/biocircuit_specifications/main_figure.png
new file mode 100644
index 00000000..31e8852c
Binary files /dev/null and b/case_studies/biocircuit_specifications/main_figure.png differ
diff --git a/case_studies/biocircuit_specifications/rfp_control.json b/case_studies/biocircuit_specifications/rfp_control.json
new file mode 100644
index 00000000..0facb2ff
--- /dev/null
+++ b/case_studies/biocircuit_specifications/rfp_control.json
@@ -0,0 +1,1 @@
+"contract0"={"InputVars": ["Sal"], "OutputVars": ["xRFP"], "assumptions": [{"constant": -1, "coefficients": {"Sal": -1}}], "guarantees": [{"constant": 
\ No newline at end of file
diff --git a/case_studies/biocircuit_specifications/specification_based_synthetic_biology.ipynb b/case_studies/biocircuit_specifications/specification_based_synthetic_biology.ipynb
new file mode 100644
index 00000000..4042cfc5
--- /dev/null
+++ b/case_studies/biocircuit_specifications/specification_based_synthetic_biology.ipynb
@@ -0,0 +1,1341 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "d52837fd",
+   "metadata": {},
+   "source": [
+    "# Scaling up designs in synthetic biology using _pacti_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0d893532",
+   "metadata": {},
+   "source": [
+    "Research in synthetic biology has accelerated in the past decade due to its enormous promise in engineering biological systems for desired behavior. Applications range from biomedicine to engineered living materials and environmental remediation. From a design standpoint, mathematical models have been used to study the behavior of biological systems and analyze various properties. However, current experimental design approaches in synthetic biology are largely heuristics and trial-and-error based, hence, difficult to scale. To alleviate this, various modeling and analysis and logic gate design tools (like Cello) have been proposed but none have yet been used to successfully design large biological circuits. We believe that the _pacti_ framework holds the potential to address this issue of scale due to its generality in writing system specifications and in decoupling the modeling details of each component from their specification. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7ad0d7ed",
+   "metadata": {},
+   "source": [
+    "![main_figure](main_figure.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db8b2972",
+   "metadata": {},
+   "source": [
+    "To demonstrate the use of _pacti_ , we present a case study on the design of engineered biological circuits for altering and observing the dynamics of bacterial protein expression in soil and its effects on plant behavior. As shown in the Figure below, we consider a rhizosphere engineering system where engineered bacteria are introduced in to the soil near the plant roots to analyze the dynamics of protein expression in the plant. The input in this system design is a target gene of interest introduced in the soil through the bacteria. Through various biochemical processes, the goal is to measure the protein expression response of the plant in response to the target gene input. Without diving too deep into the biological details, we will summarize by saying that designing such a complex engineered biological system is not trivial due to a variety of factors. Some of these factors include the lack of information about the key biological mechanisms, absence of characterization data for system parts, and unknown effects of the soil context. With the use of _pacti_ , we envision speeding up the experimental design process in three ways: \n",
+    "\n",
+    "1. Characterized library of parts as assume-guarantee contracts: Using existing experimental data from literature, we present a library of sensors as assume-guarantee contracts that can be used to reason about specifications of diverse biological circuits. For the engineered bacteria case study, we use previously published data (for the Marionette cell lines) to construct a library of 14 sensors.\n",
+    "2. Speeding up the experimental design: Current experimental design approaches in synthetic biology are heavily reliant on screening experiments where all possible choices are explored experimentally to choose the one that works. This is not only time consuming and expensive but also does not scale beyond three or four independent choices. With the use of _pacti_ , we demonstrate how scientists may describe the desired top-level behavior as contracts and then computationally choose from a library of available parts to ensure that the components meet the top-level system specification. For the engineered bacteria case study, we find the specification of the sensors that meet the top-level criteria on fold-change of the circuit response.\n",
+    "3. Finding specifications of missing parts: In synthetic biology, it is common to have parts in the system for which no characterization data is available. Using quotient operation on contracts, we can find the constraints that this missing part must satisfy to meet the desired top-level criteria. For the bacterial case study, we find the missing contract of the dCas9-mediated repression mechanism (as shown in the B panel of the Figure above) for which characterization data is not available in the literature."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6470e2ad",
+   "metadata": {},
+   "source": [
+    "# Modeling the specifications to construct a library of parts\n",
+    "\n",
+    "Using the experimental characterization data for the sensors in the _Marionette_ bacterial cell strain [1], we model the sensor behavior as assume-guarantee contracts. We observe that each sensor has three characteristic behaviors: (1) the off state, where the output of the sensor stays close to zero (the non-zero expression in this state is termed as \"leaky response\"), (2) the linear rate of output , where the sensor output responds linearly to the input (in log scale), (3) the saturation state, where the sensor output saturates to a maximum constant v\n",
+    "\n",
+    "[1] Meyer, Adam J., et al. \"Escherichia coli “Marionette” strains with 12 highly optimized small-molecule sensors.\" Nature chemical biology 15.2 (2019): 196-204."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "cb984cc7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load all required libraries\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import sympy\n",
+    "from io import StringIO\n",
+    "\n",
+    "from pacti.terms.polyhedra.loaders import read_contract, write_contract\n",
+    "from synbio_utils import display_sensor_contracts, remove_quantization_errors\n",
+    "import pacti.terms.polyhedra as gtp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "50b7286e",
+   "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>Inducer</th>\n",
+       "      <th>Plasmid</th>\n",
+       "      <th>Regulator</th>\n",
+       "      <th>Promoter</th>\n",
+       "      <th>ymax (RPU)</th>\n",
+       "      <th>ymin (RPUx10-3)</th>\n",
+       "      <th>K (µM)</th>\n",
+       "      <th>n</th>\n",
+       "      <th>Dynamic range</th>\n",
+       "      <th>start</th>\n",
+       "      <th>ymax Linear</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>DAPG</td>\n",
+       "      <td>pAJM.847</td>\n",
+       "      <td>PhlFAM</td>\n",
+       "      <td>PPhlF</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>1.700</td>\n",
+       "      <td>2.1</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>0.0300</td>\n",
+       "      <td>2.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Cuma</td>\n",
+       "      <td>pAJM.657</td>\n",
+       "      <td>CymRAM</td>\n",
+       "      <td>PCymRC</td>\n",
+       "      <td>3.70</td>\n",
+       "      <td>4.3</td>\n",
+       "      <td>8.900</td>\n",
+       "      <td>2.4</td>\n",
+       "      <td>870</td>\n",
+       "      <td>0.3000</td>\n",
+       "      <td>2.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>OC6</td>\n",
+       "      <td>pAJM.474</td>\n",
+       "      <td>LuxR</td>\n",
+       "      <td>PLuxB</td>\n",
+       "      <td>1.30</td>\n",
+       "      <td>2.4</td>\n",
+       "      <td>0.120</td>\n",
+       "      <td>1.8</td>\n",
+       "      <td>540</td>\n",
+       "      <td>0.0020</td>\n",
+       "      <td>0.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Van</td>\n",
+       "      <td>pAJM.773</td>\n",
+       "      <td>VanRAM</td>\n",
+       "      <td>PVanCC</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>2.4</td>\n",
+       "      <td>26.000</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>1200</td>\n",
+       "      <td>1.0000</td>\n",
+       "      <td>1.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>IPTG</td>\n",
+       "      <td>pAJM.336</td>\n",
+       "      <td>LacIAM</td>\n",
+       "      <td>PTac</td>\n",
+       "      <td>3.30</td>\n",
+       "      <td>4.8</td>\n",
+       "      <td>140.000</td>\n",
+       "      <td>1.8</td>\n",
+       "      <td>690</td>\n",
+       "      <td>2.0000</td>\n",
+       "      <td>1.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>aTc</td>\n",
+       "      <td>pAJM.011</td>\n",
+       "      <td>TetR</td>\n",
+       "      <td>PTet*</td>\n",
+       "      <td>2.40</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>0.013</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>500</td>\n",
+       "      <td>0.0018</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Ara</td>\n",
+       "      <td>pAJM.677</td>\n",
+       "      <td>AraCAM</td>\n",
+       "      <td>PBAD</td>\n",
+       "      <td>1.20</td>\n",
+       "      <td>2.4</td>\n",
+       "      <td>37.000</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>500</td>\n",
+       "      <td>0.2000</td>\n",
+       "      <td>0.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Cho</td>\n",
+       "      <td>pAJM.683</td>\n",
+       "      <td>BetIAM</td>\n",
+       "      <td>PBetI</td>\n",
+       "      <td>2.60</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>4100.000</td>\n",
+       "      <td>2.7</td>\n",
+       "      <td>310</td>\n",
+       "      <td>300.0000</td>\n",
+       "      <td>0.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Nar</td>\n",
+       "      <td>pAJM.661</td>\n",
+       "      <td>TtgRAM</td>\n",
+       "      <td>PTtg</td>\n",
+       "      <td>0.46</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>95.000</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>140</td>\n",
+       "      <td>2.0000</td>\n",
+       "      <td>0.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>DHBA</td>\n",
+       "      <td>pAJM.690</td>\n",
+       "      <td>PcaUAM</td>\n",
+       "      <td>P3B5B</td>\n",
+       "      <td>1.60</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>370.000</td>\n",
+       "      <td>1.8</td>\n",
+       "      <td>360</td>\n",
+       "      <td>9.0000</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>Sal</td>\n",
+       "      <td>pAJM.771</td>\n",
+       "      <td>NahRAM</td>\n",
+       "      <td>PSalTTC</td>\n",
+       "      <td>2.80</td>\n",
+       "      <td>4.7</td>\n",
+       "      <td>43.000</td>\n",
+       "      <td>1.8</td>\n",
+       "      <td>600</td>\n",
+       "      <td>0.9000</td>\n",
+       "      <td>1.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>OHC14</td>\n",
+       "      <td>pAJM.1642</td>\n",
+       "      <td>CinRAM</td>\n",
+       "      <td>PCin</td>\n",
+       "      <td>1.50</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.430</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>490</td>\n",
+       "      <td>0.0160</td>\n",
+       "      <td>0.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>Acr</td>\n",
+       "      <td>pAJM.884</td>\n",
+       "      <td>AcuRAM</td>\n",
+       "      <td>PAcu</td>\n",
+       "      <td>3.10</td>\n",
+       "      <td>37.0</td>\n",
+       "      <td>130.000</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>83</td>\n",
+       "      <td>10.0000</td>\n",
+       "      <td>1.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>Ery</td>\n",
+       "      <td>pAJM.969</td>\n",
+       "      <td>MphRAM</td>\n",
+       "      <td>PMph</td>\n",
+       "      <td>0.29</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>65.000</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>36</td>\n",
+       "      <td>2.0000</td>\n",
+       "      <td>0.18</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Inducer    Plasmid Regulator Promoter  ymax (RPU)  ymin (RPUx10-3)  \\\n",
+       "0     DAPG   pAJM.847    PhlFAM    PPhlF        2.50              2.5   \n",
+       "1     Cuma   pAJM.657    CymRAM   PCymRC        3.70              4.3   \n",
+       "2      OC6   pAJM.474      LuxR    PLuxB        1.30              2.4   \n",
+       "3      Van   pAJM.773    VanRAM   PVanCC        3.00              2.4   \n",
+       "4     IPTG   pAJM.336    LacIAM     PTac        3.30              4.8   \n",
+       "5      aTc   pAJM.011      TetR    PTet*        2.40              4.9   \n",
+       "6      Ara   pAJM.677    AraCAM     PBAD        1.20              2.4   \n",
+       "7      Cho   pAJM.683    BetIAM    PBetI        2.60              8.5   \n",
+       "8      Nar   pAJM.661    TtgRAM     PTtg        0.46              3.4   \n",
+       "9     DHBA   pAJM.690    PcaUAM    P3B5B        1.60              4.5   \n",
+       "10     Sal   pAJM.771    NahRAM  PSalTTC        2.80              4.7   \n",
+       "11   OHC14  pAJM.1642    CinRAM     PCin        1.50              3.0   \n",
+       "12     Acr   pAJM.884    AcuRAM     PAcu        3.10             37.0   \n",
+       "13     Ery   pAJM.969    MphRAM     PMph        0.29              8.0   \n",
+       "\n",
+       "      K (µM)    n  Dynamic range     start  ymax Linear  \n",
+       "0      1.700  2.1           1000    0.0300         2.00  \n",
+       "1      8.900  2.4            870    0.3000         2.00  \n",
+       "2      0.120  1.8            540    0.0020         0.50  \n",
+       "3     26.000  2.3           1200    1.0000         1.80  \n",
+       "4    140.000  1.8            690    2.0000         1.50  \n",
+       "5      0.013  3.8            500    0.0018         1.00  \n",
+       "6     37.000  1.5            500    0.2000         0.70  \n",
+       "7   4100.000  2.7            310  300.0000         0.90  \n",
+       "8     95.000  1.9            140    2.0000         0.20  \n",
+       "9    370.000  1.8            360    9.0000         1.00  \n",
+       "10    43.000  1.8            600    0.9000         1.30  \n",
+       "11     0.430  2.3            490    0.0160         0.70  \n",
+       "12   130.000  2.5             83   10.0000         1.70  \n",
+       "13    65.000  1.5             36    2.0000         0.18  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Read the data from the paper as a string\n",
+    "data = '''\n",
+    "Inducer,Plasmid,Regulator,Promoter,ymax (RPU),ymin (RPUx10-3),K (µM),n,Dynamic range,start,ymax Linear\n",
+    "DAPG,pAJM.847,PhlFAM,PPhlF,2.50,2.5,1.700,2.1,1000,0.03,2 \n",
+    "Cuma,pAJM.657,CymRAM,PCymRC,3.70,4.3,8.900,2.4,870,0.3,2\n",
+    "OC6,pAJM.474,LuxR,PLuxB,1.30,2.4,0.120,1.8,540,0.002,0.5\n",
+    "Van,pAJM.773,VanRAM,PVanCC,3.00,2.4,26.000,2.3,1200,1,1.8\n",
+    "IPTG,pAJM.336,LacIAM,PTac,3.30,4.8,140.000,1.8,690,2,1.5\n",
+    "aTc,pAJM.011,TetR,PTet*,2.40,4.9,0.013,3.8,500,0.0018,1\n",
+    "Ara,pAJM.677,AraCAM,PBAD,1.20,2.4,37.000,1.5,500,0.2,0.7\n",
+    "Cho,pAJM.683,BetIAM,PBetI,2.60,8.5,4100.00,2.7,310,300,0.9\n",
+    "Nar,pAJM.661,TtgRAM,PTtg,0.46,3.4,95.000,1.9,140,2,0.2\n",
+    "DHBA,pAJM.690,PcaUAM,P3B5B,1.60,4.5,370.000,1.8,360,9,1\n",
+    "Sal,pAJM.771,NahRAM,PSalTTC,2.80,4.7,43.000,1.8,600,0.9,1.3\n",
+    "OHC14,pAJM.1642,CinRAM,PCin,1.50,3.0,0.430,2.3,490,0.016,0.7\n",
+    "Acr,pAJM.884,AcuRAM,PAcu,3.10,37.0,130.000,2.5,83,10,1.7\n",
+    "Ery,pAJM.969,MphRAM,PMph,0.29,8.0,65.000,1.5,36,2,0.18\n",
+    "'''\n",
+    "df = pd.read_csv(StringIO(data))\n",
+    "\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1d378cf4",
+   "metadata": {},
+   "source": [
+    "Create contracts for the sensor using this experimental data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "2943f852",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def create_sensor_contracts(sensor_input=\"AHL\", output=\"FP\", K=0.0, yleak=0.0, start = 0.0, ymax_lin = 0.0):\n",
+    "    \"\"\"\n",
+    "    Creates the contracts for a Marionette sensing subsystem\n",
+    "    params:\n",
+    "        * input (str): The inducer input to the sensor\n",
+    "        * output (str): The output of the genetic construct. Inducer activates the \n",
+    "                        production of this output\n",
+    "        * K (float): The value of the Hill activation parameter K\n",
+    "        * yleak (float): The minimum expression of output even in absence of inducer\n",
+    "        * start (float): The value of inducer at which the induction starts\n",
+    "        * ymax_lin (float): The maximum expression of output by the inducer\n",
+    "                            before saturating (the end of linear regime)\n",
+    "    \"\"\"\n",
+    "    slope = (ymax_lin - yleak)/(K - start)\n",
+    "    intercept = yleak - slope*start\n",
+    "    contract_0 = {\n",
+    "            \"_comment\": \"contract when sensor is OFF\",\n",
+    "            \"InputVars\":[\n",
+    "                sensor_input\n",
+    "            ],\n",
+    "            \"OutputVars\":[\n",
+    "                output\n",
+    "            ],\n",
+    "            \"assumptions\":\n",
+    "            [\n",
+    "                {\"coefficients\":{sensor_input:1}, # \"1*input <= start\"\n",
+    "                \"constant\":start}\n",
+    "            ],\n",
+    "            \"guarantees\":[\n",
+    "                {\"coefficients\":{output:1},\n",
+    "                \"constant\":yleak}\n",
+    "            ]\n",
+    "        }\n",
+    "    contract_lin = {\n",
+    "            \"_comment\": \"contract for sensor in linear regime\",\n",
+    "            \"InputVars\":[\n",
+    "                sensor_input\n",
+    "            ],\n",
+    "            \"OutputVars\":[\n",
+    "                output\n",
+    "            ],\n",
+    "            \"assumptions\":\n",
+    "            [\n",
+    "                {\"coefficients\":{sensor_input:1},\n",
+    "                \"constant\":K},\n",
+    "                {\"coefficients\":{sensor_input:-1},\n",
+    "                \"constant\":-start}\n",
+    "            ],\n",
+    "            \"guarantees\":[\n",
+    "                {\"coefficients\":{output:-1, sensor_input:slope},\n",
+    "                \"constant\":intercept},\n",
+    "                {\"coefficients\":{output:1, sensor_input:-slope},\n",
+    "                \"constant\":-intercept}\n",
+    "            ]\n",
+    "        }\n",
+    "    contract_max = {\n",
+    "            \"_comment\": \"contract for sensor in saturation\",\n",
+    "            \"InputVars\":[\n",
+    "                sensor_input\n",
+    "            ],\n",
+    "            \"OutputVars\":[\n",
+    "                output\n",
+    "            ],\n",
+    "            \"assumptions\":\n",
+    "            [\n",
+    "                {\"coefficients\":{sensor_input:-1},\n",
+    "                \"constant\":-K}\n",
+    "            ],\n",
+    "            \"guarantees\":[\n",
+    "                {\"coefficients\":{output:-1},\n",
+    "                \"constant\":-ymax_lin}\n",
+    "            ]\n",
+    "        }\n",
+    "    return contract_0, contract_lin, contract_max"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a035e0ac",
+   "metadata": {},
+   "source": [
+    "## Composition to find optimal sensor choices\n",
+    "\n",
+    "To speed up the experimental screening, we use _pacti_ to choose a sensor from the library that meets the desired top-level behavior. Here, we have a tetracycline (aTc) sensor that expresses a dCas9 protein and the other sensor (the one we want to choose) that expresses the xRFP guide RNA. The xRFP guide RNA binds to dCas9 to repress the expression of red fluorescent protein (RFP), the output of the system. When either of the sensors are OFF, the output (RFP) is constitutively expressed (that is, output is ON). In this way, the system behavior is that of a NAND logic gate. We denote the contract for the tetracycline sensor as $C_{\\text{atc}}$ and the contract for dCas9 repression mechanism as $C_{\\text{dCas9}}$. For the top-level specification, we define the fold-change of the system as\n",
+    "$F = \\frac{\\text{RFP}_{\\text{on}}}{\\text{RFP}_{\\text{off}}}.$\n",
+    "\n",
+    "To choose the sensor that achieves the highest fold-change for the system, we compose the contract for each of the available 13 sensors in the library with the tetracycline contract and the dCas9 repression contract to obtain the top-level system contract. The composition operation in _pacti_ reports a design error for any sensor that is \"unsatisfiable in the given context\" to reject some of the choices while some other choices are rejected if they do not meet the set top-level system criteria."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "ed34b120",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create all sensor contracts:\n",
+    "sensor_names = [str(i) for i in df['Inducer']]\n",
+    "sensor_library = {}\n",
+    "sensor_library_params = {}\n",
+    "for sensor in sensor_names:\n",
+    "    sensor_params = {}\n",
+    "    yleak_s = df.loc[df[\"Inducer\"] == sensor][\"ymin (RPUx10-3)\"].values[0]*1e-3\n",
+    "    s_start = df.loc[df[\"Inducer\"] == sensor][\"start\"].values[0]\n",
+    "    s_K = df.loc[df[\"Inducer\"] == sensor][\"K (µM)\"].values[0]\n",
+    "    ymax_s = df.loc[df[\"Inducer\"] == sensor][\"ymax Linear\"].values[0]\n",
+    "    sensor_params = {\"leak\":yleak_s, \"start\":s_start, \"K\":s_K, \"ymax\":ymax_s}\n",
+    "    s_output = \"dCas9\"\n",
+    "    contract_s_0, contract_s_lin, contract_s_max = create_sensor_contracts(sensor_input=sensor, output=\"xRFP\", \n",
+    "                                                                           start=s_start, K=s_K,\n",
+    "                                                                           ymax_lin=ymax_s, yleak=yleak_s)\n",
+    "    sensor_library[sensor] = [contract_s_0, contract_s_lin, contract_s_max]\n",
+    "    sensor_library_params[sensor] = sensor_params"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "a6e060b6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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pVEwEAAAAAAAAAAAAUG5QlwUAAAAofRVKOwAAAAAAAAAAAAAAAAAAAFB+kJgIAAAAAAAAAAAAAAAAAACshsREAAAAAAAAAAAAAAAAAABgNSQmAgAAAAAAAAAAAAAAAAAAqyExEQAAAAAAAAAAAAAAAAAAWA2JiQAAAAAAAAAAAAAAAAAAwGpITAQAAAAAAAAAAAAAAAAAAFZDYiIAAAAAAAAAAAAAAAAAALAaEhMBAAAAAAAAAAAAAAAAAIDV/B8fTM6+J8BiPwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 3200x2000 with 15 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, all_ax = plt.subplots(nrows=3, ncols=5, figsize=(32,20))\n",
+    "index = 0\n",
+    "for sensor in sensor_names:\n",
+    "    ax = all_ax[index//5][index%5]\n",
+    "    index += 1\n",
+    "    yleak_s = df.loc[df[\"Inducer\"] == sensor][\"ymin (RPUx10-3)\"].values[0]*1e-3\n",
+    "    s_start = df.loc[df[\"Inducer\"] == sensor][\"start\"].values[0]\n",
+    "    s_K = df.loc[df[\"Inducer\"] == sensor][\"K (µM)\"].values[0]\n",
+    "    ymax_s = df.loc[df[\"Inducer\"] == sensor][\"ymax Linear\"].values[0]\n",
+    "    ax = display_sensor_contracts(sensor_input=sensor, output=\"\", leak=yleak_s,\n",
+    "                                  start=s_start, K=s_K, ymax_lin=ymax_s,\n",
+    "                                  xlim_min=10**-3, xlim_max=10**4, ylim_min=10**-3, ylim_max=10**2,\n",
+    "                                  ax=ax, show=False)\n",
+    "    ax.set_xlabel(sensor, fontsize=16)\n",
+    "    ax.tick_params(axis = 'both', which = 'major', labelsize = 16)\n",
+    "    ax.set_ylabel(\"Output\", fontsize=16)\n",
+    "    ax.legend(fontsize=16)\n",
+    "# plt.savefig('all_sensors.svg')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9af8e4e8",
+   "metadata": {},
+   "source": [
+    "Write the contract for the dCas9 repression mechanism"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d3736900",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dCas9_contract_off = {\n",
+    "            \"_comment\": \"xRFP:OFF, dCas9:OFF, RFP:ON\",\n",
+    "            \"InputVars\":[\n",
+    "                \"xRFP\", \"dCas9\"\n",
+    "            ],\n",
+    "            \"OutputVars\":[\n",
+    "                \"RFP\"\n",
+    "            ],\n",
+    "            \"assumptions\":\n",
+    "            [\n",
+    "                {\"coefficients\":{\"xRFP\":1},\n",
+    "                \"constant\":0.2}, # xRFP < 0.2\n",
+    "                {\"coefficients\":{\"dCas9\":1},\n",
+    "                \"constant\":0.1}, # dCas9 < 0.1\n",
+    "            ],\n",
+    "            \"guarantees\":[\n",
+    "                {\"coefficients\":{\"RFP\":-1},\n",
+    "                 \"constant\":-10}, # RFP >= 10\n",
+    "            ]\n",
+    "        }\n",
+    "\n",
+    "dCas9_contract_on = {\n",
+    "            \"_comment\": \"The dCas9 repression: xRFP:ON, dCas9:ON, RFP:OFF\",\n",
+    "            \"InputVars\":[\n",
+    "                \"xRFP\", \"dCas9\"\n",
+    "            ],\n",
+    "            \"OutputVars\":[\n",
+    "                \"RFP\"\n",
+    "            ],\n",
+    "            \"assumptions\":\n",
+    "            [\n",
+    "                {\"coefficients\":{\"xRFP\":-1},\n",
+    "                \"constant\":-0.3}, # xRFP > 0.3\n",
+    "                {\"coefficients\":{\"xRFP\":1},\n",
+    "                \"constant\":1}, # xRFP < 1\n",
+    "                {\"coefficients\":{\"dCas9\":-1},\n",
+    "                \"constant\":-0.1}, # dCas9 > 0.1\n",
+    "                {\"coefficients\":{\"dCas9\":1},\n",
+    "                \"constant\":0.8}, # dCas9 < 0.8\n",
+    "            ],\n",
+    "            \"guarantees\":[\n",
+    "                {\"coefficients\":{\"RFP\":1, \"xRFP\":2, \"dCas9\":0.1},\n",
+    "                 \"constant\":5}, # RFP < 5 - 2xRFP - 0.1dCas9\n",
+    "            ]\n",
+    "        }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "eb3dc9c2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "c_dcas9_off, c_dcas9_on = read_contract([dCas9_contract_off, dCas9_contract_on])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "5901a940",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input2 = \"aTc\"\n",
+    "sensor_ok = {}\n",
+    "fold_change = {}\n",
+    "desired_fold_change = 4\n",
+    "for sensor in sensor_library:\n",
+    "    in1_params = sensor_library_params[sensor]\n",
+    "    in2 = \"aTc\"\n",
+    "    in2_params= sensor_library_params[in2]\n",
+    "    c_atc_off, c_atc_on, c_atc_sat = create_sensor_contracts(sensor_input=\"aTc\", output=\"dCas9\", \n",
+    "                                                             start=in2_params[\"start\"],\n",
+    "                                                             K=in2_params[\"K\"],\n",
+    "                                                             ymax_lin=in2_params[\"ymax\"],\n",
+    "                                                             yleak=in2_params[\"leak\"])\n",
+    "    if sensor != \"aTc\":  \n",
+    "        try:\n",
+    "            # OFF condition\n",
+    "            c_sensor1 = read_contract(sensor_library[sensor][0])\n",
+    "            c_sensor2 = read_contract(c_atc_off)\n",
+    "            sensor_comp = c_sensor2.compose(c_sensor1)\n",
+    "            top_level_off = sensor_comp.compose(c_dcas9_off)\n",
+    "            top_level_off = remove_quantization_errors(top_level_off)\n",
+    "            sym_expr = gtp.PolyhedralTerm.to_symbolic(top_level_off.g.terms[0])\n",
+    "            rfp_vals_on = {}\n",
+    "            updated_sym = sym_expr.subs(sensor, 0.9*in1_params[\"start\"])\n",
+    "            updated_sym = updated_sym.subs(in2, 0.9*in2_params[\"start\"])\n",
+    "            subs_val = sympy.solve(updated_sym, \"RFP\")\n",
+    "            rfp_vals_on[in1_params[\"start\"], in2_params[\"start\"]] = subs_val[0]\n",
+    "            # ON condition\n",
+    "            c_sensor1 = read_contract(sensor_library[sensor][1])\n",
+    "            c_sensor2 = read_contract(c_atc_on)\n",
+    "            sensor_comp = c_sensor2.compose(c_sensor1)\n",
+    "            top_level_on = sensor_comp.compose(c_dcas9_on)\n",
+    "            top_level_on = remove_quantization_errors(top_level_on)\n",
+    "            sym_expr = gtp.PolyhedralTerm.to_symbolic(top_level_on.g.terms[0])\n",
+    "            rfp_vals_off = {}\n",
+    "            updated_sym = sym_expr.subs(sensor, 0.9*in1_params[\"K\"])\n",
+    "            updated_sym = updated_sym.subs(in2, 0.9*in2_params[\"K\"])\n",
+    "            subs_val = sympy.solve(updated_sym, \"RFP\")\n",
+    "            rfp_vals_off[in1_params[\"K\"], in2_params[\"K\"]] = subs_val[0]\n",
+    "            fold_change[sensor] = list(rfp_vals_on.values())[0]/list(rfp_vals_off.values())[0]\n",
+    "            if fold_change[sensor] > desired_fold_change:\n",
+    "                sensor_ok[sensor] = True\n",
+    "            else:\n",
+    "                sensor_ok[sensor] = False\n",
+    "        except ValueError as e:\n",
+    "            if \"unsatisfiable in context\" in str(e):\n",
+    "                sensor_ok[sensor] = False\n",
+    "            else:\n",
+    "                print(str(e))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "2fd35913",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "ax.bar(fold_change.keys(), fold_change.values(), color='C1', alpha=0.5)\n",
+    "\n",
+    "ax.set_xlabel(\"Sensor\", fontsize=14)\n",
+    "ax.set_ylabel(\"Top-level fold-change\", fontsize=14)\n",
+    "plt.xticks(rotation=45, fontsize=14)\n",
+    "plt.yticks(fontsize=14)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e2a91a03",
+   "metadata": {},
+   "source": [
+    "Note that not all sensors were compatible. Some of the sensor choices led to errors in design automatically caught by _pacti_ :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "6e2370b5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'DAPG': 8.66785005774809,\n",
+       " 'Cuma': 9.68674395539190,\n",
+       " 'OC6': 2.52677363203287,\n",
+       " 'Van': 7.26517302130202,\n",
+       " 'IPTG': 4.72491416737617,\n",
+       " 'Ara': 2.76605435260938,\n",
+       " 'Cho': 3.30535361336978,\n",
+       " 'DHBA': 3.32963257502971,\n",
+       " 'Sal': 4.07634004092162,\n",
+       " 'OHC14': 2.83319542816097,\n",
+       " 'Acr': 6.97093280769728}"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fold_change"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "0d863ce4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'DAPG': True,\n",
+       " 'Cuma': True,\n",
+       " 'OC6': False,\n",
+       " 'Van': True,\n",
+       " 'IPTG': True,\n",
+       " 'Ara': False,\n",
+       " 'Cho': False,\n",
+       " 'Nar': False,\n",
+       " 'DHBA': False,\n",
+       " 'Sal': True,\n",
+       " 'OHC14': False,\n",
+       " 'Acr': True,\n",
+       " 'Ery': False}"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sensor_ok"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4c240582",
+   "metadata": {},
+   "source": [
+    "# Finding specifications of missing parts\n",
+    "\n",
+    "For the NAND gate described above, if we write a desired top-level contract $C_{\\text{sys}}$ and assume that both sensors have been chosen (here we choose Sal and aTc) so that we have the contracts for both sensors. Then, we may use quotient operation in _pacti_ to find the ideal specifications of the dCas9 mechanism. This can guide the design of the repression mechanism.\n",
+    "\n",
+    "\n",
+    "### Contract for Sal sensor:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "625110ef",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "yleak_sal = df.loc[df[\"Inducer\"] == \"Sal\"][\"ymin (RPUx10-3)\"].values[0]*1e-3\n",
+    "sal_start = df.loc[df[\"Inducer\"] == \"Sal\"][\"start\"].values[0]\n",
+    "sal_K = df.loc[df[\"Inducer\"] == \"Sal\"][\"K (µM)\"].values[0]\n",
+    "ymax_sal = df.loc[df[\"Inducer\"] == \"Sal\"][\"ymax Linear\"].values[0]\n",
+    "\n",
+    "sal_output = \"xRFP\"\n",
+    "contract_sal_min, contract_sal_lin, contract_sal_max = create_sensor_contracts(sensor_input=\"Sal\", output=sal_output, \n",
+    "                                                                               start=sal_start, K=sal_K,\n",
+    "                                                                               ymax_lin=ymax_sal, yleak=yleak_sal)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "d694b93f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display_sensor_contracts(sensor_input=\"Input 1\", output=\"xRFP\", leak=yleak_sal,\n",
+    "                         start=sal_start, K=sal_K, ymax_lin=ymax_sal,\n",
+    "                         xlim_min=10**-3, xlim_max=10**3, ylim_min=10**-3, ylim_max=10**1);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "756ac61b",
+   "metadata": {},
+   "source": [
+    "### Contracts for aTc sensor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "0d96e571",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "yleak_atc = df.loc[df[\"Inducer\"] == \"aTc\"][\"ymin (RPUx10-3)\"].values[0]*1e-3\n",
+    "atc_start = df.loc[df[\"Inducer\"] == \"aTc\"][\"start\"].values[0]\n",
+    "atc_K = df.loc[df[\"Inducer\"] == \"aTc\"][\"K (µM)\"].values[0]\n",
+    "ymax_atc =df.loc[df[\"Inducer\"] == \"aTc\"][\"ymax Linear\"].values[0]\n",
+    "atc_output = \"dCas9\"\n",
+    "contract_atc_min, contract_atc_lin, contract_atc_max = create_sensor_contracts(sensor_input=\"aTc\", output=atc_output, \n",
+    "                                                                               start=atc_start, K=atc_K,\n",
+    "                                                                               ymax_lin=ymax_atc, yleak=yleak_atc)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "38e8e980",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display_sensor_contracts(sensor_input=\"aTc\", output=\"dCas9\", leak=yleak_atc,\n",
+    "                         start=atc_start, K=atc_K, ymax_lin=ymax_atc,\n",
+    "                         xlim_min=10**-3, xlim_max=10**0, ylim_min=10**-3, ylim_max=10**1);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b2ea06c9",
+   "metadata": {},
+   "source": [
+    "### Top-level system contract"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "941f49c5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "leak_RFP = np.max([yleak_atc, yleak_sal])\n",
+    "max_RFP = np.max([ymax_atc, ymax_sal])\n",
+    "def create_top_level_contracts(input1 = \"u1\", input2 = \"u2\", output=\"y\", \n",
+    "                               input1_params = None, input2_params = None, output_params = None):\n",
+    "    \"\"\"\n",
+    "    Creates the top level system contracts\n",
+    "    \"\"\"\n",
+    "    u1_start = input1_params[\"start\"]\n",
+    "    u1_K = input1_params[\"K\"]\n",
+    "\n",
+    "    u2_start = input2_params[\"start\"]\n",
+    "    u2_K = input2_params[\"K\"]\n",
+    "\n",
+    "    y_max = output_params[\"max\"]\n",
+    "    y_leak = output_params[\"leak\"]\n",
+    "    all_contracts = []\n",
+    "    contract_top_level_on1 = {\n",
+    "            \"_comment\": \"u1:OFF, u2:OFF, y:ON\",\n",
+    "            \"InputVars\":[\n",
+    "                input1, input2\n",
+    "            ],\n",
+    "            \"OutputVars\":[\n",
+    "                output\n",
+    "            ],\n",
+    "            \"assumptions\":\n",
+    "            [\n",
+    "                {\"coefficients\":{input1:1},\n",
+    "                \"constant\":u1_start - u1_start*0.01},\n",
+    "                {\"coefficients\":{input1:1},\n",
+    "                \"constant\":u2_start - u2_start*0.01}\n",
+    "            ],\n",
+    "            \"guarantees\":[\n",
+    "                {\"coefficients\":{output:-1},\n",
+    "                \"constant\":-y_max},\n",
+    "            ]\n",
+    "        }\n",
+    "    all_contracts.append(contract_top_level_on1)\n",
+    "    contract_top_level_on2 = {\n",
+    "            \"_comment\": \"u1:ON, u2:OFF, y:ON\",\n",
+    "            \"InputVars\":[\n",
+    "                input1, input2\n",
+    "            ],\n",
+    "            \"OutputVars\":[\n",
+    "                output\n",
+    "            ],\n",
+    "            \"assumptions\":\n",
+    "            [\n",
+    "                {\"coefficients\":{input1:-1},\n",
+    "                \"constant\":-(u1_start + u2_start*0.01)},\n",
+    "                {\"coefficients\":{input1:1},\n",
+    "                \"constant\":u1_K - u1_K*0.01},\n",
+    "                {\"coefficients\":{input2:1},\n",
+    "                \"constant\":u2_start - u2_start*0.01}\n",
+    "            ],\n",
+    "            \"guarantees\":[\n",
+    "                {\"coefficients\":{output:-1},\n",
+    "                \"constant\":-y_max},\n",
+    "            ]\n",
+    "        }\n",
+    "    all_contracts.append(contract_top_level_on2)\n",
+    "    contract_top_level_on3 = {\n",
+    "            \"_comment\": \"u1:OFF, u2:ON, y:ON\",\n",
+    "            \"InputVars\":[\n",
+    "                input1, input2\n",
+    "            ],\n",
+    "            \"OutputVars\":[\n",
+    "                output\n",
+    "            ],\n",
+    "            \"assumptions\":\n",
+    "            [\n",
+    "                {\"coefficients\":{input1:1},\n",
+    "                \"constant\":u1_start - u1_start*0.01},\n",
+    "                {\"coefficients\":{input2:-1},\n",
+    "                \"constant\":-(u2_start + u2_start*0.01)},\n",
+    "                {\"coefficients\":{input2:1},\n",
+    "                \"constant\":u2_K - u2_K*0.01},\n",
+    "            ],\n",
+    "            \"guarantees\":[\n",
+    "                {\"coefficients\":{output:-1},\n",
+    "                \"constant\":-y_max},\n",
+    "            ]\n",
+    "        }\n",
+    "    all_contracts.append(contract_top_level_on3)\n",
+    "    contract_top_level_off = {\n",
+    "            \"_comment\": \"u1:ON, u2:ON, y:OFF\",\n",
+    "            \"InputVars\":[\n",
+    "                input1, input2\n",
+    "            ],\n",
+    "            \"OutputVars\":[\n",
+    "                output\n",
+    "            ],\n",
+    "            \"assumptions\":\n",
+    "            [\n",
+    "                {\"coefficients\":{input1:-1},\n",
+    "                \"constant\":-(u1_start + u1_start*0.01)},\n",
+    "                {\"coefficients\":{input1:1},\n",
+    "                \"constant\":u1_K - u1_K*0.01},\n",
+    "                {\"coefficients\":{input2:-1},\n",
+    "                \"constant\":-(u2_start + u2_start*0.01)},\n",
+    "                {\"coefficients\":{input2:1},\n",
+    "                \"constant\":u2_K - u2_K*0.01},\n",
+    "            ],\n",
+    "            \"guarantees\":[\n",
+    "                {\"coefficients\":{output:1},\n",
+    "                \"constant\":y_leak},   \n",
+    "            ]\n",
+    "        }\n",
+    "    all_contracts.append(contract_top_level_off)\n",
+    "    return all_contracts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "f99896c7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sal_params = {\"start\":sal_start, \"K\":sal_K}\n",
+    "atc_params = {\"start\":atc_start, \"K\":atc_K}\n",
+    "RFP_params = {\"max\":max_RFP, \"leak\":leak_RFP}\n",
+    "ctop_on1, ctop_on2, ctop_on3, ctop_off = create_top_level_contracts(input1 = \"Sal\",\n",
+    "                                                                    input2 = \"aTc\",\n",
+    "                                                                    output = \"RFP\",\n",
+    "                                                                    input1_params=sal_params,\n",
+    "                                                                    input2_params=atc_params,\n",
+    "                                                                    output_params=RFP_params)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "fd81174a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "csal_OFF, csal_ON, csal_SAT = read_contract([contract_sal_min, contract_sal_lin, contract_sal_max])\n",
+    "catc_OFF, catc_ON, catc_SAT = read_contract([contract_atc_min, contract_atc_lin, contract_atc_max])\n",
+    "ctop_off_off, ctop_on_off, ctop_off_on, ctop_on_on = read_contract([ctop_on1, ctop_on2, ctop_on3, ctop_off])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8c441dc0",
+   "metadata": {},
+   "source": [
+    "### Print the contracts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "0621e8ec",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "InVars: [<Var aTc>]\n",
+      "OutVars:[<Var dCas9>]\n",
+      "A: 1*aTc <= 0.013, -1*aTc <= -0.0018\n",
+      "G: 88.84821428571429*aTc + -1.0*dCas9 <= -0.15502678571428574, -88.84821428571429*aTc + 1.0*dCas9 <= 0.15502678571428574\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(catc_ON)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "23c214e4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "InVars: [<Var Sal>]\n",
+      "OutVars:[<Var xRFP>]\n",
+      "A: 1*Sal <= 43.0, -1*Sal <= -0.9\n",
+      "G: 0.03076722090261283*Sal + -1.0*xRFP <= -0.0229904988123516, -0.03076722090261283*Sal + 1.0*xRFP <= 0.02299049881235149\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(csal_ON)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a1be2a35",
+   "metadata": {},
+   "source": [
+    "### Available composition \n",
+    "(in the NAND gate ON condition => output is OFF)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "f513dfe7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "InVars: [<Var Sal>, <Var aTc>]\n",
+      "OutVars:[<Var xRFP>, <Var dCas9>]\n",
+      "A: 1*Sal <= 43.0, -1*Sal <= -0.9, 1*aTc <= 0.0129999999999999, -1*aTc <= -0.0018000000000000238\n",
+      "G: 0.03076722090261283*Sal + -1.0*xRFP <= -0.0229904988123516, -0.03076722090261283*Sal + 1.0*xRFP <= 0.02299049881235149, 88.84821428571429*aTc + -1.0*dCas9 <= -0.15502678571428574, -88.84821428571429*aTc + 1.0*dCas9 <= 0.15502678571428574\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(csal_ON.compose(catc_ON))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "2da8d176",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "InVars: [<Var Sal>, <Var aTc>]\n",
+      "OutVars:[<Var RFP>]\n",
+      "A: -1*Sal <= -0.909, 1*Sal <= 42.57, -1*aTc <= -0.001818, 1*aTc <= 0.01287\n",
+      "G: 1*RFP <= 0.004899999999999904\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(ctop_on_on)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d1ca2865",
+   "metadata": {},
+   "source": [
+    "#### Take the quotient of the top-level contract in the ON condition from the available composition"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "07c848d9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "csal_atc = csal_ON.compose(catc_ON)\n",
+    "dCas_repression_lin = ctop_on_on.quotient(csal_atc)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "984a84c6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "InVars: [<Var xRFP>, <Var dCas9>]\n",
+      "OutVars:[<Var RFP>]\n",
+      "A: -1.0*xRFP <= -0.0509579026128266, 1.0*xRFP <= 1.33275109263658, -1.0*dCas9 <= -0.316552839285713, 1.0*dCas9 <= 1.29850330357142\n",
+      "G: 1*RFP <= 0.004899999999999904\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(dCas_repression_lin)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3015d768",
+   "metadata": {},
+   "source": [
+    "#### Find contract for the missing subsystem for other conditions of the NAND gate circuit:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "06a936d6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "InVars: [<Var Sal>]\n",
+      "OutVars:[<Var xRFP>]\n",
+      "A: 1*Sal <= 0.9\n",
+      "G: 1*xRFP <= 0.0046999999999999265\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(csal_OFF)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "c0b6daee",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "InVars: [<Var aTc>]\n",
+      "OutVars:[<Var dCas9>]\n",
+      "A: 1*aTc <= 0.013, -1*aTc <= -0.0018\n",
+      "G: 88.84821428571429*aTc + -1.0*dCas9 <= -0.15502678571428574, -88.84821428571429*aTc + 1.0*dCas9 <= 0.15502678571428574\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(catc_ON)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "2ec75565",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "InVars: [<Var Sal>, <Var aTc>]\n",
+      "OutVars:[<Var xRFP>, <Var dCas9>]\n",
+      "A: 1*Sal <= 0.8999999999999999, 1*aTc <= 0.0129999999999999, -1*aTc <= -0.0018000000000000238\n",
+      "G: 1.0*xRFP <= 0.0046999999999999265, 88.84821428571429*aTc + -1.0*dCas9 <= -0.15502678571428574, -88.84821428571429*aTc + 1.0*dCas9 <= 0.15502678571428574\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(csal_OFF.compose(catc_ON))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "20d1563f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "InVars: [<Var Sal>, <Var aTc>]\n",
+      "OutVars:[<Var RFP>]\n",
+      "A: 1*Sal <= 0.891, -1*aTc <= -0.001818, 1*aTc <= 0.01287\n",
+      "G: -1*RFP <= -1.3\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(ctop_off_on)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "ef03f7d6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "csal_atc = csal_OFF.compose(catc_ON)\n",
+    "dCas_repression_off_on = ctop_off_on.quotient(csal_atc)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "4433ee6f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "InVars: [<Var xRFP>, <Var dCas9>]\n",
+      "OutVars:[<Var RFP>]\n",
+      "A: 1.0*xRFP <= 0.0046999999999999265, -1.0*dCas9 <= -0.316552839285713, 1.0*dCas9 <= 1.29850330357142\n",
+      "G: -1*RFP <= -1.3\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(dCas_repression_off_on)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e8a1d0b7",
+   "metadata": {},
+   "source": [
+    "Write results to a JSON file:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "de6b3af6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system_json_contracts = write_contract([dCas_repression_lin, dCas_repression_off_on], \"dCas9_repression.json\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.7.9 64-bit (microsoft store)",
+   "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": "3.7.9"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "ba9e6fe06a64942683fea6d67a5847d4ab092d5208198b13bd09506138dbee13"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/case_studies/biocircuit_specifications/synbio_utils.py b/case_studies/biocircuit_specifications/synbio_utils.py
new file mode 100644
index 00000000..6366b97c
--- /dev/null
+++ b/case_studies/biocircuit_specifications/synbio_utils.py
@@ -0,0 +1,53 @@
+import matplotlib.pyplot as plt
+import numpy as np
+def display_sensor_contracts(sensor_input="u", output="y", leak=0.0, start=0.0, umin=0.0, ymin=0.0, 
+                             K=0.0, ymax_lin=0.0, xlim_min=0.0, xlim_max=0.0,
+                             ylim_min=0.0, ylim_max=0.0, show=True, ax=None):
+    """
+    Plot three contracts: lag, linear, saturation on a 2-D plane
+    """
+    if ax is None:
+        _, ax = plt.subplots()
+    slope = (ymax_lin - leak)/(K - start)
+    intercept = leak - slope*start
+    ax.hlines(y=leak, xmin=xlim_min, xmax=start, color='r', lw=2, ls='--', label='OFF')
+    ax.axvline(x=start, ls='dotted', color='k')
+    ax.plot(np.linspace(start, K,2), slope*np.linspace(start,K,2) + intercept, color='#006400', lw=2, label='Linear')
+    ax.hlines(y=ymax_lin, xmin=K, xmax=xlim_max, color='blue', lw=2, ls='--', label='Saturation')
+    ax.axvline(x=K, ls='dotted', color='k')
+    ax.set_xlabel(sensor_input, fontsize=14)
+    ax.set_xscale('log')
+    ax.set_xlim(xlim_min, xlim_max)
+    ax.set_ylabel(output, fontsize=14)
+    ax.set_yscale('log')
+    ax.set_ylim(ylim_min, ylim_max)
+    ax.legend()
+    if show:
+        plt.show()
+    return ax
+
+import copy
+def remove_quantization_errors(contract, tolerance = 1e-4):
+    """
+    Removes terms that have all coefficients lower than tolerance
+    """
+    from_contract = copy.deepcopy(contract)
+    index = 0
+    # Remove quantization error from assumptions
+    for t_i in list(from_contract.a.terms):
+        all_coeff = 0
+        for var, coeff in t_i.variables.items():
+            all_coeff += np.abs(coeff)
+        if all_coeff < tolerance:
+            from_contract.a.terms.remove(t_i)
+        index += 1
+    # Remove quantization error from guarantees
+    index = 0
+    for t_i in list(from_contract.g.terms):
+        all_coeff = 0
+        for var, coeff in t_i.variables.items():
+            all_coeff += np.abs(coeff)
+        if all_coeff < tolerance:
+            from_contract.g.terms.remove(t_i)
+        index += 1
+    return from_contract
\ No newline at end of file
diff --git a/docs/case_studies.md b/docs/case_studies.md
index 3910e384..31ebdfe0 100644
--- a/docs/case_studies.md
+++ b/docs/case_studies.md
@@ -8,5 +8,6 @@ We present a case study to evaluate the perception system of an autonomous car.
 
 # Specification-based synthetic biology
 
+A case study on modeling the specifications of biological sensors to construct a library of parts and guide biocircuit design.
 
 # Signal processing pipelines in digital ICs
diff --git a/examples/AND_logic_gate.ipynb b/examples/AND_logic_gate.ipynb
index 2c7798ab..f6f24504 100644
--- a/examples/AND_logic_gate.ipynb
+++ b/examples/AND_logic_gate.ipynb
@@ -11,7 +11,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -27,7 +27,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -82,7 +82,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -92,19 +92,19 @@
       "Contract1:\n",
       "InVars: [<Var u_1>]\n",
       "OutVars:[<Var x_1>]\n",
-      "A: -1*u_1 <= -1\n",
+      "A: -1*u_1 <= -1.0\n",
       "G: -1*x_1 <= -1.5\n",
       "Contract2:\n",
       "InVars: [<Var u_2>]\n",
       "OutVars:[<Var x_2>]\n",
-      "A: -1*u_2 <= -1\n",
+      "A: -1*u_2 <= -1.0\n",
       "G: -1*x_2 <= -0.30000000000000004\n"
      ]
     }
    ],
    "source": [
-    "c1 = readContract(contract1)\n",
-    "c2 = readContract(contract2)\n",
+    "c1 = read_contract(contract1)\n",
+    "c2 = read_contract(contract2)\n",
     "\n",
     "print(\"Contract1:\\n\" + str(c1))\n",
     "print(\"Contract2:\\n\" + str(c2))"
@@ -119,7 +119,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -128,9 +128,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "InVars: [<Var u_1>, <Var u_2>]\n",
+      "OutVars:[<Var x_1>, <Var x_2>]\n",
+      "A: -1*u_1 <= -1.0, -1*u_2 <= -1.0\n",
+      "G: -1*x_1 <= -1.5, -1*x_2 <= -0.30000000000000004\n"
+     ]
+    }
+   ],
    "source": [
     "print(contract_comp)"
    ]
@@ -144,18 +155,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
-    "contract12 = writeContract(contract_comp)"
+    "contract12 = write_contract(contract_comp)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'InputVars': ['u_1', 'u_2'],\n",
+       " 'OutputVars': ['x_1', 'x_2'],\n",
+       " 'assumptions': [{'constant': -1.0, 'coefficients': {'u_1': -1.0}},\n",
+       "  {'constant': -1.0, 'coefficients': {'u_2': -1.0}}],\n",
+       " 'guarantees': [{'constant': -1.5, 'coefficients': {'x_1': -1.0}},\n",
+       "  {'constant': -0.30000000000000004, 'coefficients': {'x_2': -1.0}}]}"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "contract12"
    ]
@@ -169,7 +196,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -198,11 +225,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Contract3:\n",
+      "InVars: [<Var x_1>, <Var x_2>]\n",
+      "OutVars:[<Var y>]\n",
+      "A: -1*x_1 <= -1.5, -1*x_2 <= -0.3\n",
+      "G: -1*y <= -1.5\n"
+     ]
+    }
+   ],
    "source": [
-    "c3 = readContract(contract3)\n",
+    "c3 = read_contract(contract3)\n",
     "print(\"Contract3:\\n\" + str(c3))"
    ]
   },
@@ -215,7 +254,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -224,9 +263,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "InVars: [<Var u_1>, <Var u_2>]\n",
+      "OutVars:[<Var y>]\n",
+      "A: -1*u_1 <= -1.0, -1*u_2 <= -1.0\n",
+      "G: -1*y <= -1.5\n"
+     ]
+    }
+   ],
    "source": [
     "print(and_gate_contract)"
    ]
@@ -242,7 +292,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -251,9 +301,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "InVars: [<Var x_1>, <Var x_2>]\n",
+      "OutVars:[<Var y>]\n",
+      "A: -1*x_1 <= -1.5, -1*x_2 <= -0.30000000000000004\n",
+      "G: -1*y <= -1.5\n"
+     ]
+    }
+   ],
    "source": [
     "print(synthesized_c3)"
    ]
@@ -269,7 +330,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -278,9 +339,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "InVars: [<Var u_1>]\n",
+      "OutVars:[<Var x_1>]\n",
+      "A: -1*u_1 <= -1.0\n",
+      "G: -1*x_1 <= -1.5\n"
+     ]
+    }
+   ],
    "source": [
     "print(synthesize_c1)"
    ]
@@ -294,7 +366,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -303,9 +375,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "InVars: [<Var u_2>]\n",
+      "OutVars:[<Var x_2>]\n",
+      "A: -1*u_2 <= -1.0\n",
+      "G: -1*x_2 <= -0.30000000000000004\n"
+     ]
+    }
+   ],
    "source": [
     "print(synthesize_c2)"
    ]
@@ -319,7 +402,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -328,16 +411,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "InVars: [<Var u_1>, <Var u_2>]\n",
+      "OutVars:[<Var x_1>, <Var x_2>]\n",
+      "A: -1*u_1 <= -1.0, -1*u_2 <= -1.0\n",
+      "G: -1*x_1 <= -1.5, -1*x_2 <= -0.30000000000000004\n"
+     ]
+    }
+   ],
    "source": [
     "print(synthesize_c12)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -346,16 +440,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "InVars: [<Var u_1>]\n",
+      "OutVars:[<Var x_1>]\n",
+      "A: -1*u_1 <= -1.0\n",
+      "G: -1*x_1 <= -1.5\n"
+     ]
+    }
+   ],
    "source": [
     "print(synthesize_c1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -369,18 +474,11 @@
    "source": [
     "print('Success')"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.10.4 64-bit",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -394,7 +492,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.4"
+   "version": "3.10.8"
   },
   "vscode": {
    "interpreter": {
diff --git a/mkdocs.yml b/mkdocs.yml
index 8a439c40..cd2d79a5 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -21,7 +21,7 @@ nav:
   - Multiagent Path Planning:
     - Multi-agent Coordination: _case_studies/multiagent_coordination/multiagent.ipynb
   - Signal Processing in Digital ICs: _case_studies/digital_signal_processing/dsp_wl.ipynb
-  #- Synthetic Biology: case_studies/synthbio.md
+  - Synthetic Biology: _case_studies/specification_based_synthetic_biology.ipynb
 - Documentation: #reference/
   - IoContract: iocontract.md
   - PolyhedralTerms : Polyhedral.md
diff --git a/src/pacti/terms/polyhedra/__init__.py b/src/pacti/terms/polyhedra/__init__.py
index d36d378c..089df09b 100644
--- a/src/pacti/terms/polyhedra/__init__.py
+++ b/src/pacti/terms/polyhedra/__init__.py
@@ -1,2 +1,2 @@
-from .loaders import readContract, string_to_polyhedra_contract, writeContract
+from .loaders import read_contract, string_to_polyhedra_contract, write_contract
 from .polyhedra import PolyhedralTerm, PolyhedralTermList
diff --git a/src/pacti/terms/polyhedra/loaders.py b/src/pacti/terms/polyhedra/loaders.py
index 2af30b10..48c3c175 100644
--- a/src/pacti/terms/polyhedra/loaders.py
+++ b/src/pacti/terms/polyhedra/loaders.py
@@ -1,3 +1,7 @@
+"""
+Consists of loader functions that can read a JSON dictionary contract
+or write a IOContract to a JSON file.
+"""
 import json
 
 import sympy
@@ -9,7 +13,7 @@
 from pacti.utils.string_contract import StrContract
 
 
-def readContract(contract: dict | list[dict]) -> list[IoContract]:
+def read_contract(contract: dict | list[dict]) -> list[IoContract]:
     """
     Converts a contract written as JSON dictionary to pacti.iocontract type.
     If a list of JSON contracts are passed, a corresponding list of iocontracts is returned.
@@ -21,18 +25,19 @@ def readContract(contract: dict | list[dict]) -> list[IoContract]:
     Returns:
         list_iocontracts: A list of input-output Pacti contract objects
     """
-    if type(contract) is not list:
+    if not isinstance(contract, list):
         contract = [contract]
     list_iocontracts = []
-    for c in contract:
-        if type(c) is not dict:
+    for c_i in contract:
+        if not isinstance(c_i, dict):
             raise ValueError("A dict type contract is expected.")
         reqs = []
         for key in ["assumptions", "guarantees"]:
-            reqs.append([PolyhedralTerm(term["coefficients"], term["constant"]) for term in c[key]])
+            reqs.append([PolyhedralTerm(term["coefficients"], \
+                        float(term["constant"])) for term in c_i[key]])
         iocont = IoContract(
-            input_vars=getVarlist(c["InputVars"]),
-            output_vars=getVarlist(c["OutputVars"]),
+            input_vars=getVarlist(c_i["InputVars"]),
+            output_vars=getVarlist(c_i["OutputVars"]),
             assumptions=PolyhedralTermList(list(reqs[0])),
             guarantees=PolyhedralTermList(list(reqs[1])),
         )
@@ -43,43 +48,52 @@ def readContract(contract: dict | list[dict]) -> list[IoContract]:
         return list_iocontracts
 
 
-def writeContract(contract: IoContract | list[IoContract], filename: str = None) -> list[dict]:
+def write_contract(contract: IoContract | list[IoContract], filename: str = None) -> list[dict]:
     """
     Converts a pacti.IoContract to a dictionary. If a list of iocontracts is passed,
     then a list of dicts is returned.
     If a filename is provided, a JSON file is written, otherwise only dictionaries are returned.
-    Args:
-        contract: Contract input of type IoContract or list of IoContracts.
-        filename: Name of file to write the output contract, defaults to None in which case, no file is written.
+    Arguments:
+        * contract (gear.IoContract, list): Contract input of type IoContract
+                                                       or list of IoContracts.
+        * filename (str, optional): Name of file to write the output contract,
+                                    defaults to None in which case,
+                                    no file is written.
     Returns:
         contract_dict: A dictionary for the given IoContract.
     """
-    if type(contract) is IoContract:
+    if isinstance(contract, IoContract):
         contract = [contract]
     contract_list = []
-    for c in contract:
-        if not isinstance(c, IoContract):
+    for c_i in contract:
+        if not isinstance(c_i, IoContract):
             return ValueError("A IoContract is expected.")
         contract_dict = {}
-        contract_dict["InputVars"] = [str(var) for var in c.inputvars]
-        contract_dict["OutputVars"] = [str(var) for var in c.outputvars]
+        contract_dict["InputVars"] = [str(var) for var in c_i.inputvars]
+        contract_dict["OutputVars"] = [str(var) for var in c_i.outputvars]
         contract_dict["assumptions"] = [
-            {"constant": term.constant, "coefficients": {str(k): v for k, v in term.variables.items()}}
-            for term in c.a.terms
+            {"constant":float(term.constant), \
+             "coefficients": {str(k): float(v) for k, v in term.variables.items()}}
+            for term in c_i.a.terms
         ]
         contract_dict["guarantees"] = [
-            {"constant": term.constant, "coefficients": {str(k): v for k, v in term.variables.items()}}
-            for term in c.g.terms
+            {"constant":float(term.constant), \
+             "coefficients": {str(k): float(v) for k, v in term.variables.items()}}
+            for term in c_i.g.terms
         ]
         contract_list.append(contract_dict)
     if filename:
-        with open(filename, "w+") as f:
+        with open(filename, "w+", encoding='utf-8') as f_i:
             count = 0
+            f_i.write("{\n")
             for c_dict in contract_list:
-                f.write('"contract' + str(count) + '"' + "=")
-                json.dump(c_dict, f)
-                f.write("\n")
+                data = json.dumps(c_dict)
+                f_i.write('"contract' + str(count) + '"' + ":")
+                f_i.write(data)
+                if c_dict != contract_list[-1]:
+                    f_i.write(",\n")
                 count += 1
+            f_i.write("\n}")
     if len(contract_list) == 1:
         return contract_list[0]
     else:


Test patch:
diff --git a/tests/test_examples.py b/tests/test_examples.py
index c0cc9859..4b3b1adc 100644
--- a/tests/test_examples.py
+++ b/tests/test_examples.py
@@ -1,6 +1,48 @@
-import os
-
+import pacti.iocontract as iocontract
+from pacti.terms.polyhedra import *
 
 def test_examples():
-    result = os.popen("jupyter nbconvert --to script --execute --stdout examples/AND_logic_gate.ipynb | python").read()
-    assert result[-1] == "\n"
+    contract1 = {
+        "InputVars":[
+            "u_1"
+        ],
+        "OutputVars":[
+            "x_1"
+        ],
+        "assumptions":
+        [
+            {"coefficients":{"u_1":-1},
+            "constant":-1}
+        ],
+        "guarantees":
+        [
+            {"coefficients":{"x_1":-1},
+            "constant":-1.5}
+        ]
+    }
+    contract2 = {
+            "InputVars":[
+                "u_2"
+            ],
+            "OutputVars":[
+                "x_2"
+            ],
+            "assumptions":
+            [
+                {"coefficients":{"u_2":-1},
+                "constant":-1}
+            ],
+            "guarantees":
+            [
+                {"coefficients":{"x_2":-1},
+                "constant":-0.3}
+            ]
+        }
+    c1 = read_contract(contract1)
+    c2 = read_contract(contract2)
+    contract_str = "Contract1:InVars: [<Var u_1>]"
+    contract_str += "\nOutVars:[<Var x_1>]\nA: -1*u_1 <= -1.0"
+    contract_str += "\nG: -1*x_1 <= -1.5"
+    assert "Contract1:" + str(c1) ==  contract_str
+    contract_comp = c1.compose(c2)
+    assert isinstance(contract_comp, iocontract.IoContract)
diff --git a/tests/test_iocontract.py b/tests/test_iocontract.py
index abd405ff..f2e507ca 100644
--- a/tests/test_iocontract.py
+++ b/tests/test_iocontract.py
@@ -1,5 +1,5 @@
 import pacti.iocontract as iocontract
-from pacti.terms.polyhedra.loaders import readContract
+from pacti.terms.polyhedra.loaders import read_contract
 
 
 def validate_iocontract(contract):
@@ -12,24 +12,22 @@ def create_contracts(num=1):
     """
     contracts = []
     for i in range(num):
-        c = {
+        c_i = {
             "InputVars": ["i" + str(i)],
             "OutputVars": ["o" + str(i)],
             "assumptions": [{"coefficients": {"i" + str(i): 1}, "constant": i}],
             "guarantees": [{"coefficients": {"o" + str(i): 1}, "constant": 1}],
         }
-        contracts.append(c)
+        contracts.append(c_i)
     if num == 1:
         return contracts[0]
     else:
         return contracts
 
-
 def test_validate_iocontract():
-    c1, c2 = readContract(create_contracts(num=2))
-    print(type(c1))
-    assert validate_iocontract(c1)
-    assert validate_iocontract(c2)
+    c_1, c_2 = read_contract(create_contracts(num=2))
+    assert validate_iocontract(c_1)
+    assert validate_iocontract(c_2)
 
 
 def test_contract_equality():
diff --git a/tests/test_py_loaders.py b/tests/test_py_loaders.py
index f381aeca..f8cd6453 100644
--- a/tests/test_py_loaders.py
+++ b/tests/test_py_loaders.py
@@ -2,7 +2,7 @@
 from test_iocontract import validate_iocontract
 
 import pacti.iocontract as iocontract
-from pacti.terms.polyhedra.loaders import readContract, writeContract
+from pacti.terms.polyhedra.loaders import read_contract, write_contract
 
 
 def create_contracts(num=1):
@@ -11,13 +11,13 @@ def create_contracts(num=1):
     """
     contracts = []
     for i in range(num):
-        c = {
+        c_i = {
             "InputVars": ["u" + str(i)],
             "OutputVars": ["x" + str(i)],
             "assumptions": [{"coefficients": {"u" + str(i): 1}, "constant": i}],
             "guarantees": [{"coefficients": {"x" + str(i): 1}, "constant": 1}],
         }
-        contracts.append(c)
+        contracts.append(c_i)
     if num == 1:
         return contracts[0]
     else:
@@ -25,26 +25,32 @@ def create_contracts(num=1):
 
 
 def test_read_contract():
+    """
+    Test read_contract
+    """
     # Create 1 contract and read it
-    c = create_contracts(1)
-    validate_iocontract(readContract(c))
+    c_i = create_contracts(1)
+    validate_iocontract(read_contract(c_i))
     # Create 5 contracts and read
-    c = create_contracts(5)
-    io_contracts = readContract(c)
+    c_i = create_contracts(5)
+    io_contracts = read_contract(c_i)
     assert len(io_contracts) == 5
     for io_c in io_contracts:
         assert isinstance(io_c, iocontract.IoContract)
         assert validate_iocontract(io_c)
     # Ensure that all contracts are dictionaries
-    c = [("InputVars", "u"), ("OutputVars", "x")]
+    c_i = [("InputVars", "u"), ("OutputVars", "x")]
     with pytest.raises(ValueError, match="A dict type contract is expected."):
-        readContract(c)
+        read_contract(c_i)
 
 
 def test_write_contract():
-    c = create_contracts(1)
-    io_c = readContract(c)
-    assert c == writeContract(io_c)
+    """
+    Test write_contract
+    """
+    c_i = create_contracts(1)
+    io_c = read_contract(c_i)
+    assert c_i == write_contract(io_c)
     all_contracts = create_contracts(5)
-    io_contracts = readContract(all_contracts)
-    assert all_contracts == writeContract(io_contracts)
+    io_contracts = read_contract(all_contracts)
+    assert all_contracts == write_contract(io_contracts)