notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Molecular Representation and Algorithm\n",
    "We will use _Adleman's_ paper ideas for the molecular representations.<br>\n",
    "For each vertex in the $G_{color}$, we will create 20 DNA bases long unique sequence as _Adleman_ did in his paper.<br>\n",
    "We will also create unique DNA sequences for the edges in $G_{color}$ as Adleman_ did in his paper - using combination of 2 vertex parts which also unique and different from other complements combinations of sequences.<br>\n",
    "By that we can represent $G_{color}$ using molecules, so let's dive into the algorithm itself:<br>\n",
    "\n",
    "**Algorithm:**<br>\n",
    "1. Create tube $T$ which will contain all the possible graph paths as we insert copies of all the vertices and edges representations (and their complements) and the _'Ligase'_ enzyme.\n",
    "2. Amplify the full paths from $c_{1}$ to $c_{end}$ using PCR process with $c_{i}$ primers and $c_{end}$ complement representation.\n",
    "3. Perform gel electrophoresis process to filter only the valid paths. For $|V|$ vertices in $G$, we need path in length of $2*|V| + 1$ of $G_{color}$, and because we represent vertex as 20 DNA bases sequence, we need exactly $40*|V| + 20$ length long path.\n",
    "4. For each edge $e=(v_i,v_j) in G$ and for each color $C from \\{C_1, C_2, C_3\\}$:\n",
    "    4.1 Pick all the DNA molecules from $T$ that contains the sequence $v_iC$ and transfer them into new tube $T_{valid}$\n",
    "    4.2 Pick all the DNA molecules from $T_{valid}$ that contains the sequence $v_jC$ and drop them out of $T_{valid}$ tube.\n",
    "    4.3 Transfer all DNA molecules from $T_{valid}$ into $T$.\n",
    "5. Perform PCR to amplify the results in tube $T$ to avoid errors.\n",
    "6. If the tube $T$ has any DNA molecules, the graph can be colored with 3 colors and return `True`. Otherwise, it cannot and return `False`.\n",
    "\n",
    "After step 1 we have all the possible paths in the graph $G_{color}$.<br>\n",
    "In step 2 we are amplifying the paths that starting with first vertex and ends with end vertex.<br>\n",
    "In step 3 we are filtering only the valid paths with contains all the vertices in the graph.<br>\n",
    "In step 4 we are filtering only the valid paths thats qualify the restriction of the problem - two vertices neighbors cannot have the same color.<br>\n",
    "In step 5 we are amplifying the results to avoid mutation errors and etc.<br>\n",
    "And in the last step, we are checking if we got any DNA molecules left, which determines if there is solution to the problem."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How to run the code:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Requirements:\n",
    "None."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Instructions:\n",
    "The code written Python 3.8 and stored in the `molecular_computation` folder."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The code can be run only from the `main.py` explicitly inside the `molecular_computation` folder without any configuration at all (it includes virtual environment which contains Python 3.8)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### To run the code from the folder explicitly:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Open Terminal or CMD and navigate to `molecular_computation` folder."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Run the next command:\n",
    "\n",
    "> Windows:\n",
    "<br>`.\\venv\\bin\\python3 main.py`\n",
    "\n",
    "> Linux / MacOS:\n",
    "<br>`./venv/bin/python3 main.py`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**OR** \n",
    "If you have `PyCharm` installed, just run the `main.py` file from the `PyCharm` editor."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What is included?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The project is organized in the following manner:\n",
    "- **algorithms** - Includes algorithms implementations using mulecular computation.\n",
    "    - _restriction_enzyme_automata_ - Includes the restriction enzyme automaton discussed in chapter 5.3.2 in the learning book.\n",
    "    - _three_sat_algorithm_ - Includes the 3-SAT algorithm discussed in chapter 5.2.2 in the learning book.\n",
    "    \n",
    "- **enzymes** - Includes enzymes implementation.\n",
    "    - _fok1_ - Includes the restriction enzyme Fok1.\n",
    "    - _ligase_ - Includes the ligase enzyme.\n",
    "    - _polymerase_ - Includes polymerase enzyme.\n",
    "    - _primer_ - Includes the primer enzyme.\n",
    "    \n",
    "- **molecules** - Includes molecules implementation.\n",
    "    - _dna_sequence_ - Includes DNA sequence (one strand of DNA bases).\n",
    "    - _dna_molecule_ - Includes DNA molecule (two strands of DNA bases).\n",
    "\n",
    "- **procedures** - Includes procedures of a mulecular lab.\n",
    "    - _gel_electrophoresis_ - Includes Gel-Electrophoresis procedure.\n",
    "    - _pcr_ - Includes the PCR procedure.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How to use?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It simple, just run the main file, and choose the option you want to run as input."
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Screenshot of main file run selection options:<br>\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each virtual computation includes visible output that should clarify what is going on, for every step of the virtual computation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**NOTE:** For better visual explanation, the explanation behind the symbols in the output should be clarify in the attached video (Also, the visual representation of the output is very clear to know so it just for better understanding)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Differences between Virtual Lab and Actual Lab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we will analyze the differences in the algorithms between the virtual lab environment to the actual lab."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3-SAT Algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The 3-SAT problem is NP-Complete problem, which is considered to be hard for computer to solve in reasonable time (Exponentional Time-complexity).<br>\n",
    "Using molecular computation in actual lab, the problem reduced to linear Time-complexity, making it seems possible to solve in reasonable time using molecular computation.<br>\n",
    "\n",
    "> **In Actual Lab**:<br>\n",
    "If the formula of the 3-SAT problem contains M clauses than the number of operations the molecular 3-SAT Algorithm performs is linear in M.<br><br>\n",
    "> **In Virtual Lab**:<br>\n",
    "Given L literals and M clauses:<br>\n",
    "> * Need to generate unique representations of DNA sequences for the literals representations - **O(L)**\n",
    "> * Generate solution graph for the problem - **O(L)**\n",
    "> * Generate all possible paths in the solution graph (Brute-force using the paths in the solution graph) - **O($2^L$)**\n",
    "> * Check only the satisfying paths of the solution graph (Filter each path, over clauses) - **O($M*2^L$)**\n",
    "\n",
    "> **Conclusion: O($M*2^L$)** Time-complexity at virtual lab against linear time in M at actual lab.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Restriction Enzyme Automata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The restriction enzyme automata uses the enzyme `Fok1` to restrict DNA molecules which represents input given to the automaton in such a way that leaves sticky part of the DNA - which represent the state of the automaton.<br>\n",
    "From there, the automaton includes also representation of DNA molecules which represent the transition rules of the automaton which can be attached to the sticky parts of the DNA molecules which represnt part of the given input.<br>\n",
    "\n",
    "In term of time-complexity, the both labs (Virtual and actual) share same characteristics.<br>\n",
    "They both need to operate on each single state in deterministic way, which make them to operate for each state without using parallelism (The power of molecular computation).<br>\n",
    "So for minor conclusion - They both may have the same results in term of time-complexity.\n",
    "\n",
    "Also in the virtual lab, the reprensetation is hardcoded and can lead to lower errors in opposite to the actual lab which can be less prone to errors which occurr by nature of biology.<br>\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}