notebooks/slim_intro.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "%load_ext slim_magic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# setup: has to be in a separate cell for some reason\n",
    "plt.rcParams['figure.figsize'] = [8, 4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SLiM\n",
    "\n",
    "SLiM stands for [\"Selection on Linked Mutations\"](https://messerlab.org/slim/),\n",
    "written by Ben Haller and Phillip Messer.\n",
    "The manual is very good, and probably describes everything you'd want to know,\n",
    "but there are published papers about [SLiM v2](https://academic.oup.com/mbe/article/34/1/230/2670194),\n",
    "[SLiM v3](https://academic.oup.com/mbe/advance-article/doi/10.1093/molbev/msy228/5229931),\n",
    "using [tree sequence recording](https://onlinelibrary.wiley.com/doi/abs/10.1111/1755-0998.12968),\n",
    "and a [tutorial for SLiM v3](https://academic.oup.com/mbe/advance-article/doi/10.1093/molbev/msy237/5258474).\n",
    "\n",
    "It was initially designed to simulate genomes under selection,\n",
    "but has matured into a good ecological simulator itself (in v3).\n",
    "We'll focus on this, but you still need to know the basics of how to set up genomes.\n",
    "\n",
    "Simulation hierarchy:\n",
    "\n",
    "- `sim` : the simulation\n",
    "    - `p1`, `p2`, ... : the \"subpopulations\"\n",
    "        - `p1.individuals` : the individuals in subpopulation `p1`\n",
    "            - `ind.genomes` : the two genomes of individual `ind`\n",
    "                - `genome.mutations` : the \"mutations\" carried by `genome`\n",
    "\n",
    "Most things are vectorized, so for instance `p1.individuals.sex` will get you a vector of sexes,\n",
    "equivalent to doing something like `for (ind in p1.individuals) { ... ind.sex ... }`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running scripts\n",
    "\n",
    "First, you need to [install SLiM](https://messerlab.org/SLiM).\n",
    "You should use SLiM's GUI for developing and visualizing!\n",
    "But, in this notebook I'll run SLiM from within the notebook.\n",
    "I'm doing this with the `slim_magic` package, in development; get this by doing (anywhere):\n",
    "```\n",
    "git clone https://github.com/andrewkern/slim_magic.git\n",
    "cd slim_magic\n",
    "pip install .\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A first recipe\n",
    "\n",
    "Here's the first recipe from the SLiM manual.\n",
    "It uses the default demography: a randomly-mating Wright-Fisher population of fixed size.\n",
    "The `%%slim` up top of this cell asks python to run the SLiM script and print the resulting output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "// Initial random seed:\n",
      "5966568885072\n",
      "\n",
      "// RunInitializeCallbacks():\n",
      "initializeMutationRate(1e-07);\n",
      "initializeMutationType(1, 0.5, \"f\", 0);\n",
      "initializeMutationType(2, 0.5, \"f\", 0.01);\n",
      "initializeGenomicElementType(1, c(m1, m2), c(1, 1));\n",
      "initializeGenomicElement(g1, 0, 99999);\n",
      "initializeRecombinationRate(1e-08);\n",
      "\n",
      "// Starting run at generation <start>:\n",
      "1 \n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%slim\n",
    "initialize()\n",
    "{\n",
    "    // set the overall mutation rate\n",
    "    initializeMutationRate(1e-7);\n",
    "\n",
    "    // m1 mutation type: neutral\n",
    "    initializeMutationType(\"m1\", 0.5, \"f\", 0.0);\n",
    "    // m2 mutation type: beneficial\n",
    "    initializeMutationType(\"m2\", 0.5, \"f\", 0.01);\n",
    "\n",
    "    // g1 genomic element type: uses m1 or m2 with equal prob for mutations\n",
    "    initializeGenomicElementType(\"g1\", c(m1, m2), c(1.0, 1.0));\n",
    "\n",
    "    // uniform chromosome of length 100 kb\n",
    "    initializeGenomicElement(g1, 0, 99999);\n",
    "   \n",
    "    // uniform recombination along the chromosome\n",
    "    initializeRecombinationRate(1e-8);\n",
    "}\n",
    "\n",
    "// create a population of 500 individuals\n",
    "1 {\n",
    "    sim.addSubpop(\"p1\", 500);\n",
    "}\n",
    "\n",
    "// run to generation 10000\n",
    "10000 {\n",
    "    sim.simulationFinished();\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise:** Put this in the SLiM GUI.\n",
    "1. Run it.\n",
    "2. Click on the \"help\" button and browse the functions.\n",
    "3. Option-click on a function to pop up the help for it.\n",
    "4. Change the mutation type to be *deleterious*, like so:\n",
    "```\n",
    "    initializeMutationType(\"m1\", 0.5, \"f\", -0.01);\n",
    "```\n",
    "and watch what happens."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## More general (non-Wright-Fisher) models\n",
    "\n",
    "There are two general types: \"Wright-Fisher\" and \"non-Wright-Fisher\".\n",
    "(Wright-Fisher is a special case of non-Wright-Fisher, but it came first, thus the name.)\n",
    "We want to have real demography, so we'll stick to \"nonWF\" models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The SLiM Life Cycle\n",
    "\n",
    "The most important thing to understand is the life cycle. \n",
    "The life cycle for Wright-Fisher models is a bit different (see the manual),\n",
    "but for nonWF models it is, a bit simplified:\n",
    "\n",
    "1. Generation of offspring, in `reproduction()` callbacks\n",
    "    \n",
    "2. Execution of `early()` events\n",
    "\n",
    "3. Fitness value recalculation with `fitness()` callbacks\n",
    "\n",
    "4. Viability/survival selection (`fitness` = probability of survival)\n",
    "\n",
    "5. Removal of fixed mutations\n",
    "\n",
    "6. Execution of `late()` events\n",
    "\n",
    "7. Generation count increment, individual age increments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The parts of a recipe\n",
    "\n",
    "Each *script block* in a SLiM recipe\n",
    "is prefaced with some code that says *when* it happens, and to *whom*.\n",
    "This might take the form:\n",
    "```\n",
    "[id] [gen1 [: gen2]] [early()] { ... }\n",
    "```\n",
    "The square brackets demarcate optional things.\n",
    "The pieces are:\n",
    "\n",
    "- `id` : a name for this script block (often omitted)\n",
    "- `gen1` : the first time step to execute this code in\n",
    "- `gen2` : the last time step to execute this code in\n",
    "- `early()` : when in the life cycle this happens; can actually be one of \n",
    "    `initialize()`, `early()`, `late()`, `fitness()`, `interaction()`,\n",
    "    `mateChoice()`, `modifyChild()`, `recombination()`, or `reproduction()`\n",
    "    (defaults to `early()`).\n",
    "\n",
    "These have somewhat different meanings: for instance,\n",
    "an `early()` script block happens once per time step,\n",
    "a `modifyChild()` script block is evaluated once *per offspring* each time step.\n",
    "Also, many of these can be restricted to a subset of indivduals.\n",
    "\n",
    "For instance:\n",
    "```\n",
    "early() {\n",
    "    // stuff here happens every early()\n",
    "}\n",
    "```\n",
    "or,\n",
    "```\n",
    "\n",
    "10:20 reproduction(p1) {\n",
    "    // stuff here happens every time step from 10 to 20 (inclusive)\n",
    "    // and only to individuals in subpopulation p1\n",
    "}\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A nonWF model\n",
    "\n",
    "Here's an example nonWF model.\n",
    "The new things are that\n",
    "\n",
    "1. We need to explicitly manage reproduction (with a `reproduction()` callback).\n",
    "2. We put in some output, that reports what the current population size is every time step.\n",
    "   (This is sent to python using the `%%slim_stats` magic.) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>pop_size</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>generation</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5120</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            pop_size\n",
       "generation          \n",
       "1                 10\n",
       "2                 20\n",
       "3                 40\n",
       "4                 80\n",
       "5                160\n",
       "6                320\n",
       "7                640\n",
       "8               1280\n",
       "9               2560\n",
       "10              5120"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%slim_stats\n",
    "initialize()\n",
    "{\n",
    "    // since the default is WF, to get a non-WF model, we need to say so\n",
    "    initializeSLiMModelType(\"nonWF\");\n",
    "    // genome is the same as above\n",
    "    initializeMutationRate(1e-7);\n",
    "    initializeMutationType(\"m1\", 0.5, \"f\", 0.0);\n",
    "    initializeGenomicElementType(\"g1\", m1, 1.0);\n",
    "    initializeGenomicElement(g1, 0, 99999);\n",
    "    initializeRecombinationRate(1e-8);    \n",
    "}\n",
    "\n",
    "reproduction() {\n",
    "    subpop.addCrossed(individual, subpop.sampleIndividuals(1));\n",
    "}\n",
    "\n",
    "1 early() {\n",
    "    sim.addSubpop(\"p1\", 10);\n",
    "    log = sim.createLogFile(\"/dev/stdout\", logInterval=1);\n",
    "    log.addGeneration();\n",
    "    log.addCustomColumn(\"pop_size\", \"p1.individualCount;\"); \n",
    "}\n",
    "\n",
    "10 {\n",
    "    sim.simulationFinished();\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SLiM's Fitness\n",
    "\n",
    "Uh-oh, I see where *that* is going.\n",
    "We need population regulation!\n",
    "The easiest way is by the `fitnessScaling` attribute of individuals.\n",
    "In nonWF SLiM models, the \"fitness\" of an individual \n",
    "is the probability of survival until the next time step.\n",
    "This is computed by multiplying the fitness calculated from the genome\n",
    "(since all our mutations are neutral, this is `1.0` for everyone)\n",
    "by the `fitnessScaling` attributes of the individual and of their subpopulation.\n",
    "\n",
    "Concretely, we'll fix a \"carrying capacity\" $K$,\n",
    "and say that the probability of survival, per individual,\n",
    "when there are $N$ individuals, is $K/N$.\n",
    "\n",
    "As before, every individual produces one offspring\n",
    "with a randomly chosen mate\n",
    "every time step.\n",
    "\n",
    "**Exercise:** What is the stable population size of this model?\n",
    "Predict before you run it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%slim_stats --out df\n",
    "initialize()\n",
    "{\n",
    "    // since the model will be non-Wright-Fisher, we need to say so\n",
    "    initializeSLiMModelType(\"nonWF\");\n",
    "    // genome is the same as above\n",
    "    initializeMutationRate(1e-7);\n",
    "    initializeMutationType(\"m1\", 0.5, \"f\", 0.0);\n",
    "    initializeGenomicElementType(\"g1\", m1, 1.0);\n",
    "    initializeGenomicElement(g1, 0, 99999);\n",
    "    initializeRecombinationRate(1e-8);    \n",
    "\n",
    "    // this will be the carrying capacity\n",
    "    defineConstant(\"K\", 1000);\n",
    "}\n",
    "\n",
    "reproduction() {\n",
    "    subpop.addCrossed(individual, subpop.sampleIndividuals(1));\n",
    "}\n",
    "\n",
    "1 early() {\n",
    "    sim.addSubpop(\"p1\", 1000);\n",
    "    log = sim.createLogFile(\"/dev/stdout\", logInterval=1);\n",
    "    log.addGeneration();\n",
    "    log.addCustomColumn(\"pop_size\", \"p1.individualCount;\");\n",
    "}\n",
    "\n",
    "early() {\n",
    "    p1.fitnessScaling = K / p1.individualCount;\n",
    "}\n",
    "\n",
    "100 {\n",
    "    sim.simulationFinished();\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(df)\n",
    "ax.set_xlabel(\"time step\")\n",
    "ax.set_ylabel(\"population size\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise:** Add to the output another entry: how many individuals are older than 2 time steps.\n",
    "(Code like it's R;\n",
    "`p1.individuals` gets you a vector of individuals; \n",
    "look in the help under Individual to see how to get their ages.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Age-dependent mortality\n",
    "\n",
    "Let's modify the above to resemble our *stage-structured* model from before.\n",
    "\n",
    "1. Individuals are *juvenile* for one full year, with 70% mortality rate.\n",
    "2. If they survive, they become *adults*, with 50% yearly mortality,\n",
    "3. and a mean birth rate of 10 per individual.\n",
    "\n",
    "This will grow exponentially, of course, so don't run it for too long."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%slim_stats --out df\n",
    "initialize() {\n",
    "    initializeSLiMModelType(\"nonWF\");\n",
    "    initializeMutationRate(1e-7);\n",
    "    initializeMutationType(\"m1\", 0.5, \"f\", 0.0);\n",
    "    initializeGenomicElementType(\"g1\", m1, 1.0);\n",
    "    initializeGenomicElement(g1, 0, 99999);\n",
    "    initializeRecombinationRate(1e-8);    \n",
    "\n",
    "    // this will be the carrying capacity\n",
    "    defineConstant(\"K\", 100);\n",
    "}\n",
    "\n",
    "reproduction() {\n",
    "    if (individual.age > 1) {\n",
    "        num_offspring = rpois(1, 10);\n",
    "        mate = sample(p1.individuals[p1.individuals.age > 1], 1);\n",
    "        // catn(\"Producing \" + num_offspring + \" babies!\");\n",
    "        for (k in seqLen(num_offspring)) {\n",
    "            p1.addCrossed(individual, mate);\n",
    "        }\n",
    "    }\n",
    "}\n",
    "\n",
    "1 early() {\n",
    "    sim.addSubpop(\"p1\", 500);\n",
    "    log = sim.createLogFile(\"/dev/stdout\", logInterval=1);\n",
    "    log.addGeneration();\n",
    "    for (age in seqLen(10)) {\n",
    "        log.addCustomColumn(\"age_\" + age, \"sum(p1.individuals.age == \" + age + \");\");\n",
    "    }\n",
    "}\n",
    "\n",
    "\n",
    "early() {\n",
    "    // survival probabilities\n",
    "    inds = p1.individuals;\n",
    "    inds[inds.age > 1].fitnessScaling = 0.5;\n",
    "    inds[inds.age <= 1].fitnessScaling = 0.3;\n",
    "}\n",
    "\n",
    "10 {\n",
    "    sim.simulationFinished();\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(df, label=df.columns)\n",
    "ax.set_xlabel(\"time step\")\n",
    "ax.set_ylabel(\"number of individuals\")\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise:** change `%%slim_stats` to `%%slim` at the top and uncomment the `catn( )` line; see what happens. (Moral: to get useful debugging output, use `catn( )`!)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise:**  Add *fecundity* population regulation to this model:\n",
    "suppose that the mean number of offspring that an individual has\n",
    "when there are $N$ adults is $10/(1 + N/K)$, where $K$ is the carrying capacity.\n",
    "Predict where equilibrium will be, then run it for 1000 generations to see."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise\n",
    "\n",
    "Read sections 1-4 of the SLiM Manual.\n",
    "Add *both* beneficial and deleterious mutations to a SLiM recipe of your choice.\n",
    "(You should be able to tell this is working if you get fitness values both above and below 1.)"
   ]
  }
 ],
 "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": "3.9.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}