{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 16a. Stochastic (Gillespie) simulation\n", "\n", "
" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "tags": [ "hide-input" ] }, "outputs": [ { "data": { "text/html": [ "\n", "
\n", " \n", " Loading BokehJS ...\n", "
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*/\n", " safe: true,\n", " /* Index of renderer in `output_area.display_order` */\n", " index: 0\n", " });\n", " }\n", "\n", " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", " if (root.Jupyter !== undefined) {\n", " const events = require('base/js/events');\n", " const OutputArea = require('notebook/js/outputarea').OutputArea;\n", "\n", " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", " register_renderer(events, OutputArea);\n", " }\n", " }\n", " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", " root._bokeh_timeout = Date.now() + 5000;\n", " root._bokeh_failed_load = false;\n", " }\n", "\n", " const NB_LOAD_WARNING = {'data': {'text/html':\n", " \"
\\n\"+\n", " \"

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\\n\"+\n", " \"\\n\"+\n", " \"\\n\"+\n", " \"from bokeh.resources import INLINE\\n\"+\n", " \"output_notebook(resources=INLINE)\\n\"+\n", " \"\\n\"+\n", " \"
\"}};\n", "\n", " function display_loaded() {\n", " const el = document.getElementById(\"p1001\");\n", " if (el != null) {\n", " el.textContent = \"BokehJS is loading...\";\n", " }\n", " if (root.Bokeh !== undefined) {\n", " if (el != null) {\n", " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", " }\n", " } else if (Date.now() < root._bokeh_timeout) {\n", " setTimeout(display_loaded, 100)\n", " }\n", " }\n", "\n", " function run_callbacks() {\n", " try {\n", " root._bokeh_onload_callbacks.forEach(function(callback) {\n", " if (callback != null)\n", " callback();\n", " });\n", " } finally {\n", " delete root._bokeh_onload_callbacks\n", " }\n", " console.debug(\"Bokeh: all callbacks have finished\");\n", " }\n", "\n", " function load_libs(css_urls, js_urls, callback) {\n", " if (css_urls == null) css_urls = [];\n", " if (js_urls == null) js_urls = [];\n", "\n", " root._bokeh_onload_callbacks.push(callback);\n", " if (root._bokeh_is_loading > 0) {\n", " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", " return null;\n", " }\n", " if (js_urls == null || js_urls.length === 0) {\n", " run_callbacks();\n", " return null;\n", " }\n", " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", "\n", " function on_load() {\n", " root._bokeh_is_loading--;\n", " if (root._bokeh_is_loading === 0) {\n", " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", " run_callbacks()\n", " }\n", " }\n", "\n", " function on_error(url) {\n", " console.error(\"failed to load \" + url);\n", " }\n", "\n", " for (let i = 0; i < css_urls.length; i++) {\n", " const url = css_urls[i];\n", " const element = document.createElement(\"link\");\n", " element.onload = on_load;\n", " element.onerror = on_error.bind(null, url);\n", " element.rel = \"stylesheet\";\n", " element.type = \"text/css\";\n", " element.href = url;\n", " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", " document.body.appendChild(element);\n", " }\n", "\n", " for (let i = 0; i < js_urls.length; i++) {\n", " const url = js_urls[i];\n", " const element = document.createElement('script');\n", " element.onload = on_load;\n", " element.onerror = on_error.bind(null, url);\n", " element.async = false;\n", " element.src = url;\n", " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", " document.head.appendChild(element);\n", " }\n", " };\n", "\n", " function inject_raw_css(css) {\n", " const element = document.createElement(\"style\");\n", " element.appendChild(document.createTextNode(css));\n", " document.body.appendChild(element);\n", " }\n", "\n", " const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.1.0.min.js\"];\n", " const css_urls = [];\n", "\n", " const inline_js = [ function(Bokeh) {\n", " Bokeh.set_log_level(\"info\");\n", " },\n", "function(Bokeh) {\n", " }\n", " ];\n", "\n", " function run_inline_js() {\n", " if (root.Bokeh !== undefined || force === true) {\n", " for (let i = 0; i < inline_js.length; i++) {\n", " inline_js[i].call(root, root.Bokeh);\n", " }\n", "if (force === true) {\n", " display_loaded();\n", " }} else if (Date.now() < root._bokeh_timeout) {\n", " setTimeout(run_inline_js, 100);\n", " } else if (!root._bokeh_failed_load) {\n", " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", " root._bokeh_failed_load = true;\n", " } else if (force !== true) {\n", " const cell = $(document.getElementById(\"p1001\")).parents('.cell').data().cell;\n", " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", " }\n", " }\n", "\n", " if (root._bokeh_is_loading === 0) {\n", " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", " run_inline_js();\n", " } else {\n", " load_libs(css_urls, js_urls, function() {\n", " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", " run_inline_js();\n", " });\n", " }\n", "}(window));" ], "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(\"p1001\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.1.0.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\nif (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(\"p1001\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Colab setup ------------------\n", "import os, sys, subprocess\n", "if \"google.colab\" in sys.modules:\n", " cmd = \"pip install --upgrade biocircuits multiprocess watermark\"\n", " process = subprocess.Popen(cmd.split(), stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n", " stdout, stderr = process.communicate()\n", "# ------------------------------\n", "\n", "try:\n", " import multiprocess\n", "except:\n", " import multiprocessing as multiprocess\n", "\n", "import tqdm\n", "\n", "import numpy as np\n", "import scipy.stats as st\n", "import numba\n", "\n", "import biocircuits\n", "\n", "# Plotting modules\n", "import iqplot\n", "import bokeh.io\n", "import bokeh.plotting\n", "\n", "bokeh.io.output_notebook()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sampling out of probability distributions\n", "\n", "Sampling out of a distribution involves using a random number generator to *simulate* the **story** that generates the distribution. So, if you know the story and you have a computer handy, you can sample to be able to get a plot of your distribution, though you will not get the analytical form.\n", "\n", "Let's demonstrate this with the Binomial distribution. We know that if we flip a coin that has probability $p$ of landing heads, the number of heads in $n$ flips is Binomially distributed. Imagine for a moment that we did not know that, but will instead take to sampling using the story. We will take $n = 25$ and $p = 0.25$ and compute $P(h \\mid n, p)$, the probability of getting $h$ heads in $n$ flips, each with probability $p$ of landing heads. We will draw 10, 30, 100, and 300 samples by simulating the story of the Binomial distribution and plot the result obtained by sampling along with the expected Binomial distribution." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
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" const render_items = [{\"docid\":\"5c4de3f8-63ea-42c8-a6ef-be802fdbd94a\",\"roots\":{\"p1405\":\"39490ef1-ebdf-406b-ba8c-9c10dc4c45c9\"},\"root_ids\":[\"p1405\"]}];\n", " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", " }\n", " if (root.Bokeh !== undefined) {\n", " embed_document(root);\n", " } else {\n", " let attempts = 0;\n", " const timer = setInterval(function(root) {\n", " if (root.Bokeh !== undefined) {\n", " clearInterval(timer);\n", " embed_document(root);\n", " } else {\n", " attempts++;\n", " if (attempts > 100) {\n", " clearInterval(timer);\n", " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", " }\n", " }\n", " }, 10, root)\n", " }\n", "})(window);" ], "application/vnd.bokehjs_exec.v0+json": "" }, "metadata": { "application/vnd.bokehjs_exec.v0+json": { "id": "p1405" } }, "output_type": "display_data" } ], "source": [ "def simulate_coinflips(n, p, size=1):\n", " \"\"\"\n", " Simulate n_samples sets of n coin flips with prob. p of heads.\n", " \"\"\"\n", " n_heads = np.empty(size, dtype=np.int64)\n", " for i in range(size):\n", " n_heads[i] = np.sum(np.random.random(size=n) < p)\n", " return n_heads\n", "\n", "\n", "size = (30, 100, 1000, 10000)\n", "n = 25\n", "p = 0.25\n", "h_plot = np.arange(26)\n", "theor_dist = st.binom.pmf(h_plot, n, p)\n", "\n", "plots = []\n", "for n_samp in size:\n", " plot = bokeh.plotting.figure(\n", " frame_height=200,\n", " frame_width=300,\n", " x_axis_label=\"h\",\n", " y_axis_label=\"P(h)\",\n", " title=f\"{n_samp} samples\",\n", " )\n", " h = simulate_coinflips(n, p, size=n_samp)\n", " plot.circle(h_plot, theor_dist)\n", " plot.segment(x0=h_plot, x1=h_plot, y0=0, y1=theor_dist)\n", " plot.circle(\n", " np.arange(h.max() + 1), np.bincount(h) / n_samp, color=\"tomato\",\n", " )\n", " plots.append(plot)\n", "\n", "bokeh.io.show(bokeh.layouts.gridplot(plots, ncols=2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we can see, if we *sample* out of the probability distribution, we can approximately calculate the actual distribution. If we sample enough, the approximation is very good.\n", "\n", "Sampling is such a powerful strategy that highly efficient algorithms with convenient APIs have been developed to sample out of named probability distributions. For example, we could have used `np.random.binom()` as a drop-in (and _much_ more efficient) replacement for the `simulate_coinflips()` function above." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sampling out of distributions defined by master equations\n", "\n", "We will use the same strategy for solving master equations. We will find a way to *sample* out of the distribution that is governed by the master equation. This technique was pioneered by Dan Gillespie in the late 1970s. For that reason, these sampling techniques are often called **Gillespie simulations**. The algorithm is sometimes referred to as a **stochastic simulation algorithm**, or SSA." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Example: Unregulated gene expression\n", "\n", "Here will explore how this algorithm works by looking at simple production of a protein. We will include the production of RNA to demonstrate how this is done with a master equation that has more than one species.\n", "\n", "For simple protein production, we have the following reactions.\n", "\n", "\\begin{align}\n", "\\text{DNA} \\rightarrow \\text{mRNA} \\rightarrow \\text{protein}\n", "\\end{align}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Macroscale equations\n", "\n", "As we have seen before, the deterministic dynamics, which describe mean concentrations over a large population of cells, are described by the ODEs\n", "\n", "\\begin{align}\n", "\\frac{\\mathrm{d}m}{\\mathrm{d}t} &= \\beta_m - \\gamma_m m, \\\\[1em]\n", "\\frac{\\mathrm{d}p}{\\mathrm{d}t} &= \\beta_p m - \\gamma_p p.\n", "\\end{align}\n", "\n", "The same equations should hold if $m$ and $p$ represent the mean *numbers* of molecules; we would just have to appropriately rescale the constants. Assuming the $m$ and $p$ are now numbers (so we are not free to pick their units), we can nondimensionalize using $\\gamma_m$ to nondimensionalize time. This leads to redefinition of parameters and variables\n", "\n", "\\begin{align}\n", "&\\beta_m V/\\gamma_m \\to \\beta_m, \\\\[1em]\n", "&\\beta_p/\\gamma_m \\to \\beta_p, \\\\[1em]\n", "&\\gamma_m t \\to t,\n", "\\end{align}\n", "\n", "where $V$ is the entire volume of the system of interest. The dimensionless equations are\n", "\n", "\\begin{align}\n", "\\frac{\\mathrm{d}m}{\\mathrm{d}t} &= \\beta_m - m, \\\\[1em]\n", "\\frac{\\mathrm{d}p}{\\mathrm{d}t} &= \\beta_p m - \\gamma p,\n", "\\end{align}\n", "\n", "with $\\gamma = \\gamma_p/\\gamma_m$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### The Master equation\n", "\n", "We can write a master equation for these dynamics. In this case, each state is defined by an mRNA copy number $m$ and a protein copy number $p$. So, we will write a master equation for $P(m, p, t)$.\n", "\n", "\\begin{align}\n", "\\frac{\\mathrm{d}P(m,p;t)}{\\mathrm{d}t} &= \\beta_m P(m-1,p;t) + (m+1)P(m+1,p;t) - \\beta_m P(m,p;t) - mP(m,p;t) \\nonumber \\\\[1em]\n", "&+\\beta_p mP(m,p-1;t) + \\gamma (p+1)P(m,p+1;t) - \\beta_p mP(m,p;t) - \\gamma p P(m,p;t).\n", "\\end{align}\n", "\n", "We implicitly define $P(m, p; t) = 0$ if $m < 0$ or $p < 0$. This is the master equation we will sample from using the stochastic simulation algorithm (SSA), also known as the Gillespie algorithm." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The Gillespie algorithm\n", "\n", "The Gillespie algorithm, also called a stochastic simulation algorithm (SSA), is a way to sample the story behind a master equation, as will become clear momentarily as we work through the algorithm and introduce some terminology.\n", "\n", "The transition probabilities are also called **propensities** in the context of stochastic simulation. The propensity for a given transition, say indexed $i$, is denoted as $a_i$. The equivalence to notation we introduced for master equations is that if transition $i$ results in the change of state from $n'$ to $n$, then $a_i = W(n\\mid n')$.\n", "\n", "To cast this problem for a Gillespie simulation, we can write each change of state (moving either the copy number of mRNA or protein up or down by 1 in this case) and their respective propensities.\n", "\n", "\\begin{align}\n", "\\begin{array}{ll}\n", "\\text{reaction, }r_i & \\text{propensity, } a_i \\\\\n", "m \\rightarrow m+1,\\;\\;\\;\\; & \\beta_m \\\\[0.3em]\n", "m \\rightarrow m-1, \\;\\;\\;\\; & m\\\\[0.3em]\n", "p \\rightarrow p+1, \\;\\;\\;\\; & \\beta_p m \\\\[0.3em]\n", "p \\rightarrow p-1, \\;\\;\\;\\; & \\gamma p.\n", "\\end{array}\n", "\\end{align}\n", "\n", "Note that specifying the reactions and their respective propensities has the same information as specifying the master equation itself.\n", "\n", "We will not carefully prove that the Gillespie algorithm samples from the probability distribution governed by the master equation, but will state the principles behind it. The basic idea is that events (such as those outlined above) are rare, discrete, separate events. I.e., each event is an arrival of a Poisson process. The Gillespie algorithm starts with some state, $(m_0,p_0)$. Then a state change, *any* state change, will happen in some time $\\Delta t$ that has a certain probability distribution (which we will show is Exponential momentarily). The probability that the state change that happens is reaction $j$ is proportional to $a_j$. That is to say, state changes with high propensities are more likely to occur. Thus, choosing which of the $n$ state changes happens in $\\Delta t$ is a matter of drawing an integer $j$ in $[1,n]$ where the probability of drawing $j$ is\n", "\n", "\\begin{align}\n", "\\frac{a_j}{\\sum_i a_i}.\n", "\\end{align}\n", "\n", "Now, how do we determine how long the state change took? The probability density function describing that a *given* state change $i$ takes place in time $t$ is\n", "\n", "\\begin{align}\n", "P(t\\mid a_i) = a_i\\, \\mathrm{e}^{-a_i t},\n", "\\end{align}\n", "\n", "since the time it takes for arrival of a [Poisson process](https://en.wikipedia.org/wiki/Poisson_point_process) is [Exponentially distributed](https://en.wikipedia.org/wiki/Exponential_distribution). The probability that it has *not* arrived in time $\\Delta t$ is the probability that the arrival time is greater than $\\Delta t$, given by the complementary cumulative distribution function for the Exponential distribution.\n", "\n", "\\begin{align}\n", "P(t > \\Delta t\\mid a_i) = \\int_{\\Delta t}^\\infty \\mathrm{d}t\\,P(t\\mid a_i) = \\mathrm{e}^{-a_i \\Delta t}.\n", "\\end{align}\n", "\n", "Now, say we have $n$ processes that arrive in time $t_1, t_2, \\ldots$. The probability that *none* of them arrive before $\\Delta t$ is\n", "\n", "\\begin{align}\n", "P(t_1 > \\Delta t, t_2 > \\Delta t, \\ldots) &=\n", "P(t_1 > \\Delta t) P(t_2 > \\Delta t) \\cdots =\n", "\\prod_i \\mathrm{e}^{-a_i \\Delta t} \n", "= \\mathrm{exp}\\left[-\\Delta t \\sum_i a_i\\right].\n", "\\end{align}\n", "\n", "This is the same as the probability of a single Poisson process with $a = \\sum_i a_i$ not arriving before $\\Delta t$. So, the probability that it *does* arrive in $\\Delta t$ is Exponentially distributed with mean $\\left(\\sum_i a_i\\right)^{-1}$.\n", "\n", "So, we know how to choose a state change and we also know how long it takes.\n", "The Gillespie algorithm then proceeds as follows.\n", "\n", "1. Choose an initial condition, e.g., $m = p = 0$.\n", "2. Calculate the propensity for each of the enumerated state changes. The propensities may be functions of $m$ and $p$, so they need to be recalculated for every $m$ and $p$ we encounter.\n", "3. Choose how much time the reaction will take by drawing out of an exponential distribution with a mean equal to $\\left(\\sum_i a_i\\right.)^{-1}$. This means that a change arises from a Poisson process.\n", "4. Choose what state change will happen by drawing a sample out of the discrete distribution where $P_i = \\left.a_i\\middle/\\left(\\sum_i a_i\\right)\\right.$. In other words, the probability that a state change will be chosen is proportional to its propensity.\n", "5. Increment time by the time step you chose in step 3.\n", "6. Update the states according to the state change you choose in step 4.\n", "7. If $t$ is less than your pre-determined stopping time, go to step 2. Else stop.\n", "\n", "Gillespie proved that this algorithm samples the probability distribution described by the master equation in his seminal papers in [1976](https://doi.org/10.1016/0021-9991(76)90041-3) and [1977](http://doi.org/10.1021/j100540a008). (We recommend reading the latter.) You can also read a concise discussion of how the algorithm samples the master equation in [section 4.2 of Del Vecchio and Murray](http://www.cds.caltech.edu/~murray/books/AM08/pdf/bfs-stochastic_14Sep14.pdf)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Coding up a Gillespie simulation\n", "\n", "To code up the Gillespie simulation of the simple gene expression example, we first make an an array that gives the changes in the counts of $m$ and $p$ for each of the four reactions. This is a way of encoding the updates in the particle counts that we get from choosing the respective state changes." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Column 0 is change in m, column 1 is change in p\n", "simple_update = np.array(\n", " [\n", " [1, 0], # Make mRNA transcript\n", " [-1, 0], # Degrade mRNA\n", " [0, 1], # Make protein\n", " [0, -1], # Degrade protein\n", " ],\n", " dtype=np.int64,\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we make a function that updates the array of propensities for each of the four reactions. We update the propensities (which are passed into the function as an argument) instead of instantiating them and returning them to save on memory allocation while running the code. It has the added benefit that it forces you to keep track of the indices corresponding to the update matrix. This helps prevent bugs. It will naturally be a function of the current population of molecules. It may in general also be a function of time, so we explicitly allow for time dependence (even though we will not use it in this simple example) as well." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "def simple_propensity(propensities, population, t, beta_m, beta_p, gamma):\n", " \"\"\"Updates an array of propensities given a set of parameters\n", " and an array of populations.\n", " \"\"\"\n", " # Unpack population\n", " m, p = population\n", "\n", " # Update propensities\n", " propensities[0] = beta_m # Make mRNA transcript\n", " propensities[1] = m # Degrade mRNA\n", " propensities[2] = beta_p * m # Make protein\n", " propensities[3] = gamma * p # Degrade protein" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Making a draw\n", "\n", "Finally, we write a general function that draws a choice of reaction and the time interval for that reaction. This is the heart of the Gillespie algorithm, so we will take some time to discuss speed. First, to get the time interval, we sample a random number from an exponential distribution with mean $\\left(\\sum_i a_i\\right)^{-1}$. This is easily done using the `np.random.exponential()` function. \n", "\n", "Next, we have to select which reaction will take place. This amounts to drawing a sample over the discrete distribution where $P_i = a_i\\left(\\sum_i a_i\\right)^{-1}$, or the probability of each reaction is proportional to its propensity. This can be done using `scipy.stats.rv_discrete`, which allows specification of an arbitrary discrete distribution. We will write a function to do this." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "def sample_discrete_scipy(probs):\n", " \"\"\"Randomly sample an index with probability given by probs.\"\"\"\n", " return st.rv_discrete(values=(range(len(probs)), probs)).rvs()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is a nice one-liner, but is it fast? There may be significant overhead in setting up the `scipy.stats` discrete random variable object to sample from each time. Remember, we can't just do this once because the array `probs` changes with each step in the SSA because the propensities change. We will therefore write a less elegant, but maybe faster way of doing it. \n", "\n", "Another way to sample the distribution is to generate a uniformly distributed random number $q$, with $0 < q < 1$ and return the value $j$ such that\n", "\n", "\\begin{align}\n", "\\sum_{i=0}^{j-1} p_i < q < \\sum_{i=0}^{j}p_i.\n", "\\end{align}\n", "\n", "We'll code this up." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "def sample_discrete(probs):\n", " \"\"\"Randomly sample an index with probability given by probs.\"\"\"\n", " # Generate random number\n", " q = np.random.rand()\n", " \n", " # Find index\n", " i = 0\n", " p_sum = 0.0\n", " while p_sum < q:\n", " p_sum += probs[i]\n", " i += 1\n", " \n", " return i - 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's compare the speeds using the `%timeit` magic function. This is a useful tool to help diagnose slow spots in your code." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Result from scipy.stats:\n", "587 µs ± 18.4 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n", "\n", "Result from hand-coded method:\n", "1.05 µs ± 5.89 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)\n" ] } ], "source": [ "# Make dummy probs\n", "probs = np.array([0.1, 0.3, 0.4, 0.05, 0.15])\n", "\n", "print('Result from scipy.stats:')\n", "%timeit sample_discrete_scipy(probs)\n", "\n", "print('\\nResult from hand-coded method:')\n", "%timeit sample_discrete(probs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Wow! The less concise method is a couple of orders of magnitude faster! So, we will ditch using `scipy.stats`, and use our hand-built sampler instead. (You can find a much more thorough discussion on speed considerations in Gillespie simulations in [Technical Appendix 16b](16b_profiling_for_speed).)\n", "\n", "Now we can write a function to do our draws." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "def gillespie_draw(propensity_func, propensities, population, t, args=()):\n", " \"\"\"\n", " Draws a reaction and the time it took to do that reaction.\n", " \n", " Parameters\n", " ----------\n", " propensity_func : function\n", " Function with call signature propensity_func(population, t, *args)\n", " used for computing propensities. This function must return\n", " an array of propensities.\n", " propensities : ndarray\n", " Propensities for each reaction as a 1D Numpy array.\n", " population : ndarray\n", " Current population of particles\n", " t : float\n", " Value of the current time.\n", " args : tuple, default ()\n", " Arguments to be passed to `propensity_func`.\n", " \n", " Returns\n", " -------\n", " rxn : int\n", " Index of reaction that occured.\n", " time : float\n", " Time it took for the reaction to occur.\n", " \"\"\"\n", " # Compute propensities\n", " propensity_func(propensities, population, t, *args)\n", " \n", " # Sum of propensities\n", " props_sum = propensities.sum()\n", " \n", " # Compute next time\n", " time = np.random.exponential(1.0 / props_sum)\n", " \n", " # Compute discrete probabilities of each reaction\n", " rxn_probs = propensities / props_sum\n", " \n", " # Draw reaction from this distribution\n", " rxn = sample_discrete(rxn_probs)\n", " \n", " return rxn, time" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Gillespie time stepping\n", "\n", "Now we are ready to write our main loop. We will only keep the counts at pre-specified time points. This saves on RAM, and we really only care about the values at given time points anyhow.\n", "\n", "Note that this function is generic. All we need to specify our system is the following.\n", "\n", "* A function to compute the propensities\n", "* How the updates for a given reaction are made\n", "* Initial population\n", "\n", "Additionally, we specify necessary parameters, an initial condition, and the time points at which we want to store our samples. So, providing the propensity function and update are analogous to providing the time derivatives when using `scipy.integrate.odeint()`." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "def gillespie_ssa(propensity_func, update, population_0, time_points, args=()):\n", " \"\"\"\n", " Uses the Gillespie stochastic simulation algorithm to sample\n", " from probability distribution of particle counts over time.\n", " \n", " Parameters\n", " ----------\n", " propensity_func : function\n", " Function of the form f(params, t, population) that takes the current\n", " population of particle counts and return an array of propensities\n", " for each reaction.\n", " update : ndarray, shape (num_reactions, num_chemical_species)\n", " Entry i, j gives the change in particle counts of species j\n", " for chemical reaction i.\n", " population_0 : array_like, shape (num_chemical_species)\n", " Array of initial populations of all chemical species.\n", " time_points : array_like, shape (num_time_points,)\n", " Array of points in time for which to sample the probability\n", " distribution.\n", " args : tuple, default ()\n", " The set of parameters to be passed to propensity_func. \n", "\n", " Returns\n", " -------\n", " sample : ndarray, shape (num_time_points, num_chemical_species)\n", " Entry i, j is the count of chemical species j at time\n", " time_points[i].\n", " \"\"\"\n", "\n", " # Initialize output\n", " pop_out = np.empty((len(time_points), update.shape[1]), dtype=np.int64)\n", "\n", " # Initialize and perform simulation\n", " i_time = 1\n", " i = 0\n", " t = time_points[0]\n", " population = population_0.copy()\n", " pop_out[0, :] = population\n", " propensities = np.zeros(update.shape[0])\n", " while i < len(time_points):\n", " while t < time_points[i_time]:\n", " # draw the event and time step\n", " event, dt = gillespie_draw(\n", " propensity_func, propensities, population, t, args\n", " )\n", "\n", " # Update the population\n", " population_previous = population.copy()\n", " population += update[event, :]\n", "\n", " # Increment time\n", " t += dt\n", "\n", " # Update the index\n", " i = np.searchsorted(time_points > t, True)\n", "\n", " # Update the population\n", " pop_out[i_time : min(i, len(time_points))] = population_previous\n", "\n", " # Increment index\n", " i_time = i\n", "\n", " return pop_out" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Running and parsing results\n", "\n", "We can now run a set of SSA simulations and plot the results. We will run 100 trajectories and store them, using $\\beta_p = \\beta_m = 10$ and $\\gamma = 0.4$. We will also use the nifty package `tqdm` to give a progress bar so we know how long it is taking." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|████████████████████████████████████████████████████████████████████████████| 100/100 [00:19<00:00, 5.17it/s]\n" ] } ], "source": [ "# Specify parameters for calculation\n", "args = (10.0, 10.0, 0.4)\n", "time_points = np.linspace(0, 50, 101)\n", "population_0 = np.array([0, 0], dtype=int)\n", "size = 100\n", "\n", "# Seed random number generator for reproducibility\n", "np.random.seed(42)\n", "\n", "# Initialize output array\n", "samples = np.empty((size, len(time_points), 2), dtype=np.int64)\n", "\n", "# Run the calculations\n", "for i in tqdm.tqdm(range(size)):\n", " samples[i, :, :] = gillespie_ssa(\n", " simple_propensity, simple_update, population_0, time_points, args=args\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now have our samples, so we can plot the trajectories. For visualization, we will plot every trajectory as a thin blue line, and then the average of the trajectories as a thick orange line." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
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frame_height=200,\n", " x_axis_label=\"dimensionless time\",\n", " y_axis_label=\"number of mRNAs\",\n", " ),\n", " bokeh.plotting.figure(\n", " frame_width=300,\n", " frame_height=200,\n", " x_axis_label=\"dimensionless time\",\n", " y_axis_label=\"number of proteins\",\n", " ),\n", "]\n", "\n", "# Plot trajectories and mean\n", "for i in [0, 1]:\n", " for x in samples[:, :, i]:\n", " plots[i].line(\n", " time_points, x, line_width=0.3, alpha=0.2, line_join=\"bevel\"\n", " )\n", " plots[i].line(\n", " time_points,\n", " samples[:, :, i].mean(axis=0),\n", " line_width=6,\n", " color=\"orange\",\n", " line_join=\"bevel\",\n", " )\n", "\n", "# Link axes\n", "plots[0].x_range = plots[1].x_range\n", "\n", "bokeh.io.show(bokeh.layouts.gridplot(plots, ncols=2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also compute the steady state properties by considering the end of the simulation. The last 50 time points are at steady state, so we will average over them." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "mRNA mean copy number = 10.0748\n", "protein mean copy number = 251.6994\n", "\n", "mRNA variance = 10.26200496\n", "protein variance = 2145.33703964\n", "\n", "mRNA noise = 0.317965262817717\n", "protein noise = 0.18402023679851773\n" ] } ], "source": [ "print(\"mRNA mean copy number =\", samples[:, -50:, 0].mean())\n", "print(\"protein mean copy number =\", samples[:, -50:, 1].mean())\n", "print(\"\\nmRNA variance =\", samples[:, -50:, 0].std() ** 2)\n", "print(\"protein variance =\", samples[:, -50:, 1].std() ** 2)\n", "print(\"\\nmRNA noise =\", samples[:, -50:, 0].std() / samples[:, -50:, 0].mean())\n", "print(\n", " \"protein noise =\", samples[:, -50:, 1].std() / samples[:, -50:, 1].mean()\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we can compute the steady state probability distributions. To plot them, we plot the empirical cumulative distribution function (ECDF) from the sampling. The theoretical distribution for mRNA is Poisson, which is overlayed in orange." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
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"markdown", "metadata": {}, "source": [ "As we expect, the mRNA copy number matches the Poisson distribution. We also managed to get a distribution for the protein copy number that we could plot. \n", "\n", "You now have the basic tools for doing Gillespie simulations. You just need to code the propensity function and the update, and you're on your way! (Note, though, that this only works for Gillespie simulations where the states are defined by particle counts, which should suffice for this course.)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Implementation in the biocircuits package\n", "\n", "This algorithm is implemented in the biocircuits package. A significant speed boost is achieved by [just-in-time compliation](https://en.wikipedia.org/wiki/Just-in-time_compilation) using [Numba](http://numba.pydata.org). To utilize this feature, you need to just-in-time compile (JIT) your propensity function. You can insist that everything is compiled (and therefore skips the comparably slow Python interpreter) by using the @numba.njit decorator. In many cases, like in this one for simple gene expression, that is all you have to do." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "@numba.njit\n", "def simple_propensity(propensities, population, t, beta_m, beta_p, gamma):\n", " \"\"\"Updates an array of propensities given a set of parameters\n", " and an array of populations.\n", " \"\"\"\n", " # Unpack population\n", " m, p = population\n", "\n", " # Update propensities\n", " propensities[0] = beta_m # Make mRNA transcript\n", " propensities[1] = m # Degrade mRNA\n", " propensities[2] = beta_p * m # Make protein\n", " propensities[3] = gamma * p # Degrade protein" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can also utilize more threads and do the Gillespie simulations in parallel if you like using the `n_threads` keyword argument (making sure that `n_threads` does not exceed the number of cores on your machine). Note that the total number of samples is `n_threads * size`. Because of the speed boosts, we will take more samples using the biocircuits implementation." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "samples = biocircuits.gillespie_ssa(\n", " simple_propensity,\n", " simple_update,\n", " population_0,\n", " time_points,\n", " size=2000,\n", " args=args,\n", " n_threads=2,\n", " progress_bar=False,\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now have 4000 trajectories, and we can again make the plots as we did before. With so many trajectories, though, we should not show them all, since there would be too many glyphs on the plot. We therefore thin out the samples for plotting, taking only every 100th trajectory." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
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frame_height=200,\n", " x_axis_label=\"dimensionless time\",\n", " y_axis_label=\"number of mRNAs\",\n", " ),\n", " bokeh.plotting.figure(\n", " frame_width=300,\n", " frame_height=200,\n", " x_axis_label=\"dimensionless time\",\n", " y_axis_label=\"number of proteins\",\n", " ),\n", "]\n", "\n", "# Plot trajectories and mean\n", "for i in [0, 1]:\n", " # Get 2.5th and 97.5th percentiles\n", " low = np.percentile(samples[:, :, i], 2.5, axis=0)\n", " high = np.percentile(samples[:, :, i], 97.5, axis=0)\n", "\n", " # Plot the envelope\n", " plots[i].patch(\n", " np.concatenate((time_points, time_points[::-1])),\n", " np.concatenate((low, high[::-1])),\n", " alpha=0.3,\n", " line_alpha=0,\n", " )\n", "\n", " # Plot the mean\n", " plots[i].line(\n", " time_points,\n", " np.mean(samples[:, :, i], axis=0),\n", " line_width=2,\n", " )\n", "\n", "# Link axes\n", "plots[0].x_range = plots[1].x_range\n", "\n", "bokeh.io.show(bokeh.layouts.gridplot(plots, ncols=2))" ] }, { "cell_type": 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\n", "\n", "## References\n", "\n", "- Del Vecchio, D. and Murray, R. M., _Biomolecular Feedback Systems_, Princeton University Press, 2014. ([link](http://www.cds.caltech.edu/~murray/books/AM08/pdf/bfs-stochastic_14Sep14.pdf))\n", "- Gillespie, D. T., A general method for numerically simulating the stochastic time evolution of coupled chemical reactions, _J. Comput. Phys._, 22, 403–434, 1976. ([link](https://doi.org/10.1016/0021-9991(76)90041-3))\n", "- Gillespie, D. T., Exact stochastic simulation of coupled chemical reactions, _J. Phys. Chem._, 81, 2340–2361, 1977. ([link](http://doi.org/10.1021/j100540a008))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Computing environment" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Python implementation: CPython\n", "Python version : 3.10.10\n", "IPython version : 8.12.0\n", "\n", "numpy : 1.23.5\n", "scipy : 1.10.1\n", "numba : 0.56.4\n", "tqdm : 4.65.0\n", "multiprocess: 0.70.14\n", "iqplot : 0.3.2\n", "biocircuits : 0.1.11\n", "bokeh : 3.1.0\n", "jupyterlab : 3.5.3\n", "\n" ] } ], "source": [ "%load_ext watermark\n", "%watermark -v -p numpy,scipy,numba,tqdm,multiprocess,iqplot,biocircuits,bokeh,jupyterlab" ] } ], "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": "3.10.10" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false } }, "nbformat": 4, "nbformat_minor": 4 }