Tutorials
We will have several computational tutorials throughout the course. These will be done using Jupyter notebooks through Google Colab, so you will need to sign into a Google Account to create new notebooks. If you would like to set up Python on your own computers, follow Tutorial 0a below. As the course progresses, we will post the discussed or related notebooks below. Note: In some browsers (Firefox primarily), the end of each sentence is clipped off, making the posted notebooks difficult to read. If this is occurring, please try using another browser (Chrome, Safari, etc).
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Tutorial 0a Setting up Python | This tutorial will walk you through how to install a Python 3.8 scientific computing environment.
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Using the Jupyter notebook | This tutorial will teach you how to write code and text in Jupyter notebooks for homework submissions.
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A Primer on Python Syntax | This tutorial will walk you through the basics of programming in Python.
Syllabus for Computational Sessions and Datasets
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Day 1: Exponential growth: numerical integration by Forward Euler
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Day 1: Bacterial growth: image segmentation and linear regression | As a Jupyter notebook | Bacterial Growth Image Dataset
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Day 2: Chemical Master Equation of Diffusion: Peaked Center and FRAP
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Day 2: Stochastic Simulations: mRNA Production and Degredation
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Day 3: Stochastic Simulations: Gillespie Simulations with Ligand-Receptor Dynamics
Tutorial Templates on Colab
We will create links to some Colab notebooks below with some lines of code already written out. Clicking the link will open a new page of the notebook that you can save to your account.
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Bacterial growth template | This template contains lines of code for downloading the E. coli growth image set.
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Kinetic proofreading template | This template contains lines of code for setting up the kinetic proofreading rates matrix.
External resources
Below is a list of useful online resources for learning the Python programming language and principles of programming in general.
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Probability Distribution Explorer by Justin Bois | Explains various probability distributions and their stories.