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.
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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.
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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 | Notebook from class
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Day 1: Bacterial growth: image segmentation and linear regression | Notebook from class
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Day 2: Chemical Master Equation of Diffusion: Peaked Center and FRAP | Notebook from class
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Day 2: Stochastic Simulations: The Wright Fisher Model of Evolution | Notebook
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Day 3: Numerical Integration: Coupled Flagella Lengths | Notebook
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Day 3: Phase Separation | Notebook
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Day 4: Stochastic Simulations: Gillespie Simulations with Ligand-Receptor Dynamics | Notebook
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Day 4: Stochastic Simulations: Kinetic Proofreading | Template, Notebook
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.