Code
During this course, you will develop a computational prowess that will aid in your understanding of physical biology. These will be done using Jupyter notebooks through Google Colab, so you will need to sign into a Google Account to create new notebooks.
We will post links to Jupyter Notebooks hosted on colab of the tutorial sessions here.
Tutorials
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Tutorial 0.0 – A Primer On Jupyter Notebooks and Google Colab| This tutorial expalins what a Jupyter Notebook is and how to use it on Google Colab
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Tutorial 0.1 – A Primer On Python Syntax| This tutorial gives a quick intro to programming in Python.
Exercises
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Tutorial 1: Basic Image Processing and Counting Elephants from Space| In this exercise, we learn some simple techniques of quantitative image processing and try to count elephants in two satellite images.
In-Class Code
Elephant Image 1
Elephant Image 2 -
Tutorial 2: Spotting Elephants Through Machine Learning| In this exercise, we cover the basics of machine learning and create an artificial neuron from scratch to identify “elephants” in a toy dataset.
In-Class Code (Day 1)
In-Class Code (Day 2)
Example Training Image
Example Training Label Image
Complete Training Dataset
Test Image
Test Label Image -
Tutorial 3: Integrating the Dynamics of Constitutive Expression| In this exercise, we use a chemical master equation to dissect dynamics of constitutive expression.
In Class -
Tutorial 4: Diffusion and Random Walks| Through stochastic simulations, we explore how diffusion can be understood as a random walk.
In Class -
Tutorial 5: The Taylor Expansion| Our in-class exercise on computing and plotting the Taylor expansion of a few functions.
In Class -
Tutorial 6: Autoactivation| Here we explore the dynamics and stability of fixed points in the context of autoregulation.
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Tutorial 7: MWC State Probabilities and the Mutal Repression Switch| Here, we use the MWC model to explore the probabilities of different states and generate our “hydrogen atom” transcriptome.
In Class -
Tutorial 7x: Gillespie simulation of the constitutive promoter| As Madhav mentioned in class, we can take a more ‘appropriate’ approach to simulating stochastic processes by considering the fact that rates are variable, and we often only speak in terms of average rates. This tutorial walks you through a Gillespie simulation for expression from a constitutive promoter.
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[**Tutorial 8: Control of flagellar lengths in Chlamydomonas</i.>**](https://colab.research.google.com/drive/1QXEEUmS4KKWEBX8uBdBl4pukyJYm3Vg6?usp=sharing)| This tutorial presents a combination of numerics and analytics to explore flagellar length control.