Repository for the 1st Annual Undergraduate Quantitative Biology (UQ-bio) Summer School.

Authors: Dr. Brian Munsky, Michael May, Linda Forero, Luis Aguilera, William Raymond, Zachary Fox, Lisa Weber, and Huy D. Vo.

Description

This repository contains the materials for the 1st Annual Undergraduate Quantitative Biology (UQ-bio) Summer School.

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Modules

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• Module 0: Bootcamp Basics to get Started with Scientific Computing in Python (June 1-4).

  • Python basics 1 (int, str, iterables, slicing)
  • Python basics 2 (loops, ranges, functions, lambdas, list comprehension)
  • Python basics 3 (importing packages, classes/modules, os navigation, reading files)
  • Python basics 4 (Numpy and Linear Algebra Review)
  • Python basics 5 (Colab Enviroment setup and navigation)
  • Python basics 6 (Visualization with matplotlib)

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• Live Tutorial 0a - Basic Image manipulation in a Python interactive notebook (Luis Aguilera)
• Live Tutorial 0b – PySB. A Python framework for systems biology modeling. (Carlos Lopez, Samantha Beik, Alexander Lubbock)
• Solutions for questions posed during hacking sessions and Tutorial 0a!

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• Module 1: Introduction to Single-Cell Optical Microscopy Experiments and Image Processing (June 7 – 11).
  • Live Tutorial 1a – Single-cell segmentation in Python (Zach Fox)
  • Live Tutorial 1b – Single-particle tracking in Python (Luis Aguilera)
    • Part I.
    • Part II.

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• Module 2: Introduction to Multivariable Statistics and Machine Learning for Single-Cell Data (June 14 – 18).
  • Live Tutorial 2a – Basic Statistical Analyses (Huy Vo)
    • Explore basic univariate probability distributions.
    • Visualization and summary statistics for multivariate and time-series data.
    • Linear Regression: estimating free diffusion coefficient from mean squared displacements.
  • Live Tutorial 2b - Machine Learning with a Simulated Nascent Chain Dataset (Will/Zach)
    • Example Simple Perceptron
    • Example Architectures with Oxford Flowers 17
    • Example Autoencoder with Oxford Flowers 17
    • Example FFNN boundary visualization

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• Module 3: Introduction to Stochastic Simulations of Single-Cell Gene Regulatory Processes (June 21 – 25).
  • Tutorial 3a – Write your own stochastic simulation (Lisa Weber)
  • Tutorial 3b – TASEP Models (Will Raymond)

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• Module 4: Introduction to Master Equation Analyses of Single-Cell Gene Regulatory Processes (June 28 – July 2).
  • Tutorial 4a – Writing and Running an FSP analysis (Michael May)
  • Tutorial 4b – Computing Likelihood of Single-cell data using FSP (Zach/Huy)

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• Module 5: Introduction to Monte Carlo Methods to Infer Models for Noisy Single-Cell Processes (July 5 – 9).
  • Tutorial 5a – Basics of Bayesian Analysis and MCMC algorithms (Steve Presse)
  • Tutorial 5b - Writing and Running an MCMC analysis (Huy Vo)

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Projects Notebooks

• Project 1: Single-cell yeast response dynamics. ](add link)

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• Project 2: Single-cell RNA FISH analysis.
  • Weeks 0-2: Extracting quantitative information from single-cell RNA-FISH images
  • Week 3: Simulating stochastic gene expression
  • Week 4:
    • Part A: FSP for bursting gene model
      • Solution to Part A: [https://colab.research.google.com/drive/1HWhBk8CwwZl77BTTt6oYomXTGcP5qjmi?usp=sharing]
    • Part B: Computing smFISH log-likelihood with FSP
      • Solution to Part B: [https://colab.research.google.com/drive/1krPeCWiAVwNuuoXrTvqrV-AOnu261Dp3?usp=sharing]

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• Project 3: Single mRNA translation dynamics,
  • Week 1: - Homework - Image processing.
    • Homework - Image processing (solutions).
  • Week 2: - Homework - Statistics / Machine Learning.
    • Homework - Statistics / Machine Learning (solutions).
  • Week 3: - Homework - Stochastic Simulations.
    • Homework - Stochastic Simulation (solutions).
  • Week 4: - Homework - Parameter inference (solutions). .
  • Week 5: - Homework. Work with your team on your final project presentation. For the final project report follow these guidelines.

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Databases

To access links to the databases will be provided by the instructors.