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Lessons

  • 0. Configuring your computer to use Python for scientific computing
  • 1. Introduction to Biological Circuit Design
  • 2. Introduction to Python for biological circuits
  • 3. Big functions from small circuits
  • 4. Finding biological circuit motifs
  • 5. Analysis of feed forward loops
  • 6. Robustness in biological circuits
  • 7. Kinetic proofreading: Multi-step processes reduce error rates in molecular recognition
  • 8. Blinking bacteria: The repressilator enables self-sustaining oscillations
  • 9. Oscillators, part II: Uses, simplifications, and elaborations of negative feedback oscillators
  • 10. Gene expression is noisy! How stochastic effects lead to heterogeneity
  • 11. Bursty gene expression
  • 12. Stochastic simulation of biological circuits
  • 13. Stochastic differentiation
  • 14. Cellular ‘bet-hedging’
  • 15. It’s about time: time-based regulation in cells
  • 16. Paradoxical regulation in intra- and intercellular circuits
  • 17. Molecular titration generates ultrasensitive responses in biological circuits
  • 20. Turing patterns
  • 21. Scaling reaction-diffusion patterns
  • Appendix A: Regulatory functions and their derivatives

Homework

  • Homework 1
  • Homework 2
  • Homework 3
  • Homework 4
  • Homework 5
  • Homework 6
  • Homework 7
  • Homework 8
    • Homework 8.1: Controlling p53 levels (70 pts)
    • Homework 8.2: Turing patterns with expanders (70 pts)
  • Homework 9
Biological Circuit Design
  • BE 150/Bi 250 b main page
  • View page source

Homework 8¶

  • Homework 8.1: Controlling p53 levels (70 pts)
  • Homework 8.2: Turing patterns with expanders (70 pts)
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Last updated on May 29, 2020.

© 2020 Michael Elowitz and Justin Bois. With the exception of pasted graphics, where the source is noted, this work is licensed under a Creative Commons Attribution License CC-BY 4.0. All code contained herein is licensed under an MIT license.

This document was prepared at Caltech with financial support from the Donna and Benjamin M. Rosen Bioengineering Center.



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