Biological results are increasingly couched in the language of quantitative data. In this course we will focus on responding to such data with quantitative models. Through several classic examples we will use simple physical principles to derive quantitative models which make polarizing predictions. These predictions will then be tested by analyzing data using tools such as image analysis and bioinformatics. No previous experience with quantitative modeling or programming is necessary.
- Course instructor: Hernan Garcia (firstname.lastname@example.org)
Download and install Matlab by logging on to the UC Berkeley Software Central
|Week||Class date||Where and when||Topic||Assignment||Materials|
|1||8/26||2pm @ 301 Barker||
Introduction to biological numeracy
E. coli by the numbers
Read "The Path To Biological Numeracy" section of Cell Biology by the Numbers (p. 10-30)
Each student present their favorite vignette from Cell Biology by the Numbers (5 minutes). Send Hernan your vignette choice by 5pm on 8/28.
|2||9/2||2pm @ 301 Barker||
Flies by the Numbers I - Part 1: Morphogen Gradients
|Vignette presentations: Ignacio, Rebecca and Elizabeth|
|3||9/9||1pm @ 301 Barker - Note earlier start||
Flies by the Numbers I - Part 2: Morphogen Gradients
Vignette presentations: Yang
|4||9/14||4pm @ 547 LSA||
Bacterial growth through simulations
Vignette presentations: Akshay, Rosalie, and Andy B
|5||9/21||4pm @ 547 LSA||
Bacterial growth through image analysis - Part 1
Vignette presentations: Andy N., Geert and Pradeep
|6||9/30||2pm @ 301 Barker||
Bacterial growth through image analysis - Part 2
Vignette presentations: Diya, Chris and Jared
|7||10/7||2pm @ 301 Barker||
Flies by the Numbers II - Part 1: Lighting Up the Central Dogma
|Vignette presentations: Ross, Dong and Julian|
|8||10/14||2pm @ 301 Barker||Flies by the Numbers II - Part 2: Lighting Up the Central Dogma||Vignette presentations: Vanessa, Simon and Ethan||Matlab code: Counting spots of nascent transcript formation|
|9||10/21||2pm @ 301 Barker||A statistical-mechanical view of binding in biology - Part 1: Ligand-receptor and ion channels||
A First Exposure to Statistical Mechanics for Life Scientists: Applications to Binding (Garcia2007b).
Ion channel data: Keller1986Data.
Channel open probability: Matlab code.
DigitizeIt: Software to extract data from plots.
|10||10/28||2pm @ 301 Barker||A statistical-mechanical view of binding in biology - Part 2: Ligand-receptor and ion channels|
|11||11/4||2pm @ 301 Barker||A statistical-mechanical view of binding in biology - Part 3: Ligand-receptor and ion channels||
Ligand-receptor binding: Matlab code
|13||11/18||2pm @ 301 Barker||A statistical-mechanical view of binding in biology - Part 4: Hemoglobin and cooperativity||
Dimoglobin simulation: Matlab code
|15||12/2||2pm @ 301 Barker||
Regulatory biology: Transcriptional regulation in bacteria
Fill out course evaluation at https://course-evaluations.berkeley.edu!
Statistical mechanics and regulatory decision-making: Phillips2015.
If you're feeling adventurous about data analysis: Full data set.
- Phillips, R., et al. (2013). Physical Biology of the Cell, 2nd Edition. New York, Garland Science.
- Alberts, B. (2015). Molecular Biology of the Cell. New York, NY, Garland Science.
- Milo, R. and Phillips, R. (to be published in 2016, can be downloaded from http://book.bionumbers.org/). Cell Biology by the Numbers. New York, NY, Garland Science.
- Mahajan, S. (2010). Street-Fighting Mathematics: The Art of Educated Guessing and Opportunistic Problem Solving. MIT Press (2010).
- Weinstein, L. and Adam, J.A. (2008). Guesstimation: Solving the World's Problems on the Back of a Cocktail Napkin. Princeton University Press.