MCB290F - Quantitative Biology Module

Biology is being revolutionized by new experimental techniques that have made it possible to measure the inner workings of molecules, cells and multicellular organisms with unprecedented precision. The objective of this short module is to explore this deluge of quantitative data through the use of biological numeracy. We will survey exciting research examples from our department and beyond in order to develop theoretical models that make precise predictions about biological phenomena. These predictions will be tested through the hands-on analysis of experimental data and by performing numerical simulations using Matlab.

Physical biology will be introduced as an exciting new tool to complement other approaches within biology such as genetics, genomics and structural biology. The module will introduce students to the enabling power of biological numeracy in scientific discovery and make it possible for them to use these tools in their own research.

Course instructor: Hernan Garcia (hggarcia@berkeley.edu)

Course TA: Armando Reimer (areimer@berkeley.edu)

Class materials:

 

Class date When and where Topic Materials
9/14 1pm @ 177 Stanley A feeling for the numbers in biology

Matlab code: Simulating bacterial growth

An obsession with dN/dt: Neidhardt1999

 
9/15 1pm @ 177 LSA Embryonic development by the numbers

Bicoid mutant dataPapers: Driever1989,Liu2013.

Matlab code: Testing the French Flag Model

9/17 1pm @ 177 Stanley A probabilistic view of biology: The coin flip metaconcept 

Carboxysomes in cyanobacteria: Savage2010. Matlab code.

Dilution to measure gene regulatory functions: Rosenfeld2005. Matlab code.

Correct derivation of partitioning error of carboxysomes between daugher nuclei.

9/21 1pm @ 177 Stanley The ubiquitous nature of binding problems in biology 

A First Exposure to Statistical Mechanics for Life Scientists: Applications to Binding (Garcia2007b).

Ion channel papers: Keller1986, Zhong1998, Perozo2002.

Ion channel data: Keller1986Data. Matlab code: Analyzing ion channel data.

Ligand-receptor binding data. Matlab code: Plotting ligand-receptor binding curves.

DigitizeIt: Software to extract data from plots.

9/22  1pm @ 177 LSA Regulatory biology - Part I

Matlab code: New version of ligand-receptor plots.

Matlab code: Hemoglobin and cooperativity.

Simple repression in bacteria: Oehler1994Garcia2011c.

Statistical mechanics and regulatory decision-making: Phillips2015.

Analyzing bacterial gene expression: Sample imagesSimple analysis data set.

If you're feeling adventurous about data analysis: Full data set.

 
9/23  1pm @ 177 Stanley Regulatory biology - Part II

Matlab code: Measuring bacterial gene expression.

Matlab code: Plotting the fold-change in gene expression.

Course exit survey

 

 

Matlab tutorials: We will assume no previous Matlab experience. However, if you're interested in learning more about Matlab you can go to these great tutorials.

 

 

 

Bibliography:

  • 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.
Semester: 
Fall 2015
Wednesday, September 2, 2015