Matlab Tutorials

Exercise 0a | Introduction to Matlab

Exercise 0b | Stochastics and ODEs

Exercise 0c | Introduction to Image Analysis

Data Sets

Data Set 1a | Images of a graticule and E. coli cells.

Data Set 1b | A phase and fluorescence image of bacterial cells.

Data Set 2 | Images of a growing bacterial colony.

Data Set 3 | An image set of fluorescent E. coli cells.

Data Set 4 | A sample data set of our LacI titration data with an experimental description.

Exercise 1a | [data set 1a] Sizing up E. coli.

Exercise 1b | [data set 1b] Project Part I: Images and thresholding.

Exercise 2a | [data set 2] Measuring colony growth rate.

Exercise 2b | [data set 1b] Project Part II: Smoothing images, labeling objects, and extracting region properties.

Exercise 3a | Demonstrating the power of the Taylor Expansion on a simple function.

Exercise 3b | A stochastic simulation of diffusion.

Exercise 3c | [data set 3][segmenter.m] Project Part III: Applying an area filter and writing a segmentation function.

Exercise 3x | A stochastic simulation of diffusion of a neurotransmitter across a synaptic junction (not done in class).

Exercise 4a | A stochastic simulation of a random walk with biased step probabilities.

Exercise 4b | [data set 3][extract_int.m] Project Part IV: Comparing total intensity versus mean intensity as a measure of gene expression.

Exercise 5a | Computing the probability of ligand binding a receptor with cooperativity.

Exercise 5b| Computing the probability of an active chemoreceptor as a function of chemoattractant concentration.

Exercise 6a |A numerical integration of mRNA expression from a constitutive promoter.

Exercise 6b | A numerical integration of the distribution of mRNA copy number as a function of time

Exercise 6c | [data set 4][lacI_titration.m] Project Part V: Computing fold-change and comparison to our statistical mechanical prediction.

Exercise 6x | Numerical integration of ligand-receptor binding (not done in class).