Introductory Materials

• Setting up Python for scientific programming | This brief tutorial contains instructions to get Python and peripherals set up on your computer.

Below are links to some courses you may find useful for learning programming and data analysis in biology.

• Introduction to Programming in the Biological Sciences | A pboc-style course in which you learn practical programming in one weeks’ time.

• Data Analysis in the Biological Sciences | A wonderful course which teaches the theory and practice of Bayesian statistics using biological data. The tutorials are shown in detail in Python.

Data sets

Please download the following data sets, unzip them, and place them in your pboc/data folder as described in the setting up Python tutorial.

• Data Set 1 | A phase contrast image of bacteria and a graticule.
• Data Set 2 | A series of phase contrast and fluorescence images of a growing E. coli colony
• Data Set 3 | Images of developing fly embryos for identification of the position of the cephalic furrow.
• Data Set 4 | A large image set of E. coli strains with varying copy numbers of the lacI repressor molecule.
• Data Set 5 | An image set of 5’ and 3’ labeled mRNA expressing in the developing Drosophila embryo.
• Data Set 6 | mRNA distributions for two genes in yeast measured by single-molecule FISH from Zenklusen et. al. (2008)
• Data Set 7 | Revised data set for transcription elongation in flies.

Course Python Utilities

As sometimes syntax can be difficult, we have written a file with a few functions written in Python that will make some of the in-class exercises less cumbersome. Please download them below and place them in your root pboc folder as described in the setting up Python tutorial.

• pboc_utils.py | Course utilities.

Course Exercises

As we go through the course, the code we write in class will be posted here. When possible, extra tutorials with more detail and explanation will be posted as well.

• Exercise 1 | Measuring the growth rate of E. coli cells growing on LB. [data set][in class]

• Exercise 2 | “Spreading the butter:” diffusion by numerically integrating the master equation and simulating FRAP. [in class]

• Exercise 3 | Numerically integrating the differential equation for mean mRNA production by a constitutive promoter.

• Exercise 4 | “Spreading the butter:” finding the full probability distribution of mRNA produced by a constitutive promoter.

• Exercise 5 | Plotting the open probability for a simple two-state ion channel.

• Exercise 6 | Part I of the project. We covered loading images, segmentation by thresholding, filtering, and labeling.