MBL Physiology Bootcamp


As part of the weeklong bootcamp for the MBL Physiology course, we will implement image analysis, theoretical modeling, and biophysical simulation code in MATLAB and learn how to leverage these tools to explore biological problems in vitro, in E. coli, and in Drosophila melanogaster.




Hernan Garcia

Assistant Professor

University of California, Berkeley


Elizabeth Eck

Graduate Student

University of California, Berkeley



Nick Lammers

Graduate Student

University of California, Berkeley


Meghan Turner

Graduate Student

University of California, Berkeley




Data Sets

    • Measuring Bacterial Growth | A time series of fluorescence images of E. coli cells growing on a rich medium substrate. One image is taken every 5 minutes.
    • Quantifying Gene Expression in Bacteria | Each image corresponds to a phase contrast and fluorescence snapshot of YFP-expressing bacteria containing varying numbers of Lac repressor. sample bacterial images | full data set
    • Measuring Cytoskeletal Filament Length Distributions | Fluorescence images of actin filaments prepared in vitro and calibration grid.
    • Testing The French Flag Model | Fluorescence images of developing Drosophila embryos expressing different dosages of the Bicoid morphogen fused to GFP. Also download a csv file containing more dosages from Thomas Gregor. sample image | full data set
    • Timing Transcription in Drosophila | Data set containing images from developing Drosophila embryos in which the 5’ and 3’ end of the hunchback gene are labeled with PP7 and MS2, respectively. | full data set
    • P granule polarization in the C. elegans germline Data set containing raw and masked fluorescence images of p granules in the C. elegans germline. Raw dataset was provided by Cliff Brangwynne. Download all 3 sets and combine for the full data set. | raw data set part 1/3 | raw data set part 2/3 | raw data set part 3/3 | mask data set
    • Using Machine Learning to Segment E. Coli Cells | Data set raw phase contrast images of E. coli  colonies, along with label files indicating regions corresponding to bacterial interiors and exteriors. full data set




The code we write in during bootcamp will be added here as the course progresses.

    • Exponential Growth of E. coli  | slides | mlx | pdf
      • Integrating Cell Division by the Euler Method | Here, we get introduced to the MATLAB programming language and learn how to numerically solve the differential growth equation. GroupB in-class script
      • Measuring Growth Rate Through Microscopy | We’ve all (probably) measured bacterial growth by recording the OD of growing cultures. In this script, we analyze a time-lapse microscopy data set by measuring the bacterial area as a function of time. GroupB in-class script
      • Chi2 minimization | We performed a chi2 minimization to estimate the growth rate of the bacteria in the analyzed movie.
    • Transcriptional Regulation in Bacteria Dissecting simple repression in the lac operon of E. coli | slides | mlx | pdf | GroupB in-class script
    • Cytoskeleton length distributions | slidesmlx | pdf
      • Predicting cytoskeletal filament length distributions using chemical master equations | GroupB in-class script
      • Testing those predictions by analyzing actin filament images.
    • How the Fly Gets Its Neck | In this tutorial we test the French flag model by predicting cephalic furrow position as a function of Bicoid activator, and testing those predictions experimentally. slides | html | mlx
    • Timing Transcription in Drosophila embryos | Using some of the image processing skill we learned while processing the growth movie, we measure the rate of transcription of the hunchback gene. html | mlx GroupB in-class script
    • P granule polarization in the C. elegans germline | Here, we learn how to track objects over time and use this skill to gain information about the mechanism for p granule localization | slides | mlx | pdf | Group C in-class script
    • Using Machine Learning to Segment E. Coli Cells | slides | mlx | pdf
      • Using logistic regressions for robust classification of data.
      • Applying image filters to experimental data.


Below are papers and books that you may find of interest during or after this bootcamp rotation.


Python Resources

If neither your home institution nor your lab has a MATLAB license, you can do all of these exercises in Python, a free, open-source programming language. Rob Phillips' lab teaches similar exercises in Python during Caltech's Physical Biology Bootcamp. On the bootcamp webpage, you can find Python tutorials, a toolbox of useful pre-built Python functions, Python exercises analogous to the MATLAB exercises above, and links to more external Python resources.

Summer 2019
Monday, June 17, 2019