IGEM:IMPERIAL/2008/Prototype/Drylab/Motility data collection

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<html><a href=http://openwetware.org/wiki/IGEM:IMPERIAL/2008/Prototype><img width=50px src=http://openwetware.org/images/f/f2/Imperial_2008_Logo.png></img</a></html> Home The Project B.subtilis Chassis Wet Lab Dry Lab Notebook

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Our Approach

Approach to Motility Data Collection

Using manual tracking we hope to acquire enough data in order to derive important B. Subtilis motility properties (i.e. run time, tumble time, run velocity, and tumbling angle). This is done by first tracking cells and obtaining coordinate points, and from these points extract out the above mentioned motility characteristics. We also aim to study the statistical distribution of these various properties, and compare them to our own statistical models of B.Subtilis motility.

Material

We will be using the Zeiss Axiovert 200 inverted microscope with a fully motorised stage, controlled by Improvision Volocity acquisition software. This system offers a full incubation chamber with temperature and CO2 control, a large range of filter sets from UV to far-red and a highly sensitive 1300x1000 pixel camera for fast low-light imaging.

Video images are captured into memory by the system at a basal video frame rate of 16.3Hz. This can be further increased by performing binning.


Microscope Video Characteristics

  • From our experience on the microscope, we observe the following:
    • Bacteria are rod shaped.
    • Some cells swim out of the focus plane
    • The images are noisy
    • Some cells seem to be undergoing division
    • Some cells appear to be dead as they are not moving or drift


Video1

Video2

Video3

Manual Tracking Methodology

  • We intend to track for each cell:
    • The centroid
    • The part of the cell in the direction of movement
  • We attempt to reduce the bias in our manual tracking
    • By disabling the view path option
    • By displacing the cursor away from the tracked cell before adding a new point

Data Clustering

  • By plotting velocity*exp(i*angle) in the complex plane for each frame, we hope to be able to segment our data into two clusters:
    • One defining the tumble phase
    • One defining the run phase
  • This segmentation would then be used by our analysis program to derive the following properties: run time, tumble time, run velocity, tumble angle.



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