Team:Imperial College/Motility

Motility Analysis
{{Imperial/Box2|Approach| As part of our chassis characterisation process, we have decided to model B. subtilis motility. In order to do this, the approach illustrated below was taken. The first phase of modelling involved data collection using microscopy techniques and cell tracking. Collected data were then analysed using algorithms which enabled us to extract distributions of parameters as defined in our model.

From Data Collection to Data Analysis

Materials
We used the Zeiss Axiovert 200 inverted microscope and Improvision Volocity acquisition software. This system offers a full incubation chamber with temperature control 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 to 27.9Hz by performing x4 binning. A short video of swimming B. subtilis is shown:

Method
In order to choose suitable tracking software, we generated a synthetic video and applied tracking algorithms to the data. We then assessed the reliability, validity and errors associated with the various tracking methods. We chose manual tracking as our method of tracking due to its high reliability and tolerable error.

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We manually tracked motile B. subtilis, obtaining two-dimensional coordinate data points which are described by the trajectory of the cells. The open source tracking software can be found here.

Data Extraction
The coordinate data obtained were then fed into algorithms to model cell trajectory and motility. Algorithms used to extract motility data and fit cell trajectory data to models can be found in the appendices section.|}}

{{Imperial/Box2|Conclusion| From our model fitting process, we can see that flagellar force is exponentially distributed. Our mechanical model though simple, fits the cell trajectory data extremely well as shown in the figure above. Further work would involve the use of a movable stage to track the movement of B. subtilis over its entire run, so as to obtain a distribution of other motility parameters associated with running and tumbling events.