IGEM:IMPERIAL/2008/Prototype/Drylab/Motility data collection: Difference between revisions

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=Our Approach=
=Motility Data Acquisition=
Using manual tracking we hope to acquire sufficient data to obtain ''B. Subtilis'' motility properties such as run velocity, run duration, tumbling angle and tumbling duration. This is done by first tracking cells and obtaining coordinate points, and from these points extracting out the above mentioned motility characteristics. The next phase would be to study the statistical distribution of these various properties, and compare them to our own statistical models of ''B.Subtilis'' motility. Our approach to collecting motility data is shown in the figure below:
[[Image:Data_Acquisition.TIF|500px|thumb|Approach to Motility Data Collection|center]]
[[Image:Data_Acquisition.TIF|500px|thumb|Approach to Motility Data Collection|center]]
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=
;[[IGEM:IMPERIAL/2008/Prototype/Drylab//Motility_data_collection/Materials |Materials]]


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.  
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.


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.
;[[IGEM:IMPERIAL/2008/Prototype/Drylab//Motility_data_collection/Methods|Method]]


It was concluded from the validation of tracking software that manual tracking provides for the most reliable form of tracking. We intend to use manual tracking to track ''B.Subtilis'' and extract two-dimensional coordinate data points which is described by the trajectory of the cells.


=Microscope Video Characteristics=
;[[IGEM:IMPERIAL/2008/Prototype/Drylab//Motility_data_collection/Data_Extraction|Data Extraction]]


*From our experience on the microscope, we observe the following:
After obtaining the coordinate points described by bacteria trajectory, we will input this data into algorithms to extract motility properties such as run velocity, run duration, tumbling angle and tumbling duration. As we are extracting data from digitised images, the error associated with our data extraction algorithm will also be assessed using synthetic videos. Once motility data extraction is complete, we may go on to model the motility of ''B.Subtilis''.
**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




===[[IGEM:IMPERIAL/2008/Prototype/Drylab/Motility_data_collection/Video1|Video1]]===


===[[IGEM:IMPERIAL/2008/Prototype/Drylab/Motility_data_collection/Video2|Video2]]===
===[[IGEM:IMPERIAL/2008/Prototype/Drylab/Motility_data_collection/Video3|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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Latest revision as of 02:06, 8 September 2008

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Motility Data Acquisition

Using manual tracking we hope to acquire sufficient data to obtain B. Subtilis motility properties such as run velocity, run duration, tumbling angle and tumbling duration. This is done by first tracking cells and obtaining coordinate points, and from these points extracting out the above mentioned motility characteristics. The next phase would be to study the statistical distribution of these various properties, and compare them to our own statistical models of B.Subtilis motility. Our approach to collecting motility data is shown in the figure below:

Approach to Motility Data Collection
Materials

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.

Method

It was concluded from the validation of tracking software that manual tracking provides for the most reliable form of tracking. We intend to use manual tracking to track B.Subtilis and extract two-dimensional coordinate data points which is described by the trajectory of the cells.

Data Extraction

After obtaining the coordinate points described by bacteria trajectory, we will input this data into algorithms to extract motility properties such as run velocity, run duration, tumbling angle and tumbling duration. As we are extracting data from digitised images, the error associated with our data extraction algorithm will also be assessed using synthetic videos. Once motility data extraction is complete, we may go on to model the motility of B.Subtilis.




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