IGEM:IMPERIAL/2008/Prototype/Drylab: Difference between revisions

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We have divided the modelling team into 3 sections:
We have divided the modelling team into 3 sections:
#Modelling Genetic Circuits - Erika
#Modelling Genetic Circuits - Erika
#Collecting Motility Data - Yanis
#Modelling ''B.Subtilis'' Growth - Prudence
#Analysis of Motlity Data and Model Fitting - Clinton & Prudence
#Modelling ''B.Subtilis'' Motility
#*Collecting Motility Data - Yanis
#*Analysis of Motlity Data and Model Fitting - Clinton


=[[/ODE_Model | Modelling the Genetic Circuit]]=
=[[/ODE_Model | Modelling the Genetic Circuit]]=
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To build the ODE model, each [[IGEM:IMPERIAL/2008/Prototype/Wetlab/test_constructs | test construct]] was [[/ODE_models_of_test_constructs | individually modelled]]. These test construct models can then be combined and the combined models compared to the experimental results from the wetlab.
To build the ODE model, each [[IGEM:IMPERIAL/2008/Prototype/Wetlab/test_constructs | test construct]] was [[/ODE_models_of_test_constructs | individually modelled]]. These test construct models can then be combined and the combined models compared to the experimental results from the wetlab.


= [[IGEM:IMPERIAL/2008/Prototype/Drylab/Motility_data_collection | Motility Data Collection & Analysis ]]=
=[[IGEM:IMPERIAL/2008/Prototype/Drylab/Modelling_the_Growth_of_B.Subtilis| Modelling the Growth of ''B.Subtilis'']]=


Using a video microscope, we have captured numerous frames of Bacillus Subtilis swimming. Using a manual tracking plug-in for ImageJ, we intend to acquire as much data as possible on the swimming motility of the cells.  
=Modelling the Motility of ''B.Subtilis''=


[[Image:Approach.jpg|300px|thumb|right|Approach to Modelling Motility]]


== [[IGEM:IMPERIAL/2008/Prototype/Drylab/Validation | Validating the Tracking Software ]] ==
The motility of ''B.Subtilis'' is hypothesised to be affected by various levels of EpsE expression. In order to model motility as a function of EpsE production, we have decided to use video microscopy techniques to analyse the motility of ''B.Subtilis''. We hope to obtain a transfer function model relating EpsE expression to bacterial motility characteristics such as run velocity, run duration, tumbling angle and tumbling duration. This modelling process is shown on the right and has been divided into 3 main sections:


For our motility analysis we will be using ImageJ (open source freeware software written by NIH). We have considered a few tracking plugins for ImageJ. The most accurate seems to be SpotTracker written by EPFL ( Ecole Polytechnique Federale de Lausanne), though not written specifically for cell tracking. Thus the need for us to '''[[IGEM:IMPERIAL/2008/Prototype/Drylab/Validation | validate ]]''' the software and delimit its range of operability.
#'''[[IGEM:IMPERIAL/2008/Prototype/Drylab/Validation | Validation of Tracking Software ]]'''
 
#*In order to assess the error associated with tracking algorithms applied to a digitised images sequence, a series of steps were taken to '''[[IGEM:IMPERIAL/2008/Prototype/Drylab/Validation |validate]]''' the tracking software.
=[[IGEM:IMPERIAL/2008/Prototype/Drylab/Modelling_the_Growth_of_B.Subtilis| Modelling the Growth of B.Subtilis]]=
#'''[[IGEM:IMPERIAL/2008/Prototype/Drylab/Motility_data_collection|Motility Data Acquisition]]'''
 
#*Data on run velocity, run duration, tumbling angle and tubmling duration were extracted from coordinate data output provided by the tracking software as part of the process of gathering data.
=Modelling the swimming motility=
#'''[[IGEM:IMPERIAL/2008/Prototype/Drylab/Data_Analysis|Model Fitting]]'''
 
#*Several alternative models were created for the purpose of model fitting. The motility data obtained is then analysed and fitted to alternative models. Preferences will then be assigned to fitted models using probabilistic methods such as Bayesian Analysis.
 
= [[IGEM:IMPERIAL/2008/Prototype/Drylab/Data_Analysis| Data Analysis]] =


=Resources=
=Resources=

Latest revision as of 10:07, 6 September 2008

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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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Team Strategy

We have divided the modelling team into 3 sections:

  1. Modelling Genetic Circuits - Erika
  2. Modelling B.Subtilis Growth - Prudence
  3. Modelling B.Subtilis Motility
    • Collecting Motility Data - Yanis
    • Analysis of Motlity Data and Model Fitting - Clinton

Modelling the Genetic Circuit

A simple ODE model was assumed in order to model the concentrations of the interacting proteins.

To build the ODE model, each test construct was individually modelled. These test construct models can then be combined and the combined models compared to the experimental results from the wetlab.

Modelling the Growth of B.Subtilis

Modelling the Motility of B.Subtilis

Approach to Modelling Motility

The motility of B.Subtilis is hypothesised to be affected by various levels of EpsE expression. In order to model motility as a function of EpsE production, we have decided to use video microscopy techniques to analyse the motility of B.Subtilis. We hope to obtain a transfer function model relating EpsE expression to bacterial motility characteristics such as run velocity, run duration, tumbling angle and tumbling duration. This modelling process is shown on the right and has been divided into 3 main sections:

  1. Validation of Tracking Software
    • In order to assess the error associated with tracking algorithms applied to a digitised images sequence, a series of steps were taken to validate the tracking software.
  2. Motility Data Acquisition
    • Data on run velocity, run duration, tumbling angle and tubmling duration were extracted from coordinate data output provided by the tracking software as part of the process of gathering data.
  3. Model Fitting
    • Several alternative models were created for the purpose of model fitting. The motility data obtained is then analysed and fitted to alternative models. Preferences will then be assigned to fitted models using probabilistic methods such as Bayesian Analysis.

Resources

The following are four tutorials which introduce us to data analysis and modelling. The tutorials are focused on the above approach. MATLAB codes used for data analysis can be found in the final link.

Dry Lab Tutorial 1: Design of a Motility Assay

Dry Lab Tutorial 2: Statistical Data Analysis

Dry Lab Tutorial 3: Testing the Tracking Software

Dry Lab Tutorial 4: Modelling the Growth Curve

MATLAB Codes

References


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