Imperial/Dry Lab/Data Analysis

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=Data Analysis=
=Data Analysis=
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==Introduction==
 
Data analysis involves manipulating experimental data with the objective of extracting useful information. This then allows us to test our original hypotheses surrounding the problem, and in doing so, test the stringency/validity of our representative model.
Data analysis involves manipulating experimental data with the objective of extracting useful information. This then allows us to test our original hypotheses surrounding the problem, and in doing so, test the stringency/validity of our representative model.
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[[Image: IC07_LeastSqr.gif|frame|left|450px| '''Fig. 1''': Curve/Shape-fitting]]
 
If the model proves to be valid, data analysis likewise provides a means of parameter extraction essential in rendering our theoretical model more realistic (as it gleans parameters from actual expimental data).
If the model proves to be valid, data analysis likewise provides a means of parameter extraction essential in rendering our theoretical model more realistic (as it gleans parameters from actual expimental data).
Our approach to data analysis utilizes curve/shape-fitting by non-linear regression (employing the least-squares method).<br><br>
Our approach to data analysis utilizes curve/shape-fitting by non-linear regression (employing the least-squares method).<br><br>
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==Principle of method of parameter extraction==
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The data analysis involved in our two projects, Infector Detector and Cell-by-Date, is performed with respect to the Cell-free Chassis. For this reason, the entire data analysis is presented in the characterisation page of this foundational chassis. Follow the image link below to the data analysis for our project.
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The method used weighted non-linear leasts-squares. This technique involves obtaining the best-fitting non-linear curve [http://mathworld.wolfram.com/LeastSquaresFitting.html] for a given set of parameters from the parameter space. This procedure involves minimizing the sum of the squares of the offsets from the chosen curve. (reference to wolfram mathworld - article) Here, the offsets refering to the difference between the chosen non-linear curve and the experimental data, at a particular value of the independent variable.
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A weighted non-linear least-squares is used, so that that the integrity of the data does not corrupt the extracted parameters. Weightings are assigned to adjacent experimental data points, according to their variance from the general trend/behaviour.
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{{Click || image = Gm curvefitting.jpg| link = Imperial/Cell-Free/Characterisation | width = 300px | height = 180px }} <br /> [[Imperial/Cell-Free/Characterisation#Principle of our Data Analysis | Data Analysis]]
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====Representative example====
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Consider the following model
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<center>| [[Imperial/Dry_Lab | Dry Lab >>]]</center>

Latest revision as of 02:16, 27 October 2007


Data Analysis

Data analysis involves manipulating experimental data with the objective of extracting useful information. This then allows us to test our original hypotheses surrounding the problem, and in doing so, test the stringency/validity of our representative model.

If the model proves to be valid, data analysis likewise provides a means of parameter extraction essential in rendering our theoretical model more realistic (as it gleans parameters from actual expimental data).

Our approach to data analysis utilizes curve/shape-fitting by non-linear regression (employing the least-squares method).

The data analysis involved in our two projects, Infector Detector and Cell-by-Date, is performed with respect to the Cell-free Chassis. For this reason, the entire data analysis is presented in the characterisation page of this foundational chassis. Follow the image link below to the data analysis for our project.


Data Analysis



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