Team:NTU-Singapore/Parts/Correlation

From 2008.igem.org

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=Introduction=
=Introduction=
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Correlations form an integral part in chemical engineering. Many phenomena observed in chemical processes have been extensively studied and relevant data collected to provide correlations between the variables of interest. This is to allow end users to be able to predict certain outcomes based on the knowledge of a specified variable if the correlation is availble. This practise is common when a process is not fully understood and a quick but efficient method to determine the process output is desired. Herein lies the aim of the next part of our project: to find a correlation between the amount of GFP produced and the fluorescence observed.
Correlations form an integral part in chemical engineering. Many phenomena observed in chemical processes have been extensively studied and relevant data collected to provide correlations between the variables of interest. This is to allow end users to be able to predict certain outcomes based on the knowledge of a specified variable if the correlation is availble. This practise is common when a process is not fully understood and a quick but efficient method to determine the process output is desired. Herein lies the aim of the next part of our project: to find a correlation between the amount of GFP produced and the fluorescence observed.
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For the 10 graphs, correlations were generated for each of them using the [http://www.mathworks.com/products/curvefitting/ Curve Fitting toolbox] found in MATLAB.
For the 10 graphs, correlations were generated for each of them using the [http://www.mathworks.com/products/curvefitting/ Curve Fitting toolbox] found in MATLAB.
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The results are found in each of the links below<br>
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The results are found in each of the links below. Each curve is shown together with the prediction bounds to a 95% cofidence level and the equation of the curve.<br>
[https://2008.igem.org/Team:NTU-Singapore/Parts/Curve1 Lactose = 1mM]<br>
[https://2008.igem.org/Team:NTU-Singapore/Parts/Curve1 Lactose = 1mM]<br>
[https://2008.igem.org/Team:NTU-Singapore/Parts/Curve2 Lactose = 2mM]<br>
[https://2008.igem.org/Team:NTU-Singapore/Parts/Curve2 Lactose = 2mM]<br>
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The curves were fitted once a R-square value of higer than 0.99 was obtained. Although the curves could be successfully obtained there remains some questions to be asked:<br>
The curves were fitted once a R-square value of higer than 0.99 was obtained. Although the curves could be successfully obtained there remains some questions to be asked:<br>
1) Is the model results truly representative of the real situation? <br>
1) Is the model results truly representative of the real situation? <br>
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2) Will temperature changes have an impact on the results? Recall that the model does not take into account temperature effects!<br>
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2) Will temperature changes have an impact on the results? However it is still possible that our model can account for temperature effects by tweaking the logistic growth input parameter. Recall that our model accounts for varying carrying capacity, and a different temperature could be represented by different paramter values.<br>
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=Initial Concept=
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The experiment we carried out was to obtain cells inoculated with lactose during the characterization process and to extract the protein from them. The RFU recorded at a particular time would be correlated with the amount of protein that the cell had within during the extraction. The protein quantification would come from a gel run of the proteins against a standard ladder.
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The experiment showed that the amount of proteins in the cells was alike and this result was interesting in a sense.
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Since the experiment did not yield the expected results, it was possible that the lactose provided the cells with the nutrients to multiply. A higher lactose concentration could mean that the cells were able to grow at a faster rate and produce a higher fluorescence reading.
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Latest revision as of 01:47, 23 October 2008


Introduction

Correlations form an integral part in chemical engineering. Many phenomena observed in chemical processes have been extensively studied and relevant data collected to provide correlations between the variables of interest. This is to allow end users to be able to predict certain outcomes based on the knowledge of a specified variable if the correlation is availble. This practise is common when a process is not fully understood and a quick but efficient method to determine the process output is desired. Herein lies the aim of the next part of our project: to find a correlation between the amount of GFP produced and the fluorescence observed.

Model results and RFU data

A simple correlation was done between the model results and the RFU collected.


RFU versus protein results

For the 10 graphs, correlations were generated for each of them using the [http://www.mathworks.com/products/curvefitting/ Curve Fitting toolbox] found in MATLAB.

The results are found in each of the links below. Each curve is shown together with the prediction bounds to a 95% cofidence level and the equation of the curve.
Lactose = 1mM
Lactose = 2mM
Lactose = 3mM
Lactose = 4mM
Lactose = 5mM
Lactose = 6mM
Lactose = 7mM
Lactose = 8mM
Lactose = 9mM
Lactose = 10mM

The curves were fitted once a R-square value of higer than 0.99 was obtained. Although the curves could be successfully obtained there remains some questions to be asked:
1) Is the model results truly representative of the real situation?

2) Will temperature changes have an impact on the results? However it is still possible that our model can account for temperature effects by tweaking the logistic growth input parameter. Recall that our model accounts for varying carrying capacity, and a different temperature could be represented by different paramter values.
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