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.

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.

@@ Line 7: / Line 7: @@
 =Introduction=
-An important part of the iGEM project is to perform characterization of Biobricks. The concept is simple enough, for the desired promoter a GFP gene is attached behind it and the resulting fluorescence is measured under different conditions. By reviewing the different websites and protocols from different teams, we realise that there are a myriad of methods out there. The problem is that each team may possess different machines or equipment that take fluoresence readings. Here we propose a method
 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.
@@ Line 35: / Line 33: @@
 ) Is the model results truly representative of the real situation? <br>
 ) 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>
-=Initial Concept=
-==Quantifying the concentration of Protein formed==
-In an attempt to quantify the concentration of GFP protein formed relative to the RFU measurements, we took cell samples of varying RFU values and store the cells in minus 20 deg till a corresponding range of RFU values were collected. After which, varying protein standards of known concentration were prepared and a gel was conducted as depicted in the top gel run. As shown in the gel run, with an increasing protein concentration, the thickness of the band will also increase. The next two gel runs in the diagram are for the range of RFU investigated. At higher RFU readings, we would expect a higher protein concentration present. Hence, a thicker band should also be seen. However, for the increasing range of RFU readings, we do not obtain an expected increase in thickness of the band.
-This could be due to the following reasons<br>
-) The increase in RFU was because of the increase in cell number and not the increase in concentration of the GFP protein.<br>
-) The increase in band thickness was not distinctive, as the concentration GFP protein when compared to the other proteins in the cell may be low.<br>
-[[Image:NTU_Characterization_RFU_GEL_run.JPG|thumb|center|700px|Characterization RFU GEL Run]]
-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.
-The experiment showed that the amount of proteins in the cells was alike and this result was interesting in a sense.
-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.
-What this means for us is that the increase in RFU(flouresence)could be linked more closely to the ability for the cell to reproduce under different lactose inoculations, and the models which describe faster or better protein production itself could be flawed.

Team:NTU-Singapore/Parts/Correlation

From 2008.igem.org

Latest revision as of 01:47, 23 October 2008

Introduction

Model results and RFU data