Team:SFLS Shenzhen/Modeling

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• Porject
  • Description
  • Design
  • Experiments
  • Results
  • Applied Design
• Parts
  • Basic Parts
  • Composite Parts
• Human Practices
  • HP Silver
  • HP Gold
  • Integrated HP
  • Public Engagement
• Modeling
  • Prediction Model
• Team
  • About Our Team
  • Collaborations
  • Notebook
  • Attributions
• Judging
  • Achievement
  • Interlab
  • Improvement

Prediction Model

According to the formula below:
$$F = \phi * I *
as \phi representing the fluorescence efficiency of the tested substance, \CapI representing the intensity of light inserted by the testing machine “Molecular Devices FilterMax F3” per second per squared centimeter, \epsilon representing the fluorescence Molar Absorption Coefficient of the fluorescence substance, and with c representing the the concentration of the sample and \l representing the thickness of the samples. As the setting of the experiment goes, these factors except the concentration and the thickness of the samples are merely the same - which doesn’t count anymore. The relationship between the Fluorescence intensity and the concentration of the sample can be viewed as linear.

As the thickness of the samples differs significantly less than the concentration of the sample does, we don't consider the thickness of the sample a determinant factor.

We therefore predicted that the higher concentration the sample gets, the higher the fluorescence intensity the result might shows. As the design of our project shows: the concentration of the fluorescence substance have to correspond to the existence of both the bio-marker miR-155 and bio-marker miR-10b. So the amount of the fluorescence substance should not differ when the amount of both bio-markers remains the same and should be linear to the amount of both bio-markers when both added in the experiment. Concentration curve using partially purified wtGFP protein. Dilutions of wtGFP protein were made using 10 mM Tris, 10 mM EDTA buffer as the diluent. Samples were read using an FL600 fluorescence plate reader with reader function controlled by KC4 data reduction software on an external PC. Fluorescence was determined using a 400 nm, 30 nm bandpass excitation filter and a 508 nm, 20 nm bandpass emission filter with an instrument sensitivity setting of 175.【1】

Further prediction and evaluation of the effectiveness of the model:

Thus we can predict the relationship between the amount of both biomarker miR-155 and miR-10b added into the sample and the florescence intensity as : 1, When either of the bio-marker does not exist, there will be no such Fluorescence intensity.
2, When both of the bio-marker exist in the sample, the value of fluorescence intensity would act accordingly to the amount of the bio-marker that is lower in the case, and would follow the linear relationship as we have predicted between the concentration of green fluorescence protein and the fluorescence intensity.
We can check our prediction by the graph of the results shows below. We can see in the result that we get below that prediction one is perfectly correct, but the graph doesn’t follow accordingly to prediction two- there is no perfect linear relationship between the amount of the lower-amount biomarker. But this error is allowable. Conclusion: Our model have successively predicted the result of our experiment. But we can also work on exploring why the error existed and improve our experiment in further studies.