Objective

This model aims to find out how much FRET protein should be added into our screening system.

Method

Chemical equilibrium is used to determine the florescence level and the minimal FRET protein activity required to produce the florescence that can be detected. This model assumes the portion of active protein in all protein is constant.

Result

The optimal ratio of the amount of one FRET protein to that of the other is the following:

The figures in the table indicates the optimal ratio of the protein on the top over the protein on the left.

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Objective

The efficiency of FRET is inversely proportional to the sixth power of the distance between donor and acceptor, making FRET extremely sensitive to small changes in distance. Therefore, simulating structure of protein-protein interactions is important. This model aims to predict the FRET efficiency in order to determine which molecules and which terminals should be used.

Method

pyDockWEB is used for structural prediction of protein-protein interactions and the prediction of distances between donor and acceptor. Förster theory is used determine the FRET efficiency.

Result

The FRET efficiency is given by the following table.

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[3]

Objective

We made a gene that connects DKK1 promoter to mCherry. The gene expression rate of DKK1 promoter is affected by testosterone activity and affects the expression level of mCherry. The expression level of mCherry should be greater than a threshold so that its florescence can be detected by devices. In order to achieve this threshold, sufficient amount of testosterone should be in our screening system; this is the sensitivity of our screening system. This model aims to find out the sensitivity of our screening system.

Method

We simulate the kinetics of transcription signal and then simulate the expression of DKK1. Testosterone efficiency is used in this model instead of testosterone activity. Florescence decay is not considered in this model because ……..

Result

[DKK1] = 5 + 0.006 [DHT]^2

where [DKK1] indicate activity in ng/ml and [DHT] indicates activity in nM.

[mCherry] = c_1 + c_2 [Testosterone]^2

where square brackets indicate activity in M, and c_1 and c_2 are constants to be determined.

Note: This model is accurate only when [DHT] is less than 50 nM.

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[2]

Objective

Type I 5-alpha-reductase is presented in DP cells instead of type II when cultured in vitro, [1] but their efficiency in converting testosterone to DHT differ greatly. Since we want to simulate the in vivo behaviors of DP cells, we transfect an extra plasmid containing type II 5-alpha-reductase gene. The promoter should be chosen to produce the exact activity of type II 5-alpha-reductase we want. Therefore, we use a model to find out which promoter should be chosen.

Method

We compare the strength of a variety of promoters, determined the strength we need and find the corresponding promoter.

Result

The strength we want is _______, and the corresponding promoter is _______.

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1. Shicheng Liu, Hitoshi Yamauchi. (2008). "Different patterns of 5-alpha-reductase expression, cellular distribution, and testosterone metamolism in human follicular dermal papilla cells." Biochemical and Biophysical Research Communications 368 (2008) 858–864
2. Jane Yuxia Qin (2010). "Systematic Comparison of Constitutive Promoters and the Doxycycline-Inducible Promoter." PLOS ONE. May 12, 2010. https://doi.org/10.1371/journal.pone.0010611
3. Meehan KL1, Sadar MD. (2003). "Androgens and androgen receptor in prostate and ovarian malignancies." Front Biosci. 2003 May 1;8:d780-800.