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Based on modeling work, we find that negative feedback can contribute to the network’s adaptation to DNA copy number variation. So we analysed the following three-node networks, A and B. The difference between A and B is the output nodes of network A has negative feedback.
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We transfected this two circuits into mammalian cells (Hela cell). By testing the mean value of EYFP (Enhance Yellow Fluorescent Protein, the output of our circuits), and the relationship between the EYFP and the DNA copy number, we can prove the hypothesis.
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We constructed the following circuit A ,B and C .The circuit A corresponds to the network A ,while the circuit B is the implementation of network B. Circuit C is used as a control design to testify the function of A and B.
 
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In circuit A, as we can see, the input is miR-21, which can repress the plasmid pz371 and K1116002(The plasmid’s information can be found in parts). K1116002 induced by rtTA and Dox, serves as an auxiliary node, producing the LacI gene to inhibit the expression of EYFP. EYFP(Enhance Yellow Fluorescent Protein )is used as output. Besides, the miR-FF3 restrains the expression of LacI. The reason that we get the most of post-transcriptional control can be seen in Supplementary text.
 
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                In circuit B, however, the plasmid K1116003 does not have FF3 target, leading to the contrast between circuit A and B. We can see miR-21 can’t target at pZ349 and pZ331 in circuit C , that is, there is no input in circuit C. The miR-21, used to distinguish cancer cell from normal cells ,is endogenous in Hela cell.
 
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miRNAs function as posttranscriptional regulators which have distinguished features compared to transcriptional regulators, intervening late in gene expression process, with the capability to counteract variation from the upstream processes (Margaret et al., 2012).
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Research shows that while conducting experiment on an incoherent feedforward motif in mammalian cells, posttranscriptional regulation results in superior adaptation behavior, higher absolute expression levels and lower intrinsic fluctuations (Bleris et al., 2011).
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miRNAs can serve as buffers against variation during gene expression; transient increases in transcription factor activity would propagate to increases in target miRNA transcription while would be counteracted by increased miRNA and vice versa. Therefore, under the miRNA posttranscriptional regulation, protein output can be uncoupled from fluctuations in transcription factor concentration or activity (Margaret et al., 2012).
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miRNAs also possess good stability which, consistent with theoretical constraints, meets the need for enough molecules of a regulator to achieve a small reduction in the noise of a target gene (Lestas et al., 2010).
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                                We took advantage of another fluorescent protein(mkate ) as reference gene, which has no influence in our design. Published literature generally supports the view that in transient transfections, fluorescence depends linearly on the copy number of transfected plasmids (Tseng et al, 1997; Pollard et al, 1998;Cohenet al,2009; Schwakeet al, 2010). While strictly speaking, this reporter level also depends on many other potentially fluctuating parameters such as global synthesis and degradation rates(Leonidas Bleris et al,2011),it is more legitimately use the normalized quotient to instead of the value of EYFP.
 
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                                We first checked the function of the above-mentioned three circuit. The results shows that circuit A,B and C works as expected in Figure 1.
 
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Apparently, the output of circuit C is lower than circuit B .Having the negative feedback compared with circuit B, the expression of EYFP in circuit A is strongest. 
 
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                                Then we analysed the output of constructed designs varies with the DNA copy number. Facing with the difficulty of counting the copy number directly, we employed the reference gene to reflect .We think the copy number is high when the expression intensity of mkate is strong. One hundred thousand positive Hela cells was collected to obtain the relationship between EYFP and make.
 
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From the figure 2, we learned with the increase of the expression intensity of makte, the specific valve of EYFP and mkate decreases. In another word, the circuit A’s output reaches saturation fast with the increase of copy number. We came to a conclusion circuit A’s adaptation to DNA copy number is higher than circuit B’s. So, the negative feedback works.
 
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Due to some restrictions in wetlab, we only finished the above-mentioned experiment. We found that the number of Hela cells who possesses high copy number is  comparatively low. We also noticed the circuit C’s output is higher than expected in Figure 1.This may cause wrong judge when use the design to detect miR-21. Some measures will be taken to solve this question.
 
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        Besides, we are going to endeavor to construct the other networks mentioned in modeling work.
 
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Optimal network functions as a smart and reliable sorter, while ordinary network may make more mistakes sorting different cells. Which one is better?

                                         

                                         

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