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                 βCsp(T) was obtained by fitting previous studies on related cold shock promoter in human. The curve of βCsp(T) reaches a maximum at 10℃ and a minimum at 20℃ (Fig. 2). In other words, its temperature-responsive range is between 10℃ and 20℃. The value of βHsp(T) is defined by us, reaching a maximum at 37℃ and a minimum at 30℃ (Fig. 3). This setting of parameter is based on certain physiological considerations where the optimal growth temperature of Saccharomyces cerevisiae is 30℃ and where heat shock response is observed at temperatures higher than 37℃. Next, comparing Fig. 2and Fig. 3, it is obvious that the activity range of these two promoters are not overlapping, which is an critical problem to our genetic circuit. We believed that this phenomenon is going to be the flaw of our genetic circuit because two signals produced by two promoters are not able to crosstalk. Hence, the expression profile of GFP along these temperatures might not be changed. This suspect will be proved by simulation in the next paragraph.

                 βCsp(T) was obtained by fitting previous studies on related cold shock promoter in human. The curve of βCsp(T) reaches a maximum at 10℃ and a minimum at 20℃ (Fig. 2). In other words, its temperature-responsive range is between 10℃ and 20℃. The value of βHsp(T) is defined by us, reaching a maximum at 37℃ and a minimum at 30℃ (Fig. 3). This setting of parameter is based on certain physiological considerations where the optimal growth temperature of Saccharomyces cerevisiae is 30℃ and where heat shock response is observed at temperatures higher than 37℃. Next, comparing Fig. 2and Fig. 3, it is obvious that the activity range of these two promoters are not overlapping, which is an critical problem to our genetic circuit. We believed that this phenomenon is going to be the flaw of our genetic circuit because two signals produced by two promoters are not able to crosstalk. Hence, the expression profile of GFP along these temperatures might not be changed. This suspect will be proved by simulation in the next paragraph.

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             <div class="tip"> Fig. 2: Fitting result of βCsp(T). </div>

             <div class="tip"> Fig. 2: Fitting result of βCsp(T). </div>

             <div class="tip"> Fig. 3: Fitting result of βHsp(T) </div>

             <div class="tip"> Fig. 3: Fitting result of βHsp(T) </div>

             <legend><b>Neither Hsp nor constitutive promoter suits our purpose under this circuit structure.</b></legend>

             <legend><b>Neither Hsp nor constitutive promoter suits our purpose under this circuit structure.</b></legend>

                 To better understand the role of Hsp and constitutive promoter in our circuit, we analyze the expression pattern difference between repressor-regulated-Csp and Csp alone. By an steady state approach, we may validate if our genetic circuit is in effect changing the sensitivity of Csp. In order to define "markers" that help us discriminate between "bad" results and "good" results, "GFP maximal concentration" (abbreviated as <b>GFP_max</b>) and "temperature corresponding to half of the maximal concentration of GFP" (abbreviated as T1/2) as taken into consideration (Fig. 4). As the value of GFPmax goes up, we are more able to observe the signal under low temperatures; as T1/2 goes down, the temperature-responsive range of Csp narrows down which implies more <b>sensitive</b>. However, in our constitutive promoter model, the two markers do not become "better". The repressor suppresses the activity of Csp significantly when αR becomes small (Fig. 5). Likewise, the markers of Hsp model are "bad" too. Since the active ranges of Csp and Hsp are not overlapping, expression of GFP cannot be suppressed at all as predicted in the last paragraph (Fig. 6). We are going to solve this problem using another genetic circuit!  

                 To better understand the role of Hsp and constitutive promoter in our circuit, we analyze the expression pattern difference between repressor-regulated-Csp and Csp alone. By an steady state approach, we may validate if our genetic circuit is in effect changing the sensitivity of Csp. In order to define "markers" that help us discriminate between "bad" results and "good" results, "GFP maximal concentration" (abbreviated as <b>GFP_max</b>) and "temperature corresponding to half of the maximal concentration of GFP" (abbreviated as T1/2) as taken into consideration (Fig. 4). As the value of GFPmax goes up, we are more able to observe the signal under low temperatures; as T1/2 goes down, the temperature-responsive range of Csp narrows down which implies more <b>sensitive</b>. However, in our constitutive promoter model, the two markers do not become "better". The repressor suppresses the activity of Csp significantly when αR becomes small (Fig. 5). Likewise, the markers of Hsp model are "bad" too. Since the active ranges of Csp and Hsp are not overlapping, expression of GFP cannot be suppressed at all as predicted in the last paragraph (Fig. 6). We are going to solve this problem using another genetic circuit!  

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             <div class="tip"> Fig. 4: Expression pattern of GFP under various αA and αR－ a constitutive promoter integrated model. The X, Y axis are values of αA and αR scanned. The Z axis is the maximal GFP concentration. The color bar represents the temperature corresponding to half of the maximal GFP concentration</div>

             <div class="tip"> Fig. 4: Expression pattern of GFP under various αA and αR－ a constitutive promoter integrated model. The X, Y axis are values of αA and αR scanned. The Z axis is the maximal GFP concentration. The color bar represents the temperature corresponding to half of the maximal GFP concentration</div>

             <div class="tip"> Fig. 5: Expression pattern of GFP under various αA and αR－ an Hsp integrated model. The X, Y axis are values of αA and αR scanned. The Z axis is the maximal GFP concentration. The color bar represents the temperature corresponding to half of the maximal GFP concentration</div>

             <div class="tip"> Fig. 5: Expression pattern of GFP under various αA and αR－ an Hsp integrated model. The X, Y axis are values of αA and αR scanned. The Z axis is the maximal GFP concentration. The color bar represents the temperature corresponding to half of the maximal GFP concentration</div>

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