Difference between revisions of "Team:Tianjin/Model"

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                                     Because the fluorescent protein we wanted to choose had to be optimized in yeast and had to be given by parts, the following fluorescent proteins in <a href="#2">Figure2 were all what we could choose from.
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                                     Because the fluorescent protein we wanted to choose had to be optimized in yeast and had to be given by parts, the following fluorescent proteins in <a href="#2">Figure2</a> were all what we could choose from.
 
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                                         There were three groups in our experiment. They were blank control group, partial control group and experimental group. After getting all the data, first, we drew a histogram and a scattergram of time and maximum OD<sub>600</sub>&nbsp; values (Figure8, 9). These results were very instructive to experiments that these results told us the best measuring point and the best measuring interval.
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                                         There were three groups in our experiment. They were blank control group, partial control group and experimental group. After getting all the data, first, we drew a histogram and a scattergram of time and maximum OD<sub>600</sub>&nbsp; values (<a href="#8">Figure8, 9</a>). These results were very instructive to experiments that these results told us the best measuring point and the best measuring interval.
 
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                                         Oscillation in KaiC phosphorylation is the best-observed parameter in this system and represents a key state variable for the clock in vivo. Thus we have sought to closely mimic this output in our project. Nakajima et al. <sup><a href="#re6">[6]</a></sup> suggest, given the dual function of KaiC and ‘‘cooperation between KaiA and KaiB,’’ that autonomous oscillation of KaiC phosphorylation might be achieved. We established a model based on known biological and biochemical observations and our experiments that did not involve transcription or translation. In <a href="#14">Figure14, we summarized the key steps of three Kai proteins oscillation when ATP is provided in excess. It was well established that we used three circles to represent all possible combinations of three Kai proteins, just like Mars and its two satellites. This was also why we call it Mars Model.
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                                         Oscillation in KaiC phosphorylation is the best-observed parameter in this system and represents a key state variable for the clock in vivo. Thus we have sought to closely mimic this output in our project. Nakajima et al. <sup><a href="#re6">[6]</a></sup> suggest, given the dual function of KaiC and ‘‘cooperation between KaiA and KaiB,’’ that autonomous oscillation of KaiC phosphorylation might be achieved. We established a model based on known biological and biochemical observations and our experiments that did not involve transcription or translation. In <a href="#14">Figure14</a>, we summarized the key steps of three Kai proteins oscillation when ATP is provided in excess. It was well established that we used three circles to represent all possible combinations of three Kai proteins, just like Mars and its two satellites. This was also why we call it Mars Model.
 
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                                         The model (<a href="#14">Figure 14) contained twelve processes (R1-R12) describing all the protein-protein interactions and phosphorylation-dephosphorylation between the Kai proteins. KaiXY represents KaiX and KaiY compound and KaiC* represents fully phosphorylated KaiC. Process R1, R2 and R3 are six aggregations of KaiC protein, two aggregations of KaiA protein and four aggregations of KaiB protein respectively. In process R4, KaiC<sub>6</sub> binds KaiA<sub>2</sub>, forming KaiA<sub>2</sub>C<sub>6</sub> compound. Since KaiA<sub>2</sub> facilitates the autokinase activity of KaiC<sub>6</sub>, KaiA<sub>2</sub>C<sub>6</sub> first converts to partial phosphorylated form, KaiA<sub>2</sub>C<sub>6</sub>C<sub>6</sub>*, by process R5, and then rapidly converts to fully phosphorylated form, KaiA<sub>2</sub>C<sub>6</sub>*, by process R10. Then, fully phosphorylated protein KaiA<sub>2</sub>C<sub>6</sub>* combines with KaiB<sub>4</sub>, forming KaiA<sub>2</sub>B<sub>4</sub>C<sub>6</sub>*, by process R6. In process R7, KaiA<sub>2</sub> is displaced from KaiA<sub>2</sub>B<sub>4</sub>C<sub>6</sub>*. When KaiA<sub>2</sub> no longer exists in KaiA<sub>2</sub>B<sub>4</sub>C<sub>6</sub>*, KaiB<sub>4</sub> dissociates from KaiB<sub>4</sub>C<sub>6</sub>*, by process R8. Process R9, R11, and R12 are depolymerization of KaiC<sub>6</sub>, KaiA<sub>2</sub> and KaiB<sub>4</sub> protein, respectively<sup><a href="#re7">[7]</a></sup>.
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                                         The model (<a href="#14">Figure 14</a>) contained twelve processes (R1-R12) describing all the protein-protein interactions and phosphorylation-dephosphorylation between the Kai proteins. KaiXY represents KaiX and KaiY compound and KaiC* represents fully phosphorylated KaiC. Process R1, R2 and R3 are six aggregations of KaiC protein, two aggregations of KaiA protein and four aggregations of KaiB protein respectively. In process R4, KaiC<sub>6</sub> binds KaiA<sub>2</sub>, forming KaiA<sub>2</sub>C<sub>6</sub> compound. Since KaiA<sub>2</sub> facilitates the autokinase activity of KaiC<sub>6</sub>, KaiA<sub>2</sub>C<sub>6</sub> first converts to partial phosphorylated form, KaiA<sub>2</sub>C<sub>6</sub>C<sub>6</sub>*, by process R5, and then rapidly converts to fully phosphorylated form, KaiA<sub>2</sub>C<sub>6</sub>*, by process R10. Then, fully phosphorylated protein KaiA<sub>2</sub>C<sub>6</sub>* combines with KaiB<sub>4</sub>, forming KaiA<sub>2</sub>B<sub>4</sub>C<sub>6</sub>*, by process R6. In process R7, KaiA<sub>2</sub> is displaced from KaiA<sub>2</sub>B<sub>4</sub>C<sub>6</sub>*. When KaiA<sub>2</sub> no longer exists in KaiA<sub>2</sub>B<sub>4</sub>C<sub>6</sub>*, KaiB<sub>4</sub> dissociates from KaiB<sub>4</sub>C<sub>6</sub>*, by process R8. Process R9, R11, and R12 are depolymerization of KaiC<sub>6</sub>, KaiA<sub>2</sub> and KaiB<sub>4</sub> protein, respectively<sup><a href="#re7">[7]</a></sup>.
 
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                                     Input reaction rate constants <em>k<sub>1</sub>-k<sub>12</sub></em> and initial concentration of every protein, oscillatory curve of every protein could be obtained as shown in <a href="#16">Figure16.
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                                     Input reaction rate constants <em>k<sub>1</sub>-k<sub>12</sub></em> and initial concentration of every protein, oscillatory curve of every protein could be obtained as shown in <a href="#16">Figure16</a>.
 
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                                     From <a href="#16">Figure16 it was known that although the peak time of each protein varies, the oscillation period of every protein is the same. Therefore, in the following analysis, we take KaiC as an example to show the change of periods.
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                                     From <a href="#16">Figure16</a> it was known that although the peak time of each protein varies, the oscillation period of every protein is the same. Therefore, in the following analysis, we take KaiC as an example to show the change of periods.
 
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                                     (1)Assume the reaction rate constants change proportionally with temperature changing, the period of protein oscillation shortens as shown in <a href="#17">Figure17.<br>
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                                     (1)Assume the reaction rate constants change proportionally with temperature changing, the period of protein oscillation shortens as shown in <a href="#17">Figure17</a>.<br>
 
                                     (<em>Note: All the blue curves represent the initial data and red curves represent the revised data.</em>)
 
                                     (<em>Note: All the blue curves represent the initial data and red curves represent the revised data.</em>)
 
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                                         In the following table, we list the impact of <em>k<sub>5</sub></em>-<em>k<sub>12</sub></em> changing to the oscillation. It can be seen in <a href="#22">Figure22 that <em>k<sub>5</sub></em> and <em>k<sub>6</sub></em> have great influence on the disappearance of the oscillation. The impact of <em>k<sub>5</sub></em>-<em>k<sub>8</sub></em> may relates to phosphorylation and temperature changing as well as other factors. Therefore, figuring out biological factors related to these reaction rate constants is one of our future work.
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                                         In the following table, we list the impact of <em>k<sub>5</sub></em>-<em>k<sub>12</sub></em> changing to the oscillation. It can be seen in <a href="#22">Figure22</a> that <em>k<sub>5</sub></em> and <em>k<sub>6</sub></em> have great influence on the disappearance of the oscillation. The impact of <em>k<sub>5</sub></em>-<em>k<sub>8</sub></em> may relates to phosphorylation and temperature changing as well as other factors. Therefore, figuring out biological factors related to these reaction rate constants is one of our future work.
 
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Revision as of 06:43, 17 October 2018

<!DOCTYPE html> Team:Tianjin - 2018.igem.org

MODEL

Overview

The models we built included four parts. First, we established a fluorescent protein model to screen out the most suitable fluorescent protein, the main modeling method here is grayscale analysis. Then, for the large amount of measured OD values, we drew the growth curve of yeasts and it fitted logistic model. It described the growth situation of the yeasts after plasmid introduction, and we compare it with yeasts without any foreign plasmid. The growth curve also offers the best measuring point and the best measuring interval. What’s more, we drew the degradation curve of the fluorescent protein, which helps us know different characteristics of the two chosen fluorescent proteins better. Finally, we constructed a model to illustrate the oscillation of KaiA, KaiB and KaiC protein called Mars Model, it explained the reason why the cycle reduced in yeasts nicely. Modeling work integrated with experiments tightly made our project complete and convincing.