Difference between revisions of "Team:BIT-China/Model/GPCRpathway"

Latest revision as of 13:50, 1 November 2017

BIT-CHINA

Mating pheromone pathway model

We expected to use mRFP intensity to predict the sweetness based on our system. So we needed an insight into relationship between mRFP and sweetness. But lots of factor can impact the signal output so that we decided to divide our system into two parts, single cell model and yeast growth model.

Single cell model

Purpose

To simulate RFP intensity under different sweetness, we needed to set a model in a single cell firstly. By establishing this model, we could learn about how the sweetness signal transmit in the in yeast coupling pheromone pathway^[3], and know each step of the signal transmit in detail, which provides supports for regulating the signal and improving our bio-meter.

Single cell model:

In single cell model, we pay main attention to the signal transduction in pheromone pathway based on^[4].And in order to simulate the signal transduction in mathematical way conveniently, we set some hypothesizes of this model:

1. We assumed that T1R2/T1R3 receptor does not have synergistic effect when it binds with sweeteners.

2. We hypothesized that the number of binding sweetener are consistent when binding to T1R2/T1R3 receptor or pheromone receptor.

3. We supposed that the combining rate and the initial binding concentration of sweetener are as same as pheromone receptor’s.

4. There is no influence between cell growth and protein expression in a single cell.

5. Only concern conservation relations of protein concentration in a single cell. The protein involving in the signal transduction is not considers its production or degradation.

Method and discussion

Fig 8． Sweetness testing pathway in Sugar Hunter

In order to simulate our project systematically, we divided our whole system into four blocks: (a) the activation of T1R2/T1R3 receptor; (b) the activation of G-protein cycle; (c) the cascade reaction of MAPK; and (d) the expression of RFP. And the simulating process and result of each part were shown below.

1. The activation of T1R2/T1R3 receptor:

Fig. 9 The mechanism of T1R2/T1R3 receptor’s activation

In this process, T1R2/T1R3 receptor has four different states. And the receptor transfer between these states under the different sweetener-binding conditions. The equations of this process were shown as follow:

(T1R2/3: the T1R2-T1R3 receptor. All parameters of this part were listed in Table 1)

Table 1． The value of parameters in activation of T1R2/T1R3 receptor
Parameter	Description	Value
k₁	Rate constant of sweetness binding on receptor	0.0012
k₂	Rate constant of activated receptor	0.6
k₃	Rate constant of downregulated receptor	0.24
k₄	Rate constant of receptor degradation	0.024

The result of T1R2/T1R3 receptor’s activation was shown below(Fig. 10). It demonstrated that T1R2/T1R3 receptor could respond to different concentration of ligand.

Fig. 10． The simulating result of receptor’s activation under the different concentration of ligand

2. The activation of G-protein cycle:

Fig. 11 The process of G-protein cycle’s activation

After upstream signal was produced, the activated G exchanges GTP in place of GDP^[5]. Then the G and G_βγ dimer are dissociated from receptor and then active downstream pathway. Here, we selected the G_βγ dimer as the output of this part. And the equations of this process were listed as follow:

(The parameters of this part were listed in Table 2)

Table 2． The value of parameters in G-protein cycle’s activation
Parameter	Description	Value
k₅	Rate constant of G_αβγ's dissociated	0.0036
k₆	Rate constant of G_αβγ's Synthetized	2000
k₇	Rate constant of G_βγ's bind with Ste5	0.1
k₈	Rate constant of G_βγ's unbind with Ste5	5

The result of G-protein cycle’s activation was shown below (Fig. 12). According to the figure, we indicated that our system could transduce upstream signal accurately.

Fig. 12． The result of G-protein cycle’s activation under the different concentration of ligand

The cascade reaction of MAPK:

Fig. 13 The cascade reaction of MAPK

All proteins in this part belong to the category of kinase and the signal was transmitted through phosphorylation. Finally, Fus3 activates the expression of Ste12 which was regarded as the output of this part. And all equations in this process were listed as follow:

(The parameters of this part were listed in Table 3)

Table 3． The value of parameters in cascade reaction of MAPK
Parameter	Description	Value
k₇	Rate constant of G_βγ's bind with Ste5	0.1
k₈	Rate constant of G_βγ's unbind with Ste5	5
k₉	Rate constant of Ste11's Phosphorylated	10
k₁₀	Rate constant of Ste7's double Phosphorylated	47
k₁₁	Rate constant of Fus3's double Phosphorylated	345
k₁₂	Rate constant of double Phosphorylated Fus3's dissociation.	140
k₁₃	Rate constant of double Phosphorylated Fus3's synthesis.	260
k₁₄	Rate constant of Fus3's dephosphorylated	50
k₁₅	Rate constant of double pp-Fus3's bind with Ste12	18
k₁₆	Rate constant of double pp-Fus3's unbind with Ste12	10

The result of the cascade reaction of MAPK was shown as follow (Fig. 14).

Fig. 14 The result of Ste12’s activation under the different concentration of ligand

4. Expression of mRFP:

Fig. 15 The process of RFP’s expression

Ste12 could accept signal from upstream pathway, it leads to the activation of relevant promoter P_fus and expression of downstream gene. There we regarded the expression of RFP as the output. The equations in this process were listed as follow:

(The parameters of this part were listed in Table 4)

Table 4． The value of parameters in RFP expression
Parameter	Description	Value
k₁₇	Rate constant of mRFP_mRNA Synthetize	0.382
k₁₈	Rate constant of mRFP_mRNA Degradation	8.39
k₁₉	Rate constant of nascent RFP synthetize	0.012
k₂₀	Rate constant of mature mRFP synthetize	0.0012
k₂₁	Rate constant of mature mRFP degradation	0.018

Result

Integrating four models of each block, we obtained completed result about signal transduction in single cell. The result was shown as follow (Fig. 16).

Fig. 16． The result of RFP intensity under different concentration ligand Our modeling result exhibited that different concentration of ligand could result in different RFP intensity, which demonstrated that our system could response to the different signal strength specifically. And it also demonstrated that our system could work in a single cell in theory.

Yeast growth model

Purpose

After constructing the model of signal transduction in a single cell, we considered to combine single cell model with the growth of yeast to simulate our system’s practical condition. So in this part, we looked forward to construct a simple model to describe the growth of yeast cells and provided some bases to the next step.

Method

Practical data measurement

We refered the model established by Imperial College 2016. This model was used to describe the growth condition of two kinds of cell which are competitive in a limit culture.

We re-proposed some hypotheses to fit our system.

1. The condition of cell was divided into two states, activated and non-activated, and there is no conversion between two states. Each state of cell consume the nutrition independently.

2. Only the activated state could combine sweetener.

3. The nutrition in culture was limited.

4. Each group cell had same growth condition.

Then we set the ODEs as following:

(The parameters of this model were listed in Table 5)

Table 5． The value of parameter in yeast growth part
Parameter	Description	Value
r₁	Rate of non-active yeast growth	1
r₂	Rate of active yeast growth	2
n₁	Culture time for non-active yeast	30
n₂	Culture time for active yeast	30
s₁	Rate constant of nutrition consumption for non-active yeast	0.45
s₂	Rate constant of nutrition consumption for active yeast	2

Result

The result of yeast growth model was showed as follow. (Fig. 17)

Fig. 17 The result of yeast cell growth model

Discussion

Analyzing the trend of the curve, it shown that our model can simulate the growth of yeast in some certain condition. And we considered activated cell as effective bio-meter in our project.

Combination Model coupling single cell and yeast growth

Based on the above results, we combined these two models together to simulate the performance of whole system in population level. We multiplied the value of Yeast_active and the value of RFP_mature directly.

And we altered some parameters to fit the experimental data.

The result of this combined model was shown below (Fig. 18).

Fig. 18 The simulation result of RFP intensity in population level.

Discussion

Combining two models, we discovered that the RFP intensity is almost the same as base line (value is 0) at the beginning. And after 15 hours, the fluorescence intensity were reflected from single cell level to population level. because the cells entered into stationary phase.

As for the curve after 22 hours, the RFP intensity starts to decrease slowly in all ligand concentration. It may due to the death of cells.

We decided to select the RFP intensity at 22 hours as the final output value of sweetness signal based on our model. In order to avoid the cell growth impacts the RFP intensity, we selected this specific moment as the sampling time.

SWEETENESS MODEL

Purpose

We expected to set a model based on our above model to simulate the RFP intensity of sweetener. Make a comparison between the results of simulation and practical measurement, Our system could not only work like people gustatory sensation system with universality but also is more accurate and less interference than people.

Method

Although we finished the GPCR model, the combination rate between the sweetener and receptor had not reported or measured yet. So we needed to measure this data from wet lab.

But because of the instability of our system, we only got a useful group of data. More discussion was established in Project Page. The result was shown (Table 6). Then we utilized these values to optimize our pheromone model.

Table 6． The result of sweetness measurement using Sugar Hunter at 16 hours
Group Name	RFP intensity/ unit
2% Sucrose	42.5
0% Sucrose (Control)	27.5

Analyzing previous methods of sweetness measurement, we made two assumptions for this sweetness model:

1. Sweetness of all sweeteners could be transformed into the different concentration of standard sucrose (10% dissolving in water) with the same sweetness.

2. The bind between different ligands and GPCR will not impact signal transduction in pheromone pathway.

Based on these, the most significant work is to find out the RFP intensity corresponding to the standard sucrose.

Combining the experimental data, we made a simple calculation and got the RFP intensity of standard sweetness amounting to the fluorescence intensity induced by 750nM ideal ligand in our model.

Fig. 20． The relationship between sweetness and ideal ligand

We set a correction factor K_corret=750 on calculation result. Then we reset the input according to following equation:

Then we prefered to predict different sweetness of sweetener. The sweetness data was obtained from previous study^[7]. (Table 7．)

Table 7． The sweetness of sweetener measured by people taste
Sweetener	Sweetness
Sucrose	1
Aspartame	200
Stevioside	150
Sucralose	600
Glycyrrhizic acid	170
Acesulfame	200
Cyclamate	30

Result

The result of simulated RFP intensity induced by different sweeteners was shown in Fig. 20．

Fig. 21． The modeling result of RFP intensity induced by different sweeteners

Discussion

After 22 hours, the peak of all curve were displayed as the pheromone model. And different sweetness also could induce different fluorescence intensity. But this model still need optimization in further, based on the following aspects:

(1) Through molecular simulation to discover the combination constant of sweetener binding process.

(2) The correct factor is to be considered the molecular weight, binding sites and combination numbers of the sweetener.

(3) Combine the protein expression and cells growth together. Consider the interaction between two models.

SUMMARY

Based on our models, we successfully got following results:

1. We simulated the structure of T1R2/T1R3 receptor and bound it with different sweeteners to observe the combining situation of different sweeteners through molecular docking.

2. We constructed the signal transduction model to simulate pheromone response pathway in two different levels and proved that our system could work as we expected.

3. We also provided an ideal relationship between RFP intensity and sweetness based on the wet lab data and our signal transduction model , which also demonstrated that our system could detect different range of sweetness. Although sweeteners are various, but we still can taste all of them and sense different sweetness due to the powerful sweetness receptors, T1R2/T1R3. Based on our model, we successfully proved that using sweetness receptor as a “meter” and coupling the signal pathway to determine the sweetness in yeast cell is available. Last, let’s set foot on the trip of beating sweet-monsters in parallel space with our

Sugar Hunter!!!

References

1. Dubois G E. Molecular mechanism of sweetness sensation.[J]. Physiology & Behavior, 2016, 164(Pt B):453.

2. Nie Y, Vigues S, Hobbs J R, et al. Distinct contributions of T1R2 and T1R3 taste receptor subunits to the detection of sweet stimuli.[J]. Current Biology Cb, 2005, 15(21):1948-52.

3. Richardson, Kathryn. Mechanisms of GPCR signal regulation in fission yeast[J]. University of Warwick, 2014.

4. Kofahl B, Klipp E. Modelling the dynamics of the yeast pheromone pathway.[J]. Yeast, 2004, 21(10):831.

5. Audet M, Bouvier M. Restructuring G-Protein- Coupled Receptor Activation [J]. Cell, 2012, 151(1):14-23.

6. Carocho M, Morales P, Icfr F. Sweeteners as food additives in the XXI century: A review of what is known, and what is to come[J]. Food & Chemical Toxicology An International Journal Published for the British Industrial Biological Research Association, 2017, 107.

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 <style>
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+          height: auto;
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 	    <section class="content_container" id="mytop">
 			<h2 class="title-h2">Mating pheromone pathway model</h2>
-				<p class="my-content-p">We expected to use mRFP intensity to predict the sweetness based on our system. So we needed an insight into relationship between mRFP and sweetness. But lots of factor can impact the signal output so that we decided to divide our system into two parts, single cell model and yeast growth model. </p>
+				<p class="my-content-p">We expected to use mRFP intensity to predict the sweetness based on our system. So we needed an insight into relationship between mRFP and sweetness. But lots of factor can impact the signal output so that we decided to divide our system into two parts, <b>single cell model</b> and <b>yeast growth model</b>. </p>
@@ Line 20: / Line 21: @@
 				<h3 class="title-h3">Single cell model</h3>
 				<h4 class="title-h4">Purpose</h4>
-				<p class="my-content-p">To simulate mRFP intensity under different sweetness, we needed to set a model in a single cell firstly. By establishing this model, we can understand how does the sweetness signal transmit in the in yeast mating pheromone pathway[3], and know the details of each step of the signal transmit thorough which provides a help for us to regulate the signal, and for improving our bio-meter.</p>
+				<p class="my-content-p">To simulate RFP intensity under different sweetness, we needed to set a model in a single cell firstly. By establishing this model, we could learn about how the sweetness signal transmit in the in yeast coupling pheromone pathway<sup>[3]</sup>, and know each step of the signal transmit in detail, which provides supports for regulating the signal and  improving our bio-meter.</p>
 				<h4 class="title-h4">Single cell model:</h4>
-				<p class="my-content-p">In single cell model, we pay main attention on the signal transduction in pheromone pathway based on the work from Dubois G E[2]. And for simulating the signal transduction in mathematical way, we set some hypothesizes of this model:</p>
+				<p class="my-content-p">In single cell model, we pay main attention to the signal transduction in pheromone pathway based on<sup>[4]</sup>.And in order to simulate the signal transduction in mathematical way conveniently, we set some hypothesizes of this model:    </p>
-					<li class="my-content-li2">1. We assume that T1R2/T1R3 receptor does not have synergistic effect when sweeteners bind.</li>
+					<li class="my-content-li2">1. We assumed that T1R2/T1R3 receptor does not have synergistic effect when it binds with sweeteners.</li>
-					<li class="my-content-li2">2. We hypothesize that the binding number of sweetener likes pheromones receptor are consistent when it binds to T1R2/T1R3.</li>
+					<li class="my-content-li2">2. We hypothesized that the number of binding sweetener are consistent when binding to T1R2/T1R3 receptor or pheromone receptor.</li>
-					<li class="my-content-li2">3. We assume that the combination rate and the initial concentration of sweetener binding is the same as pheromone receptor.</li>
+					<li class="my-content-li2">3. We supposed that the combining rate and the initial binding concentration of sweetener are as same as pheromone receptor’s.</li>
-					<li class="my-content-li2">4. There is no influence between the cell growth and protein expression in a single cell.</li>
+					<li class="my-content-li2">4. There is no influence between cell growth and protein expression in a single cell.</li>
-					<li class="my-content-li2">5. Only concern conservation relations of protein concentration in a single cell. The protein involving in the signal transduction is not considers its production or degradation. </li>
+					<li class="my-content-li2">5. Only concern conservation relations of protein concentration in a single cell. The protein involving in the signal transduction is not considers its production or degradation.  </li>
 				<h4 class="title-h4">Method and discussion </h4>
 				<div class="my-content-box">
-	                <img class="formula50" src="https://static.igem.org/mediawiki/2017/b/b3/T--BIT-China--2017modeling_pic8.png" />
+	                <img class="formula50" src="https://static.igem.org/mediawiki/2017/8/85/T-BIT-China-2017MODEL-16.png" />
-	                <span>Fig 8． The schematic diagram of Sugar hunter signal pathway</span>
+	                <span>Fig 8． Sweetness testing pathway in Sugar Hunter</span>
           		</div>
-				<p class="my-content-p">We established the reaction kinetic equations between variable states of protein based on conservation relations law. Use ordinary differential equations (ODEs) to describe the signal (the different states of protein) variation. We remade the pheromone signaling transduction in yeast MAPK pathway being divided into four modules: T1R2/T1R3 receptor activation, G-protein cycle activation, the MAPK cascade, and expression of mRFP. (Fig 8.) </p>
+				<p class="my-content-p">In order to simulate our project systematically, we divided our whole system into four blocks: (a) the activation of T1R2/T1R3 receptor; (b) the activation of G-protein cycle; (c) the cascade reaction of MAPK; and (d) the expression of RFP. And the simulating process and result of each part were shown below.  </p>
-				<p class="my-content-p">Next, we will introduce four parts in detail.  </p>
-				<p class="my-content-p">T1R2/T1R3 receptor activation part:</p>
+				<p class="my-content-p">1. The activation of T1R2/T1R3 receptor:</p>
 				<div class="my-content-box">
-	              <img class="formula50" src="https://static.igem.org/mediawiki/2017/a/ad/T--BIT-China--2017modeling_pic9.png" />
+	              <img class="formula50" src="https://static.igem.org/mediawiki/2017/f/fc/T-BIT-China-2017MODEL-17.png" />
-	              <span>Fig. 9 The conventional diagram of T1R2-T1R3 receptor activation reaction </span>
+	              <span>Fig. 9 The mechanism of T1R2/T1R3 receptor’s activation</span>
 	            </div>
 				<div class="my-content-box">
-					<p class="my-content-p">We divided this activation process of inducing signal into four states of T1R2-T1R3 as shown in Fig. 9． And ODEs are shown follow.</p>
+					<p class="my-content-p">In this process, T1R2/T1R3 receptor has four different states. And the receptor transfer between these states under the different sweetener-binding conditions. The equations of this process were shown as follow:</p>
-					<img class="formula2" src="https://static.igem.org/mediawiki/2017/6/67/T--BIT-China--2017modeling_pic22.png" alt="">
+					<img class="formula2" src="https://static.igem.org/mediawiki/2017/a/a4/T--BIT-China--2017formula_pic1.png" alt="">
 				</div>
   				<div class="my-img-box">
-  					<span>(T1R2/3: the T1R2-T1R3 heterodimer)</span>
+  					<span>(T1R2/3: the T1R2-T1R3 receptor. All parameters of this part were listed in Table 1)</span>
   				</div>
-				<p class="my-content-p">The parameters of this part are listed in the Table1.</p>
 				<div class="my-img-box" style="justify-content: flex-start;">
 		            <table class="table-co">
-		                <caption>Table 1.  The sweeteners added for detecting function of the system </caption>
+		                <caption>Table 1． The value of parameters in activation of T1R2/T1R3 receptor </caption>
 		                <thead>
 		                    <tr>
@@ Line 61: / Line 63: @@
 		                      <th>Description</th>
 		                      <th>Value</th>
-		                      <th>Unit</th>
 		                    </tr>
 		                </thead>
@@ Line 67: / Line 69: @@
 		                    <tr>
 		                        <td>k<sub>1</sub></td>
-		                        <td>Rate constant of sweetness bind on receptor</td>
+		                        <td>Rate constant of sweetness binding on receptor</td>
 		                        <td>0.0012</td>
-		                        <td>min<sup>-1</sup>nM<sup>-1</sup></td>
 		                    </tr>
 		                    <tr>
 		                        <td>k<sub>2</sub></td>
-		                        <td>Rate constant of receptor is not activated</td>
+		                        <td>Rate constant of activated receptor</td>
 		                        <td>0.6</td>
-		                        <td>min<sup>-1</sup></td>
 		                    </tr>
 		                    <tr>
 		                      <td>k<sub>3</sub></td>
-		                      <td>Rate constant of sweetness Unbind on receptor</td>
+		                      <td>Rate constant of downregulated receptor</td>
 		                      <td>0.24</td>
-		                      <td>min<sup>-1</sup></td>
 		                    </tr>
 		                    <tr>
@@ Line 87: / Line 89: @@
 		                    	<td>Rate constant of receptor degradation</td>
 		                    	<td>0.024</td>
-		                    	<td>min<sup>-1</sup></td>
 		                    </tr>
 		                </tbody>
@@ Line 96: / Line 98: @@
-				<p class="my-content-p">The output of the T1R2/T1R3 receptor activation part is showed. (Fig. 10)</p>
+				<p class="my-content-p">The result of T1R2/T1R3 receptor’s activation was shown below(Fig. 10). It demonstrated that T1R2/T1R3 receptor could respond to different concentration of ligand.</p>
 				<div class="my-content-box">
 		            <img class="formula50" src="https://static.igem.org/mediawiki/2017/d/d4/T--BIT-China--2017modeling_pic10.png" />
-		            <span>Fig. 10． The output signal, activated state of T1R2-T1R3 under different concentration ligand</span>
+		            <span>Fig. 10． The simulating result of receptor’s activation under the different concentration of ligand</span>
 		         </div>
-				<p class="my-content-p">As we can see, the receptor can sense different concentration of ligand and release correspond output.</p>
-				<h4 class="title-h4">G-protein cycle activation part:</h4>
+				<h4 class="title-h4">2. The activation of G-protein cycle:</h4>
 				<div class="my-content-box">
-	              <img class="formula50" src="https://static.igem.org/mediawiki/2017/8/80/T--BIT-China--2017modeling_pic11.png" />
+	              <img class="formula50" src="https://static.igem.org/mediawiki/2017/f/fa/T-BIT-China-2017MODEL-18.png" />
-	              <span>Fig. 11 The schematic diagram of G-protein cycle activation</span>
+	              <span>Fig. 11 The process of G-protein cycle’s activation</span>
 	          	</div>
 				<div class="my-content-box">
-					<p class="my-content-p">After signal produced, the GDP is replaced by GTP in G [5]. Then the Gβγ subunits can be released from membrane to active the downstream protein. Here, we selected the Gβγ as the output of this part. And the reaction equations of this process are listed follow. </p>
+					<p class="my-content-p">After upstream signal was produced, the activated G exchanges GTP in place of GDP<sup>[5]</sup>. Then the G and G<sub>βγ</sub> dimer are dissociated from receptor and then active downstream pathway. Here, we selected the G<sub>βγ</sub> dimer as the output of this part. And the equations of this process were listed as follow: </p>
-					<img class="formula2" src="https://static.igem.org/mediawiki/2017/0/0c/T--BIT-China--2017modeling_pic23.png" alt="">
+					<img class="formula2" src="https://static.igem.org/mediawiki/2017/3/32/T--BIT-China--2017formula_pic2.png" alt="">
 				</div>
+				<p class="my-content-p">(The parameters of this part were listed in Table 2)</p>
-				<p class="my-content-p">The parameters of this part are listed in the Table2.</p>
 				<div class="my-img-box" style="justify-content: flex-start;">
 		            <table class="table-co">
-		                <caption>Table 2． The value of parameter in G-protein cycle activation part</caption>
+		                <caption>Table 2． The value of parameters in G-protein cycle’s activation</caption>
 		                <thead>
 		                    <tr>
@@ Line 128: / Line 128: @@
 		                      <th>Description</th>
 		                      <th>Value</th>
-		                      <th>Unit</th>
 		                    </tr>
 		                </thead>
@@ Line 134: / Line 134: @@
 		                    <tr>
 		                        <td>k<sub>5</sub></td>
-		                        <td>Rate constant of G<sub>αβγ</sub> dissociated</td>
+		                        <td>Rate constant of G<sub>αβγ</sub>'s dissociated</td>
 		                        <td>0.0036</td>
-		                        <td>min<sup>-1</sup>nM<sup>-1</sup></td>
 		                    </tr>
 		                    <tr>
 		                        <td>k<sub>6</sub></td>
-		                        <td>Rate constant of G<sub>αβγ</sub> Synthetized</td>
+		                        <td>Rate constant of G<sub>αβγ</sub>'s Synthetized</td>
 		                        <td>2000</td>
-		                        <td>min<sup>-1</sup>nM<sup>-1</sup></td>
 		                    </tr>
 		                    <tr>
 		                      <td>k<sub>7</sub></td>
-		                      <td>Rate constant of G<sub>βγ</sub> bind with Ste5</td>
+		                      <td>Rate constant of G<sub>βγ</sub>'s bind with Ste5</td>
 		                      <td>0.1</td>
-		                      <td>min<sup>-1</sup>nM<sup>-1</sup></td>
 		                    </tr>
 		                    <tr>
 		                    	<td>k<sub>8</sub></td>
-		                    	<td>Rate constant of G<sub>βγ</sub> unbind with Ste5</td>
+		                    	<td>Rate constant of G<sub>βγ</sub>'s unbind with Ste5</td>
 		                    	<td>5</td>
-		                    	<td>min<sup>-1</sup></td>
 		                    </tr>
 		                </tbody>
@@ Line 163: / Line 163: @@
-				<p class="my-content-p">The output of G-protein cycle activation part is shown. (Fig. 12)</p>
+				<p class="my-content-p">The result of G-protein cycle’s activation was shown below (Fig. 12). According to the figure, we indicated that our system could transduce upstream signal accurately.</p>
 				<div class="my-content-box">
 		            <img class="formula50" src="https://static.igem.org/mediawiki/2017/6/62/T--BIT-China--2017modeling_pic12.png" />
-		            <span>Fig. 12． The output signal, Gβγ under different concentration ligand</span>
+		            <span>Fig. 12． The result of G-protein cycle’s activation under the different concentration of ligand</span>
 	          	</div>
-				<p class="my-content-p">The result is similar to the first part which means our system can conserve the signal precision.</p>
-				<h4 class="title-h4">The MAPK cascade part:</h4>
+				<h4 class="title-h4">The cascade reaction of MAPK:</h4>
 				<div class="my-content-box">
-	              <img class="formula50" src="https://static.igem.org/mediawiki/2017/c/c9/T--BIT-China--2017modeling_pic13.png" />
+	              <img class="formula50" src="https://static.igem.org/mediawiki/2017/9/9d/T-BIT-China-2017MODEL-19.png" />
-	              <span>Fig. 13 The schematic diagram of MAPK cascade reaction</span>
+	              <span>Fig. 13 The cascade reaction of MAPK</span>
 	         	</div>
 				<div class="my-content-box">
-					<p class="my-content-p">The all protein in this part belongs to kinase and the signal is transmitted through phosphorylation. Finally, Fus3, will activate the Ste12 which is the output of this part. And all reaction equations of this process are listed as follow.</p>
+					<p class="my-content-p">All proteins in this part belong to the category of kinase and the signal was transmitted through phosphorylation. Finally, Fus3 activates the expression of Ste12 which was regarded as the output of this part. And all equations in this process were listed as follow:</p>
-					<img class="formula2" src="https://static.igem.org/mediawiki/2017/e/e4/T--BIT-China--2017modeling_pic24.png" alt="">
+					<img class="formula2" src="https://static.igem.org/mediawiki/2017/4/45/T--BIT-China--2017formula_pic3.png" alt="">
 				</div>
@@ Line 187: / Line 187: @@
+				<p class="my-content-p">(The parameters of this part were listed in Table 3)</p>
-				<p class="my-content-p">The parameters of this part are listed in the Table 3.</p>
 				<div class="my-img-box" style="justify-content: flex-start;">
 		            <table class="table-co">
-		                <caption>Table 3． The value of parameter in MAPK cascade part</caption>
+		                <caption>Table 3． The value of parameters in cascade reaction of MAPK</caption>
 		                <thead>
 		                    <tr>
@@ Line 198: / Line 197: @@
 		                      <th>Description</th>
 		                      <th>Value</th>
-		                      <th>Unit</th>
 		                    </tr>
 		                </thead>
@@ Line 204: / Line 203: @@
 		                    <tr>
 		                      <td>k<sub>7</sub></td>
-		                      <td>Rate constant of G<sub>βγ</sub> bind with Ste5</td>
+		                      <td>Rate constant of G<sub>βγ</sub>'s bind with Ste5</td>
 		                      <td>0.1</td>
-		                      <td>min<sup>-1</sup>nM<sup>-1</sup></td>
 		                    </tr>
 		                    <tr>
 		                    	<td>k<sub>8</sub></td>
-		                    	<td>Rate constant of G<sub>βγ</sub> unbind with Ste5</td>
+		                    	<td>Rate constant of G<sub>βγ</sub>'s unbind with Ste5</td>
 		                    	<td>5</td>
-		                    	<td>min<sup>-1</sup></td>
 		                    </tr>
 		                    <tr>
 		                      <td>k<sub>9</sub></td>
-		                      <td>Rate constant of Ste11 Phosphorylated</td>
+		                      <td>Rate constant of Ste11's Phosphorylated</td>
 		                      <td>10</td>
-		                      <td>min<sup>-1</sup></td>
 		                    </tr>
 		                    <tr>
 		                    	<td>k<sub>10</sub></td>
-		                    	<td>Rate constant of Ste7 double Phosphorylated</td>
+		                    	<td>Rate constant of Ste7's double Phosphorylated</td>
 		                    	<td>47</td>
-		                    	<td>min<sup>-1</sup></td>
 		                    </tr>
 		                    <tr>
 		                      <td>k<sub>11</sub></td>
-		                      <td>Rate constant of Fus3 double Phosphorylated</td>
+		                      <td>Rate constant of Fus3's double Phosphorylated</td>
 		                      <td>345</td>
-		                      <td>min<sup>-1</sup></td>
 		                    </tr>
 		                    <tr>
 		                    	<td>k<sub>12</sub></td>
-		                    	<td>Rate constant of double Phosphorylated Fus3 dissociation.</td>
+		                    	<td>Rate constant of double Phosphorylated Fus3's dissociation.</td>
 		                    	<td>140</td>
-		                    	<td>min<sup>-1</sup></td>
 		                    </tr>
 		                    <tr>
 		                      <td>k<sub>13</sub></td>
-		                      <td>Rate constant of double Phosphorylated Fus3 synthesis.</td>
+		                      <td>Rate constant of double Phosphorylated Fus3's synthesis.</td>
 		                      <td>260</td>
-		                      <td>min<sup>-1</sup></td>
 		                    </tr>
 		                    <tr>
 		                    	<td>k<sub>14</sub></td>
-		                    	<td>Rate constant of Fus3 dephosphorylated</td>
+		                    	<td>Rate constant of Fus3's dephosphorylated</td>
 		                    	<td>50</td>
-		                    	<td>min<sup>-1</sup></td>
 		                    </tr>
 		                    <tr>
 		                      <td>k<sub>15</sub></td>
-		                      <td>Rate constant of double pp-Fus3 bind with Ste12</td>
+		                      <td>Rate constant of double pp-Fus3's bind with Ste12</td>
 		                      <td>18</td>
-		                      <td>min<sup>-1</sup></td>
 		                    </tr>
 		                    <tr>
 		                    	<td>k<sub>16</sub></td>
-		                    	<td>Rate constant of double pp-Fus3 unbind with Ste12</td>
+		                    	<td>Rate constant of double pp-Fus3's unbind with Ste12</td>
 		                    	<td>10</td>
-		                    	<td>min<sup>-1</sup></td>
 		                    </tr>
 		                </tbody>
@@ Line 269: / Line 268: @@
-				<p class="my-content-p">The output of MAPK cascade part is showed. (Fig. 14)</p>
+				<p class="my-content-p">The result of the cascade reaction of MAPK was shown as follow (Fig. 14).</p>
 			    <div class="my-content-box">
 	              <img class="formula50" src="https://static.igem.org/mediawiki/2017/4/4e/T--BIT-China--2017modeling_pic14.png" />
-	              <span>Fig. 14 The output signal, activated state of Ste12 under different concentration ligand</span>
+	              <span>Fig. 14 The result of Ste12’s activation under the different concentration of ligand</span>
 	          	</div>
-				<h4 class="title-h4">Expression of mRFP part:</h4>
+				<h4 class="title-h4">4. Expression of mRFP:</h4>
 				<div class="my-content-box">
-	              <img class="formula50" src="https://static.igem.org/mediawiki/2017/2/26/T--BIT-China--2017modeling_pic15.png" />
+	              <img class="formula50" src="https://static.igem.org/mediawiki/2017/8/80/T-BIT-China-2017MODEL-20.png" />
-	              <span>Fig. 15． The Gene expression reaction diagram</span>
+	              <span>Fig. 15 The process of RFP’s expression</span>
 	            </div>
 				<div class="my-content-box">
-					<p class="my-content-p">As we mentioned, the activated Ste12 can initial the transcription of the P<sub>Fus</sub>. Due   the Ste12 can transmit the signal to the mRFP, so we selected the mRFP intensity to be output. And the equations about this process are listed as follow.</p>
+					<p class="my-content-p">Ste12 could accept signal from upstream pathway, it leads to the activation of relevant promoter  <i>P<sub>fus</sub></i>  and expression of downstream gene. There we regarded the expression of RFP as the output. The equations in this process were listed as follow:</p>
-					<img class="formula2" src="https://static.igem.org/mediawiki/2017/a/a5/T--BIT-China--2017modeling_pic25.png" alt="">
+					<img class="formula2" src="https://static.igem.org/mediawiki/2017/f/fa/T--BIT-China--2017formula_pic4.png" alt="">
 				</div>
@@ Line 291: / Line 290: @@
-				<p class="my-content-p">The parameters of this part are listed in the Table 4.</p>
+				<p class="my-content-p"> (The parameters of this part were listed in Table 4)</p>
 				<div class="my-img-box" style="justify-content: flex-start;">
 		            <table class="table-co">
-		                <caption>Table 4． The value of parameter in gene expression part</caption>
+		                <caption>Table 4． The value of parameters in RFP expression</caption>
 		                <thead>
 		                    <tr>
@@ Line 300: / Line 299: @@
 		                      <th>Description</th>
 		                      <th>Value</th>
-		                      <th>Unit</th>
 		                    </tr>
 		                </thead>
@@ Line 308: / Line 307: @@
 		                      <td>Rate constant of mRFP_mRNA Synthetize</td>
 							  <td>0.382</td>
-		                      <td>min<sup>-1</sup>nM<sup>-1</sup></td>
 		                    </tr>
 		                    <tr>
@@ Line 314: / Line 313: @@
 		                    	<td>Rate constant of mRFP_mRNA Degradation</td>
 								<td>8.39</td>
-		                    	<td>min<sup>-1</sup></td>
 		                    </tr>
 		                    <tr>
@@ Line 320: / Line 319: @@
 		                      <td>Rate constant of nascent RFP synthetize</td>
 								<td>0.012</td>
-		                      <td>min<sup>-1</sup></td>
 		                    </tr>
 		                    <tr>
@@ Line 326: / Line 325: @@
 		                    	<td>Rate constant of mature mRFP synthetize</td>
 								<td>0.0012</td>
-		                    	<td>min<sup>-1</sup></td>
 		                    </tr>
 		                    <tr>
@@ Line 332: / Line 331: @@
 		                      <td>Rate constant of mature mRFP degradation</td>
 								<td>0.018</td>
-		                      <td>min<sup>-1</sup></td>
 		                    </tr>
 		                </tbody>
@@ Line 342: / Line 341: @@
 				<h4 class="title-h4">Result </h4>
-				<p class="my-content-p">After calculating, we got the initial model of the signal transduction in single cell. The result is shown as follow.</p>
+				<p class="my-content-p"> Integrating four models of each block, we obtained completed result about signal transduction in single cell. The result was shown as follow (Fig. 16).</p>
 				<div class="my-content-box">
 	              <img class="formula50" src="https://static.igem.org/mediawiki/2017/0/0d/T--BIT-China--2017modeling_pic16.png" />
-	              <span>Fig. 16． The output signal, mRFP intensity under different concentration ligand</span>
+	              <span>Fig. 16． The result of RFP intensity under different concentration ligand
+Our modeling result exhibited that different concentration of ligand could result in different RFP intensity, which demonstrated that our system could response to the different signal strength specifically. And it also demonstrated that our system could work in a single cell in theory.</span>
 	         	</div>
-				<p class="my-content-p">The result shows that the different concentration of ligand can result in different mRFP intensity, which illustrates that the detect device (<i>P<sub>fus</sub></i>-<i>mRFP-CYC1t</i>) can reflect the signal strength as we expect. This result demonstrates that our system can work inside a single cell in theory.</p>
 			</div>
@@ Line 356: / Line 354: @@
 				<h3 class="title-h3">Yeast growth model</h3>
 				<h4 class="title-h4">Purpose</h4>
-				<p class="my-content-p">After build up the model of signal transduction in a single cell, we plan to combine with the growth situation, because in practical situation, measuring a single cell is so difficult and costly that nobody be pleasure to try it. So in this part, we hope to build up a simple model to describe the growth of our yeast cells and reflect the mRFP-sweetness relationship in population level.  </p>
+				<p class="my-content-p">After constructing the model of signal transduction in a single cell, we considered to combine single cell model with the growth of yeast to simulate our system’s practical condition. So in this part, we looked forward to construct a simple model to describe the growth of yeast cells and provided some bases to the next step. </p>
 				<h4 class="title-h4">Method </h4>
 				<h5 class="title-h5">Practical data measurement</h5>
-				<p class="my-content-p">For more accurate prediction, we measured the fluorescence intensity of our engineered yeast in population level, which was knocked out gene sst2, far1 corresponding to our single model, by inducing by α pheromone. More detail of this part result is shown in the host engineered. There is only a mRFP intensity curve showing in Fig. 17． </p>
+				<p class="my-content-p">We refered the model established by Imperial College 2016. This model was used to describe the growth condition of two kinds of cell which are competitive in a limit culture.  </p>
-				<div class="my-content-box">
+				<p class="my-content-p">We re-proposed some hypotheses to fit our system. </p>
-	              <img class="formula50" src="https://static.igem.org/mediawiki/2017/0/03/T--BIT-China--2017modeling_pic17.png" />
-	              <span>Fig. 17． The mRFP intensity of Sugar hunter system in practical situation</span>
-	         	</div>
-				<h5 class="title-h5">Model for simulation </h5>
+					<li class="my-content-li2">1. The condition of cell was divided into two states, activated and non-activated, and there is no conversion between two states. Each state of cell consume the nutrition independently.</li>
-				<p class="my-content-p">We referred to the model sat by iGEM team Imperial College 2016. This model was to describe the growth condition of two kinds of cell with competition in a limit culture. </p>
+					<li class="my-content-li2">2. Only the activated state could combine sweetener.</li>
-				<p class="my-content-p">We remade some hypothesis to make this model fitting our system. </p>
+					<li class="my-content-li2">3. The nutrition in culture was limited.</li>
-					<li class="my-content-li2">1. The condition of cell is divided into two state, activated and non-activated, and there is no conversion between two states. Two states of cell will consume the nutrition respectively.</li>
+					<li class="my-content-li2">4. Each group cell had same growth condition.  </li>
-					<li class="my-content-li2">2. Only the state of activation can combine with single cell model.</li>
-					<li class="my-content-li2">3. The nutrition in culture is limited.</li>
-					<li class="my-content-li2">4. The growth of each group cell shares the same growth situation.  </li>
 				<div class="my-content-box">
 					<p class="my-content-p">Then we set the ODEs as following:</p>
-					<img class="formula2" src="https://static.igem.org/mediawiki/2017/a/a7/T--BIT-China--2017modeling_pic26.png" alt="">
+					<img class="formula2" src="https://static.igem.org/mediawiki/2017/e/ef/T--BIT-China--2017formula_pic5.png" alt="">
 				</div>
+				<p class="my-content-p">(The parameters of this model were listed in Table 5)</p>
-				<p class="my-content-p">The parameters of this model are listed in the Table 5.</p>
 				<div class="my-img-box" style="justify-content: flex-start;">
 		            <table class="table-co">
@@ Line 388: / Line 381: @@
 		                      <th>Description</th>
 		                      <th>Value</th>
-		                      <th>Unit</th>
 		                    </tr>
 		                </thead>
@@ Line 408: / Line 401: @@
 		                        <td>Culture time for non-active yeast</td>
 								<td>30</td>
-		                        <td>Hour</td>
 		                    </tr>
 		                    <tr>
@@ Line 414: / Line 407: @@
 		                    	<td>Culture time for active yeast</td>
 								<td>30</td>
-		                    	<td>Hour</td>
 		                    </tr>
 		                    <tr>
@@ Line 431: / Line 424: @@
 		            </table>
 		        </div>
 				<h4 class="title-h4">Result </h4>
-				<p class="my-content-p">The result of yeast growth model is showed as follow. (Fig. 18)</p>
+				<p class="my-content-p">The result of yeast growth model was showed as follow. (Fig. 17)</p>
 				<div class="my-content-box">
-	            	<img class="formula50" src="https://static.igem.org/mediawiki/2017/c/c8/T--BIT-China--2017modeling_pic18.png" />
+	            	<img class="formula50" src="https://static.igem.org/mediawiki/2017/9/9d/T-BIT-China-2017MODEL-21.png" />
-	                <span>Fig. 18 The result of yeast cells growth model, the active cell.</span>
+	                <span>Fig. 17 The result of yeast cell growth model</span>
 	         	</div>
 				<h4 class="title-h4">Discussion </h4>
-				<p class="my-content-p">The tendency is similar to our practical state meaning that our model can simulate the yeast growth in some certain condition. In the frame of model, we can regard the active cells as the living cells with ability to make normal bio-process. </p>
+				<p class="my-content-p">Analyzing the trend of the curve, it shown that our model can simulate the growth of yeast in some certain condition. And we considered activated cell as effective bio-meter in our project. </p>
 			</div>
@@ Line 449: / Line 441: @@
 			<div class="cd-section" id="Combination">
 				<h3 class="title-h3">Combination Model coupling single cell and yeast growth</h3>
-				<p class="my-content-p">After finishing the model of single cell signal transduction and the yeast growth simulation, the next work was to combine two models together to simulate the RFP intensity in population level.</p>
+				<p class="my-content-p">Based on the above results, we combined these two models together to simulate the performance of whole system in population level. We multiplied the value of Yeast<sub>active</sub> and the value of RFP<sub>mature</sub> directly. </p>
-				<p class="my-content-p">Based on two assumptions we had sat, we simply combined through multiplying the value of  and the value of. </p>
+				<img class="formula2" style="margin-top: -50px; margin-bottom: -60px" src="https://static.igem.org/mediawiki/2017/8/82/T--BIT-China--2017modeling_pic27.png" alt="">
-				<p class="my-content-p">And we modified some parameters to meet the experimental data.</p>
+				<p class="my-content-p">And we altered some parameters to fit the experimental data.</p>
-				<div class="my-content-box">
-					<p class="my-content-p">The result of this combination model is showed follow.</p>
+				<p class="my-content-p">The result of this combined model was shown below (Fig. 18).</p>
-					<img class="formula2" style="margin-top: -50px; margin-bottom: -60px" src="https://static.igem.org/mediawiki/2017/8/82/T--BIT-China--2017modeling_pic27.png" alt="">
-				</div>
 				<div class="my-content-box">
 		            <img class="formula50" src="https://static.igem.org/mediawiki/2017/b/ba/T--BIT-China--2017modeling_pic19.png" />
-		            <span>Fig. 19 The simulation result of RFP intensity in population level.</span>
+		            <span>Fig. 18 The simulation result of RFP intensity in population level.</span>
 		        </div>
 				<h4 class="title-h4">Discussion</h4>
-				<p class="my-content-p">Combining two models, we find that the RFP intensity is almost the same as base line (value is 0) at the beginning. And at about 15 hours, because the cells have entered stationary phase, distinguish in single cell level will reflect in population level. </p>
+				<p class="my-content-p">Combining two models, we discovered that the RFP intensity is almost the same as base line (value is 0) at the beginning. And after 15 hours, the fluorescence intensity were reflected from single cell level to population level.  because the cells entered into stationary phase. </p>
-				<p class="my-content-p">As for the curve after 22 hours, the RFP intensity starts to decrease slowly in all ligand concentration. It may due to the death of cells.</p>
+				<p class="my-content-p">As for the curve after 22 hours, the RFP intensity starts to decrease slowly in all ligand concentration. It may due to the death of cells.  </p>
-				<p class="my-content-p">We decided to select the RFP intensity at 22 hours as the final output value of sweetness signal based on our model. In order to avoid the cell growth impacts the RFP intensity, we selected this specific moment as the sampling time.</p>
+				<p class="my-content-p">We decided to select the RFP intensity at 22 hours as the final output value of sweetness signal based on our model. In order to avoid the cell growth impacts the RFP intensity, we selected this specific moment as the sampling time.   </p>
 			</div>
@@ Line 471: / Line 463: @@
 			<div class="cd-section" id="SWEETENER">
-				<h3 class="title-h3">SWEETENER MODEL</h3>
+				<h3 class="title-h3">SWEETENESS MODEL</h3>
 				<h4 class="title-h4">Purpose</h4>
-				<p class="my-content-p">We hoped to set a model modified from our mating pheromone transduction model to predict the RFP intensity of sweetener. Make a comparison between our simulation result and practical measurement results to illustrate that our system not only can work like people gustatory sensation system with universality but also is more accurate and less interference than people. </p>
+				<p class="my-content-p">We expected to set a model based on our above model to simulate the RFP intensity of sweetener. Make a comparison between the results of simulation and practical measurement, Our system could not only work like people gustatory sensation system with universality but also is more accurate and less interference than people. </p>
 				<h4 class="title-h4">Method </h4>
-				<p class="my-content-p">Although we had finished the GPCR model, the combination rate between the sweetener and receptor had not been reported or measured yet. So we needed to measure this data from wet lab.</p>
+				<p class="my-content-p">Although we finished the GPCR model, the combination rate between the sweetener and receptor had not reported or measured yet. So we needed to measure this data from wet lab.</p>
-				<p class="my-content-p">But because of the instability of our system, we only got a useful group of data. The more discussion is established in Project website. The result was showed. (Table 6．) Then we utilized this group values to correct our pheromone model</p>
+				<p class="my-content-p">But because of the instability of our system, we only got a useful group of data. More discussion was established in Project Page. The result was shown (Table 6). Then we utilized these values to optimize our pheromone model.</p>
 				<div class="my-img-box" style="justify-content: flex-start;">
@@ Line 511: / Line 503: @@
-				<p class="my-content-p">Analysis the previous method of sweetness measurement, we made two assumptions for this sweetness model:</p>
+				<p class="my-content-p">Analyzing previous methods of sweetness measurement, we made two assumptions for this sweetness model:</p>
-					<li class="my-content-li2">1. Sweetness of all sweeteners can be transformed into the different concentration of standard sucrose (10% dissolving in water) with the same sweetness.</li>
+					<li class="my-content-li2">1. Sweetness of all sweeteners could be transformed into the different concentration of standard sucrose (10% dissolving in water) with the same sweetness.</li>
 					<li class="my-content-li2">2. The bind between different ligands and GPCR will not impact signal transduction in pheromone pathway.</li>
 				<p class="my-content-p">Based on these, the most significant work is to find out the RFP intensity corresponding to the standard sucrose.</p>
-				<p class="my-content-p">Combining the wet experiment data, we made a simple calculation and got the RFP intensity of standard sweetness amounting to the fluorescence intensity induced by 750nM ideal ligand in our model.</p>
+				<p class="my-content-p">Combining the experimental data, we made a simple calculation and got the RFP intensity of standard sweetness amounting to the fluorescence intensity induced by 750nM ideal ligand in our model. </p>
 				<div class="my-content-box">
-	              <img class="formula50" src="https://static.igem.org/mediawiki/2017/9/9c/T--BIT-China--2017modeling_pic20.png" />
+	              <img style="width: 50%; height: auto;" src="https://static.igem.org/mediawiki/2017/a/ac/T-BIT-China-2017MODEL-22.png" />
-	              <span>Fig. 20．  The relation between sweetness and idea ligand in model </span>
+	              <span>Fig. 20．  The relationship between sweetness and ideal ligand</span>
 	         	</div>
 				<div class="my-content-box">
-					<p class="my-content-p">We sat a correction factor  on calculation result. Then we rewrote the input according to follow equation. </p>
+					<p class="my-content-p">We set a correction factor K<sub>corret</sub>=750  on calculation result. Then we reset the input according to following equation:</p>
 					<img class="formula2" src="https://static.igem.org/mediawiki/2017/d/da/T--BIT-China--2017modeling_pic28.png" alt="">
 				</div>
-				<p class="my-content-p">Then we wanted to predict different sweetness of sweetener. The sweetness data is obtained from previous study. (Table 7．) </p>
+				<p class="my-content-p">Then we prefered to predict different sweetness of sweetener. The sweetness data was obtained from previous study<sup>[7]</sup>.  (Table 7．) </p>
 				<div class="my-img-box" style="justify-content: flex-start;">
 		            <table class="table-co">
@@ Line 580: / Line 571: @@
 				<h4 class="title-h4">Result </h4>
-				<p class="my-content-p">The result of predicted RFP intensity of sweeteners is showed in Fig. 21．</p>
+				<p class="my-content-p">The result of simulated RFP intensity induced by different sweeteners was shown in Fig. 20．</p>
 				<div class="my-content-box">
 	              <img class="formula50" src="https://static.igem.org/mediawiki/2017/b/b9/T--BIT-China--2017modeling_pic21.png" />
-	              <span>Fig. 21．  The predicted RFP intensity of sweeteners with different sweetness calculated by corrected model</span>
+	              <span>Fig. 21．  The modeling result of RFP intensity induced by different sweeteners</span>
 	            </div>
 				<h4 class="title-h4">Discussion </h4>
-				<p class="my-content-p">When the detect time arrive at 22 hours, the peak of all curve is displayed as the pheromone model. And the different sweetness also can induce different RFP expression level. But this model needs to be modified further, because we only corrected model based on a group wet lab data, which is not enough for a model especial a model of bio-system actually.</p>
-				<p class="my-content-p">What is more? A apart from keeping measuring the RFP intensity under different sweetness to correct model, we have considered some further work about improving this model in the future. </p>
+				<p class="my-content-p">After 22 hours, the peak of all curve were displayed as the pheromone model. And different sweetness also could induce different fluorescence intensity. But this model still need optimization in further, based on the following aspects:</p>
-					<li class="my-content-li2">(1) Through structure information discovery to find the combination constant of sweetener binding process.</li>
+					<li class="my-content-li2">(1) Through molecular simulation to discover the combination constant of sweetener binding process.</li>
 					<li class="my-content-li2">(2) The correct factor is to be considered the molecular weight, binding sites and combination numbers of the sweetener.</li>
-					<li class="my-content-li2">(3) Combine the protein expression and cells growth together. Consider the impact between two modules. </li>
+					<li class="my-content-li2">(3) Combine the protein expression and cells growth together. Consider the interaction between two models. </li>
 			</div>
@@ Line 598: / Line 589: @@
 			<div class="cd-section" id="SUMMARY">
 				<h3 class="title-h3">SUMMARY</h3>
-				<p class="my-content-p">With our models, we successfully get the results as follows:</p>
+				<p class="my-content-p">Based on our models, we successfully got following results:</p>
-					<li class="my-content-li2">1. We simulated the receptor structure model of T1R2/T1R3 and allowed it to bind to the sweetener to view the combining situation of different sweeteners through molecular docking. </li>
+					<li class="my-content-li2">1. We simulated the structure of T1R2/T1R3 receptor and bound it with different sweeteners to observe the combining situation of different sweeteners through molecular docking. </li>
-					<li class="my-content-li2">2. We have constructed the signal transduction model in mating pheromone response pathway in two different levels and proved that our system can work as we expectation. </li>
+					<li class="my-content-li2">2. We constructed the signal transduction model to simulate pheromone response pathway in two different levels and proved that our system could work as we expected.  </li>
-					<li class="my-content-li2">3. We also provide an ideal relationship between RFP intensity and sweetness based on the wet lab data and our signal transduction model simulation, which also demonstrates that our system can detect different sweetness in a appropriate sweetness range. </li>
+					<li class="my-content-li2">3. We also provided an ideal relationship between RFP intensity and sweetness based on the wet lab data and our signal transduction model , which also demonstrated that our system could detect different range of sweetness.
-					<li class="my-content-li2">4. Give some advice for other iGEMers about future model of our sweetness bio- meter, Sugar Hunter.</li>
+					Although sweeteners are various, but we still can taste all of them and sense different sweetness due to the powerful sweetness receptors, T1R2/T1R3. Based on our model, we successfully proved that using sweetness receptor as a “meter” and coupling the signal pathway to determine the sweetness in yeast cell is available. Last, let’s set foot on the trip of beating sweet-monsters in parallel space with our  </li>
-				<p class="my-content-p">To make a long story short, the sweeteners vary so much, but we always can taste all kinds of sweeteners and sense different sweetness because we have powerful sweetness receptors, T1R2/T1R3. Based on the pheromone signal transduction model, we successfully proves that using sweetness receptor as a "meter" and coupling the mating signal pathway to determine the sweetness in yeast cell is a feasible design. </p>
-				<p class="my-content-p">Last, let’s set foot on the trip of beating sweet-monsters in parallel space with our Sugar Hunter!!!</p>
+				<p class="my-content-p">Sugar Hunter!!!</p>
 				<h4 class="title-h4">References</h4>
-					<li class="my-content-li2">1.	Dubois G E. Molecular mechanism of sweetness sensation.[J]. Physiology & Behavior, 2016, 164(Pt B):453.</li>
+					<li class="my-content-li2"><span>1.   Dubois G E. Molecular mechanism of sweetness sensation.[J]. Physiology & Behavior, 2016, 164(Pt B):453.</span></li>
-					<li class="my-content-li2">2.	Kofahl B, Klipp E. Modelling the dynamics of the yeast pheromone pathway.[J]. Yeast, 2004, 21(10):831.</li>
+					<li class="my-content-li2"><span>2.	Nie Y, Vigues S, Hobbs J R, et al. Distinct contributions of T1R2 and T1R3 taste receptor subunits to the detection of sweet stimuli.[J]. Current Biology Cb, 2005, 15(21):1948-52.</span></li>
-					<li class="my-content-li2">3.	Richardson, Kathryn. Mechanisms of GPCR signal regulation in fission yeast[J]. University of Warwick, 2014.</li>
+					<li class="my-content-li2"><span>3.   Richardson, Kathryn. Mechanisms of GPCR signal regulation in fission yeast[J]. University of Warwick, 2014.</span></li>
-					<li class="my-content-li2">4.	Nie Y, Vigues S, Hobbs J R, et al. Distinct contributions of T1R2 and T1R3 taste receptor subunits to the detection of sweet stimuli.[J]. Current Biology Cb, 2005, 15(21):1948-52.</li>
+					<li class="my-content-li2"><span>4.	Kofahl B, Klipp E. Modelling the dynamics of the yeast pheromone pathway.[J]. Yeast, 2004, 21(10):831.</span></li>
-					<li class="my-content-li2">5. Audet M, Bouvier M. Restructuring G-Protein- Coupled Receptor Activation [J]. Cell, 2012, 151(1):14-23.</li>
+					<li class="my-content-li2"><span>5.   Audet M, Bouvier M. Restructuring G-Protein- Coupled Receptor Activation [J]. Cell, 2012, 151(1):14-23.</span></li>
+                    <li class="my-content-li2"><span>6.   Carocho M, Morales P, Icfr F. Sweeteners as food additives in the XXI century: A review of what is known, and what is to come[J]. Food & Chemical Toxicology An International Journal Published for the British Industrial Biological Research Association, 2017, 107.</span></li>
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