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

Revision as of 11:31, 28 October 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 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.

Single cell model:

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:

1. We assume that T1R2/T1R3 receptor does not have synergistic effect when sweeteners bind.

2. We hypothesize that the binding number of sweetener likes pheromones receptor are consistent when it binds to T1R2/T1R3.

3. We assume that the combination rate and the initial concentration of sweetener binding is the same as pheromone receptor.

4. There is no influence between the 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． The schematic diagram of Sugar hunter signal pathway

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.

Next, we will introduce four parts in detail.

T1R2/T1R3 receptor activation part:

Fig. 9 The conventional diagram of T1R2-T1R3 receptor activation reaction

We divided this activation process of inducing signal into four states of T1R2-T1R3 as shown in Fig. 9． And ODEs are shown follow.

(T1R2/3: the T1R2-T1R3 heterodimer)

The parameters of this part are listed in the Table1.

Table 1. The sweeteners added for detecting function of the system
Parameter	Description	Value	Unit
k₁	Rate constant of sweetness bind on receptor	0.0012	min^-1nM^-1
k₂	Rate constant of receptor is not activated	0.6	min^-1
k₃	Rate constant of sweetness Unbind on receptor	0.24	min^-1
k₄	Rate constant of receptor degradation	0.024	min^-1

The output of the T1R2/T1R3 receptor activation part is showed. (Fig. 10)

Fig. 10． The output signal, activated state of T1R2-T1R3 under different concentration ligand

As we can see, the receptor can sense different concentration of ligand and release correspond output.

G-protein cycle activation part:

Fig. 11 The schematic diagram of G-protein cycle activation

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.

The parameters of this part are listed in the Table2.

Table 2． The value of parameter in G-protein cycle activation part
Parameter	Description	Value	Unit
k₅	Rate constant of G_αβγ dissociated	0.0036	min^-1nM^-1
k₆	Rate constant of G_αβγ Synthetized	2000	min^-1nM^-1
k₇	Rate constant of G_βγ bind with Ste5	0.1	min^-1nM^-1
k₈	Rate constant of G_βγ unbind with Ste5	5	min^-1

The output of G-protein cycle activation part is shown. (Fig. 12)

Fig. 12． The output signal, Gβγ under different concentration ligand

The result is similar to the first part which means our system can conserve the signal precision.

The MAPK cascade part:

Fig. 13 The schematic diagram of MAPK cascade reaction

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.

The parameters of this part are listed in the Table 3.

Table 3． The value of parameter in MAPK cascade part
Parameter	Description	Value	Unit
k₇	Rate constant of G_βγ bind with Ste5	0.1	min^-1nM^-1
k₈	Rate constant of G_βγ unbind with Ste5	5	min^-1
k₉	Rate constant of Ste11 Phosphorylated	10	min^-1
k₁₀	Rate constant of Ste7 double Phosphorylated	47	min^-1
k₁₁	Rate constant of Fus3 double Phosphorylated	345	min^-1
k₁₂	Rate constant of double Phosphorylated Fus3 dissociation.	140	min^-1
k₁₃	Rate constant of double Phosphorylated Fus3 synthesis.	260	min^-1
k₁₄	Rate constant of Fus3 dephosphorylated	50	min^-1
k₁₅	Rate constant of double pp-Fus3 bind with Ste12	18	min^-1
k₁₆	Rate constant of double pp-Fus3 unbind with Ste12	10	min^-1

The output of MAPK cascade part is showed. (Fig. 14)

Fig. 14 The output signal, activated state of Ste12 under different concentration ligand

Expression of mRFP part:

Fig. 15． The Gene expression reaction diagram

As we mentioned, the activated Ste12 can initial the transcription of the P_Fus. 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.

The parameters of this part are listed in the Table 4.

Table 4． The value of parameter in gene expression part
Parameter	Description	Value	Unit
k₁₇	Rate constant of mRFP_mRNA Synthetize	0.382	min^-1nM^-1
k₁₈	Rate constant of mRFP_mRNA Degradation	8.39	min^-1
k₁₉	Rate constant of nascent RFP synthetize	0.012	min^-1
k₂₀	Rate constant of mature mRFP synthetize	0.0012	min^-1
k₂₁	Rate constant of mature mRFP degradation	0.018	min^-1

Result

After calculating, we got the initial model of the signal transduction in single cell. The result is shown as follow.

Fig. 16． The output signal, mRFP intensity under different concentration ligand

The result shows that the different concentration of ligand can result in different mRFP intensity, which illustrates that the detect device (P_fus-mRFP-CYC1t) can reflect the signal strength as we expect. This result demonstrates that our system can work inside a single cell in theory.

Yeast growth model

Purpose

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.

Method

Practical data measurement

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．

Fig. 17． The mRFP intensity of Sugar hunter system in practical situation

Model for simulation

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.

We remade some hypothesis to make this model fitting our system.

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.

2. Only the state of activation can combine with single cell model.

3. The nutrition in culture is limited.

4. The growth of each group cell shares the same growth situation.

Then we set the ODEs as following:

The parameters of this model are listed in the Table 5.

Table 5． The value of parameter in yeast growth part
Parameter	Description	Value	Unit
r₁	Rate of non-active yeast growth	1
r₂	Rate of active yeast growth	2
n₁	Culture time for non-active yeast	30	Hour
n₂	Culture time for active yeast	30	Hour
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 is showed as follow. (Fig. 18)

Fig. 18 The result of yeast cells growth model, the active cell.

Discussion

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.

Combination Model coupling single cell and yeast growth

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.

Based on two assumptions we had sat, we simply combined through multiplying the value of and the value of.

And we modified some parameters to meet the experimental data.

The result of this combination model is showed follow.

Fig. 19 The simulation result of RFP intensity in population level.

Discussion

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.

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.

SWEETENER MODEL

Purpose

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.

Method

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.

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

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

Analysis the previous method of sweetness measurement, we made two assumptions for this sweetness model:

1. Sweetness of all sweeteners can 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 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.

Fig. 20． The relation between sweetness and idea ligand in model

We sat a correction factor on calculation result. Then we rewrote the input according to follow equation.

Then we wanted to predict different sweetness of sweetener. The sweetness data is obtained from previous study. (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 predicted RFP intensity of sweeteners is showed in Fig. 21．

Fig. 21． The predicted RFP intensity of sweeteners with different sweetness calculated by corrected model

Discussion

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.

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.

(1) Through structure information discovery to find 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 impact between two modules.

SUMMARY

With our models, we successfully get the results as follows:

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.

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.

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.

4. Give some advice for other iGEMers about future model of our sweetness bio- meter, Sugar Hunter.

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.

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

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

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.

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

TOP

@@ Line 13: / Line 13: @@
 	    <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 20: @@
 				<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 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>
 				<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 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>
 					<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">2. We hypothesize that the binding number of sweetener likes pheromones receptor are consistent when it binds to T1R2/T1R3.</li>
@@ Line 34: / Line 34: @@
 	                <span>Fig 8． The schematic diagram of Sugar hunter signal pathway</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">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.  </p>
 				<p class="my-content-p">Next, we will introduce four parts in detail.  </p>