Difference between revisions of "Team:CIEI-BJ/Model"

 
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<li><a href="#a1">Model</a>
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<li><a href="#a2">Modeling of introduction</a>
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<li><a href="#a3">Modeling with aflatoxin effect</a>
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<li><a href="#a4">Modeling of difference as time goes</a>
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<li><a href="#a5">Modeling of Device</a>
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<div class="first-level" id="a1"  >Model</div>
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<div class="second-level" id="a2" >Modeling of introduction</div>
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<p class="my-content" >This project is aimed at finding out the impact of aflatoxin (AFT-B1) on different kinds of fungi. We are going to show the effect of aflatoxin by presenting diagrams of the growing situation of fungi with AFT-B1 and without AFT-B1. Such contrast would effectively reflect the difference and provide an intuitionistic visual sense.</p>
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<p class="my-content" >The following contents can be divided into three parts.</p>
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<p class="my-content" >1. Modeling of aflatoxin effect: We analyze five contrasts, including one situation with His as variable and four situations with different kinds of enzymes added.</p>
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<p class="my-content" >2. Modeling of effect as time goes: we find the difference in two situations (with AFT-B1 and without AFT-B1) as time goes.</p>
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<p class="my-content" >3. Modeling of Device: A device model is also established. This model would illustrate the variables controlled and let the users know the growing condition of those fungi.</p>
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<div class="second-level" id="a3" >Modeling with aflatoxin effect</div>
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<p class="my-content" >In this project, we analyze five contrasts, including one situation both with AFT-B1 but one with His added and one without His added, and four situations with different kinds of enzymes added. With the increase of time, the fungi would grow at a different speed. We use optical density (OD value) to reflect the growing speed of the fungi. The faster the fungi grow, the higher the OD value, since culture medium with a faster growing speed would be more turbid. Consequently, the diagram would provide a visualized contrast.</p>
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<p class="my-content" >The four enzymes we used are ADTZ, BacC, MsMeg5998 and Mnp. By recording date of these four kinds of enzymes, we could more efficiently analyze the impact of AFT-B1 on different enzymes.</p>
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<p class="my-content" >1. ScFv1&ScFv2 (fungi both with AFT-B1)</p>
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<img class="my-img" src="https://static.igem.org/mediawiki/2018/8/89/T--CIEI-BJ--MO--fig1.PNG" />
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<p class="my-content" >Figure 1</p>
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<p class="my-content" >As is shown in the graph, two curves show the difference when there are both AFT-B1 to be added. The blue curve represents the situation without His added, while the orange curve represents the situation with His added. It is very obvious that the fungi grow much faster without His, which means when with AFT-B1, His would inhibit the fungi to grow.</p>
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<p class="my-content" >2. ADTZ</p>
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<img class="my-img" src="https://static.igem.org/mediawiki/2018/2/20/T--CIEI-BJ--MO--fig2.PNG" />
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<p class="my-content" >Figure 2</p>
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<p class="my-content" >When the enzymes added is ADTZ, the difference between fungi with AFT-B1 and fungi without AFT-B1 become less obvious.</p>
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<p class="my-content" >3. BacC</p>
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<img class="my-img" src="https://static.igem.org/mediawiki/2018/c/c5/T--CIEI-BJ--MO--fig3.PNG" />
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<p class="my-content" >Figure 3</p>
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<p class="my-content" >When BacC is added, the difference between two situations would become larger as time goes by.</p>
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<p class="my-content" >4. Ms Meg5998</p>
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<img class="my-img" src="https://static.igem.org/mediawiki/2018/3/31/T--CIEI-BJ--MO--fig4.PNG" />
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<p class="my-content" >Figure 4</p>
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<p class="my-content" >Two curves are relatively stable, which means the difference between growing speed is constant.</p>
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<p class="my-content" >5. Mnp</p>
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<img class="my-img" src="https://static.igem.org/mediawiki/2018/7/72/T--CIEI-BJ--MO--fig5.PNG" />
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<p class="my-content" >Figure 5</p>
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<p class="my-content" >Two curves have very small difference and even overlap a little, which means AFT-B1 has no obvious effect.</p>
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<p class="my-content" >6. AFT-B1 added</p>
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<img class="my-img" src="https://static.igem.org/mediawiki/2018/9/90/T--CIEI-BJ--MO--fig6.PNG" />
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<p class="my-content" >Figure 6</p>
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<p class="my-content" >The graph shows the growing speed of fungi in 5 different situations, in which the blue curve represents situation when AFT-B1 is not added. As is shown in the graph, when no AFT-B1 is added, the growing speed of fungi without enzyme added is fastest.</p>
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<p class="my-content" >7. without AFT-B1 added</p>
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<img class="my-img" src="https://static.igem.org/mediawiki/2018/e/ee/T--CIEI-BJ--MO--fig7.PNG" />
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<p class="my-content" >Figure 7</p>
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<p class="my-content" >When there is no AFT-B1 added, five curves seem have no obvious difference.</p>
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<div class="second-level" id="a4" >Modeling of difference as time goes</div>
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<p class="my-content" >Besides simply contrast the difference, we made diagrams of the difference between OD value as time goes and find out the fitted curve, calculating the coefficient of each factor. We chose the AH109 sample.</p>
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<p class="my-content" >By subtracting the OD value of fungi without AFT-B1 from that of fungi without AFT-B1, we can observe the difference between them in different time point, in order to find out the influence of AFT-B1 on growing of fungi.</p>
 +
<p class="my-content" >Then we apply the data into SPSS to find the fitted curve. The way to find the fitted curve is shown below:</p>
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<p class="my-content" >We tried to define the curve as a1r1(x)+a2r2(x)+…+amrm(x).</p>
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<p class="my-content" >We tried linear, quadratic, cubic to find which could best fit the data, and result shows that cubic function would show the data most precisely.</p>
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<p class="my-content" >The figure we created:</p>
  
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<img class="my-img" src="https://static.igem.org/mediawiki/2018/a/a5/T--CIEI-BJ--MO--fig9.PNG" />
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<p class="my-content" >The independent variable is hours.</p>
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<img class="my-img" src="https://static.igem.org/mediawiki/2018/a/ac/T--CIEI-BJ--MO--fig10.PNG" />
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<p class="my-content" >The graph we made:</p>
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<img class="my-img" src="https://static.igem.org/mediawiki/2018/7/76/T--CIEI-BJ--MO--fig8.PNG" style="width: 600px"/><p class="my-content" >Figure 8</p >
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<p class="my-content" >Therefore, we could simulate the growing situation for fungi.</p>
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<div class="second-level" id="a5" >Modeling of Device</div>
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<p class="my-content" >The model we built should be able to present the difference of growing speed in different situation.</p>
 +
<p class="my-content" >The variables we are going to control are:</p>
 +
<p class="my-content" >1. Temperature  2. The initial amount of fungi  3. The concentration of AFT-B1</p>
 +
<p class="my-content" >Temperature</p>
 +
<p class="my-content" >Temperature is an important factor of the growing speed. We controlled the temperature to be 28C to provide a same growing environment for the fungi.</p>
 +
<p class="my-content" >The initial amount of fungi</p>
 +
<p class="my-content" >In order to learn about the growing speed, we need to calculate with the OD value, so the initial value must be same in order to ensure a more accurate answer.</p>
 +
<p class="my-content" >The concentration of AFT-B1</p>
 +
<p class="my-content" >Different concentration would influence the growing speed of the fungi, so it’s necessary to control the concentration of AFT-B1 added.</p>
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</div>
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Latest revision as of 17:09, 17 October 2018

Top
Model
Modeling of introduction

This project is aimed at finding out the impact of aflatoxin (AFT-B1) on different kinds of fungi. We are going to show the effect of aflatoxin by presenting diagrams of the growing situation of fungi with AFT-B1 and without AFT-B1. Such contrast would effectively reflect the difference and provide an intuitionistic visual sense.

The following contents can be divided into three parts.

1. Modeling of aflatoxin effect: We analyze five contrasts, including one situation with His as variable and four situations with different kinds of enzymes added.

2. Modeling of effect as time goes: we find the difference in two situations (with AFT-B1 and without AFT-B1) as time goes.

3. Modeling of Device: A device model is also established. This model would illustrate the variables controlled and let the users know the growing condition of those fungi.

Modeling with aflatoxin effect

In this project, we analyze five contrasts, including one situation both with AFT-B1 but one with His added and one without His added, and four situations with different kinds of enzymes added. With the increase of time, the fungi would grow at a different speed. We use optical density (OD value) to reflect the growing speed of the fungi. The faster the fungi grow, the higher the OD value, since culture medium with a faster growing speed would be more turbid. Consequently, the diagram would provide a visualized contrast.

The four enzymes we used are ADTZ, BacC, MsMeg5998 and Mnp. By recording date of these four kinds of enzymes, we could more efficiently analyze the impact of AFT-B1 on different enzymes.

1. ScFv1&ScFv2 (fungi both with AFT-B1)

Figure 1

As is shown in the graph, two curves show the difference when there are both AFT-B1 to be added. The blue curve represents the situation without His added, while the orange curve represents the situation with His added. It is very obvious that the fungi grow much faster without His, which means when with AFT-B1, His would inhibit the fungi to grow.

2. ADTZ

Figure 2

When the enzymes added is ADTZ, the difference between fungi with AFT-B1 and fungi without AFT-B1 become less obvious.

3. BacC

Figure 3

When BacC is added, the difference between two situations would become larger as time goes by.

4. Ms Meg5998

Figure 4

Two curves are relatively stable, which means the difference between growing speed is constant.

5. Mnp

Figure 5

Two curves have very small difference and even overlap a little, which means AFT-B1 has no obvious effect.

6. AFT-B1 added

Figure 6

The graph shows the growing speed of fungi in 5 different situations, in which the blue curve represents situation when AFT-B1 is not added. As is shown in the graph, when no AFT-B1 is added, the growing speed of fungi without enzyme added is fastest.

7. without AFT-B1 added

Figure 7

When there is no AFT-B1 added, five curves seem have no obvious difference.

Modeling of difference as time goes

Besides simply contrast the difference, we made diagrams of the difference between OD value as time goes and find out the fitted curve, calculating the coefficient of each factor. We chose the AH109 sample.

By subtracting the OD value of fungi without AFT-B1 from that of fungi without AFT-B1, we can observe the difference between them in different time point, in order to find out the influence of AFT-B1 on growing of fungi.

Then we apply the data into SPSS to find the fitted curve. The way to find the fitted curve is shown below:

We tried to define the curve as a1r1(x)+a2r2(x)+…+amrm(x).

We tried linear, quadratic, cubic to find which could best fit the data, and result shows that cubic function would show the data most precisely.

The figure we created:

The independent variable is hours.

The graph we made:

Figure 8

Therefore, we could simulate the growing situation for fungi.

Modeling of Device

The model we built should be able to present the difference of growing speed in different situation.

The variables we are going to control are:

1. Temperature 2. The initial amount of fungi 3. The concentration of AFT-B1

Temperature

Temperature is an important factor of the growing speed. We controlled the temperature to be 28C to provide a same growing environment for the fungi.

The initial amount of fungi

In order to learn about the growing speed, we need to calculate with the OD value, so the initial value must be same in order to ensure a more accurate answer.

The concentration of AFT-B1

Different concentration would influence the growing speed of the fungi, so it’s necessary to control the concentration of AFT-B1 added.