Difference between revisions of "Team:CIEI-BJ/Model"
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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> | <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> | ||
<p class="my-content" >The figure we created:</p> | <p class="my-content" >The figure we created:</p> | ||
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<p class="my-content" >The independent variable is hours.</p> | <p class="my-content" >The independent variable is hours.</p> | ||
| − | <p class="my-content" > | + | <p class="my-content" >这里差第二个表格图</p> |
<p class="my-content" >The graph we made:</p> | <p class="my-content" >The graph we made:</p> | ||
<img class="my-img" src="https://static.igem.org/mediawiki/2018/7/76/T--CIEI-BJ--MO--fig8.PNG" /> | <img class="my-img" src="https://static.igem.org/mediawiki/2018/7/76/T--CIEI-BJ--MO--fig8.PNG" /> | ||
Revision as of 09:38, 17 October 2018
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 we can divide 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.
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.
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:
这里差第一个表格图
The independent variable is hours.
这里差第二个表格图
The graph we made:
Figure 8
Therefore, we could simulate the growing situation for fungi.
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.