Team:TCU Taiwan/Modeling

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<div class="wrapper" style="background-color: white;"><font size="3" face="Verdana" color="#333">TEST.</font></div>
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        <td align="center"><img src="https://static.igem.org/mediawiki/2014/d/dd/TCU_MD_Training_error.png" width="1003" height="334"></td>
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        <td align="center"><font size="3" face="Verdana"><strong>Fig.1&nbsp;&nbsp;Training Error</strong></font></td>
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         <td align="center"><font size="3" face="Verdana"><strong>Fig.1&nbsp;&nbsp;Model1</strong></font></td>
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         <td align="center"><font size="3" face="Verdana"><strong>Fig4.1&nbsp;&nbsp;Model1</strong></font></td>
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         <td align="center"><font size="3" face="Verdana"><strong>Fig.2&nbsp;&nbsp;Model2</strong></font></td>
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         <td align="center"><font size="3" face="Verdana"><strong>Fig4.2&nbsp;&nbsp;Model2</strong></font></td>
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Revision as of 08:49, 16 October 2014



 
Modeling
  
 
1.Introduction
TEST.
 
 
2.Software

MATLAB

MATLAB(MATrix LABoratory) is a high-level language and interactive environment for numerical computation, visualization, and programming. Using MATLAB, you can analyze data, develop algorithms, and create models and applications. The language, tools, and built-in math functions enable you to explore multiple approaches and reach a solution faster than with spreadsheets or traditional programming languages, such as C/C++ or Java™.





ANFIS

The architecture and learning procedure underlying ANFIS (Adaptive Network-based Fuzzy Inference System) is presented, which is a fuzzy inference system implemented in the framework of adaptive networks. By using a hybrid learning procedure, the proposed ANFIS can construct an input-output mapping based on both human knowledge (in the form of fuzzy if-then rules) and stipulated input-output data pairs. In the simulation, the ANFIS architecture is employed to model nonlinear functions, identify nonlinear components on-line in a control system, and predict a chaotic time series, all yielding remarkable results. Comparisons with artificial neural networks and earlier work on fuzzy modeling are listed and discussed.
 

TEST.
Fig.1  Training Error

 
4.Results

Model 1 : Release

We chose two variables to find the best release amount of phage after helper phage infecting. The first variable is the time after we input helper phages M13KO7 into E.Coli (JM101) with phagemid pBluescript II SK(-).(We have put BBa_I13521 inside as an reporter gene.)This is the time for helper phages to infect bacterium.

After the infection, we added kanamycin into these JM101 for selection becauseJM101 can get kanamycin resistance only when they are infected by M13KO7. Then we incubated these JM101 so they can have time to release phagemid-carrying phage, and the incubating time is the second variable in our test.

As we can see in this figure, the most amount of phage being released is at the time when we add kanamycin after 30 minutes of infection and then incubate them for 14 hours. Under this condition, the best releasing amount of phage is 4×1010 pfu/ml.



Fig4.1  Model1

 

 



Model 2 : Infection Rate

Then we came up with another question: Under which condition will we get the best infection rate of our phagemid-carrying phage? We believe this is influenced by the MOI between phages and bacterium. MOI means multiplicity of infection, it is the ratio of agents (phage-carrying phage) to infection targets (E.coli JM101). So we choose MOI as the variable, and incubate bacterium for 1 hour after infecting.
In our test, we found that as long as the MOI is higher than 6 pfu/cfu, the infection rate can access 100%. This is an exciting result!!
Fig4.2  Model2

 
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Team Members Project Parts Human Pratics Modeling Safety Notebook Attributions

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