"
Team:NCTU Formosa/modeling
From 2013.igem.org
(→Temperature-regulated system) |
(→ANFIS Introduction) |
||
| Line 15: | Line 15: | ||
===ANFIS Introduction=== | ===ANFIS Introduction=== | ||
<p>Adaptive-Network-Based Fuzzy Inference System, in short ANFIS, is a power tool for constructing a set of fuzzy if-then rules to generate stipulated output and input pairs. Unlike system modeling using mathematical rules that lacks the ability to deal with ill-defined and uncertain system, ANFIS can transform human knowledge into rule base, and therefore, ANFIS can effectively tune membership functions, minimizing the output error.</p> | <p>Adaptive-Network-Based Fuzzy Inference System, in short ANFIS, is a power tool for constructing a set of fuzzy if-then rules to generate stipulated output and input pairs. Unlike system modeling using mathematical rules that lacks the ability to deal with ill-defined and uncertain system, ANFIS can transform human knowledge into rule base, and therefore, ANFIS can effectively tune membership functions, minimizing the output error.</p> | ||
| + | |||
| + | the above | ||
</div></div> | </div></div> | ||
Revision as of 03:42, 26 October 2013
Modeling was our first step forward. When validated with our experimental data, modeling is also a verification of the accuracy of our experiments.
Contents |
MATLAB Introduction
MATLAB (matrix laboratory) is a numerical computing environment and fourth-generation programming language. It is developed by MathWorks, a company in United States. MATLAB allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages, including C, C++, Java, and Fortran. Although MATLAB is intended primarily for numerical computing, an optional toolbox uses the MuPAD symbolic engine, allowing access to symbolic computing capabilities. An additional package, Simulink, adds graphical multi-domain simulation and Model-Based Design for dynamic and embedded systems.
ANFIS Introduction
Adaptive-Network-Based Fuzzy Inference System, in short ANFIS, is a power tool for constructing a set of fuzzy if-then rules to generate stipulated output and input pairs. Unlike system modeling using mathematical rules that lacks the ability to deal with ill-defined and uncertain system, ANFIS can transform human knowledge into rule base, and therefore, ANFIS can effectively tune membership functions, minimizing the output error.
the above
Light-regulated System
Red Promoter
Temperature-regulated system
From Figure 1, the maximum output is obtained at 37oC. Under the same time frame, the output (the normalized expression of the reporter gene) is maximized at 37oC while minimized at 25oC. There is a dramatic decrease in the output near 30o and the outputs around 37oC are much higher. This modeling demonstrates that using 37oC RBS is a plausible approach for achieving gene expression through temperature.
Small RNA-regulated System
rRBS efficiency
