Team:NCTU Formosa/modeling

Contents 1 Introduction of Modeling 2 Model software 2.1 MATLAB 2.2 ANFIS 3 Modeling for biobricks 3.1 Theoretical biobrick 3.2 9 PBAN biobrick and modeling result 4 Device modeling 4.1 Reference

Introduction of Modeling

In the modeling part, we make two models in our project to optimize our result and enhance the convenience of the device usage. In the first model, we demonstrate a model for our biobricks which is composed of Pcons, RBS, 9 PBAN, GFP, and terminator. And in the second model, a device model for two kinds of natural factor which are temperature and the wavelength of light. Before introducing the model, we would like to make a brief introduction for our modeling method and the modeling tool we used. The following contents we can devided into three parts: (1)Model software (2)Modeling for biobrick (3)Modeling for device.

Model software

MATLAB

MATLAB (matrix laboratory) is a numerical computing environment and fourth-generation programmingnk= 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

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.

Modeling for biobricks

In this project, 9 kinds of PBAN are used to attract 9 different kinds of insect into our device. Even though these 9 PBAN didn’t works the same in attracting different kinds of insect, 9 PBAN are all produced by E.coli that should get same production rate. Thus, we use a “Pcons+ RBS + GFP+ Term” as the theoretical condition of our PBAN biobrick (Pcons+ RBS+ PBAN+ GFP+ Term). By detecting the expression value from the theoretical biobrick, and modified by our PBAN biobrick expression. On the other hand, this modified model can not only fit a theoretical condition which prevents our model from operating bias, but also fit to a real condition. The following contents we devided into three parts: (1) Theoretical biobrick (2) 9 PBAN biobrick and modeling result

Theoretical biobrick

9 PBAN biobrick and modeling result

PBAN(BM)

<\p>result

PBAN(MB)

result

PBAN(AI)

result

PBAN(LD)

result

PBAN(HAH)

result

PBAN(AS)

result

PBAN(SI)

result

PBAN(AA)

result

Device modeling

We chose to use two different variables light and temperature to simulate the condition of Taiwan’s farmer normal use of our device, hope to find out the optimum conditions of the device

Variable Light-- we divide the wave of visible light into five parts－750, 630, 590, 525 and 460 nm, hoping to modeling all of visible light condition.

Temperature—we select five temperature between the highest and lowest average temperature last year (17.03。C /30.1。C) of the major city in Taiwan, and want to modeling all of temperature condition.

Experiment data

Reference