Team:USTC-Software/Project/Method

From 2013.igem.org

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An example about filtring and consistency is presented in “Example”.
An example about filtring and consistency is presented in “Example”.
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     <p><h4 id="2.3">2.3 Regulation Calculation</h4></p>
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     <p><h4 id="2.3" style="display:block;">2.3 Regulation Calculation</h4></p>
     <p align="justify">If there is a three-unit network and they interact with each other as it is shown in the figure.
     <p align="justify">If there is a three-unit network and they interact with each other as it is shown in the figure.
The regulation is described by the GRN matrix.</p>
The regulation is described by the GRN matrix.</p>

Revision as of 01:07, 28 October 2013

Header2


Methodologies

Methodologies

In order to simulate the GRN's working and analyze the changing after exogenous gene imported, some advanced algorithms and classical methods are employed in the software. These algorithms and methods include Binary Tree method, Needle-Wunsch Algorithm, Decision Tree method, Hill Equation and PSO Algorithm.

There are four parts of methodologies: Database, Operon Theory and Regulatory Model, Forward Analysis and Reverse Analysis.

Database

Abstract
Fetch Regulation
Fetch Gene Info
Fetch Promoter Info
Integration

Our software integrates all information we picked out about genes and generates a file named “all_info” —— all information about genes —— for the output graphical interface's reading. In the meanwhile, the array of objects containing all information has been stored in computer memory which greatly improve the computing speed of our software.

  The format of all_info database:
    No.    promoter_sequence    gene_sequence    gene_name    ID    left_position    right_position    promoter_name     description
The fetching module generates three files: old_GRN, all_info and uncertain_database.

Operon Theory and Regulatory Model

Operon Theory
Regulatory Model
Similarity and Homology

Forward Analysis

Construct New GRN
Network Model
Evaluate Network

Reverse Analysis

Virtual Gene
Expression Range
Particle Swarm Optimaztion
Locate Optimal Target