Team:USTC-Software/Project/Method

From 2013.igem.org

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<h2>Reverse Analysis</h2>
<h2>Reverse Analysis</h2>
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<div class="jobs_trigger"><strong>Virtual Gene</strong></div>
<div class="jobs_trigger"><strong>Virtual Gene</strong></div>
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<p align="justify">Before reverse analysis, we use the same idea about constructing a new GRN. So we create a virtual gene which replace the gene what users want to get. In calculation, it means that we add a row and a column to the matrix of GRN.</p>
<p align="justify">Before reverse analysis, we use the same idea about constructing a new GRN. So we create a virtual gene which replace the gene what users want to get. In calculation, it means that we add a row and a column to the matrix of GRN.</p>
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<div class="jobs_trigger"><strong>Expression Range</strong></div>
<div class="jobs_trigger"><strong>Expression Range</strong></div>
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It is a must that figuring out the strongest and weakest expression strength before inputting the extreme cases into the target expression. The way to find out the strongest and weakest expression is modeling the GRN's steady state by a large amount of random regulation from -1 and 1. We ran it for 1000 times to get the range of gene expression. On the other hand, the expression of genes unpicked by the users should be stable as much as possible. The initial strength of expression is calculated by modeling the original GRN with Hill's equation.
It is a must that figuring out the strongest and weakest expression strength before inputting the extreme cases into the target expression. The way to find out the strongest and weakest expression is modeling the GRN's steady state by a large amount of random regulation from -1 and 1. We ran it for 1000 times to get the range of gene expression. On the other hand, the expression of genes unpicked by the users should be stable as much as possible. The initial strength of expression is calculated by modeling the original GRN with Hill's equation.
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<div class="jobs_trigger"><strong>Particle Swarm Optimaztion</strong></div>
<div class="jobs_trigger"><strong>Particle Swarm Optimaztion</strong></div>
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<div class="jobs_trigger" style="display:none;"><strong>Locate Optimal Target</strong></div>
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<p align="justify">To improve the efficiency of choosing a suitable gene after getting a series of regulatory value, our software picks out some obvious regulation. The value of regulation is between -1 to 1 in which -1 means negative effect and 1 means positive effect. As a result, what our software has done is filtering out the absolute value which is lower than 0.9. Because the difference of regulatory intensity lower than 0.1 has very little effect to the stable expression, the final result of regulation is indicated by Boolean value.</br></br>
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The format of regulatory prediction in “Result”:</br>
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Gene_name->Gene_name    regulation(+/-)
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Revision as of 15:10, 27 October 2013

Slide

Take a gNAP before wearing your gloves! Genetic Network Analyze and Predict
The sketch and final GUI of gNAP!
We compare the result of our software with gene expression profile in literature.
We are USTC-Software!


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