# Team:USTC-Software/Project/Method

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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.

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.

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There are four parts of methodologies: Database, Operon Theory and Regulatory Model, Forward Analysis and Reverse Analysis. There are four parts of methodologies: Database, Operon Theory and Regulatory Model, Forward Analysis and Reverse Analysis. Line 100: Line 100:   The format of regulation database:
The format of regulation database:
TF_name    TF_name    +/-/+-

TF_name    TF_name    +/-/+-

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The regulation of TFs has been put into a square matrix whose row is the regulator and column is the one regulated by. To make our GRN as complete as possible, the regulation between TF and genes has joined into the matrix. The one-way interaction results that we must read the TF in order to fulfill the regulator before completing the TF to gene's regulation in the same way of TF to TF.

The regulation of TFs has been put into a square matrix whose row is the regulator and column is the one regulated by. To make our GRN as complete as possible, the regulation between TF and genes has joined into the matrix. The one-way interaction results that we must read the TF in order to fulfill the regulator before completing the TF to gene's regulation in the same way of TF to TF.

The format of regulation database:
The format of regulation database:
TF_name    Gene_name    +/-/+-

TF_name    Gene_name    +/-/+-

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At last, a regulatory matrix whose row represents regulate gene (TF) and whose column represents gene regulated by (TF+Gene) has been output into a file called “old_GRN” in root directory. The values in GRN matrix are regulations in which “1” means positive activation, “-1” means repression and “0” means no relationship. There have been some regulations both positive and negative identified regulations are determined by the experimental environment. As a result, our software picks out those uncertain genes and stores them into a file named “uncertain_database”.

At last, a regulatory matrix whose row represents regulate gene (TF) and whose column represents gene regulated by (TF+Gene) has been output into a file called “old_GRN” in root directory. The values in GRN matrix are regulations in which “1” means positive activation, “-1” means repression and “0” means no relationship. There have been some regulations both positive and negative identified regulations are determined by the experimental environment. As a result, our software picks out those uncertain genes and stores them into a file named “uncertain_database”.

The format of uncertain database:
The format of uncertain database:
Line 118: Line 118:   The format of Gene Info database:
The format of Gene Info database:
ID_assigned_by_RegulonDB    Gene_name    Left_end_position    Right_end_position    DNA_strand    Product_type     Product_name    Start_codon_sequence     Stop_codon_sequence    Gene_sequence

ID_assigned_by_RegulonDB    Gene_name    Left_end_position    Right_end_position    DNA_strand    Product_type     Product_name    Start_codon_sequence     Stop_codon_sequence    Gene_sequence

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The label of the map vector is gene name which will be picked out based on the names read in regulation matrix before. It is really fast using the binary tree method to find the specific genetic information and store them into a specific object. Those information includes gene ID, left position, right position, gene description and gene sequence. The gene ID is used to link to RegulonDB's gene details; The left position is used to find its specific transcription unit; The right position is used to figure out the base amount; The description of genes is used to distinguish the RNA and protein; The sequence is used to predict the regulation by alignment. The label of the map vector is gene name which will be picked out based on the names read in regulation matrix before. It is really fast using the binary tree method to find the specific genetic information and store them into a specific object. Those information includes gene ID, left position, right position, gene description and gene sequence. The gene ID is used to link to RegulonDB's gene details; The left position is used to find its specific transcription unit; The right position is used to figure out the base amount; The description of genes is used to distinguish the RNA and protein; The sequence is used to predict the regulation by alignment. Line 130: Line 130:   The format of TU info database:
The format of TU info database:
Operon_name    Unit_name    promoter_name    Transcription_start_site ......

Operon_name    Unit_name    promoter_name    Transcription_start_site ......

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The principle of fetching information of promoters is same as fetching genes's. Our software stores the promoter information from the file named “promoter_info” in a “map” which could be used to pick out the promoter sequence by searching promoter name through binary tree method.

The principle of fetching information of promoters is same as fetching genes's. Our software stores the promoter information from the file named “promoter_info” in a “map” which could be used to pick out the promoter sequence by searching promoter name through binary tree method.

The format of Promoter Info database:
The format of Promoter Info database:
Promoter_ID_assigned_by_RegulonDB    Promoter_name

Promoter_ID_assigned_by_RegulonDB    Promoter_name

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The sequence of promoter will be used in the alignment method in next module which could make a prediction of exogenous genes' regulation pattern. The sequence of promoter will be used in the alignment method in next module which could make a prediction of exogenous genes' regulation pattern.

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

## Reference

Lei Z, Dai Y. Assessing protein similarity with Gene Ontology and its use in subnuclear localization prediction[J]. BMC bioinformatics, 2006, 7(1): 491.

Ramoni M F, Sebastiani P, Kohane I S. Cluster analysis of gene expression dynamics[J]. Proceedings of the National Academy of Sciences, 2002, 99(14): 9121-9126.

Thieffry D, Huerta A M, Pérez‐Rueda E, et al. From specific gene regulation to genomic networks: a global analysis of transcriptional regulation in Escherichia coli[J]. Bioessays, 1998, 20(5): 433-440.

Eberhart R, Kennedy J. A new optimizer using particle swarm theory[C]//Micro Machine and Human Science, 1995. MHS'95., Proceedings of the Sixth International Symposium on. IEEE, 1995: 39-43.

Jacob F, Perrin D, Sánchez C, et al. L'opéron: groupe de gènes à expression coordonnée par un opérateur [CR Acad. Sci. Paris 250 (1960) 1727–1729][J]. Comptes rendus biologies, 2005, 328(6): 514-520.

Needleman S B, Wunsch C D. A general method applicable to the search for similarities in the amino acid sequence of two proteins[J]. Journal of molecular biology, 1970, 48(3): 443-453.

Gama-Castro S, Jiménez-Jacinto V, Peralta-Gil M, et al. RegulonDB (version 6.0): gene regulation model of Escherichia coli K-12 beyond transcription, active (experimental) annotated promoters and Textpresso navigation[J]. Nucleic acids research, 2008, 36(suppl 1): D120-D124.

Martınez-Antonio A, Collado-Vides J. Identifying global regulators in transcriptional regulatory networks in bacteria[J]. Current opinion in microbiology, 2003, 6(5): 482-489.

Salgado H, Moreno-Hagelsieb G, Smith T F, et al. Operons in Escherichia coli: genomic analyses and predictions[J]. Proceedings of the National Academy of Sciences, 2000, 97(12): 6652-6657.

Thieffry D, Salgado H, Huerta A M, et al. Prediction of transcriptional regulatory sites in the complete genome sequence of Escherichia coli K-12[J]. Bioinformatics, 1998, 14(5): 391-400.