Team:USTC-Software/Project/Method

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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.