Network-based regulatory pathways analysis

Xiong, Momiao; Zhao, Jinying; Xiong, Hao

doi:10.1093/bioinformatics/bth201

Cited by 20 publications

(13 citation statements)

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“…For this purpose, we consider ten gene regulatory networks as shown in Fig. 1 [13], Fig. 2 [23], Fig.…”

Section: Resultsmentioning

confidence: 99%

“…S6 [28]. The results have been compared with that of the existing extreme pathway analysis [12], [13]. Biological validation of the results is also included.…”

Section: Resultsmentioning

confidence: 99%

“…1 represents a part of apoptosis regulation [13]. Apoptosis is one of the main types of programmed cell death, which involves a series of biochemical events leading to specific cell morphology, characteristics and ultimately death of cells.…”

Section: Resultsmentioning

confidence: 99%

“…The effectiveness of the present method is demonstrated on ten gene regulatory networks. The results are compared with those obtained from the existing extreme pathway analysis [12], [13]. Results have been validated appropriately from biological point of view.…”

Section: Introductionmentioning

confidence: 98%

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Gradient Descent Optimization in Gene Regulatory Pathways

Das

Mukhopadhyay

2010

PLoS ONE

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BackgroundGene Regulatory Networks (GRNs) have become a major focus of interest in recent years. Elucidating the architecture and dynamics of large scale gene regulatory networks is an important goal in systems biology. The knowledge of the gene regulatory networks further gives insights about gene regulatory pathways. This information leads to many potential applications in medicine and molecular biology, examples of which are identification of metabolic pathways, complex genetic diseases, drug discovery and toxicology analysis. High-throughput technologies allow studying various aspects of gene regulatory networks on a genome-wide scale and we will discuss recent advances as well as limitations and future challenges for gene network modeling. Novel approaches are needed to both infer the causal genes and generate hypothesis on the underlying regulatory mechanisms.MethodologyIn the present article, we introduce a new method for identifying a set of optimal gene regulatory pathways by using structural equations as a tool for modeling gene regulatory networks. The method, first of all, generates data on reaction flows in a pathway. A set of constraints is formulated incorporating weighting coefficients. Finally the gene regulatory pathways are obtained through optimization of an objective function with respect to these weighting coefficients. The effectiveness of the present method is successfully tested on ten gene regulatory networks existing in the literature. A comparative study with the existing extreme pathway analysis also forms a part of this investigation. The results compare favorably with earlier experimental results. The validated pathways point to a combination of previously documented and novel findings.ConclusionsWe show that our method can correctly identify the causal genes and effectively output experimentally verified pathways. The present method has been successful in deriving the optimal regulatory pathways for all the regulatory networks considered. The biological significance and applicability of the optimal pathways has also been discussed. Finally the usefulness of the present method on genetic engineering is depicted with an example.

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“…For this purpose, we consider ten gene regulatory networks as shown in Fig. 1 [13], Fig. 2 [23], Fig.…”

Section: Resultsmentioning

confidence: 99%

“…S6 [28]. The results have been compared with that of the existing extreme pathway analysis [12], [13]. Biological validation of the results is also included.…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

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Gradient Descent Optimization in Gene Regulatory Pathways

Das

Mukhopadhyay

2010

PLoS ONE

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“…,Özögür et al (2005),Xiong et al (2004). If there are N reactions, if v i (E) is the rate of the ith gene in the reaction network (i =…”

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confidence: 99%

Optimization of gene-environment networks in the presence of errors and uncertainty with Chebychev approximation

Weber

Taylan

Gök

et al. 2008

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Current awareness on comparative and functional genomics

2005

Comp Funct Genom

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Weed genomics: New tools to understand weed biology (Review). Trends Plant Sci 9: (8) 391. Baxevanis AD. 2003. Using genomic databases for sequence-based biological discovery (Review). Mol Med 9: (9-12) 185. Benning C. 2004. Genetic mutant screening by direct metabolite analysis (Review). Anal Biochem 332: (1) 1. Brouzes E, Farge E. 2004. Interplay of mechanical deformation and patterned gene expression in developing embryos. Curr Opin Genet Dev 14: (4) 367. Brunner AM, Nilsson O. 2004. Revisiting tree maturation and floral initiation in the poplar functional genomics era (Review). New Phytol 164: (1) 43.

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Network-based regulatory pathways analysis

Cited by 20 publications

References 19 publications

Gradient Descent Optimization in Gene Regulatory Pathways

Gradient Descent Optimization in Gene Regulatory Pathways

Optimization of gene-environment networks in the presence of errors and uncertainty with Chebychev approximation

Current awareness on comparative and functional genomics

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