Optimal structural inference of signaling pathways from unordered and overlapping gene sets

Acharya, Lipi; Judeh, Thair; Wang, Guangdi; Zhu, Dongxiao

doi:10.1093/bioinformatics/btr696

Cited by 8 publications

(14 citation statements)

References 47 publications

(39 reference statements)

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“…Even without knowing the structure of the background molecular interaction network, some heuristic methods are able to reconstruct signaling pathways. For example, with the simulated annealing algorithm, Acharya et al [47] detected directed signaling pathways from all possible gene sets related to the pathways, and this method outperforms Bayesian networks on benchmark datasets.…”

Section: Heuristic Methodsmentioning

confidence: 99%

Computational Approaches for Identifying Signaling Pathways from Molecular Interaction Networks

Zhang¹,

Zhao²

2013

Current Bioinformatics

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Signaling pathways play central roles in responding to stimuli or transmitting signals from outside to inside of cells, which in turn regulate a series of complex biological processes that are vital for the function of cells. Unfortunately, the structure and function of most pathways are not complete or even not available. In the past decade, the availability of large amounts of high-throughput 'omics' data enables it possible to infer signaling pathways with computational methodologies that can help guide experiments in lab with low cost within short time. In this review, we present the latest progress being made in computational methodologies that are proposed for identifying signaling pathways from molecular interaction networks.

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Section: Heuristic Methodsmentioning

confidence: 99%

Computational Approaches for Identifying Signaling Pathways from Molecular Interaction Networks

Zhang¹,

Zhao²

2013

Current Bioinformatics

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“…Algorithm 1, based on the algorithm Network2GeneSets (47,48), extracts root to leaf linear paths or subpathways from the directed edges of the KEGG nonmetabolic pathways. A root r has zero incoming links and a positive number of outgoing links, whereas a leaf l has a positive number of incoming links and zero outgoing links (please refer to Supplementary Figure S3 for an example of Algorithm 1).…”

Section: Methodsmentioning

confidence: 99%

TEAK: Topology Enrichment Analysis frameworK for detecting activated biological subpathways

Judeh

Johnson

Kumar

et al. 2012

Nucleic Acids Research

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To mine gene expression data sets effectively, analysis frameworks need to incorporate methods that identify intergenic relationships within enriched biologically relevant subpathways. For this purpose, we developed the Topology Enrichment Analysis frameworK (TEAK). TEAK employs a novel in-house algorithm and a tailor-made Clique Percolation Method to extract linear and nonlinear KEGG subpathways, respectively. TEAK scores subpathways using the Bayesian Information Criterion for context specific data and the Kullback-Leibler divergence for case–control data. In this article, we utilized TEAK with experimental studies to analyze microarray data sets profiling stress responses in the model eukaryote Saccharomyces cerevisiae. Using a public microarray data set, we identified via TEAK linear sphingolipid metabolic subpathways activated during the yeast response to nitrogen stress, and phenotypic analyses of the corresponding deletion strains indicated previously unreported fitness defects for the dpl1Δ and lag1Δ mutants under conditions of nitrogen limitation. In addition, we studied the yeast filamentous response to nitrogen stress by profiling changes in transcript levels upon deletion of two key filamentous growth transcription factors, FLO8 and MSS11. Via TEAK we identified a nonlinear glycerophospholipid metabolism subpathway involving the SLC1 gene, which we found via mutational analysis to be required for yeast filamentous growth.

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“…In addition, using gene sets automatically accounts for the many-to-many gene dependency. Recent publications have demonstrated the promising potential of gene set based approaches (e.g., [ 25 – 27 ]). These network discovery approaches take gene sets as the direct structural information emitted from the underlying network, and infer the structure using computational approaches.…”

Section: Introductionmentioning

confidence: 99%

“…While there have been many successes in developing computational approaches for identifying potential genes and proteins involved in cell signaling [ 28 , 29 ], new methods are needed for identifying network structures that depict underlying signal cascading mechanisms. Besides few exceptions [ 25 , 26 , 30 , 31 ] most of the existing network inference approaches center around statistical causal interactions and pairwise similarities without explicit consideration of signal cascading activities within their frameworks. Although many annotated signaling pathways and tools for their analysis have become available in recent years [ 32 – 37 ], our current knowledge about signaling mechanisms is still very limited.…”

Section: Introductionmentioning

confidence: 99%

“…Indeed, a total of network structures can be constructed from m gene sets of lengths L , which may be a very large candidate pool even when the values of m and L are not high. And second, genetic algorithm may be more advantageous compared to previously proposed sampling or search strategies [ 25 , 26 ] in terms of its ability to avoid being trapped in local solution since it works with a population of solutions at each generation instead of a single solution. As a result, we translate our goal of signaling network inference into a maximization problem and devise a genetic algorithm based search scheme to locate the structure with the maximum synergy among active paths (the true network).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Network inference through synergistic subnetwork evolution

Acharya¹,

Reynolds

Zhu

2015

J Bioinform Sys Biology

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Study of signaling networks is important for a better understanding of cell behaviors e.g., growth, differentiation, metabolism, proptosis, and gaining deeper insights into the molecular mechanisms of complex diseases. While there have been many successes in developing computational approaches for identifying potential genes and proteins involved in cell signaling, new methods are needed for identifying network structures that depict underlying signal cascading mechanisms. In this paper, we propose a new computational approach for inferring signaling network structures from overlapping gene sets related to the networks. In the proposed approach, a signaling network is represented as a directed graph and is viewed as a union of many active paths representing linear and overlapping chains of signal cascading activities in the network. Gene sets represent the sets of genes participating in active paths without prior knowledge of the order in which genes occur within each path. From a compendium of unordered gene sets, the proposed algorithm reconstructs the underlying network structure through evolution of synergistic active paths. In our context, the extent of edge overlapping among active paths is used to define the synergy present in a network. We evaluated the performance of the proposed algorithm in terms of its convergence and recovering true active paths by utilizing four gene set compendiums derived from the KEGG database. Evaluation of results demonstrate the ability of the algorithm in reconstructing the underlying networks with high accuracy and precision.

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Optimal structural inference of signaling pathways from unordered and overlapping gene sets

Abstract: Source codes are available from http://dl.dropbox.com/u/16000775/sa_sc.zip.

Cited by 8 publications

References 47 publications

Computational Approaches for Identifying Signaling Pathways from Molecular Interaction Networks

Computational Approaches for Identifying Signaling Pathways from Molecular Interaction Networks

TEAK: Topology Enrichment Analysis frameworK for detecting activated biological subpathways

Network inference through synergistic subnetwork evolution

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