Discovering regulatory and signalling circuits in molecular 
interaction networks

Ideker, Trey; Ozier, Owen; Schwikowski, Benno; Siegel, Andrew F.

doi:10.1093/bioinformatics/18.suppl_1.s233

Cited by 1,083 publications

(1,142 citation statements)

References 19 publications

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“…Cytoscape with the jActiveModule plug-in was used to identify neighborhoods in the networks associated with differentially expressed genes (34,62). The merged human interactome developed by Garrow (http://www.cytoscape.org/cgi-bin/moin.cgi/Data_Sets), which contains 10,344 nodes and 53,526 interactions, was used in this analysis.…”

Section: Principal Component Analysismentioning

confidence: 99%

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Physiological and toxicological transcriptome changes in HepG2 cells exposed to copper

Song

Freedman

2009

Physiological Genomics

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Copper is an essential trace element; however, at supraphysiological levels, it can be extremely toxic. Microarray data from HepG2 cells exposed to 100, 200, 400, and 600 μM copper for 4, 8, 12 and 24 h were generated and analyzed. Principal components, K-means, and hierarchical clustering, interactome, and pathway mapping analyses indicated that these exposure conditions induce physiological and toxicological changes in the HepG2 transcriptome. As a general trend, when the level of toxicity increases, the number and diversity of affected genes, Gene Ontology categories, regulatory pathways, and complexity of interactomes increase. Physiological responses to copper include transition metal ion binding and responses to stress/stimulus, whereas toxicological responses include apoptosis, morphogenesis, and negative regulation of biomolecule metabolism. The global gene expression profile was overlaid onto biomolecular interaction networks and signal transduction cascades using pathway mapping and interactome identification. This analysis indicated that copper modulates signal transduction pathways associated with MAPK, NF-κB, death receptor, IGF-I, hypoxia, IL-10, IL-2, IL-6, EGF, Toll-like receptor, protein ubiquitination, xenobiotic metabolism, leukocyte extravasation, complement and coagulation, and sonic hedgehog signaling. These results provide insights into the global and molecular mechanisms regulating the physiological and toxicological responses to metal exposure.

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Section: Principal Component Analysismentioning

confidence: 99%

“…Interactomes were identified based on copper concentration or exposure time using Cytoscape and the jActiveModule (34). The top 15 interactomes for each copper concentration or each exposure time are shown in the Supplemental Material (Supplemental Tables 1 and 2, respectively).…”

Section: Integration Of Microarray Data Into Protein Interaction Netwmentioning

confidence: 99%

Physiological and toxicological transcriptome changes in HepG2 cells exposed to copper

Song

Freedman

2009

Physiological Genomics

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“…Several methods were developed to identify modules of nodes which are jointly affected by the condition of interest. Two publicly available examples are the Cytoscape plugin jActiveModules (Ideker et al ., 2002) and the R package BioNet (Beisser et al ., 2010). …”

Section: From Omics To Systems Biologymentioning

confidence: 99%

Integration of ‘omics’ data in aging research: from biomarkers to systems biology

et al. 2015

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SummaryAge is the strongest risk factor for many diseases including neurodegenerative disorders, coronary heart disease, type 2 diabetes and cancer. Due to increasing life expectancy and low birth rates, the incidence of age‐related diseases is increasing in industrialized countries. Therefore, understanding the relationship between diseases and aging and facilitating healthy aging are major goals in medical research. In the last decades, the dimension of biological data has drastically increased with high‐throughput technologies now measuring thousands of (epi) genetic, expression and metabolic variables. The most common and so far successful approach to the analysis of these data is the so‐called reductionist approach. It consists of separately testing each variable for association with the phenotype of interest such as age or age‐related disease. However, a large portion of the observed phenotypic variance remains unexplained and a comprehensive understanding of most complex phenotypes is lacking. Systems biology aims to integrate data from different experiments to gain an understanding of the system as a whole rather than focusing on individual factors. It thus allows deeper insights into the mechanisms of complex traits, which are caused by the joint influence of several, interacting changes in the biological system. In this review, we look at the current progress of applying omics technologies to identify biomarkers of aging. We then survey existing systems biology approaches that allow for an integration of different types of data and highlight the need for further developments in this area to improve epidemiologic investigations.

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“…This observation is exploited to identify novel genetic markers based on network connectivity [8,9,10]. For the identification of differentially expressed subnetworks with respect to GAL80 deletion in yeast, Ideker et al [11] propose a method that is based on searching for connected subgraphs with high aggregate significance of individual differential expression. Variations of this method are shown to be effective in identifying multiple genetic markers in prostate cancer [12], melanoma [13], diabetes [14], and others [15,16,17].…”

Section: Introductionmentioning

confidence: 99%

Subnetwork State Functions Define Dysregulated Subnetworks in Cancer

Chowdhury

Nibbe

Chance

et al. 2010

Lecture Notes in Computer Science

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Abstract. Emerging research demonstrates the potential of proteinprotein interaction (PPI) networks in uncovering the mechanistic bases of cancers, through identification of interacting proteins that are coordinately dysregulated in tumorigenic and metastatic samples. When used as features for classification, such coordinately dysregulated subnetworks improve diagnosis and prognosis of cancer considerably over single-gene markers. However, existing methods formulate coordination between multiple genes through additive representation of their expression profiles and utilize greedy heuristics to identify dysregulated subnetworks, which may not be well suited to the potentially combinatorial nature of coordinate dysregulation. Here, we propose a combinatorial formulation of coordinate dysregulation and decompose the resulting objective function to cast the problem as one of identifying subnetwork state functions that are indicative of phenotype. Based on this formulation, we show that coordinate dysregulation of larger subnetworks can be bounded using simple statistics on smaller subnetworks. We then use these bounds to devise an efficient algorithm, Crane, that can search the subnetwork space more effectively than simple greedy algorithms. Comprehensive cross-classification experiments show that subnetworks identified by Crane significantly outperform those identified by greedy algorithms in predicting metastasis of colorectal cancer (CRC).

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Discovering regulatory and signalling circuits in molecular interaction networks

Cited by 1,083 publications

References 19 publications

Physiological and toxicological transcriptome changes in HepG2 cells exposed to copper

Physiological and toxicological transcriptome changes in HepG2 cells exposed to copper

Integration of ‘omics’ data in aging research: from biomarkers to systems biology

Subnetwork State Functions Define Dysregulated Subnetworks in Cancer

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