Article

Differential network biology

Departments of Medicine and Bioengineering, University of California San Diego, La Jolla, CA, USA.
Molecular Systems Biology (Impact Factor: 10.87). 01/2012; 8(article 565):565. DOI: 10.1038/msb.2011.99
Source: PubMed

ABSTRACT

Protein and genetic interaction maps can reveal the overall physical and functional landscape of a biological system. To date, these interaction maps have typically been generated under a single condition, even though biological systems undergo differential change that is dependent on environment, tissue type, disease state, development or speciation. Several recent interaction mapping studies have demonstrated the power of differential analysis for elucidating fundamental biological responses, revealing that the architecture of an interactome can be massively re-wired during a cellular or adaptive response. Here, we review the technological developments and experimental designs that have enabled differential network mapping at very large scales and highlight biological insight that has been derived from this type of analysis. We argue that differential network mapping, which allows for the interrogation of previously unexplored interaction spaces, will become a standard mode of network analysis in the future, just as differential gene expression and protein phosphorylation studies are already pervasive in genomic and proteomic analysis.

Download full-text

Full-text

Available from: Nevan Krogan, Dec 25, 2013
  • Source
    • "Recently, a " differential network " (DN) technique (Ideker and Krogan, 2012) has been developed to extract disease-related edges by comparing interactions occurring across different static networks. Since genes and gene products operate not as a single unit but as part of a biochemical interaction, we assume that molecular interactions are disrupted by epigenetic or genetic factors; the disruption ultimately leads to molecular dysfunction. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Despite recent advances in osteosarcoma diagnosis and therapy, much remains unclear about the molecular mechanisms involved in the disorder, and the discovery of novel drug-targeted genes is essential. We explored the potential molecular mechanisms and target genes involved in the development and progression of osteosarcoma. First, we identified the differentially expressed genes in osteosarcoma patients and matching normal controls. We then constructed a differential expression network based on differential and non-differential interactions. Pathway-enrichment analysis was performed based on the nodes contained in the main differential expression network. Centrality analysis was used to select hub genes that may play vital roles in the progression of human osteosarcoma. Our research revealed a total of 176 differentially expressed genes including 82 upregulated and 94 downregulated genes. A differential expression network was constructed that included 992 gene pairs (1043 nodes). Pathway-enrichment analysis indicated that the nodes in the differential expression network were mainly enriched in several pathways such as those involved in cancer, cell cycle, ubiquitin-mediated proteolysis, DNA replication, ribosomes, T-cell receptor signaling, spliceosomes, neurotrophin signaling, oxidative phosphorylation, and tight junctions. Six hub genes (APP, UBC, CAND1, RPA, YWHAG, and NEDD8) were discovered; of these, two genes (UBC and RPA) were also found to be disease genes. Our study predicted that UBC and RPA had potential as target genes for the diagnosis and treatment of osteosarcoma.
    Preview · Article · Dec 2015 · Genetics and molecular research: GMR
  • Source
    • "One of the more prominent initiatives is The Cancer Genome Atlas (TCGA) (Weinstein et al., 2013), which is actively mapping genomic, transcriptomic, proteomic, and epigenomic changes in cancerous tissues compared to normal tissues. These measurements shed light on the parts of the interactome that are active in these diverse contexts, although direct experimentation is required to reveal the actual PPI changes, in particular the formation of novel interactions (Ideker and Krogan, 2012). Below we discuss efforts to harness these context-specific molecular expression profiles to elucidate network properties of human tissues and to identify interactionbased disease mechanisms. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Protein interaction networks are an important framework for studying protein function, cellular processes, and genotype-to-phenotype relationships. While our view of the human interaction network is constantly expanding, less is known about networks that form in biologically important contexts such as within distinct tissues or in disease conditions. Here we review efforts to characterize these networks and to harness them to gain insights into the molecular mechanisms underlying human disease.
    Full-text · Article · Sep 2015 · Frontiers in Genetics
  • Source
    • "In an attempt to overcome these challenges , several groups introduced the use of GEMs to place omics data in the context of the cellular metabolism ( Palsson , 2009 ; Yizhak et al . , 2010 ; Ideker and Krogan , 2012 ) . GEMs can be reconstructed based on high - throughput omics data , but they also serve as a computational framework to analyze and interpret such data as a network where the nodes represent the substrates / products and the edges the reactions , like the schematic representation in Figure 1D . "
    [Show abstract] [Hide abstract]
    ABSTRACT: Systems biology is an important approach for deciphering the complex processes in health maintenance and the etiology of metabolic diseases. Such integrative methodologies will help better understand the molecular mechanisms involved in growth and development throughout childhood, and consequently will result in new insights about metabolic and nutritional requirements of infants, children and adults. To achieve this, a better understanding of the physiological processes at anthropometric, cellular and molecular level for any given individual is needed. In this respect, novel omics technologies in combination with sophisticated data modeling techniques are key. Due to the highly complex network of influential factors determining individual trajectories, it becomes imperative to develop proper tools and solutions that will comprehensively model biological information related to growth and maturation of our body functions. The aim of this review and perspective is to evaluate, succinctly, promising data analysis approaches to enable data integration for clinical research, with an emphasis on the longitudinal component. Approaches based on empirical and mechanistic modeling of omics data are essential to leverage findings from high dimensional omics datasets and enable biological interpretation and clinical translation. On the one hand, empirical methods, which provide quantitative descriptions of patterns in the data, are mostly used for exploring and mining datasets. On the other hand, mechanistic models are based on an understanding of the behavior of a system's components and condense information about the known functions, allowing robust and reliable analyses to be performed by bioinformatics pipelines and similar tools. Herein, we will illustrate current examples, challenges and perspectives in the applications of empirical and mechanistic modeling in the context of childhood metabolic health research.
    Full-text · Article · Aug 2015
Show more

We use cookies to give you the best possible experience on ResearchGate. Read our cookies policy to learn more.