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


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.

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Available from: Nevan Krogan, Dec 25, 2013
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    • "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. "
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    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.
    Frontiers in Genetics 09/2015; 6:257. DOI:10.3389/fgene.2015.00257
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    • "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 . "
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    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.
    08/2015; 2:44. DOI:10.3389/fmolb.2015.00044
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    • "For each cluster, we then evaluate whether it carries a statistically significant gender effect, i.e. coordinated up-or down-regulation of all metabolites in the cluster. The general approach of evaluating phenotypic effects on interaction networks has recently been described as the new field of ''differential network biology'' (Ideker and Krogan 2012). "
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    ABSTRACT: The susceptibility for various diseases as well as the response to treatments differ considerably between men and women. As a basis for a gender-specific personalized healthcare, an extensive characterization of the molecular differences between the two genders is required. In the present study, we conducted a large-scale metabolomics analysis of 507 metabolic markers measured in serum of 1756 participants from the German KORA F4 study (903 females and 853 males). One-third of the metabolites show significant differences between males and females. A pathway analysis revealed strong differences in steroid metabolism, fatty acids and further lipids, a large fraction of amino acids, oxidative phosphorylation, purine metabolism and gamma-glutamyl dipeptides. We then extended this analysis by a network-based clustering approach. Metabolite interactions were estimated using Gaussian graphical models to get an unbiased, fully data-driven metabolic network representation. This approach is not limited to possibly arbitrary pathway boundaries and can even include poorly or uncharacterized metabolites. The network analysis revealed several strongly gender-regulated submodules across different pathways. Finally, a gender-stratified genome-wide association study was performed to determine whether the observed gender differences are caused by dimorphisms in the effects of genetic polymorphisms on the metabolome. With only a single genome-wide significant hit, our results suggest that this scenario is not the case. In summary, we report an extensive characterization and interpretation of gender-specific differences of the human serum metabolome, providing a broad basis for future analyses.
    Metabolomics 08/2015; 11(6). DOI:10.1007/s11306-015-0829-0 · 3.86 Impact Factor
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