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.

Download full-text


Available from: Nevan Krogan, Dec 25, 2013
1 Follower
49 Reads
  • 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.
    Frontiers in Genetics 09/2015; 6:257. DOI:10.3389/fgene.2015.00257
  • 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.
    08/2015; 2:44. DOI:10.3389/fmolb.2015.00044
  • Source
    • "Inferred networks can be used to generate testable hypotheses that are context-specific in the sense of reflecting regulatory events in the specific cells under study (Maher, 2012; Hill et al., 2012). In disease biology, such context-specific networks can be used to shed light on disease-specific processes and thereby inform drug targeting and personalised medicine approaches (Ideker and Krogan, 2012; Akbani et al., 2014). "
    [Show abstract] [Hide abstract]
    ABSTRACT: Graphical models are widely used to study biological networks. Interventions on network nodes are an important feature of many experimental designs for the study of biological networks. In this paper, we put forward a causal variant of dynamic Bayesian networks (DBNs) for the purpose of modeling time-course data with interventions. The models inherit the simplicity and computational efficiency of DBNs but allow interventional data to be integrated into network inference. We show empirical results, on both simulated and experimental data, that demonstrate the need to appropriately handle interventions when interventions form part of the design.
    The Annals of Applied Statistics 04/2015; 9(1). DOI:10.1214/15-AOAS806 · 1.46 Impact Factor
Show more