Differential network biology

By Trey Ideker, Nevan J. Krogan. Molecular Systems Biology, 2012

777 citations recorded by the source corpus.

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.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/c7cb4ab6226ffb5220ae1d9d6ab7552d7bea2e0a

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Citations in the corpus

Systemic Immunity is Required for Effective Cancer Immunotherapy (2017, neutral): “Network analyses have proven effective for identifying dominant elements of a systems-level response (Hotson et al., 2016; Ideker and Krogan, 2012).”
Detecting subnetwork-level dynamic correlations (2017, background): “However, the expression levels of the biological units are the outcome of complex biological regulatory networks, the links of which may be turned on and off in response to certain biological conditions (Barzel and Barabasi, 2013; Ideker and Krogan, 2012; Luscombe et al., 2004; Ocone et al., 2013).”
Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review (2012, background): “The examination of differential networks of cancer stages, or networks of drug treated and un-treated cells, is one of the first steps in possible solutions (Ideker & Krogan, 2012).”
NetworkAnalyst - integrative approaches for protein–protein interaction network analysis and visual exploration (2014, background): “Therefore, this approach is unable to identify new interactions that are condition-specific (30).”
Joint estimation of multiple networks from time course data (2013, background): “1) requires a penalty functional d : G × G → R and a prior network G ∈ G, with the former capturing how close a candidate network G ∈ G is to the latter (Imoto et al., 2003; Mukherjee and Speed, 2008). We discuss choice of G below. Given G, a simple choice of penalty function d is the structural Ham”
Global Testing and Large-Scale Multiple Testing for High-Dimensional Covariance Structures (2017, methodology): “…inference for a given Gaussian graph, it is of significant interest in many applications to understand whether and how the network changes between disease states (de la Fuente 2010; Ideker and Krogan 2012; Hudson et al. 2009; Li et al. 2007; Danaher et al. 2014; Zhao et al. 2014; Xia et al. 2015).”
RegNetwork: an integrated database of transcriptional and post-transcriptional regulatory networks in human and mouse (2015, neutral): “certain disease) regulatory machineries (31).”
Translation of Genotype to Phenotype by a Hierarchy of Cell Subsystems (2016, background): “Second, some biological processes are likely not required for the phenotype tested— growth of cells in rich media—but instead may drive a wide variety of other phenotypes (Dowell et al., 2010; Hillenmeyer et al., 2008; Ideker and Krogan, 2012; Lee et al., 2014).”
Human protein interaction networks across tissues and diseases (2015, background): “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).”
Potential unsatisfiability of cyclic constraints on stochastic biological networks biases selection towards hierarchical architectures (2015, background): “This fact has, in part, motivated computational biologists to develop a large collection of algorithms to infer aspects of this structure [1,42] and experimental biologists to compare networks on the basis of their hierarchical and modular architecture [43].”
Integrative approaches for finding modular structure in biological networks (2013, methodology): “Analogous to differential expression analysis, differential network analysis involves pairwise subtraction of interactions that have been mapped in different experimental conditions 129 .”