Topic
Interaction network
About: Interaction network is a research topic. Over the lifetime, 2700 publications have been published within this topic receiving 113372 citations.
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TL;DR: The topology of this network is hub-based, and much more connected than previously thought, and the wide tissue expression pattern of NRs create a highly competitive environment for the common heterodimerising partners.
Abstract: The nuclear receptors are a large family of eukaryotic transcription factors that constitute major pharmacological targets. They exert their combinatorial control through homotypic heterodimerisation. Elucidation of this dimerisation network is vital in order to understand the complex dynamics and potential cross-talk involved. Phylogeny, protein-protein interactions, protein-DNA interactions and gene expression data have been integrated to provide a comprehensive and up-to-date description of the topology and properties of the nuclear receptor interaction network in humans. We discriminate between DNA-binding and non-DNA-binding dimers, and provide a comprehensive interaction map, that identifies potential cross-talk between the various pathways of nuclear receptors. We infer that the topology of this network is hub-based, and much more connected than previously thought. The hub-based topology of the network and the wide tissue expression pattern of NRs create a highly competitive environment for the common heterodimerising partners. Furthermore, a significant number of negative feedback loops is present, with the hub protein SHP [NR0B2] playing a major role. We also compare the evolution, topology and properties of the nuclear receptor network with the hub-based dimerisation network of the bHLH transcription factors in order to identify both unique themes and ubiquitous properties in gene regulation. In terms of methodology, we conclude that such a comprehensive picture can only be assembled by semi-automated text-mining, manual curation and integration of data from various sources.
41 citations
TL;DR: It is found that the ABA dependency of PYL-PP2C interactions is contingent on the identity of the PP2Cs, and among 238 candidate substrates for ABA-activated protein kinases, 69 are putative ZmSnRK2 substrates.
Abstract: Key message
We defined a comprehensive core ABA signaling network in monocot maize, including the gene expression, subcellular localization and interaction network of ZmPYLs, ZmPP2Cs, ZmSnRK2s and the putative substrates.
41 citations
TL;DR: A simple method is presented to harness two-hybrid data to obtain negative protein-protein interaction datasets, which are validated using other available experimental data and illustrate the use of a negative dataset in the evaluation of the InterPreTS interaction prediction method.
41 citations
TL;DR: This mini-review discusses the available techniques and methods for qualitative and quantitative elucidation of protein-protein interaction networks, and summarizes the down-stream computational strategies for identification and quantification of interactions from those techniques.
Abstract: Studying protein-protein interaction networks provide key evidence for the underlying molecular mechanisms. Mass spectrometry-based proteomic approaches have been playing a pivotal role in deciphering these interaction networks, along with precise quantification for individual interactions. In this mini-review we discuss the available techniques and methods for qualitative and quantitative elucidation of protein-protein interaction networks. We then summarize the down-stream computational strategies for identification and quantification of interactions from those techniques. Finally, we highlight the challenges and limitations of current computational pipelines in eliminating false positive interactors, followed by a summary of the innovative algorithms to address these issues, along with the scope for future improvements.
41 citations
TL;DR: In this article, the emergence of global patterns in large groups in first and second-order multiagent systems is studied, focusing on two ingredients that influence the dynamics: the interaction network and the state space.
Abstract: In the present chapter, we study the emergence of global patterns in large groups in first- and second-order multiagent systems, focusing on two ingredients that influence the dynamics: the interaction network and the state space. The state space determines the types of equilibrium that can be reached by the system. Meanwhile, convergence to specific equilibria depends on the connectivity of the interaction network and on the interaction potential. When the system does not satisfy the necessary conditions for convergence to the desired equilibrium, control can be exerted, both on finite-dimensional systems and on their mean-field limit.
41 citations