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This model of network arrangement differs from the client-server model, where communication is usually to and from a central server.
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We believe this novel finding supports the model of network robustness through gene duplication [ 53], and it also has very interesting implications regarding the selection of the right candidate genes and experimental method in future validation studies.
We refer to this model of local network knowledge as 1-local view [1].
Note, that there are two types of time scales in this model of PPI network evolution: one which is slow corresponds to the long time decay of ancestral interactions between "old" genes, while the other one is faster (e.g. 10 100 MY) and corresponds to the spontaneous symmetry breaking between "old" and "new" duplicate copies and the concommitant deletion of many "new" duplicates.
The networks could be more complex than this model of distinct networks.
We believe that this model of contextual PPI network will serve as a new model for improving the biological significance and the predictive powers of the current PPI networks and may provide insights into their dynamics.
In this work, we address the modeling of network traffic using a multi-time-scale framework.
In this paper we present a model of network evolution that takes into account gene duplications and losses and their effect on regulatory network structures.
In this paper, we present a model of network evolution that is based on observed rates of link and duplication dynamics.
A Unified Model of Network Bargaining and Network Formation.
In support of this model, network analysis of the data from the 48 h timepoint identified a gene network with STAT1, STAT3, and the transcription factor PU.1 at three interacting centers.
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CEO of Professional Science Editing for Scientists @ prosciediting.com