Identify Dynamic Network Modules with Temporal and Spatial Constraints

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Despite the rapid accumulation of systems-level biological data, understanding the dynamic nature of cellular activity remains a difficult task. The reason is that most biological data are static, or only correspond to snapshots of cellular activity. In this study, we explicitly attempt to detangle the temporal complexity of biological networks by using compilations of time-series gene expression profiling data.We define a dynamic network module to be a set of proteins satisfying two conditions: (1) they form a connected component in the protein-protein interaction (PPI) network; and (2) their expression profiles form certain structures in the temporal domain. We develop the ... continued below

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Jin, R; McCallen, S; Liu, C; Almaas, E & Zhou, X J September 24, 2007.

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Despite the rapid accumulation of systems-level biological data, understanding the dynamic nature of cellular activity remains a difficult task. The reason is that most biological data are static, or only correspond to snapshots of cellular activity. In this study, we explicitly attempt to detangle the temporal complexity of biological networks by using compilations of time-series gene expression profiling data.We define a dynamic network module to be a set of proteins satisfying two conditions: (1) they form a connected component in the protein-protein interaction (PPI) network; and (2) their expression profiles form certain structures in the temporal domain. We develop the first efficient mining algorithm to discover dynamic modules in a temporal network, as well as frequently occurring dynamic modules across many temporal networks. Using yeast as a model system, we demonstrate that the majority of the identified dynamic modules are functionally homogeneous. Additionally, many of them provide insight into the sequential ordering of molecular events in cellular systems. We further demonstrate that identifying frequent dynamic network modules can significantly increase the signal to noise separation, despite the fact that most dynamic network modules are highly condition-specific. Finally, we note that the applicability of our algorithm is not limited to the study of PPI systems, instead it is generally applicable to the combination of any type of network and time-series data.

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PDF-file: 15 pages; size: 0.3 Mbytes

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  • Presented at: RECOMB 2008, Singapore, Singapore, Mar 30 - Apr 02, 2008

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  • Report No.: UCRL-PROC-235067
  • Grant Number: W-7405-ENG-48
  • Office of Scientific & Technical Information Report Number: 928543
  • Archival Resource Key: ark:/67531/metadc894650

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  • September 24, 2007

Added to The UNT Digital Library

  • Sept. 27, 2016, 1:39 a.m.

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  • Nov. 29, 2016, 1:51 p.m.

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Jin, R; McCallen, S; Liu, C; Almaas, E & Zhou, X J. Identify Dynamic Network Modules with Temporal and Spatial Constraints, article, September 24, 2007; Livermore, California. (digital.library.unt.edu/ark:/67531/metadc894650/: accessed October 17, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.