title: Systematic identification of an integrative network module during senescence from time-series gene expression.
abstract: Cellular senescence irreversibly arrests growth of human diploid cells. In addition, recent studies have indicated that senescence is a multi-step evolving process related to important complex biological processes. Most studies analyzed only the genes and their functions representing each senescence phase without considering gene-level interactions and continuously perturbed genes. It is necessary to reveal the genotypic mechanism inferred by affected genes and their interaction underlying the senescence process.
  
  
  
  We suggested a novel computational approach to identify an integrative network which profiles an underlying genotypic signature from time-series gene expression data. The relatively perturbed genes were selected for each time point based on the proposed scoring measure denominated as perturbation scores. Then, the selected genes were integrated with protein-protein interactions to construct time point specific network. From these constructed networks, the conserved edges across time point were extracted for the common network and statistical test was performed to demonstrate that the network could explain the phenotypic alteration. As a result, it was confirmed that the difference of average perturbation scores of common networks at both two time points could explain the phenotypic alteration. We also performed functional enrichment on the common network and identified high association with phenotypic alteration. Remarkably, we observed that the identified cell cycle specific common network played an important role in replicative senescence as a key regulator.
  
  
  
  Heretofore, the network analysis from time series gene expression data has been focused on what topological structure was changed over time point. Conversely, we focused on the conserved structure but its context was changed in course of time and showed it was available to explain the phenotypic changes. We expect that the proposed method will help to elucidate the biological mechanism unrevealed by the existing approaches.
authors.affiliation: Well-Aging Research Center, Samsung Advanced Institute of Technology, Samsung Electronics, Suwon, South Korea.|Biomedical HPC Technology Research Center, Korean Institute of Science and Technology Information, Daejeon, South Korea.
authors.forename: Chihyun
authors.initials: C
authors.lastname: Park
authors.affiliation: Well-Aging Research Center, Samsung Advanced Institute of Technology, Samsung Electronics, Suwon, South Korea.
authors.forename: So Jeong
authors.initials: SJ
authors.lastname: Yun
authors.affiliation: Well-Aging Research Center, Samsung Advanced Institute of Technology, Samsung Electronics, Suwon, South Korea.
authors.forename: Sung Jin
authors.initials: SJ
authors.lastname: Ryu
authors.affiliation: Well-Aging Research Center, Samsung Advanced Institute of Technology, Samsung Electronics, Suwon, South Korea.|Mobile Healthcare Lab., Samsung Advanced Institute of Technology, Samsung Electronics, Suwon, South Korea.
authors.forename: Soyoung
authors.initials: S
authors.lastname: Lee
authors.affiliation: Well-Aging Research Center, Samsung Advanced Institute of Technology, Samsung Electronics, Suwon, South Korea.|Department of New Biology, DGIST, Daegu, South Korea.
authors.forename: Young-Sam
authors.initials: YS
authors.lastname: Lee
authors.affiliation: Department of Computer Engineering, Gachon University, Seongnam-si, South Korea. ymyoon@gachon.ac.kr.
authors.forename: Youngmi
authors.initials: Y
authors.lastname: Yoon
authors.affiliation: Well-Aging Research Center, Samsung Advanced Institute of Technology, Samsung Electronics, Suwon, South Korea. blueocean2016@dgist.ac.kr.|Department of New Biology, DGIST, Daegu, South Korea. blueocean2016@dgist.ac.kr.
authors.forename: Sang Chul
authors.initials: SC
authors.lastname: Park
mesh_terms: D016922:Cellular Senescence / Q000235:genetics*; D019295:Computational Biology / Q000379:methods*; D004171:Diploidy; D018450:Disease Progression; D005347:Fibroblasts / Q000166:cytology; D020869:Gene Expression Profiling; D006801:Humans; D059630:Mesenchymal Stem Cells / Q000166:cytology / Q000473:pathology; D009369:Neoplasms / Q000235:genetics / Q000473:pathology; D010641:Phenotype; D013997:Time Factors
keywords: Data integration; Network analysis; Senescence; Time-series gene expression