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Home>Research>Subarea 5: Computational and Systems Biology of Aging
Research focus of Subarea 5.

Subarea 5: Computational and Systems Biology of Aging

Subarea 5 focuses on the development of methods to analyse and understand complex biological systems. This work includes the design of computer algorithms and biostatistical approaches as well as the development of novel Omic strategies (i.e. genomics/epigenomics, transcriptomics, proteomics, and metabolomics) to study aging and aging-related diseases. According to the FLI, due to the Subarea's expertise in computational data analysis, it is deeply interconnected with all other Subareas. The Subarea hosts two critical core facilities (Life Science Computing, Proteomics) and provides consulting services in statistics. Furthermore, it organizes courses on data analysis and statistics.

The research is defined by five focus areas:

  • Mapping extrinsic and intrinsic factors influencing stem cells during aging,
  • Integration of spatiotemporal proteomics and transcriptomics data,
  • Comprehensive evaluation of qualitative and quantitative expression changes,
  • Identification and analysis of epigenomic alterations during aging and age-related diseases, and
  • Network analysis of genomic, transcriptomic and epigenomic alterations during aging.

Research focus of Subarea 5.

The biology of aging can be viewed as a multilayered array of networks at the level of organs, cells, molecules, and genes. The FLI wants to meet this complexity by establishing the new Subarea on “Computational and Systems Biology of Aging”. The overall goal is to interconnect research at different scales, taking place in Subareas 1-4 of the Institute’s research program. The new group on Systems Biology will integrate data from networks at multiple scales and will thus point to mechanisms and interactions that would not be seen in unilayer approaches.

Publications

(since 2016)

2019

  • Karyopherin α2-dependent import of E2F1 and TFDP1 maintains protumorigenic stathmin expression in liver cancer.
    Drucker E, Holzer K, Pusch S, Winkler J, Calvisi DF, Eiteneuer E, Herpel E, Goeppert B, Roessler S, Ori A, Schirmacher P, Breuhahn K, Singer S
    Cell Commun Signal 2019, 17(1), 159
  • Conservation and divergence of the p53 gene regulatory network between mice and humans.
    Fischer M
    Oncogene 2019, 38(21), 4095-109
  • Profiling of gallbladder carcinoma reveals distinct miRNA profiles and activation of STAT1 by the tumor suppressive miRNA-145-5p.
    Goeppert B, Truckenmueller F, Ori A, Fritz V, Albrecht T, Fraas A, Scherer D, Silos RG, Sticht C, Gretz N, Mehrabi A, Bewerunge-Hudler M, Pusch S, Bermejo JL, Dietrich P, Schirmacher P, Renner M, Roessler S
    Sci Rep 2019, 9(1), 4796
  • Quantitative Proteome Landscape of the NCI-60 Cancer Cell Lines.
    Guo T, Luna A, Rajapakse VN, Koh CC, Wu Z, Liu W, Sun Y, Gao H, Menden MP, Xu C, Calzone L, Martignetti L, Auwerx C, Buljan M, Banaei-Esfahani A, Ori A, Iskar M, Gillet L, Bi R, Zhang J, Zhang H, Yu C, Zhong Q, Varma S, Schmitt U, Qiu P, Zhang Q, Zhu Y, Wild PJ, Garnett MJ, Bork P, Beck M, Liu K, Saez-Rodriguez J, Elloumi F, Reinhold WC, Sander C, Pommier Y, Aebersold R
    iScience 2019, 21, 664-80
  • Nucleoporin Nup155 is part of the p53 network in liver cancer.
    Holzer K, Ori A, Cooke A, Dauch D, Drucker E, Riemenschneider P, Andres-Pons A, DiGuilio AL, Mackmull MT, Baßler J, Roessler S, Breuhahn K, Zender L, Glavy JS, Dombrowski F, Hurt E, Schirmacher P, Beck M, Singer S
    Nat Commun 2019, 10(1), 2147
  • Genomic and transcriptomic changes complement each other in the pathogenesis of sporadic Burkitt lymphoma.
    López C, Kleinheinz K, Aukema SM, Rohde M, Bernhart SH, Hübschmann D, Wagener R, Toprak UH, Raimondi F, Kreuz M, Waszak SM, Huang Z, Sieverling L, Paramasivam N, Seufert J, Sungalee S, Russell RB, Bausinger J, Kretzmer H, Ammerpohl O, Bergmann AK, Binder H, Borkhardt A, Brors B, Claviez A, Doose G, Feuerbach L, Haake A, Hansmann ML, Hoell J, Hummel M, Korbel JO, Lawerenz C, Lenze D, Radlwimmer B, Richter J, Rosenstiel P, Rosenwald A, Schilhabel MB, Stein H, Stilgenbauer S, Stadler PF, Szczepanowski M, Weniger MA, Zapatka M, Eils R, Lichter P, Loeffler M, Möller P, Trümper L, Klapper W, ICGC MMML-Seq Consortium, Hoffmann S, Küppers R, Burkhardt B, Schlesner M, Siebert R
    Nat Commun 2019, 10(1), 1459
  • Comparison of protein quantification in a complex background by DIA and TMT workflows with fixed instrument time.
    Muntel* J, Kirkpatrick* J, Bruderer R, Huang T, Vitek O, Ori** A, Reiter** L
    J Proteome Res 2019, 18(3), 1340-51 * equal contribution, ** co-senior authors
  • Author Correction: Metastatic-niche labelling reveals parenchymal cells with stem features.
    Ombrato L, Nolan E, Kurelac I, Mavousian A, Bridgeman VL, Heinze I, Chakravarty P, Horswell S, Gonzalez-Gualda E, Matacchione G, Weston A, Kirkpatrick J, Husain E, Speirs V, Collinson L, Ori A, Lee JH, Malanchi I
    Nature 2019, 575(7784), E8
  • Metastatic-niche labelling reveals parenchymal cells with stem features.
    Ombrato L, Nolan E, Kurelac I, Mavousian A, Bridgeman VL, Heinze I, Chakravarty P, Horswell S, Gonzalez-Gualda E, Matacchione G, Weston A, Kirkpatrick J, Husain E, Speirs V, Collinson L, Ori A, Lee JH, Malanchi I
    Nature 2019, 572(7771), 603-8
  • Disentangling Genetic and Environmental Effects on the Proteotypes of Individuals.
    Romanov N, Kuhn M, Aebersold R, Ori A, Beck M, Bork P
    Cell 2019, 177(5), 1308-18