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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)

2016

  • Differential transcriptional responses to Ebola and Marburg virus infection in bat and human cells.
    Hölzer M, Krähling V, Amman F, Barth E, Bernhart SH, Carmelo VAO, Collatz M, Doose G, Eggenhofer F, Ewald J, Fallmann J, Feldhahn LM, Fricke M, Gebauer J, Gruber AJ, Hufsky F, Indrischek H, Kanton S, Linde J, Mostajo N, Ochsenreiter R, Riege K, Rivarola-Duarte L, Sahyoun AH, Saunders SJ, Seemann SE, Tanzer A, Vogel B, Wehner S, Wolfinger MT, Backofen R, Gorodkin J, Grosse I, Hofacker I, Hoffmann S, Kaleta C, Stadler PF, Becker S, Marz M
    Sci Rep 2016, 6, 34589
  • Structure of the ribosome post-recycling complex probed by chemical cross-linking and mass spectrometry.
    Kiosze-Becker K, Ori A, Gerovac M, Heuer A, Nürenberg-Goloub E, Rashid UJ, Becker T, Beckmann R, Beck M, Tampé R
    Nat Commun 2016, 7, 13248
  • Semantic multi-classifier systems for the analysis of gene expression profiles
    Lausser* L, Schmid* F, Platzer M, Sillanpää JM, Kestler AH
    Data Science Series A (Online-First) 2016, 1(1) * equal contribution
  • BiTrinA - multiscale binarization and trinarization with quality analysis.
    Mϋssel C, Schmid F, Blätte TJ, Hopfensitz M, Lausser** L, Kestler** HA
    Bioinformatics 2016, 32(3), 465-8 ** co-senior authors
  • Spatiotemporal variation of mammalian protein complex stoichiometries.
    Ori A, Iskar M, Buczak K, Kastritis P, Parca L, Andrés-Pons A, Singer S, Bork P, Beck M
    Genome Biol 2016, 17(1), 47 featured in Research Highlights
  • Cool-temperature-mediated activation of phospholipase C-γ2 in the human hereditary disease PLAID.
    Schade A, Walliser C, Wist M, Haas J, Vatter P, Kraus JM, Filingeri D, Havenith G, Kestler HA, Milner JD, Gierschik P
    Cell Signal 2016, 28(9), 1237-51
  • The endosomal transcriptional regulator RNF11 integrates degradation and transport of EGFR.
    Scharaw S, Iskar M, Ori A, Boncompain G, Laketa V, Poser I, Lundberg E, Perez F, Beck M, Bork P, Pepperkok R
    J Cell Biol 2016, 215(4), 543-58 published during change of institution
  • GiANT: Gene set uncertainty in enrichment analysis.
    Schmid F, Schmid M, Müssel C, Sträng JE, Buske C, Bullinger L, Kraus JM, Kestler HA
    Bioinformatics 2016, 32(12), 1891-4
  • Epigenetic stress responses induce muscle stem-cell ageing by Hoxa9 developmental signals.
    Schwörer S, Becker F, Feller C, Baig AH, Köber U, Henze H, Kraus JM, Xin B, Lechel A, Lipka DB, Varghese CS, Schmidt M, Rohs R, Aebersold R, Medina KL, Kestler HA, Neri F, von Maltzahn** J, Tümpel** S, Rudolph** KL
    Nature 2016, 540(7633), 428-32 ** co-corresponding authors
  • Genetic Factors of the Disease Course After Sepsis: Rare Deleterious Variants Are Predictive.
    Taudien* S, Lausser* L, Giamarellos-Bourboulis EJ, Sponholz C, Schöneweck F, Felder M, Schirra LR, Schmid F, Gogos C, Groth S, Petersen BS, Franke A, Lieb W, Huse K, Zipfel PF, Kurzai O, Moepps B, Gierschik P, Bauer M, Scherag A, Kestler** HA, Platzer** M
    EBioMedicine 2016, 12, 227-38 * equal contribution, ** co-senior authors