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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)

2025

  • Rapid brain tumor classification from sparse epigenomic data.
    Brändl B, Steiger M, Kubelt C, Rohrandt C, Zhu Z, Evers M, Wang G, Schuldt B, Afflerbach AK, Wong D, Lum A, Halldorsson S, Djirackor L, Leske H, Magadeeva S, Smičius R, Quedenau C, Schmidt NO, Schüller U, Vik-Mo EO, Proescholdt M, Riemenschneider MJ, Zadeh G, Ammerpohl O, Yip S, Synowitz M, van Bömmel** A, Kretzmer** H, Müller** FJ
    Nat Med 2025, 31(3), 840-8 ** co-corresponding authors
  • Fetal-like reversion in the regenerating intestine is regulated by mesenchymal asporin.
    Iqbal S, Andersson S, Nesta E, Pentinmikko N, Kumar A, Kumar Jha S, Borshagovski D, Webb A, Gebert N, Viitala EW, Ritchie A, Scharaw S, Kuuluvainen E, Larsen HL, Saarinen T, Juuti A, Ristimäki A, Jeltsch M, Ori A, Varjosalo M, Pietiläinen KH, Ollila S, Jensen KB, Oudhoff MJ, Katajisto P
    Cell Stem Cell 2025, 32(4), 613-626.e8
  • DNA damage response regulator ATR licenses PINK1-mediated mitophagy.
    Marx* C, Qing* X, Gong* Y, Kirkpatrick J, Siniuk K, Beznoussenko GV, Kidiyoor GR, Kirtay M, Buder K, Koch P, Westermann M, Bruhn C, Brown EJ, Xu X, Foiani M, Wang ZQ
    Nucleic Acids Res 2025, 53(5), gkaf178 * equal contribution
  • Signatures of Nonlinear Aging: Molecular Stages of Life: Sudden Changes During Aging as Potential Biomarkers for an Age Classification System.
    Olecka** M, Morrison H, Hoffmann** S
    Bioessays 2025 (epub ahead of print) ** co-corresponding authors
  • Machine Learning and Omic Data for Prediction of Health and Chronic Diseases
    Olenik M, Dönertaş HM
    In: Encyclopedia of Bioinformatics and Computational Biology (edited by Shoba R, Mario C, Asif MK) 2025, 6, 365-388, Elsevier, Amsterdam
  • iSODA: A Comprehensive Tool for Integrative Omics Data Analysis in Single- and Multi-Omics Experiments.
    Olivier-Jimenez D, Derks RJE, Harari O, Cruchaga C, Ali M, Ori A, Di Fraia D, Cabukusta B, Henrie A, Giera M, Mohammed Y
    Anal Chem 2025 (epub ahead of print)
  • Protecting cell cycle integrity: enhanced start-codon stringency in mitosis.
    Omrani O, Siniuk K, Fischer M
    Signal Transduct Target Ther 2025, 10(1), 34
  • The master male sex determinant Gdf6Y of the turquoise killifish arose through allelic neofunctionalization.
    Richter A, Mörl H, Thielemann M, Kleemann M, Geißen R, Schwarz R, Albertz C, Koch P, Petzold A, Kroll T, Groth M, Hartmann N, Herpin A, Englert C
    Nat Commun 2025, 16(1), 540
  • Author Correction: Large-scale benchmarking of circRNA detection tools reveals large differences in sensitivity but not in precision.
    Vromman M, Anckaert J, Bortoluzzi S, Buratin A, Chen CY, Chu Q, Chuang TJ, Dehghannasiri R, Dieterich C, Dong X, Flicek P, Gaffo E, Gu W, He C, Hoffmann S, Izuogu O, Jackson MS, Jakobi T, Lai EC, Nuytens J, Salzman J, Santibanez-Koref M, Stadler P, Thas O, Vanden Eynde E, Verniers K, Wen G, Westholm J, Yang L, Ye CY, Yigit N, Yuan GH, Zhang J, Zhao F, Vandesompele J, Volders PJ
    Nat Methods 2025 (epub ahead of print)
  • Gene regulation by convergent promoters.
    Wiechens E, Vigliotti* F, Siniuk* K, Schwarz R, Schwab K, Riege K, van Bömmel A, Görlich I, Bens M, Sahm A, Groth M, Sammons MA, Loewer A, Hoffmann S, Fischer M
    Nat Genet 2025, 57(1), 206-17 * equal contribution