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)

2020

  • Analyses of non-coding somatic drivers in 2,658 cancer whole genomes.
    Rheinbay E, Nielsen MM, Abascal F, Wala JA, Shapira O, Tiao G, Hornshøj H, Hess JM, Juul RI, Lin Z, Feuerbach L, Sabarinathan R, Madsen T, Kim J, Mularoni L, Shuai S, Lanzós A, Herrmann C, Maruvka YE, Shen C, Amin SB, Bandopadhayay P, Bertl J, Boroevich KA, Busanovich J, Carlevaro-Fita J, Chakravarty D, Chan CWY, Craft D, Dhingra P, Diamanti K, Fonseca NA, Gonzalez-Perez A, Guo Q, Hamilton MP, Haradhvala NJ, Hong C, Isaev K, Johnson TA, Juul M, Kahles A, Kahraman A, Kim Y, Komorowski J, Kumar K, Kumar S, Lee D, Lehmann KV, Li Y, Liu EM, Lochovsky L, Park K, Pich O, Roberts ND, Saksena G, Schumacher SE, Sidiropoulos N, Sieverling L, Sinnott-Armstrong N, Stewart C, Tamborero D, Tubio JMC, Umer HM, Uusküla-Reimand L, Wadelius C, Wadi L, Yao X, Zhang CZ, Zhang J, Haber JE, Hobolth A, Imielinski M, Kellis M, Lawrence MS, von Mering C, Nakagawa H, Raphael BJ, Rubin MA, Sander C, Stein LD, Stuart JM, Tsunoda T, Wheeler DA, Johnson R, Reimand J, Gerstein M, Khurana E, Campbell PJ, López-Bigas N, PCAWG Drivers and Functional Interpretation Working Group, PCAWG Structural Variation Working Group, Weischenfeldt J, Beroukhim R, Martincorena I, Pedersen JS, Getz G, PCAWG Consortium
    Nature 2020, 578(7793), 102-11
  • Dissecting the DNA binding landscape and gene regulatory network of p63 and p53.
    Riege K, Kretzmer H, Sahm A, McDade SS, Hoffmann S, Fischer M
    Elife 2020, 9, e63266
  • Pan-cancer analysis of whole genomes identifies driver rearrangements promoted by LINE-1 retrotransposition.
    Rodriguez-Martin B, Alvarez EG, Baez-Ortega A, Zamora J, Supek F, Demeulemeester J, Santamarina M, Ju YS, Temes J, Garcia-Souto D, Detering H, Li Y, Rodriguez-Castro J, Dueso-Barroso A, Bruzos AL, Dentro SC, Blanco MG, Contino G, Ardeljan D, Tojo M, Roberts ND, Zumalave S, Edwards PA, Weischenfeldt J, Puiggròs M, Chong Z, Chen K, Lee EA, Wala JA, Raine KM, Butler A, Waszak SM, Navarro FCP, Schumacher SE, Monlong J, Maura F, Bolli N, Bourque G, Gerstein M, Park PJ, Wedge DC, Beroukhim R, Torrents D, Korbel JO, Martincorena I, Fitzgerald RC, Van Loo P, Kazazian HH, Burns KH, PCAWG Structural Variation Working Group, Campbell PJ, Tubio JMC, PCAWG Consortium
    Nat Genet 2020, 52(3), 306-19
  • Tumor suppressor p53: from engaging DNA to target gene regulation.
    Sammons*/** MA, Nguyen*/** TAT, McDade*/** SS, Fischer*/** M
    Nucleic Acids Res 2020, 48(16), 8848-69 * equal contribution, ** co-corresponding authors
  • Elevated Hedgehog activity contributes to attenuated DNA damage responses in aged hematopoietic cells.
    Scheffold A, Baig AH, Chen Z, von Löhneysen SE, Becker F, Morita Y, Avila AI, Groth M, Lechel A, Schmid F, Kraus JM, Kestler HA, Stilgenbauer S, Philipp M, Burkhalter MD
    Leukemia 2020, 34(4), 1125-34
  • Proteomic analyses of muscle stem cells and their niche reveal novel factors affecting regeneration of skeletal muscle during aging
    Schüler SC
    Dissertation 2020, Jena, Germany
  • Stem Cell Aging: The Upcoming Era of Proteins and Metabolites.
    Schüler* SC, Gebert* N, Ori A
    Mech Ageing Dev 2020, 190, 111288 * equal contribution
  • Increased prostaglandin-D2 in male STAT3-deficient hearts shifts cardiac progenitor cells from endothelial to white adipocyte differentiation.
    Stelling E, Ricke-Hoch M, Erschow S, Hoffmann S, Bergmann AK, Heimerl M, Pietzsch S, Battmer K, Haase A, Stapel B, Scherr M, Balligand JL, Binah O, Hilfiker-Kleiner D
    PLoS Biol 2020, 18(12), e3000739
  • Butler enables rapid cloud-based analysis of thousands of human genomes.
    Yakneen S, Waszak SM, PCAWG Technical Working Group, Gertz M, Korbel JO, PCAWG Consortium
    Nat Biotechnol 2020, 38(3), 288–292
  • Comprehensive molecular characterization of mitochondrial genomes in human cancers.
    Yuan Y, Ju YS, Kim Y, Li J, Wang Y, Yoon CJ, Yang Y, Martincorena I, Creighton CJ, Weinstein JN, Xu Y, Han L, Kim HL, Nakagawa H, Park K, Campbell PJ, Liang H, PCAWG Consortium
    Nat Genet 2020, 52(3), 342-52