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)
2023
- Author Correction: 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 2023, 614(7948), E40 - The Tgf-β family member Gdf6Y determines the male sex in Nothobranchius furzeri by suppressing oogenesis-inducing genes
Richter A, Mörl H, Thielemann M, Kleemann M, Geißen R, Schwarz R, Albertz C, Koch P, Petzold A, Groth M, Hartmann N, Herpin A, Englert C
bioRxiv 2023, 10.1101/2023.05.26.542338 - Author Correction: 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 2023, 55(6), 1080 - Metabololipidomic and proteomic profiling reveals aberrant macrophage activation and interrelated immunomodulatory mediator release during aging.
Schädel P, Czapka A, Gebert N, Jacobsen ID, Ori** A, Werz** O
Aging Cell 2023, 22(7), e13856 ** co-corresponding authors - Short-Term Caloric Restriction and Subsequent Re-Feeding Compromise Liver Health and Associated Lipid Mediator Signaling in Aged Mice.
Schädel P, Wichmann-Costaganna M, Czapka A, Gebert N, Ori A, Werz O
Nutrients 2023, 15(16), 3660 - Multi-omics analysis identifies RFX7 targets involved in tumor suppression and neuronal processes.
Schwab K, Coronel L, Riege K, Sacramento EK, Rahnis N, Häckes D, Cirri E, Groth M, Hoffmann S, Fischer M
CELL DEATH DISCOV 2023, 9(1), 80 - Immunoproteasome function maintains oncogenic gene expression in KMT2A-complex driven leukemia.
Tubío-Santamaría N, Jayavelu AK, Schnoeder TM, Eifert T, Hsu CJ, Perner F, Zhang Q, Wenge DV, Hansen FM, Kirkpatrick JM, Jyotsana N, Lane SW, von Eyss B, Deshpande AJ, Kühn MWM, Schwaller J, Cammann C, Seifert U, Ebstein F, Krüger E, Hochhaus A, Heuser M, Ori A, Mann M, Armstrong SA, Heidel FH
Mol Cancer 2023, 22(1), 196 - 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 2023, 20(8), 1159-69 - A protocol for the use of cloud-based quantum computers for logical network analysis of biological systems.
Weidner FM, Rossini M, Ankerhold J, Kestler HA
STAR Protoc 2023, 4(3), 102438 - Leveraging quantum computing for dynamic analyses of logical networks in systems biology.
Weidner FM, Schwab JD, Wölk S, Rupprecht F, Ikonomi N, Werle SD, Hoffmann S, Kühl M, Kestler HA
PATTERNS 2023, 4(3), 100705
