title: Computational systems biology of aging.
abstract: Computational systems biology is expected to make major contributions to unravel the complex molecular mechanisms underlying the progression of aging in cells, tissues, and organisms. The development of computational approaches is, however, challenged by a wide spectrum of aging mechanisms participating on different levels of biological organization. The tight connectivity between the molecular constituents, functions, and cell states requires frameworks and strategies that extend beyond current practice to model, simulate, and predict the progression of aging and the emerging aging phenotypes. We provide a general overview of the specific computational tasks and opportunities in aging research, and discuss some illustrative systems level concepts in more detail. One example provided here is the assembly of a conceptual whole cell model that considers the temporal dynamics of the aging process grounded on molecular mechanisms. Another application is the assembly of interactomes, such as protein networks that allow us to analyze changes in network topology and interaction of proteins that have been implicated in aging with other cellular constituents and processes. We introduce the necessary key steps to build these applications and discuss their merits and future extensions for aging research. WIREs Syst Biol Med 2011 3 414-428 DOI: 10.1002/wsbm.126
authors.affiliation: School of Biomedical Engineering, Science and Health Systems, Drexel University, Bossone Research Center, Philadelphia, PA, USA. andres.kriete@drexel.edu
authors.forename: Andres
authors.initials: A
authors.lastname: Kriete
authors.forename: Mark
authors.initials: M
authors.lastname: Lechner
authors.forename: Drew
authors.initials: D
authors.lastname: Clearfield
authors.forename: Dirk
authors.initials: D
authors.lastname: Bohmann
mesh_terms: D000375:Aging / Q000473:pathology / Q000502:physiology*; D000818:Animals; D019295:Computational Biology; D053263:Gene Regulatory Networks; D006801:Humans; D008954:Models, Biological