  The gradual accumulation of damage and dysregulation during the aging of
living organisms can be quantified. Even so, the aging process is complex and
has multiple interacting physiological scales -- from the molecular to cellular
to whole tissues. In the face of this complexity, we can significantly advance
our understanding of aging with the use of computational models that simulate
realistic individual trajectories of health as well as mortality. To do so,
they must be systems-level models that incorporate interactions between
measurable aspects of age-associated changes. To incorporate individual
variability in the aging process, models must be stochastic. To be useful they
should also be predictive, and so must be fit or parameterized by data from
large populations of aging individuals. In this perspective, we outline where
we have been, where we are, and where we hope to go with such computational
models of aging. Our focus is on data-driven systems-level models, and on their
great potential in aging research.
