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.

可以量化生物体衰老过程中损伤和失调的逐渐积累。即便如此，衰老过程是复杂的，并且具有多个相互作用的生理尺度——从分子到细胞再到整个组织。面对这种复杂性，我们可以通过使用模拟真实的个人健康轨迹和死亡率的计算模型来显着提高我们对衰老的理解。为此，它们必须是系统级模型，其中包含与年龄相关的变化的可衡量方面之间的相互作用。为了在老化过程中纳入个体差异，模型必须是随机的。为了有用，它们还应该具有预测性，因此必须通过来自大量老龄化个体的数据进行拟合或参数化。从这个角度来看，我们概述了我们去过的地方，我们在哪里，我们希望用这种衰老计算模型去哪里。我们的重点是数据驱动的系统级模型，以及它们在老化研究中的巨大潜力。