The paths of mortality: how understanding the biology of aging can help explain systems behavior of single cells

Abstract

Aging is a fundamental aspect of life, yet also one of the most confounding. In individual cells, aging results in a progressive decline which affects all organelles and reduces a cell's ability to maintain homeostasis. Because of the interconnected nature of cellular systems, the failure of even a single organelle can have cascading effects. We are just beginning to understand the dramatic physiological changes that occur during aging. Because most aging research has focused on population dynamics, or differences between wild-type and mutant populations, single-cell behavior has been largely overlooked. An open question is whether aging cells are defined by predictable sequences of physiological changes, or whether they proceed along divergent aging trajectories defined by whichever system begins to fail first. Can aging be best characterized by a cell-cycle like model with stereotyped states all cells progress through, or a Waddington landscape with divergent trajectories? Here we present work on understanding the changing physiological states of aging cells, why it will impact systems and synthetic biologists, and how the systems community can contribute significantly to the study of aging.

Figure 1

Replicative aging and physiological failures. A) Budding yeast undergo asymmetric divisions in which mother cells retain the majority of damaged proteins and organelles. After a number of divisions, a mother cell will stop dividing and die. B) A replicative survival curve of budding yeast (BY4742) from the Kaeberlein lab showing the enormous variation in lifespan. Isogenic cells, grown in an identical environment, may only bud 3–4 times, or 50–60. C) Categories of physiological changes that cells undergo as they age. Cells grow continuously in volume, experience increased protein damage, organelles like the mitochondria become less effective (purple), and in the nucleus there is increasing instability with increased rDNA circles (red) and reduced silencing.

Figure 2

Potential aging trajectories and systems models. A) With a “cell-cycle” model of aging, all cells move through stereotyped physiological states, but at different rates. The rate of progression through each state determines the replicative lifespan of a cell. B) In a “Waddington” model of aging, cells can move through a distinct set of physiological states that may not overlap. Both the rate of progression through states, and which physiological states an individual cell visits determines the lifespan. C) Within a “cell-cycle” model, normal cells (top) move through physiological states with certain probabilities, and interventions that increase lifespan (bottom) reduce some or all transition probabilities. D) In the “Waddington” model of aging, individual cells can proceed along different paths (top), and lifespan extending interventions might not only reduce transition probabilities, but also change which physiological states cells are likely to visit as they age (bottom).