Coming to Terms with Complexity: Limits to a Reductionist View of Aging

If agIng Is “not a process,” then what Is It? My response would be that it is an agedependent trajectory of interacting system states – the sum of all molecular and physiological states and their interaction networks, many but not all of which shift in a consistent direction over time. This definition broadens our focus to include components that do not themselves depend on age, but which cohabit networks containing components that do. Gene–environment interactions are a case in point, wherein environmental variation can help to shape the age-structure of a population despite being quite obviously independent of age. Perhaps the best-established genetic pathway to influence lifespan is insulin-like signaling, believed to have evolved at least in part for its ability to maximize reproduction under favorable environments while postponing both reproduction and individual mortality under conditions of crowding or insufficient food (Kenyon, 2005; Kim, 2007; Hanover et al., 2010; Magwire et al., 2010). Since natural populations are polymorphic for ostensibly rate-limiting components of this pathway (Bonafe and Olivieri, 2009), it is likely that individuals genetically predisposed to low insulin-like signaling should survive famine better than those geared for higher signaling and shorter lifespan. This is a conclusion of some import for population biologists, since the age-composition of any population must then be modified by the availability of food. A particularly instructive example is the near-ubiquitous evolutionary requirement for species or their constituent populations to survive extended periods of famine (de Grey, 2005). Groups experiencing more prolonged famines (or just over-wintering, if their lifespans are measured in weeks) will have more diverse age structures, including an increased number of individuals for whom reproduction has been delayed. The same potential also exists for gene–gene interactions (including genes that dictate dietary preferences) to affect long-term survival. For example, only one component of a gene network may actually be age-dependent, while other genes create the background context of homeostatic states and their oscillations within which age-dependent genes must function. An increased probability of death with age could then arise from components undergoing essentially monotonic age-dependent declines, confronting extreme-value system states (in variable but age-independent parameters) to which they cannot respond adequately in any essential tissue or organ. Alternatively, an age-dependent increase in the variance of system oscillations may exceed the response range of one or more age-independent gene functions. In either case, the precise cause of death or debility will vary in a stochastic way, appearing as the “weakest link” in any one tissue or organism, although the underlying age-associated changes may be common to many or all cell types and individuals (Shmookler Reis, 1989). LIve smarter, LIve Longer? There remains, in my view, one last “trivial” explanation for the plateau in mortality, which I believe should be dealt with. I will term it “the perseverance of acquired characteristics,” but I really just mean learning in its several forms. If we posit that individuals are to some degree capable, as a function of time, of developing and improving their ability to avoid evitable causes of mortality, then those individuals who managed to survive until late age could ipso facto have reduced their late-life risk of death – although aging per se might continue unabated. Examples of such learning would include immune memory, strategies to avoid situations and behaviors that place one at increased risk of injury or death, and a reduction in speed of movement or action in recognition of slower response times. My suggestion that immune memory might be involved, in organisms with an adaptive immune system, agrees with the observed age-dependent increase in memory T cells but appears contradicted by the decline with age in recruitment to this niche (NikolichZugich and Rudd, 2010). However, if individuals exist who retain sufficient naïve T cell reserves to augment immune memory at late age, and if those are among the longest-lived in a population, then that subset of the population should see a reduction in their force of mortality. Of course, the strongest evidence for a cessation of aging comes from insects, which lack an adaptive immune system and may be thought incapable of learning. Experimental evidence clearly supports learning by drosophila (Shuai et al., 2011; van Swinderen, 2011), however, and in the rather simple and uniform environments in which they are mainComing to terms with complexity: limits to a reductionist view of aging