A complex systems approach to aging biology

Abstract

Having made substantial progress understanding molecules, cells, genes and pathways, aging biology research is now moving toward integration of these parts, attempting to understand how their joint dynamics may contribute to aging. Such a shift of perspective requires the adoption of a formal complex systems framework, a transition being facilitated by large-scale data collection and new analytical tools. Here, we provide a theoretical framework to orient researchers around key concepts for this transition, notably emergence, interaction networks and resilience. Drawing on evolutionary theory, network theory and principles of homeostasis, we propose that organismal function is accomplished by the integration of regulatory mechanisms at multiple hierarchical scales, and that the disruption of this ensemble causes the phenotypic and functional manifestations of aging. We present key examples at scales ranging from sub-organismal biology to clinical geriatrics, outlining how this approach can potentially enrich our understanding of aging.

Figure 1.. Shift to complex systems approaches in ecology and aging biology.

In both North Atlantic food webs (ecology) and oxidative stress (aging biology), the failure of linear approaches (food chains/molecular pathways) led to systems approaches with networks that captured the multifactorial and interactive nature of these systems. Bottom-up/top-down thinking gives way to the consideration of emergent properties. Intervention on selected network components, such as seal hunting to restore cod stocks or antioxidant supplements to slow aging, are unsuccessful because they ignore the consequences of propagation to other interconnected parts.

Figure 2.. Key examples of advances in aging biology using complex systems perspectives or methods.

A. High complexity in heart rate variability (top) is characteristic of young individuals, whereas decreased complexity is characteristic of older individuals. This finding led to measures of heart rate variability as indicators of health and aging. B. Physiological dysregulation, measured as the Mahalanobis distance of clinical biomarkers, increases with age in similar ways across 11 primate species. This shows conservation of physiological signatures of homeostasis and characterizes overall homeostatic state as an emergent property. C. Early warning signs of critical transitions, as measured through variance, temporal autocorrelation and cross-correlation in self-rated health data, are strongly associated with frailty status. This confirms predictions that system dynamics change jointly before adverse events. D. Different yeast cells show different cell fate trajectories along one of two canalized pathways or “modes”. (see text). E. Bayesian approaches to estimating brain immune and microglia networks provide predictive insights into late-onset AD pathophysiology. F. Different Caenorhabditis elegans survival curves at different temperatures (top) overlap perfectly when adjusted to the temporal scale of the lifespan (bottom). Accordingly, lifespan can be understood as an emergent property of underlying processes that adjust temporal scale. G. Directional network analysis of proteome and transcriptome in yeast shows loss of stoichiometry in protein complexes with aging, implying broadscale reorganization or dysregulation of regulatory networks. H. Simulated networks of interacting deficits propagated across the lifespan recapitulate observed dynamics of frailty and mortality (filled circles indicate damaged nodes).

Fig. 3. Multi-scale causality: the example of Alzheimer’s Disease (AD).

Dementia provides one illustration of how the science of complex systems can help us better understand dementia’s biological processes. First, healthy brain function can be viewed as an emergent property of a complex system, requiring interactions across multiple scales from neurons to networks to social and environmental components, while disease (in this case dementia) arises from the breakdown of these interacting components. Thus, dementia may result from the loss of complex interactions among determinants of healthy cognitive function operating on different scales in time or space. This includes the breakdown of components shown here, including at a cellular scale (interactions among neuroinflammation, the immune context, and the accumulation of Amyloid and Tau that disrupts neuronal integrity); at a tissue scale (brain atrophy); at a person-scale (decreased sleep, reduced physical activity, depression, and cardiovascular risk factors); and at a societal scale (social isolation, environmental toxins, and other influences). The complex nature of healthy function enables compensation and resiliency, which protects against cognitive decline until the loss of complexity reaches a threshold at which overt disease and disability emerge. This AD model is an example of the hierarchical principles in the Box 1 Fig, but remains speculative.

Fig. 4.. Bowtie structure of aging pathways.

Numerous upstream signals (pink) are integrated via a limited number of intermediate pathways (blue) to simultaneously adjust multiple downstream outputs (green) in a coordinated fashion. Many of the downstream components are related to aging, and the result is that this network fine-tunes many molecular aspects of aging. This is a computationally efficient way to optimize many outputs simultaneously, similar to a neural network autoencoder. This representation is qualitative: inputs and outputs are not exhaustively listed, and the actual structure of pathways is a bit more complicated (e.g., interactions among inputs and among outputs). Adapted from; see also.

Box 1 Fig.

Parallel biological hierarchies.