Recent Advances in the Systems Biology of Aging

SIGNIFICANCE

Reductionist studies have contributed greatly to our understanding of the basic biology of aging in recent years but we still do not understand fundamental mechanisms for many identified drugs and pathways. Use of systems approaches will help us move forward in our understanding of aging. Recent Advances: Recent work described here has illustrated the power of systems biology to inform our understanding of aging through the study of (i) diet restriction, (ii) neurodegenerative disease, and (iii) biomarkers of aging.

CRITICAL ISSUES

Although we do not understand all of the individual genes and pathways that affect aging, as we continue to uncover more of them, we have now also begun to synthesize existing data using systems-level approaches, often to great effect. The three examples noted here all benefit from computational approaches that were unknown a few years ago, and from biological insights gleaned from multiple model systems, from aging laboratories as well as many other areas of biology.

FUTURE DIRECTIONS

Many new technologies, such as single-cell sequencing, advances in epigenetics beyond the methylome (specifically, assay for transposase-accessible chromatin with high throughput sequencing ), and multiomic network studies, will increase the reach of systems biologists. This suggests that approaches similar to those described here will continue to lead to striking findings, and to interventions that may allow us to delay some of the many age-associated diseases in humans; perhaps sooner that we expect. Antioxid. Redox Signal. 29, 973-984.

Aging and computational systems biology

Aging research is undergoing a paradigm shift, which has led to new and innovative methods of exploring this complex phenomenon. The systems biology approach endeavors to understand biological systems in a holistic manner, by taking account of intrinsic interactions, while also attempting to account for the impact of external inputs, such as diet. A key technique employed in systems biology is computational modeling, which involves mathematically describing and simulating the dynamics of biological systems. Although a large number of computational models have been developed in recent years, these models have focused on various discrete components of the aging process, and to date no model has succeeded in completely representing the full scope of aging. Combining existing models or developing new models may help to address this need and in so doing could help achieve an improved understanding of the intrinsic mechanisms which underpin aging.

The heuristic value of redundancy models of aging

Molecular studies of aging aim to unravel the cause(s) of aging bottom-up, but linking these mechanisms to organismal level processes remains a challenge. We propose that complementary top-down data-directed modelling of organismal level empirical findings may contribute to developing these links. To this end, we explore the heuristic value of redundancy models of aging to develop a deeper insight into the mechanisms causing variation in senescence and lifespan. We start by showing (i) how different redundancy model parameters affect projected aging and mortality, and (ii) how variation in redundancy model parameters relates to variation in parameters of the Gompertz equation. Lifestyle changes or medical interventions during life can modify mortality rate, and we investigate (iii) how interventions that change specific redundancy parameters within the model affect subsequent mortality and actuarial senescence. Lastly, as an example of data-directed modelling and the insights that can be gained from this, (iv) we fit a redundancy model to mortality patterns observed by Mair et al. (2003; Science 301: 1731-1733) in Drosophila that were subjected to dietary restriction and temperature manipulations. Mair et al. found that dietary restriction instantaneously reduced mortality rate without affecting aging, while temperature manipulations had more transient effects on mortality rate and did affect aging. We show that after adjusting model parameters the redundancy model describes both effects well, and a comparison of the parameter values yields a deeper insight in the mechanisms causing these contrasting effects. We see replacement of the redundancy model parameters by more detailed sub-models of these parameters as a next step in linking demographic patterns to underlying molecular mechanisms.