Trajectories of Aging: How Systems Biology in Yeast Can Illuminate Mechanisms of Personalized Aging

Searching for the Mechanisms of Mammalian Cellular Aging Through Underlying Gene Regulatory Networks

Aging attracts the attention throughout the history of humankind. However, it is still challenging to understand how the internal driving forces, for example, the fundamental building blocks of life, such as genes and proteins, as well as the environments work together to determine longevity in mammals. In this study, we built a gene regulatory network for mammalian cellular aging based on the experimental literature and quantify its underlying driving force for the dynamics as potential and flux landscape. We found three steady-state attractors: a fast-aging state attractor, slow-aging state attractor, and intermediate state attractor. The system can switch from one state attractor to another driven by the intrinsic or external forces through the genetics and the environment. We identified the dominant path from the slow-aging state directly to the fast-aging state. We also identified the dominant path from slow-aging to fast-aging through an intermediate state. We quantified the evolving landscape for revealing the dynamic characteristics of aging through certain regulation changes in time. We also predicted the key genes and regulations for fast-aging and slow-aging through the analysis of the stability for landscape basins. We also found the oscillation dynamics between fast-aging and slow-aging and showed that more energy is required to sustain such oscillations. We found that the flux is the dynamic cause and the entropy production rate the thermodynamic origin of the phase transitions or the bifurcations between the three-state phase and oscillation phase. The landscape quantification provides a global and physical approach to explore the underlying mechanisms of cellular aging in mammals.