Network-Driven Dynamics of Replicative Aging

Project Summary This project aims at integrating computational modeling and innovative measurement technologies to understand the complexity of single-cell aging and the emergent dynamics from the underlying regulatory networks. Aging is closely associated with many diseases, such cancer, diabetes, and neurodegenerative diseases. Advances in understanding the basic biology of aging will facilitate the development of new interventional strategies to mitigate age-related diseases and prolong human healthspan. Although studies in model organisms have identified many genes and factors that influence lifespan in eukaryotes, emerging challenges are to understand how these genes and factors interact and operate dynamically to drive the aging process and to determine the lifespan. During the previous funding period, our multidisciplinary team, using microfluidic and imaging technologies combined with computational modeling, discovered that isogenic yeast cells age with two distinct forms: one with decreased ribosomal DNA (rDNA) silencing and nucleolar decline (Mode 1) whereas the other with heme depletion and mitochondrial decline (Mode 2). We further identified a core molecular circuit, consisting of the lysine deacetylase Sir2 and the heme-activated protein (HAP) transcriptional complex, that governs the fate decision toward one of the aging paths in single cells. Building upon these results, for the next funding period, we will investigate the age-dependent dynamics of the energy homeostasis and protein homeostasis systems, two conserved aging hallmark pathways in eukaryotes, and their interactions with the Sir2-HAP fate-decision circuit. In Aim 1, we will quantitatively characterize the interactions between aging and the energy homeostasis system and develop a model that simulates the aging dynamics of the system. In Aim 2, we will quantitatively characterize the interactions between aging and the protein homeostasis system and develop a dynamic model of proteostasis in aging based on the data collected. In Aim 3, we will combine experiments with modeling to characterize, simulate, and predict single-cell aging trajectories and lifespan under complex environmental conditions, with a combination of different nutrients and stresses. The proposed research will advance a quantitative and predictive understanding of regulatory networks underlying single-cell aging under complex environmental conditions, laying the foundation for interventional strategies for ameliorating age-related diseases and promoting longevity.