Mathematical modeling of senescence in metabolic networks

Senescence is one of the greatest mysteries humanity aims to solve. Senescence manifests itself on multiple levels of biological organization: from cells to tissues and whole organisms. Ultimately all life processes depend crucially on metabolism and its regulation. Therefore, the metabolic network is the ultimate biological network that experiences senescence. The aim of this thesis is to provide a theoretical background for understanding senescence in metabolic networks. Two approaches to modeling metabolic networks were used. In the first approach I modeled dynamics of metabolites concentrations. In metabolic networks without regulation even with complex topology (including branches, cycles) dynamics or metabolite concentrations is simple. Namely, convergence to a set of steady states. On contrary, even in simple metabolic pathways with positive and negative feedback dynamics or metabolite concentrations can be quite complex (e.g., ocillations, chaos). Metabolic regulation Provides robustness to various perturbations on a day to day basis, but this may be due to the cost of emergence of complex dynamics or metabolite concentrations in later ages. In the second approach I modeled the evolution of control or metabolic flux. Two strategies to control metabolic flux evolve under different conditions: rate-limiting (single-enzyme influence flux) and distributed (multiple enzymes influence flux). The presence of senescence in the form of degradation of enzyme activity or metabolic regulation influences the evolution of flux control. General properties of metabolic networks and their robustness are related to their interplay and interplay between metabolism and senescence.