Slow relaxation and aging in the model of randomly connected cycles network

We propose a statistical model of large random network with high connectivity in order to describe the behavior E. coli cells after exposure acute stress. The building blocks this are feedback cycles typical genetic and metabolic networks cell. Each node on is spin degree freedom representing component cell's that can be one two states: active or inactive. interconnected by regulation exchange metabolites. Stress realized an external magnetic field drives nodes into inactive state, time magnetization passes zero value for first represents division event cell stress period. numerical analytical solutions passage problem reproduce aging dynamics observed experimental data.