Exploring The Homeostasis Mechanism For Cellular Metabolic Network At Metabolic Level And Uncovering New Constraints Beween Enzymatic Kinetic Parameters And Rate Constants

Metabolic homeostasis, or low-level metabolic steady state, has long been taken for granted, and research priority has always been given to understand metabolic flux control and regulation of the network. In the past, this has not caused concerns because the metabolic networks studied were invariably associated with living cells. Nowadays, there are needs to reconstruct metabolic networks, and so metabolic homeostasis cannot be taken for granted. For metabolic steady state, enzyme feedback control has been known to explain why metabolites in metabolic pathways can avoid accumulation. However, we reasoned that there are further contributing mechanisms. As a new methodology developed, we separated cofactor intermediates (CIs) from non-cofactor intermediates, and identified an appropriate type of open systems for operating putative reaction topologies. Furthermore, we elaborated the criteria to tell if a multi-enzyme over-all reaction path is of in vivo nature or not at the metabolic level. As new findings, we discovered that there are interactions between the enzyme feedback inhibition and the CI turnover, and such interactions may well lead to metabolic homeostasis, an emergent property of the system. To conclude, this work offers a new perspective for understanding the role of CIs and the presence of metabolic homeostasis in the living cell. In perspective, this work might provide clues for constructing non-natural metabolic networks using multi-enzyme reactions or by degenerating metabolic reaction networks from the living cell. Besides, based on enzymatic elementary reactions, the work has uncovered the constraints between kinetic parameters and rates constants for irreversible enzyme reaction. One one hand, this kind of constraint will enhance the understanding on the nature of enzyme reaction and definition of kinetic parameters. On the other hand, based on existing enzyme kinetic database, it will also decrease the uncertainties when it concerns in-silico modeling cellular metabolism dynamically.