|Title||Top-down analysis of temporal hierarchy in biochemical reaction networks.|
|Publication Type||Journal Article|
|Year of Publication||2008|
|Authors||Jamshidi N, Palsson BØ|
|Journal||PLoS computational biology|
|Keywords||Algorithms, Biochemical Phenomena, Biochemistry, Computational Biology, Computer Simulation, Erythrocytes, Folic Acid, Glycolysis, Humans, Kinetics, Metabolic Networks and Pathways, Models, Biological, Saccharomyces cerevisiae, Thermodynamics|
The study of dynamic functions of large-scale biological networks has intensified in recent years. A critical component in developing an understanding of such dynamics involves the study of their hierarchical organization. We investigate the temporal hierarchy in biochemical reaction networks focusing on: (1) the elucidation of the existence of "pools" (i.e., aggregate variables) formed from component concentrations and (2) the determination of their composition and interactions over different time scales. To date the identification of such pools without prior knowledge of their composition has been a challenge. A new approach is developed for the algorithmic identification of pool formation using correlations between elements of the modal matrix that correspond to a pair of concentrations and how such correlations form over the hierarchy of time scales. The analysis elucidates a temporal hierarchy of events that range from chemical equilibration events to the formation of physiologically meaningful pools, culminating in a network-scale (dynamic) structure-(physiological) function relationship. This method is validated on a model of human red blood cell metabolism and further applied to kinetic models of yeast glycolysis and human folate metabolism, enabling the simplification of these models. The understanding of temporal hierarchy and the formation of dynamic aggregates on different time scales is foundational to the study of network dynamics and has relevance in multiple areas ranging from bacterial strain design and metabolic engineering to the understanding of disease processes in humans.
|Alternate Journal||PLoS Comput. Biol.|