Extreme pathways are a unique, network-based, mathematical definition of metabolic pathways and thus can be used to rigorously define and study the emergent properties of biological systems. They are derived directly from the stoichiometric matrix that represents a biochemical network and completely characterize all possible steady-state flux distributions through the network. Extreme pathways have already given many insightful conclusions about the topological properties of reconstructed reaction networks and their relationships to biological functionalities. However, the utility of extreme pathways is currently limited by the fact that the complete enumeration of genome-scale extreme pathways is computationally challenging. Additionally, the development of new analysis tools is needed to strengthen the link between extreme pathway properties and experimental data. Accordingly, our specific aims are to: (1) enable the efficient calculation of extreme pathways from genome-scale models; (2) apply the techniques developed in Specific Aim #1 to compute the extreme pathways for: (a) organelles (the mitochondria from Saccharomyces cerevisiae and chloroplasts from Arabidopsis thaliana), (b) growth condition dependent human pathogens (Helicobacter pylori and Haemophilus influenzae and others that may become available during the period of this proposal), and (c) a fully autonomous organism Escherichia coil); and (3) develop analysis tools to yield more biological meaning and relevance of extreme pathways by analyzing singular value decomposition (SVD) of extreme pathway matrices and converting the flux cone into a cone of kinetic constants (the K-cone) using measured concentrations (proteomics and metabolomic data). The concepts and analysis methods developed and verified to date must be moved forward to tie concepts directly to biological data and applications. If implemented, this proposed program will result in a significant advancement in our ability to study and characterize the capabilities of reconstructed networks and to relate in silico results to actual cellular functions.
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