(484g) Examining Rate Constants for Genome Scale Networks On Human Red Cell

Biological systems is lacking in network scale kinetic models. Kinetic parameters cannot be measured accurately and consistently, which hinders traditional approaches to gathering in vivo kinetic data. Current methods are available to attempt to overcome this lack of kinetic data though the use of a k-space and high throughput data. Methods such as the k-Cone method provide a way to collect a set of possible solutions to a network scale model, but the method needs improvement when dealing with enzyme kinetics. An improvement on the current constraint-based method of generating enzyme limiting reaction rates involves incorporating in vivo concentration data of both metabolites and proteins. Proteins in the biological system are treated as reactants, and each reaction is composed of either binding/dissociation events or isomerization reactions. This new approach was applied to the human red blood cell for which an established kinetic model is available. The study explores the effects of Gaussian noise in high throughput data, as well as the effects of missing metabolites and cofactors within a network. The study also performs a sensitivity analysis on the kinetic parameters and incorporates regulation and inhibition on the network scale. This approach could then be applied to biological systems for which kinetic data is unknown, allowing for such studies as designing activators or inhibitors for enzymes, examining genetic defects for cells diseases, and gaining insight into organisms targeted for biofuels production.