467602 Deductive Determination of Dynamic Cellular Objectives from Biological Data
This analysis simultaneously relates dynamic metabolic changes with the regulation of enzymes that influence those metabolic changes. It provides objective functions that take the form of optimal weightings which represent the return on investment for various metabolic pathways that are competing for same pool of resources. To give biological context to these returns on investment, a method to mine gene expression data and explain the objective function using pathway enrichment analysis is proposed. We use this approach to analyze three systems: two diauxic growth scenarios and prostaglandin metabolism in a mammalian cell line. Pathway enrichment analysis of the genes mined using this procedure provides objective functions that show agreement with already established experimental knowledge related to the behavior of these systems in their respective conditions. The ability to determine objective functions deductively from the data allows for the formulation of robust cybernetic descriptions of systems where objective functions are difficult to determine inductively. This approach has applications in specifying objective functions for more complicated metabolic systems in multi-cellular organisms.