(475e) ciFBA: A Scalable Framework for Integrated Analysis of Metabolism and Regulation

High-throughput data from genome sequencing and expression profiling have enabled the reconstruction of cellular regulatory and metabolic networks. While flux balance analysis and other metabolic reconstruction techniques have successfully predicted growth rates for several organisms using constraint-based linear programming, these methods lack an efficient means for seamlessly integrating a regulatory network to examine the full effect of a metabolic environment on an organism. This presentation will detail a novel, scalable extension to flux balance analysis, ciFBA (concurrent, integrated Flux Balance Analysis), that incorporates both a stoichiometric metabolic reconstruction and a constraint-based representation of Boolean gene regulatory network. ciFBA uses a single mixed integer linear program to optimize both an organism's internal flux distribution and external media composition; this approach is uniquely suited to efficiently calculate: (1) optimal and minimal growth media, (2) necessary environments for expression of a specific set of genes, and (3) media-specific growth rates. Many of these predictions can be verified by simple growth experiments, requiring only different media compositions. Ultimately, ciFBA's integration of regulation and metabolism will constitute a more complete description of an environment's effect on an organism, thereby creating a more flexible platform for in silico metabolic engineering.