Experimental technologies such as Phenotype Micro-arrays have provided unprecedented physiological data under different growth environments and genetic backgrounds. Similar advances in computational modeling techniques have allowed the development of genome-scale metabolic models of several organisms.  In this talk, we will show examples of how large-scale growth profiling data can be used to inform genome-scale models. For example, we illustrate the use of PM data to obtain a highly accurate and expanded metabolic model of Bacillus subtilis.  The integrated analysis of the large-scale substrate utilization and gene essentiality data with the genome-scale metabolic model revealed the requirement of 89 specific enzymes (transport, 63; intracellular reactions, 26) that were not in the genome annotation. Subsequent sequence analysis resulted in the identification of genes that could be putatively assigned to 13 intracellular enzymes.

In the another example, fitness profiling data from growth competition experiments between strains of different genetic backgrounds were used to refine the metabolic model of Escherichia coli. Constraint-based models of metabolism seldom incorporate capacity constraints on intracellular fluxes due to lack of kinetic data. Here, the optimal capacity constraint identification (OCCI) algorithm was developed to identify capacity constraints necessary for accurately predicting experimental fitness profiles. In a case study, fitness profiles from 14 different genetic backgrounds in the same growth environment were used to identify capacity constraints in central metabolism of E. coli. This algorithm can be readily extended to handle Phenotype Micro-array data from multiple growth environments.