Table 1. Strengths and limitations of the metabolic GEM applications.

Application	What the model can do	What the model cannot do
	Strengths of the E. coli GEM	Areas for future progress
Metabolic engineering	Gene deletion (combinatorial)	Limited coverage of molecular biology
	Gene addition	Predicting the effects of perturbations to regulatory elements
	Gene over- and under-expression	Predicting allosteric inhibition
	Rapidly test the systemic effects of heterologous pathway additions	There is no explicit representation of metabolite concentrations
	Design biomarkers/biosensors for characteristic function	Account for enzyme kinetics
	Determine media supplementation strategiesMap high-throughput data to identify bottlenecks	Cannot accurately predict the performance of nonnative genes/proteins in E. coli
	Design strains through evolution
Biological discovery	Predict growth on different carbon sources/media conditions	Predict the regulation of isozymes/parallel pathways
	Guide the functional assignment of network gaps	Predict enzyme promiscuity
	Guide the discovery of previously uncharacterized gene product functions (graph theory analysis)	Predictive power is inherently limited, because the model is not complete in scope
	Guide the reannotations of incorrectly annotated genes	Predict the expression of genes
	Connect orphan metabolites to known reactions	Predict the functional state of proteins (e.g., posttranslational modification)
Phenotypic behavior	Predict optimal cellular behavior	Differentiate between computed alternate optimal flux distributions of the cell a priori
	Understand energetics and occurrence of suboptimal behavior	Explain the reasons for suboptimal performance a priori
	Infer impact of regulationProvide a context for which experimental data can be interpreted	Provide a framework for incorporating additional regulatory interactions that are currently under development
	Predict and understand absolute and conditional gene essentiality
	Predict and understand shifts in growth conditions
Network analysis	Evaluate metabolic networks from a systems view through node and link dependencies, essentialities, overall network robustness	Does not always include the biological mechanisms behind the network connectionsFew predictions can be experimentally validated
	Describe the complex interactions of the components of the metabolic network
	Evaluate modularity of function
	Evaluate regulation based on network structure
Bacterial evolution	Predict essential genes	Account for changes in regulatory elements
	Predict the endpoint of evolution	Predict the time-course of evolution
	Understand the basis for epistatic interactions and mutational effects	Predict location of mutations in the genomePredict the effects of mutations in the genome
	Provide insights into evolutionary trajectories	Account for strain-specific genomic differences
Interspecies interaction	Model the exchange of metabolites	Model interactions that affect metabolic regulation
	Analyze high-throughput data from different strains	Inability to measure flux exchange in multi cell-type systems
	Determine the cost/benefit ratio for different types of commensalism	There are still too many unknowns to accurately build an interactions network
		Limited ability to define individual genetic content in large communities
		Limited spatial knowledge in large communities