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 |