Optimality criteria for the prediction of metabolic fluxes in yeast mutants.
Constraint-based models of cellular metabolism, such as flux balance analysis (FBA), use convex analysis and optimization to study metabolic networks at a genome scale. The availability of reaction lists for numerous organisms, along with a variety of network analysis and optimization tools, is making these approaches increasingly popular for metabolic engineering and biomedical applications, as well as for addressing fundamental biological questions. It is therefore very important to assess the predictive capacity of these models and to understand how to interpret them in a biologically relevant manner. Typically, model assessment is limited to gauging the ability to predict phenotypes, such as viability under different environmental and genetic conditions. These types of assessments, for the most part, focus only on the growth phenotype of the cells, but ignore the underlying flux predictions. While this may be sufficient for certain types of study, the question of whether flux balance models can reliably predict intracellular and transport fluxes is crucial for more detailed analysis, and remains largely unanswered. Here we compare FBA model predictions of yeast metabolic fluxes to a previously published set of experimentally determined fluxes for 13 different single gene deletion mutants across a variety of possible objective functions. We find that the specific optimization criteria used to determine fluxes have a significant impact on the accuracy of the predicted fluxes. Interestingly, while different optimization methods provide very different levels of agreement relative to experimental fluxes, they tend to provide similar predictions with respect to the effect of the perturbation on growth. This demonstrates that assessment of models at the level of flux predictions is a critical step in assessing the biological validity of different models and optimization criteria.
Graduate Program in Bioinformatics, Boston University, 44 Cummington St., Boston, Massachusetts 02215, USA. esnitkin@bu.edu
Evan S
ES
Snitkin
Daniel
D
Segrè
D001419:Bacteria / Q000235:genetics / Q000378:metabolism; D001671:Biochemistry / Q000639:trends; D004777:Environment; D005818:Genetic Engineering / Q000379:methods; D016681:Genome, Fungal; D005947:Glucose / Q000378:metabolism; D007700:Kinetics; D008957:Models, Genetic; D008967:Molecular Biology / Q000639:trends; D009154:Mutation; D010641:Phenotype; D015003:Yeasts / Q000235:genetics* / Q000254:growth & development / Q000378:metabolism*
D005947:Glucose