Genome-scale metabolic reconstructions are typically validated by comparing in silico growth predictions across different mutants utilizing different carbon sources with in vivo growth data. This comparison results in two types of model-prediction inconsistencies; either the model predicts growth when no growth is observed in the experiment (GNG inconsistencies) or the model predicts no growth when the experiment reveals growth (NGG inconsistencies). In this project, we developed an optimization-based framework, GrowMatch, to automatically reconcile GNG predictions (by suppressing functionalities in the model) and NGG predictions (by adding functionalities to the model). We use GrowMatch to resolve inconsistencies between the predictions of the latest in silico Escherichia coli (iAF1260) model and the in vivo data available in the Keio collection and improved the consistency of in silico with in vivo predictions from 90.6% to 96.7%.

In a subsequent work, we demonstrated that comparison of the in silico growth predictions in presence of higher order gene deletions (i.e., synthetic lethals) with available experimental data reveals a number of additional ways that model and experiment may differ in their predictions. For example, GSL inconsistencies refer to cases where the simultaneous deletion of a gene pair results in a viable strain (i.e., Growth), where their deletion is lethal (i.e., Synthetic Lethal) based on experimental data. ESSL represent mismatches where the single deletion of one of the genes in silico is lethal (i.e., ESsential), however, their simultaneous deletion in vivo results in a lethal phenotype (i.e., Syntheticl Lethal). Finally, SLG and SLES denote inconsistencies where the model implies that only the double gene mutation is lethal (i.e., Syntheticl Lethal) but experimental observations support either growth (G) or lethality of any of the two single gene deletions (i.e., ESsential), respectively. We generalized the GrowMatch procedure to resolve each one of these new inconsistencies for higher order deletions. In addition, the generalize GrowthMatch can work directly with gene (rather than reaction) deletions. Application of this procedure to the iMM904 model of the yeast resulted in identification of 120 distinct model modifications including various regulatory constraints for minimal and YP. Incorporation of these modifications into the model led to significant improvements in consistency of in silico predictions and in vivo observations for both single and double gene deletions.

For many cases, GrowMatch and generalized GrowMatch identified fairly non-intuitive model modification hypotheses that would have been difficult to pinpoint through inspection alone. In addition, these procedures can be used during the construction phase of new, as opposed to existing, genome-scale metabolic models, leading to more expedient and accurate reconstructions.

Related Publications:

Satish Kumar, V. and C.D. Maranas (2009), "GrowMatch: An automated method for reconciling in silico/in vivo growth predictions," PLoS Computational Biology, 5(3): e1000308.

Zomorrodi, A and C.D. Maranas (2010), "Improving the iMM904 S. cerevisiae metabolic model using essentiality and synthetic lethality data," BMC Systems Biology, 4:178

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