Essentiality (ES) and Synthetic Lethality (SL) information identify combination of genes whose deletion inhibits cell growth. This information is important for both identifying drug targets for tumor and pathogenic bacteria suppression and for flagging and avoiding gene deletions that are non-viable in biotechnology. In this study, we performed a comprehensive ES and SL analysis of two important eukaryotic models (S. cerevisiae and CHO cells) using a bilevel optimization approach introduced earlier. Information gleaned from this study is used to propose specific model changes to remedy inconsistent with data model predictions. Even for the highly curated Yeast 7.11 model we identified 50 changes (metabolic and GPR) leading to the correct prediction of an additional 28% of essential genes and 36% of synthetic lethals along with a 53% reduction in the erroneous identification of essential genes. The proposed model modifications on Yeast 7.11 involve 50 literature-supported changes that improve the sensitivity, specificity of Yeast 7.11 by 2.66% and 20.4% and decrease the false viable rate (FVR) by 8.42%.

Related Publications:

Chowdhury R., Chowdhury A., Maranas C.D. (2015), "Using gene essentiality and synthetic lethality information to correct yeast and CHO cell genome-scale models", Metabolites, 5(4):536-570. PMID: 26426067.

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