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Updated: Apr 1, 2026
Rapid Identification of Chemical Genetic Interactions in Saccharomyces cerevisiae
Published on: April 5, 2015
Using Gene Essentiality and Synthetic Lethality Information to Correct Yeast and CHO Cell Genome-Scale Models
Ratul Chowdhury1, Anupam Chowdhury2, Costas D Maranas3
1Department of Chemical Engineering, The Pennsylvania State University, University Park, Pennsylvania, PA 16802, USA. ratul@psu.edu.
Abstract:
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. Due to the paucity of mutant growth phenotype data only 12 changes were made for the CHO 1.2 model leading to an additional correctly predicted 11 essential and eight non-essential genes. Overall, we find that CHO 1.2 was 76% less accurate than the Yeast 7.11 metabolic model in predicting essential genes. Based on this analysis, 14 (single and double deletion) maximally informative experiments are suggested to improve the CHO cell model by using information from a mouse metabolic model. This analysis demonstrates the importance of single and multiple knockout phenotypes in assessing and improving model reconstructions. The advent of techniques such as CRISPR opens the door for the global assessment of eukaryotic models.
Insights
Essentiality and synthetic lethality analyses improve computational models of cell metabolism. This study enhances the accuracy of yeast and CHO cell models, identifying key gene targets for therapeutic development and biotechnology applications.
Area of Science:
- Systems biology
- Metabolic modeling
- Computational biology
Background:
- Essentiality (ES) and synthetic lethality (SL) data are crucial for identifying gene interactions that impact cell viability.
- This information is vital for drug target discovery in oncology and infectious diseases, as well as for optimizing biotechnological processes.
- Understanding gene essentiality aids in predicting the effects of gene knockouts in cellular systems.
Purpose of the Study:
- To conduct a comprehensive analysis of essentiality and synthetic lethality in eukaryotic models, specifically Saccharomyces cerevisiae (yeast) and Chinese Hamster Ovary (CHO) cells.
- To refine existing metabolic models by identifying and rectifying inconsistencies between model predictions and experimental data.
- To propose targeted experimental strategies for improving the accuracy of cellular models.
Main Methods:
- Utilized a bilevel optimization approach for comprehensive essentiality and synthetic lethality analysis.
- Performed model curation by incorporating identified gene-essentiality and synthetic-lethality data to correct discrepancies.
- Suggested maximally informative single and double deletion experiments to enhance model reconstruction.
Main Results:
- For the Yeast 7.11 model, 50 metabolic and gene-protein-reaction (GPR) changes improved predictions, correctly identifying 28% more essential genes and 36% more synthetic lethals, while reducing false positives by 53%.
- The CHO 1.2 model, due to limited phenotype data, underwent 12 changes, improving predictions for 11 essential and 8 non-essential genes.
- The CHO 1.2 model demonstrated 76% lower accuracy than the Yeast 7.11 model in predicting essential genes.
Conclusions:
- Model reconstruction and accuracy are significantly improved by incorporating single and multiple knockout phenotype data.
- The study highlights the lower predictive accuracy of the CHO 1.2 model compared to the yeast model, emphasizing the need for more experimental data.
- Suggests 14 targeted experiments to enhance the CHO cell model, leveraging insights from a mouse metabolic model and the potential of CRISPR technology for global model assessment.
