Reconciling a Salmonella enterica metabolic model with experimental data confirms that overexpression of the glyoxylate shunt can rescue a lethal ppc deletion mutant.
|Title||Reconciling a Salmonella enterica metabolic model with experimental data confirms that overexpression of the glyoxylate shunt can rescue a lethal ppc deletion mutant.|
|Publication Type||Journal Article|
|Year of Publication||2013|
|Authors||Fong NL, Lerman JA, Lam I, Palsson BO, Charusanti P|
|Journal||FEMS Microbiol Lett|
|Date Published||2013 May|
|Keywords||Carbohydrate Metabolism, Inborn Errors, Gene Deletion, Gene Expression, Glyoxylates, Liver Diseases, Metabolic Networks and Pathways, Microbial Viability, Models, Theoretical, Phosphoenolpyruvate Carboxykinase (GTP), Salmonella typhimurium, Systems Biology|
The in silico reconstruction of metabolic networks has become an effective and useful systems biology approach to predict and explain many different cellular phenotypes. When simulation outputs do not match experimental data, the source of the inconsistency can often be traced to incomplete biological information that is consequently not captured in the model. To address this problem, general approaches continue to be needed that can suggest experimentally testable hypotheses to reconcile inconsistencies between simulation and experimental data. Here, we present such an approach that focuses specifically on correcting cases in which experimental data show a particular gene to be essential but model simulations do not. We use metabolic models to predict efficient compensatory pathways, after which cloning and overexpression of these pathways are performed to investigate whether they restore growth and to help determine why these compensatory pathways are not active in mutant cells. We demonstrate this technique for a ppc knockout of Salmonella enterica serovar Typhimurium; the inability of cells to route flux through the glyoxylate shunt when ppc is removed was correctly identified by our approach as the cause of the discrepancy. These results demonstrate the feasibility of our approach to drive biological discovery while simultaneously refining metabolic network reconstructions.
|Alternate Title||FEMS Microbiol. Lett.|