Several human diseases are caused by metabolism defects. Discovering the mechanisms that govern the onset and progression of human metabolism-related diseases is not a straightforward process. Computational approaches, such as the flux balance analysis, have been successfully used to extract useful knowledge on the metabolic dysregulation processes from genome-scale network models. In this work, we propose a novel approach which integrates constraint-based techniques with model checking methods, with the aim to extract relevant qualitative information from a metabolic network model. As a case study, we applied our methodology to the simulation and analysis of the primary hyperoxaluria type I, an inherited disease in which the lack of a particular liver enzyme causes the kidney to accumulate excessive amounts of oxalate.

Combining Flux Balance Analysis and Model Checking for Metabolic Network Validation and Analysis.

PERON, ADRIANO;DI BERNARDO, DIEGO
2015

Abstract

Several human diseases are caused by metabolism defects. Discovering the mechanisms that govern the onset and progression of human metabolism-related diseases is not a straightforward process. Computational approaches, such as the flux balance analysis, have been successfully used to extract useful knowledge on the metabolic dysregulation processes from genome-scale network models. In this work, we propose a novel approach which integrates constraint-based techniques with model checking methods, with the aim to extract relevant qualitative information from a metabolic network model. As a case study, we applied our methodology to the simulation and analysis of the primary hyperoxaluria type I, an inherited disease in which the lack of a particular liver enzyme causes the kidney to accumulate excessive amounts of oxalate.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/576860
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