Author(s):
Machado, Carlos 
; Costa, Rafael S. ; Ferreira, E. C. ; Rocha, I. ; Tidor, Bruce
Date: 2012
Persistent ID: http://hdl.handle.net/1822/22325
Origin: RepositóriUM - Universidade do Minho
Document details
Exploring the gap between dynamic and constraint-based models of metabolism
Description
Systems biology provides new approaches for metabolic engineering through the development of models and methods for simulation and optimization of microbial metabolism. Here we explore the relationship between two modeling frameworks in common use namely, dynamic models with kinetic rate laws and constraint-based flux models. We compare and analyze dynamic and constraint-based formulations of the same model of the central carbon metabolism of E. coli. Our results show that, if unconstrained, the space of steady states described by both formulations is the same. However, the imposition of parameter-range constraints can be mapped into kinetically feasible regions of the solution space for the dynamic formulation that is not readily transferable to the constraint-based formulation. Therefore, with partial kinetic parameter knowledge, dynamic models can be used to generate constraints that reduce the solution space below that identied by constraint-based models, eliminating infeasible solutions and increasing the accuracy of simulation and optimization methods.
Systems biology provides new approaches for metabolic engineering through the development of models and methods for simulation and optimization of microbial metabolism. Here we explore the relationship between two modeling frameworks in common use namely, dynamic models with kinetic rate laws and constraint-based flux models. We compare and analyze dynamic and constraint-based formulations of the same model of the central carbon metabolism of E. coli. Our results show that, if unconstrained, the space of steady states described by both formulations is the same. However, the imposition of parameter-range constraints can be mapped into kinetically feasible regions of the solution space for the dynamic formulation that is not readily transferable to the constraint-based formulation. Therefore, with partial kinetic parameter knowledge, dynamic models can be used to generate constraints that reduce the solution space below that identied by constraint-based models, eliminating infeasible solutions and increasing the accuracy of simulation and optimization methods.
Document Type
Article
Language English
Language English
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