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Exploring the gap between dynamic and constraint-based models of metabolism

Paper ID Volume ID Publish Year Pages File Format Full-Text
31580 44821 2012 8 PDF Available
Title
Exploring the gap between dynamic and constraint-based models of metabolism
Abstract

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 Escherichia 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 identified by constraint-based models, eliminating infeasible solutions and increasing the accuracy of simulation and optimization methods.

► Constraint-based and full kinetic models share the same set of steady states. ► Partial rate-constant knowledge can further constrain kinetic models. ► Such reductions can be transferred from kinetic to constraint-based formulations.

Keywords
Systems biology; Metabolic networks; Dynamic models; Constraint-based models
First Page Preview
Exploring the gap between dynamic and constraint-based models of metabolism
Publisher
Database: Elsevier - ScienceDirect
Journal: Metabolic Engineering - Volume 14, Issue 2, March 2012, Pages 112–119
Authors
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Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering