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Dynamic metabolic modeling is a useful approach for studying the regulation of metabolism by integration of biological knowledge from different biochemical systems.
The main concept that underlies whole-network metabolic modeling is the identification and mathematical definition of constraints.
Genome scale metabolic modeling is shown to predict overexpression and deletion mutants which enhance recombinant protein production with high accuracy.
Genome-scale metabolic modeling is being used to gain this understanding, both by investigating the overall metabolic potential of microorganisms for chemical production and by incorporating increasingly complex information on the physical limits of metabolism (i.e. stoichiometry, thermodynamics, and kinetics).
Metabolic modeling is our attempt to test the impact of active site variation in a more meaningful way as such methods get more sophisticated.
Nevertheless, this metabolic modeling is not a detailed or meticulous modeling, considering all the individual kinetic constants and allosteric effects of different enzymes or, for instance, individual diffusion rates.
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However, an integrated approach to reconstruct the metabolic network by integrating metagenomic data with genome-scale metabolic modeling was introduced recently [ 7].
The in silico genome-scale metabolic model is developed using genomic annotation, metabolic reactions, literature information, and experimental data.
Accordingly, extreme pathway analysis reduces the metabolic network into a macro reaction scheme, on which a dynamic metabolic model is built.
The parameter set of the dopamine metabolic model is comprised of kinetic orders and rate constants.
Hence, the link between the hydrodynamics of steady swimming and the exponential-based metabolic model is defined.
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