Background: The mechanistic description of enzyme kinetics in a
dynamic model of metabolism requires specifying the numerical values of a
large number of kinetic parameters. The parameterization challenge is often
addressed through the use of simplifying approximations to form reaction
rate laws with reduced numbers of parameters. Whether such simplified models
can reproduce dynamic characteristics of the full system is an important
question.
Results: In this work, we compared the local transient response properties
of dynamic models constructed using rate laws with varying levels of
approximation. These approximate rate laws were: 1) a Michaelis-Menten rate
law with measured enzyme parameters, 2) a Michaelis-Menten rate law with
approximated parameters, using the convenience kinetics convention, 3) a
thermodynamic rate law resulting from a metabolite saturation assumption,
and 4) a pure chemical reaction mass action rate law that removes the role
of the enzyme from the reaction kinetics. We utilized in vivo data for the
human red blood cell to compare the effect of rate law choices against the
backdrop of physiological flux and concentration differences. We found that
the Michaelis-Menten rate law with measured enzyme parameters yields an
excellent approximation of the full system dynamics, while other assumptions
cause greater discrepancies in system dynamic behavior. However, iteratively
replacing mechanistic rate laws with approximations resulted in a model that
retains a high correlation with the true model behavior. Investigating this
consistency, we determined that the order of magnitude differences among
fluxes and concentrations in the network were greatly influential on the
network dynamics. We further identified reaction features such as thermodynamic
reversibility, high substrate concentration, and lack of allosteric regulation,
which make certain reactions more suitable for rate law approximations.
Conclusions: Overall, our work generally supports the use of approximate
rate laws when building large scale kinetic models, due to the key role that
physiologically meaningful flux and concentration ranges play in determining
network dynamics. However, we also showed that detailed mechanistic models
show a clear benefit in prediction accuracy when data is available. The work
here should help to provide guidance to future kinetic modeling efforts on the
choice of rate law and parameterization approaches.
@article{Du2016,
author = {Du, Bin and Zielinski, Daniel and Dr\"ager, Andreas and Tan, Justin and
Zhang, Zhen and Ruggiero, Kayla and Arzumanyan, Garry and Palsson, Bernhard O.},
title = {Evaluation of Rate Law Approximations in Bottom-up Kinetic Models of
Metabolism},
journal = {BMC Systems Biology},
month = jun,
year = {2016},
abstract = {Background: The mechanistic description of enzyme kinetics in a
dynamic model of metabolism requires specifying the numerical values of a
large number of kinetic parameters. The parameterization challenge is often
addressed through the use of simplifying approximations to form reaction
rate laws with reduced numbers of parameters. Whether such simplified models
can reproduce dynamic characteristics of the full system is an important
question.
Results: In this work, we compared the local transient response properties
of dynamic models constructed using rate laws with varying levels of
approximation. These approximate rate laws were: 1) a Michaelis-Menten rate
law with measured enzyme parameters, 2) a Michaelis-Menten rate law with
approximated parameters, using the convenience kinetics convention, 3) a
thermodynamic rate law resulting from a metabolite saturation assumption,
and 4) a pure chemical reaction mass action rate law that removes the role
of the enzyme from the reaction kinetics. We utilized in vivo data for the
human red blood cell to compare the effect of rate law choices against the
backdrop of physiological flux and concentration differences. We found that
the Michaelis-Menten rate law with measured enzyme parameters yields an
excellent approximation of the full system dynamics, while other assumptions
cause greater discrepancies in system dynamic behavior. However, iteratively
replacing mechanistic rate laws with approximations resulted in a model that
retains a high correlation with the true model behavior. Investigating this
consistency, we determined that the order of magnitude differences among
fluxes and concentrations in the network were greatly influential on the
network dynamics. We further identified reaction features such as thermodynamic
reversibility, high substrate concentration, and lack of allosteric regulation,
which make certain reactions more suitable for rate law approximations.
Conclusions: Overall, our work generally supports the use of approximate
rate laws when building large scale kinetic models, due to the key role that
physiologically meaningful flux and concentration ranges play in determining
network dynamics. However, we also showed that detailed mechanistic models
show a clear benefit in prediction accuracy when data is available. The work
here should help to provide guidance to future kinetic modeling efforts on the
choice of rate law and parameterization approaches.},
keywords = {metabolic modeling, kinetic modeling, approximate rate laws,
Michaelis-Menten kinetics, mass action kinetics},
volume = {10},
number = {1},
pages = {1--15},
issn = {1752-0509},
doi = {10.1186/s12918-016-0283-2},
url = {http://dx.doi.org/10.1186/s12918-016-0283-2},
pdf = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4895898/pdf/12918_2016_Article_283.pdf},
}