@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    = {https://doi.org/10.1186/s12918-016-0283-2},
  pdf    = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4895898/pdf/12918_2016_Article_283.pdf},
}