Model for the Michaelis Menten Enzyme Kinetics Model

Usage

michaelis_menten_model(
  data,
  formula = michaelis_menten_formula(),
  prior = michaelis_menten_prior(),
  init = michaelis_menten_init(),
  iter = 8000,
  control = list(adapt_delta = 0.99),
  stanvar_function = c(BayesPharma::michaelis_menten_stanvar(),
    BayesPharma::michaelis_menten_genquant()),
  expose_functions = TRUE,
  ...
)

Arguments

data: data.frame of experimental data. must contain columns time and P and any predictors specified in the formula.
formula: brms::brmsformula object. To create a dose-response brms::brmsformula, use the michaelis_menten_formula function.
prior: brms::brmsprior for kcat, and kM. Use michaelis_menten_formula() to create priors to be used here.
init: list of lists, numeric value, or "random" for the initial values of the parameters being modeled.
iter: numeric of iterations the model runs. Increasing iter can help with model convergence.
control: a named list of parameters to control the sampler's behavior. Adding max_treedepth and giving a greater value than 10 can improve model convergence.
stanvar_function: stan code for the model
expose_functions: logical. Expose the BayesPharma functions for the model
...: additional arguments passed to brms::brm().

Value

bpfit object, which is a wrapper around a brms::brmsfit object.

References

Choi, B., Rempala, G.A. & Kim, J.K. Beyond the Michaelis-Menten equation: Accurate and efficient estimation of enzyme kinetic parameters. Sci Rep 7, 17018 (2017). https://doi.org/10.1038/s41598-017-17072-z

Model for the Michaelis Menten Enzyme Kinetics Model

Usage

Arguments

Value

References

See also