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Stan Code for the tQ Enzyme Kinetics Model

tQ_stanvar.Rd

The tQ is an ordinary differential equation model for the total quasi-steady-state assumption kinetics defined in (Choi et al., 2017), which is related to the Michaelis-Menten kinetics model, but doesn't assume the enzyme concentration is negligibly small.

To implement the tQ model in Stan, the function tQ_ode is defined and then passed to tQ_single to integrate it using the stiff backward differentiation formula (BDF) method. To fit multiple time series in one model, the tQ_multiple can be used. Note that to handle fitting time-series with different numbers of observations, an additional integer series_index argument is used. Note that observations in the same time-series should be in sequential order in the supplied data.

Usage

tQ_stanvar()

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

See also

tQ_model,

Examples

if (FALSE) { # \dontrun{
brms::brm(
  data = ...,
  formula = brms::brmsformula(
    P ~ tQ_multiple(series_index, time, kcat, kM, ET, ST),
    kcat + kM ~ 1,
    nl = TRUE,
    loop = FALSE),
  prior = ...,
  init =  ...,
  stanvars = BayesPharma::tQ_stanvar())
} # }