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SIfEK (version 0.1.0)

Statistical Inference for Enzyme Kinetics

Description

Functions for estimating catalytic constant and Michaelis-Menten constant (MM constant) of stochastic Michaelis-Menten enzyme kinetics model are provided. The likelihood functions based on stochastic simulation approximation (SSA), diffusion approximation (DA), and Gaussian processes (GP) are provided to construct posterior functions for the Bayesian estimation. All functions utilize Markov Chain Monte Carlo (MCMC) methods with Metropolis- Hastings algorithm with random walk chain and robust adaptive Metropolis-Hastings algorithm based on Bayesian framework.

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Version

Install

install.packages('SIfEK')

Monthly Downloads

19

Version

0.1.0

License

GPL-3

Maintainer

Donghyun Ra

Last Published

November 22nd, 2018

Functions in SIfEK (0.1.0)

SSA.combi

Simultaneous estimation of Michaelis-Menten constant and catalytic constant using combined data and the likelihood function with the Stochastic Simulation Approximation method.
DA.combi

Simultaneous estimation of Michaelis-Menten constant and catalytic constant using combined data and the likelihood function with the diffusion approximation method.
DA.multi

Simultaneous estimation of Michaelis-Menten constant and catalytic constant using the likelihood function with diffusion approximation method
DA.MM

Estimation of single Michaelis-Menten constant using diffusion approximation
DA.cat

Estimation of single catalytic constant using the diffusion approximation
GP.MM

Estimation of single Michaelis-Menten constant using the Gaussian process method The function estimates single Michaelis-Menten constant using the likelihood function with the Gaussian process method.
GP.cat

Estimation of single catalytic constant using Gaussian processes
SSA.cat

Estimation of single catalytic constant using the stochastic simulation approximation method
GP.combi

Simultaneous estimation of Michaelis-Menten constant and catalytic constant using combined data and the likelihood function with the Gaussian process method
SSA.MM

Estimation of single Michaelis-Menten constant using the stochastic simulation approximation
Chymo_high

Product concentration of 101 observed time
SSA.multi

Simultaneous estimation of Michaelis-Menten constant and catalytic constant using the likelihood function with the stochastic simulation approximation method
GP.multi

Simultaneous estimation of Michaelis-Menten constant and catalytic constant using the likelihood function with the Gaussian process method.
Chymo_low

Product concentration of 101 observed time