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.
|Author||Donghyun Ra, Kyunghoon Kim, Boseung Choi|
|Maintainer||Donghyun Ra <[email protected]>|
|Package repository||View on CRAN|
Install the latest version of this package by entering the following in R:
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.