  We present an R-package developed for modeling of biochemical reaction
networks, uncertainty quantification (UQ) and sensitivity analysis (SA).
Estimating parameters and quantifying their uncertainty (and resulting
prediction uncertainty), is required for data-driven systems biology modeling.
Sampling methods need to be efficient when confronted with high-dimensional,
correlated parameter distributions. We have developed the UQSA package to be
fast for this problem class and work well with other tools for modelling. We
aim for simplicity, and part of that is our use of the SBtab format for the
unified storage of model and data. Our tool-set is modular enough, that parts
can be replaced. We use intermediate formats that are not hidden from the user
to make this feasible. UQ is performed through Markov chain Monte Carlo (MCMC)
sampling in an Approximate Bayesian Computation (ABC) setting. This can be
followed by a variance-decomposition based global sensitivity analysis. If
needed, complex parameter distributions can be described, evaluated, and
sampled from, with the help of Vine-copulas that are available in R. This
approach is especially useful when new experimental data become available, and
a previously calibrated model needs to be updated.
  Implementation: R is a high-level language and allows the use of
sophisticated statistical methods. The ode solver we used is written in C
(gsl_odeiv2, interface to R is ours). We use the SBtab tabular format for the
model description, as well as the data and an event system to be able to model
inputs frequently encountered in systems biology and neuroscience. The code has
been tested on one node with 256 cores of a computing cluster, but smaller
examples are included in the repository that can be run on a laptop.
  Source code: https://github.com/icpm-kth/uqsa
