UQSA

Uncertainty quantification via ABC-MCMC with copulas as well as global sensitivity analysis for ODE models in systems biology. This R package can approximate the posterior probability density of Parameters for Ordinary Differential Equation models. The ABC sampler used here is developed to be fairly model agnostic, but the supplied tool set and R functions specifically target ODEs as they are fast enough to simulate to permit Bayesian methods. Bayesian methods for parameter estimation are resource intensive and therefore require some consideration of efficiency in simulation. Other modeling frameworks exist, with benefits of higher accuracy in specific scenarios (e.g. low molecule count), or reduced complexity (rule based models). We have written a sibling library for R that facilitates the simulation of systems biology specific models using the GNU scientific library solvers (and models written in C). With powerful enough computing hardware, or small enough models, these frameworks can be combined with this package. We write models using the SBtab format and automatically generate C-code as well as R-code for them, the R-code can be used with deSolve (an R package) while the C-code is compatible with gsl_odeiv2 solvers. Code generation is done via SBtabVFGEN (an R package) and vfgen (a standalone software). In addition, we are writing our own substitution for vfgen, to avoid single points of failure. But the model setup phase can be completely sidestepped by writing the C-code manually (or generating it in any other way).