This thesis describes a novel Monte Carlo simulation algorithm for the estimation ofthe model parameters of kinetic rate equation systems, describing biochemical reaction networks;and for the quantitative prediction of the time-dependent behavior of real biochemicalsystems described by such kinetics models. This simulation method, referred to as thesuper-ensemble approach, combines Monte Carlo sampling of the kinetics model parameterspace with a simultaneous Galerkin-type variational Monte Carlo solution of the underlyingkinetic rate equation system. Unlike the recently proposed and closely related standardensemble simulation method, the super-ensemble does not rely on the high-volume executionof a conventional serial ordinary differential equation(ODE) solver algorithm, and it istherefore amenable to an efficient scalable parallelization by straightforward time domaindecomposition techniques. With minor modifications, the super-ensemble algorithm can alsobe deployed as a parallelizable variational ODE solution method, in a conventional ODEsolver setting where a unique ODE solution is sought for given initial conditions and givenrate functions. Test applications of the super-ensemble algorithm in both ODE solver modeand in parameter estimation mode, for a simple enzyme catalysis model, will be discussed.