In this project we perform a comprehensive benchmark of mainly Markov chain Monte Carlo (MCMC) methods for dynamical models in systems biology. The benchmark compares a number of state-of-the-art algorithms on both simulated data and real data scenarios. The scenarios covers a range of commonly encountered and challenging features such as multimodality, bifurcations, large scale problems and chaotic regimes. In addition, we develop a workflow package, based on the PEtab standard for parameter estimation problems, to allow users to easily perform reproducible and fair benchmarks. Main supervisor: Marija Cvijovic at the Department of Mathematical Sciences, Chalmers University of Technology and the University of Gothenburg.