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Ordinary differential equation systems (ODEs) are frequently used for dynamical system modeling in many science fields such as economics, physics, engineering, and systems biology. A special challenge in systems biology is that ODE systems typically contain kinetic rate parameters, which are unknown and have to be estimated from data. However, non-linearity of ODE systems together with noise in the data raise severe identifiability issues. Hence, Markov Chain Monte Carlo (MCMC) approaches havedoi:10.3389/fams.2019.00055 fatcat:oni6ecfdfjbpvnhxlil5ksbw4y