The qualitative and quantitative information conveyed by metabolic networks are important for regulating the metabolism of an organism to achieve desired targets. One approach to quantification is the 13C tracer experiment which aims to provide information on metabolic fluxes. The flux estimation problem is addressed in steady state and dynamic conditions in this presentation. The problem formulation in the steady state leads to a latent variable model structure which is utilised in applying the stochastic estimation framework to solve the flux quantification problem. A natural algorithm to solve this problem is the expectationmaximisation algorithm which is applied first. This is extended to the Markov Chain Monte Carlo algorithm to account for nonGaussian measurement noise. Finally, a sequential Monte Carlo filter is used to determine the fluxes under dynamic conditions. Results are presented for the central metabolism of Cornybacterium Glutamicum in the steady state and using a simulated metabolic network for the dynamic case.