Marino, Ines PZaikin, AlexeyMíguez Arenas, Joaquín2023-10-062023-10-062017-08-10Mariño, I. P., Zaikin, A., & Mı́guez, J. (2017). A comparison of Monte Carlo-based Bayesian parameter estimation methods for stochastic models of genetic networks. PLOS ONE, 12(8), e0182015.1932-6203https://hdl.handle.net/10016/38565We compare three state-of-the-art Bayesian inference methods for the estimation of the unknown parameters in a stochastic model of a genetic network. In particular, we introduce a stochastic version of the paradigmatic synthetic multicellular clock model proposed by Ullner et al., 2007. By introducing dynamical noise in the model and assuming that the partial observations of the system are contaminated by additive noise, we enable a principled mechanism to represent experimental uncertainties in the synthesis of the multicellular system and pave the way for the design of probabilistic methods for the estimation of any unknowns in the model. Within this setup, we tackle the Bayesian estimation of a subset of the model parameters. Specifically, we compare three Monte Carlo based numerical methods for the approximation of the posterior probability density function of the unknown parameters given a set of partial and noisy observations of the system. The schemes we assess are the particle Metropolis-Hastings (PMH) algorithm, the nonlinear population Monte Carlo (NPMC) method and the approximate Bayesian computation sequential Monte Carlo (ABC-SMC) scheme. We present an extensive numerical simulation study, which shows that while the three techniques can effectively solve the problem there are significant differences both in estimation accuracy and computational efficiency.25eng© 2017 Mariño et al.Atribución 3.0 Españaresearch articleTelecomunicacioneshttps://doi.org/10.1371/journal.pone.0182015open access18, e018201525PLoS One12AR/0000020434