BioBayes: a software package for Bayesian inference in systems biology. MOTIVATION: There are several levels of uncertainty involved in the mathematical modelling of biochemical systems. There often may be a degree of uncertainty about the values of kinetic parameters, about the general structure of the model and about the behaviour of biochemical species which cannot be observed directly. The methods of Bayesian inference provide a consistent framework for modelling and predicting in these uncertain conditions. We present a software package for applying the Bayesian inferential methodology to problems in systems biology. RESULTS: Described herein is a software package, BioBayes, which provides a framework for Bayesian parameter estimation and evidential model ranking over models of biochemical systems defined using ordinary differential equations. The package is extensible allowing additional modules to be included by developers. There are no other such packages available which provide this functionality

References in zbMATH (referenced in 14 articles )

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  1. Campillo-Funollet, Eduard; Venkataraman, Chandrasekhar; Madzvamuse, Anotida: Bayesian parameter identification for Turing systems on stationary and evolving domains (2019)
  2. Li, Yingyi; Zhang, Haibin; Li, Zhibao; Gao, Huan: Proximal gradient method with automatic selection of the parameter by automatic differentiation (2018)
  3. Niu, Mu; Macdonald, Benn; Rogers, Simon; Filippone, Maurizio; Husmeier, Dirk: Statistical inference in mechanistic models: time warping for improved gradient matching (2018)
  4. Ghosh, Sanmitra; Dasmahapatra, Srinandan; Maharatna, Koushik: Fast approximate Bayesian computation for estimating parameters in differential equations (2017)
  5. Grzegorczyk, Marco: A non-homogeneous dynamic Bayesian network with a hidden Markov model dependency structure among the temporal data points (2016)
  6. Mazur, Johanna; Kaderali, Lars: The importance and challenges of Bayesian parameter learning in systems biology (2013)
  7. Vanlier, J.; Tiemann, C. A.; Hilbers, P. A. J.; van Riel, N. A. W.: Parameter uncertainty in biochemical models described by ordinary differential equations (2013)
  8. Higham, Desmond J.: Stochastic ordinary differential equations in applied and computational mathematics (2011)
  9. Calderhead, Ben; Girolami, Mark: Estimating Bayes factors via thermodynamic integration and population MCMC (2009)
  10. Intep, Somkid; Higham, Desmond J.; Mao, Xuerong: Switching and diffusion models for gene regulation networks (2009)
  11. Girolami, Mark: Bayesian inference for differential equations (2008)
  12. Vyshemirsky, Vladislav; Girolami, Mark: Biobayes: A software package for Bayesian inference in systems biology. (2008) ioport
  13. Vyshemirsky, Vladislav; Girolami, Mark: Bayesian ranking of biochemical system models. (2008) ioport
  14. Vyshemirsky, Vladislav; Girolami, Mark A.: Bayesian ranking of biochemical system models. (2008) ioport