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 25 articles )

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  1. Niu, Mu; Wandy, Joe; Daly, Rónán; Rogers, Simon; Husmeier, Dirk: R package for statistical inference in dynamical systems using kernel based gradient matching: KGode (2021)
  2. Arnold, Andrea: Using Monte Carlo particle methods to estimate and quantify uncertainty in periodic parameters (research) (2020)
  3. Campillo-Funollet, Eduard; Venkataraman, Chandrasekhar; Madzvamuse, Anotida: Bayesian parameter identification for Turing systems on stationary and evolving domains (2019)
  4. Clerx, M., Robinson, M., Lambert, B., Lei, C.L., Ghosh, S., Mirams, G.R. and Gavaghan, D.J.: Probabilistic Inference on Noisy Time Series (PINTS) (2019) not zbMATH
  5. Ke, Yuqin; Tian, Tianhai: Approximate Bayesian computational methods for the inference of unknown parameters (2019)
  6. Macdonald, Benn; Husmeier, Dirk: Model selection via marginal likelihood estimation by combining thermodynamic integration and gradient matching (2019)
  7. Sanguinetti, Guido (ed.); Huynh-Thu, Vân Anh (ed.): Gene regulatory networks. Methods and protocols (2019)
  8. Li, Yingyi; Zhang, Haibin; Li, Zhibao; Gao, Huan: Proximal gradient method with automatic selection of the parameter by automatic differentiation (2018)
  9. Niu, Mu; Macdonald, Benn; Rogers, Simon; Filippone, Maurizio; Husmeier, Dirk: Statistical inference in mechanistic models: time warping for improved gradient matching (2018)
  10. Aderhold, Andrej; Husmeier, Dirk; Grzegorczyk, Marco: Approximate Bayesian inference in semi-mechanistic models (2017)
  11. Ghosh, Sanmitra; Dasmahapatra, Srinandan; Maharatna, Koushik: Fast approximate Bayesian computation for estimating parameters in differential equations (2017)
  12. Grzegorczyk, Marco: A non-homogeneous dynamic Bayesian network with a hidden Markov model dependency structure among the temporal data points (2016)
  13. Aderhold, Andrej; Husmeier, Dirk; Grzegorczyk, Marco: Statistical inference of regulatory networks for circadian regulation (2014)
  14. Mazur, Johanna; Kaderali, Lars: The importance and challenges of Bayesian parameter learning in systems biology (2013)
  15. 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)
  16. Rybiński, Mikołaj; Gambin, Anna: Model-based selection of the robust JAK-STAT activation mechanism (2012)
  17. Higham, Desmond J.: Stochastic ordinary differential equations in applied and computational mathematics (2011)
  18. Calderhead, Ben; Girolami, Mark: Estimating Bayes factors via thermodynamic integration and population MCMC (2009)
  19. Ii, David J. Klinke: An empirical Bayesian approach for model-based inference of cellular signaling networks (2009) ioport
  20. Intep, Somkid; Higham, Desmond J.; Mao, Xuerong: Switching and diffusion models for gene regulation networks (2009)

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