BioBayes: A software package for Bayesian inference in systems biology
Open Access
- 16 July 2008
- journal article
- research article
- Published by Oxford University Press (OUP) in Bioinformatics
- Vol. 24 (17) , 1933-1934
- https://doi.org/10.1093/bioinformatics/btn338
Abstract
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. Availability:http://www.dcs.gla.ac.uk/BioBayes/ Contact:vvv@dcs.gla.ac.ukKeywords
This publication has 9 references indexed in Scilit:
- Bayesian ranking of biochemical system modelsBioinformatics, 2007
- On population-based simulation for static inferenceStatistics and Computing, 2007
- An assessment of the role of computing in systems biologyIBM Journal of Research and Development, 2006
- Bayesian Inference for Stochastic Kinetic Models Using a Diffusion ApproximationBiometrics, 2005
- The systems biology markup language (SBML): a medium for representation and exchange of biochemical network modelsBioinformatics, 2003
- Symbolic Model Checking of Biochemical NetworksPublished by Springer Nature ,2003
- WinBUGS - A Bayesian modelling framework: Concepts, structure, and extensibilityStatistics and Computing, 2000
- Bayesian Data AnalysisPublished by Taylor & Francis ,1995
- Monte Carlo sampling methods using Markov chains and their applicationsBiometrika, 1970