Toggle Main Menu Toggle Search

Open Access padlockePrints

CaliBayes and BASIS: integrated tools for the calibration, simulation and storage of biological simulation models

Lookup NU author(s): Dr Yuhui Chen, Dr Conor LawlessORCiD, Dr Colin GillespieORCiD, Professor Jingyi Wu, Professor Richard Boys, Professor Darren Wilkinson


Full text for this publication is not currently held within this repository. Alternative links are provided below where available.


Dynamic simulation modelling of complex biological processes forms the backbone of systems biology. Discrete stochastic models are particularly appropriate for describing sub-cellular molecular interactions, especially when critical molecular species are thought to be present at low copy-numbers. For example, these stochastic effects play an important role in models of human ageing, where ageing results from the long-term accumulation of random damage at various biological scales. Unfortunately, realistic stochastic simulation of discrete biological processes is highly computationally intensive, requiring specialist hardware, and can benefit greatly from parallel and distributed approaches to computation and analysis. For these reasons, we have developed the BASIS system for the simulation and storage of stochastic SBML models together with associated simulation results. This system is exposed as a set of web services to allow users to incorporate its simulation tools into their workflows. Parameter inference for stochastic models is also difficult and computationally expensive. The CaliBayes system provides a set of web services (together with an R package for consuming these and formatting data) which addresses this problem for SBML models. It uses a sequential Bayesian MCMC method, which is powerful and flexible, providing very rich information. However this approach is exceptionally computationally intensive and requires the use of a carefully designed architecture. Again, these tools are exposed as web services to allow users to take advantage of this system. In this article, we describe these two systems and demonstrate their integrated use with an example workflow to estimate the parameters of a simple model of Saccharomyces cerevisiae growth on agar plates.

Publication metadata

Author(s): Chen YH, Lawless C, Gillespie CS, Wu J, Boys RJ, Wilkinson DJ

Publication type: Article

Publication status: Published

Journal: Briefings in Bioinformatics

Year: 2010

Volume: 11

Issue: 3

Pages: 278-289

Print publication date: 07/01/2010

ISSN (print): 1467-5463

ISSN (electronic): 1477-4054

Publisher: Oxford University Press


DOI: 10.1093/bib/bbp072


Altmetrics provided by Altmetric


Funder referenceFunder name
BBSB16 550Biotechnology and Biological Sciences Research Council
BEP17 042Biotechnology and Biological Sciences Research Council
BBC0082 001Biotechnology and Biological Sciences Research Council