A framework for parameter estimation and model selection from experimental data in systems biology using approximate Bayesian computation.

ABSTRACT: As modeling becomes a more widespread practice in the life sciences and biomedical sciences, researchers need reliable tools to calibrate models against ever more complex and detailed data. Here we present an approximate Bayesian computation (ABC) framework and software environment, ABC-SysBio, which is a Python package that runs on Linux and Mac OS X systems and that enables parameter estimation and model selection in the Bayesian formalism by using sequential Monte Carlo (SMC) approaches. We outline the underlying rationale, discuss the computational and practical issues and provide detailed guidance as to how the important tasks of parameter inference and model selection can be performed in practice. Unlike other available packages, ABC-SysBio is highly suited for investigating, in part

SUBMITTER: Liepe J 

PROVIDER: S-EPMC5081097 | biostudies-literature | 2014 Feb

REPOSITORIES: biostudies-literature