Simulation-based model selection for dynamical systems in systems and population biology.

ABSTRACT:

Motivation

Computer simulations have become an important tool across the biomedical sciences and beyond. For many important problems several different models or hypotheses exist and choosing which one best describes reality or observed data is not straightforward. We therefore require suitable statistical tools that allow us to choose rationally between different mechanistic models of, e.g. signal transduction or gene regulation networks. This is particularly challenging in systems biology where only a small number of molecular species can be assayed at any given time and all measurements are subject to measurement uncertainty.

Results

Here, we develop such a model selection framework based on approximate Bayesian computation and employing sequential Monte Carlo sampling. We

SUBMITTER: Toni T 

PROVIDER: S-EPMC2796821 | biostudies-literature | 2010 Jan

REPOSITORIES: biostudies-literature