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

By Tina Toni, M. P. Stumpf. Bioinform., 2009

306 citations recorded by the source corpus.

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 show that our approach can be applied across a wide range of biological scenarios, and we illustrate its use on real data describing influenza dynamics and the JAK-STAT signalling pathway. Bayesian model selection strikes a balance between the complexity of the simulation models and their ability to describe observed data. The present approach enables us to employ the whole formal apparatus to any system that can be (efficiently) simulated, even when exact likelihoods are computationally intractable. Contact: ttoni@imperial.ac.uk; m.stumpf@imperial.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

Subfields: Molecular Biology, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/68360ffe390c8dfe8107f0d4e332166bebc2708f

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Citations in the corpus

Topological sensitivity analysis for systems biology (2014, unknown): “t=t1 b_ xnðtÞ− fnðx̂ðtÞ; t; θÞ 2 : [4]”
Statistical inference for stochastic simulation models--theory and application. (2011, result): “We have concentrated our discussion mainly on parameter estimation, but once appropriate likelihood approximations are established, model selection and uncertainty estimation can, in principle, be done in the same way as in other statistical applications (e.g. Beaumont 2010; Toni & Stumpf 2010).”
Approximate Bayesian computational methods (2011, background): “Fearnhead and Prangle (2010) rely on a randomised version of the summary statistics from which they derive a well-calibrated version of ABC, i.e. an algorithm that gives proper predictions of given quantities.”
Approximate Bayesian Computation (2013, methodology): “Again, computational improvements for ABC in the space of models have been proposed, such as constructing a particle filter in the joint space of models and parameters [17].”
Adversarial Variational Optimization of Non-Differentiable Simulators (2017, unknown): “We compare our algorithm against ABC-SMC (Toni and Stumpf, 2009) and BOLFI (Gutmann and Corander, 2016) as baselines.”