Journal
STATISTICAL APPLICATIONS IN GENETICS AND MOLECULAR BIOLOGY
Volume 13, Issue 3, Pages 379-390Publisher
WALTER DE GRUYTER GMBH
DOI: 10.1515/sagmb-2013-0061
Keywords
cellular noise; parametric inference; approximate Bayesian computation
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Funding
- MRC [MR/J013617/1] Funding Source: UKRI
- Medical Research Council [MR/J013617/1] Funding Source: researchfish
- Medical Research Council [MR/J013617/1] Funding Source: Medline
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Stochastic systems in biology often exhibit substantial variability within and between cells. This variability, as well as having dramatic functional consequences, provides information about the underlying details of the system's behavior. It is often desirable to infer properties of the parameters governing such systems given experimental observations of the mean and variance of observed quantities. In some circumstances, analytic forms for the likelihood of these observations allow very efficient inference: we present these forms and demonstrate their usage. When likelihood functions are unavailable or difficult to calculate, we show that an implementation of approximate Bayesian computation (ABC) is a powerful tool for parametric inference in these systems. However, the calculations required to apply ABC to these systems can also be computationally expensive, relying on repeated stochastic simulations. We propose an ABC approach that cheaply eliminates unimportant regions of parameter space, by addressing computationally simple mean behavior before explicitly simulating the more computationally demanding variance behavior. We show that this approach leads to a substantial increase in speed when applied to synthetic and experimental datasets.
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