Prior Distributions
Probability distributions encoding existing knowledge or assumptions about model parameters before observing new experimental data.
Prior Distributions are probability distributions that represent what is known or assumed about model parameters before any new data is collected, forming the starting point for Bayesian inference 1.
How It Works
In Bayesian modeling, every parameter is assigned a prior distribution. Uninformative (or weakly informative) priors, such as broad normal or uniform distributions, express minimal prior knowledge and let the data drive the inference. Informative priors encode specific knowledge — for example, setting a log-normal prior on a protein degradation rate centered on published measurements.
The choice of prior matters most when data are limited. With abundant data, the likelihood dominates and the posterior becomes insensitive to the prior. With sparse biological data — common in synthetic biology where only a few time points may be available — the prior significantly shapes parameter estimates and uncertainty quantification 1.
Hierarchical priors enable sharing of information across related experiments. For instance, if the same promoter is characterized in multiple contexts, a hierarchical model can learn a population-level prior for its parameters while allowing context-specific variation 2.
Computational Considerations
Automated prior construction pipelines can mine published literature and parameter databases (e.g., BRENDA, BioNumbers) using NLP to extract reported parameter ranges and construct informative priors. Prior predictive checks — simulating from the model using only the prior — help verify that chosen priors produce biologically plausible behaviors before fitting data 1.
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Informative priors from literature databases and previous experiments can be constructed automatically using NLP-based extraction from published studies.