Figure 2.

Bayesian parameter estimation. Left: a kinetic parameter θ (abscissa) determines an observed parameter x (ordinate). Adding Gaussian noise to the true value x yields the experimental value x*, which then gives rise to a likelihood function p(x*|θ) (red). Prior distribution p(θ) (light blue) and likelihood function lead to a posterior distribution p(θ|x*)(dark blue), which represents a refined estimate of the original parameter. Right: parameters and data determine the likelihood function for a metabolic network model. Each set of system parameters θ^kin and state parameters θ^met (left) will lead to predictions x and y of the observable quantities (centre), which can be compared to the corresponding experimental values x* and y* (right).