Measurement noise distribution as a metric for parameter estimation in dynamical systems

Approximate Bayesian computation (ABC) has been demonstrated by several authors as an effective approach to infer unknown parameters in dynamical models of biological systems. ABC methods require the choice of a metric, which measures the distance between the model simulations and the experimental data. This choice is arbitrary, and the Euclidean metric (least-squares) tends to be the preferred one. In this paper, we propose the use of a specific metric based on the distribution of the measurement noise that is superimposed to the data points. We demonstrate our approach on a simple model of the p53 gene regulatory network, and we show that it can lead to better performance than ABC with the standard least-squares metric.