Reverse-engineering biological interaction networks from noisy data using Regularized Least Squares and Instrumental Variables

The problem of reverse engineering the topology of a biological network from noisy time-series measurements is one of the most important challenges in the field of Systems Biology. In this work, we develop a new inference approach which combines the Regularized Least Squares (RLS) technique with a technique to avoid the introduction of bias and non-consistency due to measurement noise in the estimation of the parameters in the standard Least Squares (LS) formulation, the Instrumental Variables (IV) method. We test our approach on a set of nonlinear in silico networks and show that the combined exploitation of RLS and IV methods improves the predictions with respect to other standard approaches.