Network Reconstruction From Time-Course Perturbation Data Using Multivariate Gaussian Processes

In this work, we appropriate the popular tool of Gaussian processes to solve the problem of reconstructing networks from time-series perturbation data. To this end, we propose a construction for multivariate Gaussian processes to describe the continuous-time trajectories of the states of the network entities. We then show that this construction admits a state-space representation for the network dynamics. By exploiting Kalman filtering techniques, we are able to infer the underlying network in a computationally efficient manner.