International audienceMOTIVATION: Reverse engineering of gene regulatory networks remains a central challenge in computational systems biology, despite recent advances facilitated by benchmark in-silico challenges that have aided in calibrating their performance. A number of approaches using either perturbation (knock-out) or wild-type time series data have appeared in the literature addressing this problem, with the latter employing linear temporal models. Nonlinear dynamical models are particularly appropriate for this inference task given the genera- tion mechanism of the time series data. In this study, we introduce a novel nonlinear autoregressive model based on operator-valued ker- nels that simultaneously learns the model parameters,...