Skip to Main Content
Dynamic Bayesian networks offer a powerful modeling tool to unravel cellular mechanisms. In particular, Linear Gaussian Networks allow researchers to avoid information loss associated with discretization and render the learning process computationally tractable even for hundreds of variables. Yet, are linear models suitable to learn the complex dynamic interactions among genes and proteins? We here present a study on simulated data produced by a mathematical model of cell cycle control in budding yeast: the results obtained confirmed the robustness of the linear model and its suitability for a first level, genome-wide analysis of high throughput dynamic data.