Inference of Boolean Networks from Time Series Data with Realistic Characteristics

In this paper, we infer Boolean networks based on simulated data. Depending on the approach, the simulated data is either extracted from Boolean networks or from a biochemical network model. Noise was added using the existing hierarchical error model (HEM). We use Boolean inference algorithms based on best-fit to find the connections between nodes. In order to validate the inference processes, we compare the ground truth and inferred connections. The results show that the best-fit inference algorithm is suitable for Boolean approach, but it fails when using the biochemical network model.