Bayesian networks have been extensively used in genetics and systems biology to describe the interplay between the components of complex phenomena. Practical applications often concentrate on causal model learning and inference on a variety of static and dynamic data. In this talk we will explore the fundamental ideas of Bayesian networks and how they can be successfully applied to model protein signalling data and sequence profiles in GWAS and genomic selection studies.