  Biological networks are a very convenient modelling and visualisation tool to
discover knowledge from modern high-throughput genomics and postgenomics data
sets. Indeed, biological entities are not isolated, but are components of
complex multi-level systems. We go one step further and advocate for the
consideration of causal representations of the interactions in living
systems.We present the causal formalism and bring it out in the context of
biological networks, when the data is observational. We also discuss its
ability to decipher the causal information flow as observed in gene expression.
We also illustrate our exploration by experiments on small simulated networks
as well as on a real biological data set.
