Network inference algorithms, and in particular Bayesian networks algorithms, are being applied with growing regularity in computational molecular biology to recover gene regulatory networks from gene expression data. However, the basic task at hand -- to predict causal relationships based on repeated concurrent measurements of multiple variables -- -is not necessarily limited to the molecular realm. Here, I present my research on using Bayesian network inference algorithms to recover networks on several different levels of biological organization: genes, neurons, and ecosystems.