Advances in biotechnology have enabled researchers to study molecular biology from the point of view of systems, from focused efforts at functional annotation to the study of pathways, regulatory networks, protein-protein interaction networks, etc. However, direct observation of these systems has proved difficult, time-consuming, and often unreliable. Thus computational methods have been developed to infer such systems from high-throughput data, such as sequences, gene expression levels, ChIP-Seq signals, etc. For the most part, these methods have not yet proved accurate and reliable enough to be used in automated analysis pipelines. Most methods used to infer biological networks rely on data for a single organism; a few attempt to leverage existing knowledge about some related organisms. Today, however, we have data about a large variety of organisms as well as good consensus about the evolutionary relationships among these organisms, so that the latter can be used to integrate the former in a well founded manner, thereby gaining significant power in the analysis. We have coined the term Phylogenetic Transfer of Knowledge (PTK) for this approach to inference and analysis. A PTK analysis considers a family of organisms with known evolutionary relationships and "transfers" biological knowledge among the organisms in accordance with these relationships. The output of a PTK analysis thus includes both predicted (or refined) target data (such as networks) for the extant organisms and inferred details about their evolutionary history. While a few ad hoc inference methods used a PTK approach almost a dozen years ago, we first provided a global perspective on such methods just six years ago. The last few years have seen a significant increase in research in this area, as well as new applications. The time is thus right for a review of recent work that falls under this heading, a characterization of the solutions proposed, and a description of remaining challenges.