A Bayesian Framework for Combining Protein and Network Topology Information for Predicting Protein-Protein Interactions.

Computational methods for predicting protein-protein interactions are important tools that can complement high-throughput technologies and guide biologists in designing new laboratory experiments. The proteins and the interactions between them can be described by a network which is characterized by several topological properties. Information about proteins and interactions between them, in combination with knowledge about topological properties of the network, can be used for developing computational methods that can accurately predict unknown protein-protein interactions.

Transcriptional plasticity of a soil arthropod across different ecological conditions.

Ecological functional genomics, dealing with the responses of organisms to their natural environment is confronted with a complex pattern of variation and a large number of confounding environmental factors. For gene expression studies to provide meaningful information on conditions deviating from normal, a baseline or normal operating range (NOR) response needs to be established which indicates how an organism's transcriptome reacts to naturally varying ecological factors. Here we determine the transcriptional plasticity of a soil arthropod, Folsomia candida, exposed to various natural environments, as part of a first attempt in establishing such a NOR.