The study of complex biological networks and prediction of gene function has been enabled by high-throughput (HTP) methods for detection of genetic and protein interactions. Sparse coverage in HTP datasets may, however, distort network properties and confound predictions. Although a vast number of well substantiated interactions are recorded in the scientific literature, these data have not yet been distilled into networks that enable system-level inference. We describe here a comprehensive database of genetic and protein interactions, and associated experimental evidence, for the budding yeast Saccharomyces cerevisiae, as manually curated from over 31,793 abstracts and online publications.

To elucidate cellular machinery on a global scale, we performed a multiple comparison of the recently available protein-protein interaction networks of Caenorhabditis elegans, Drosophila melanogaster, and Saccharomyces cerevisiae. This comparison integrated protein interaction and sequence information to reveal 71 network regions that were conserved across all three species and many exclusive to the metazoans. We used this conservation, and found statistically significant support for 4,645 previously undescribed protein functions and 2,609 previously undescribed protein interactions. We tested 60 interaction predictions for yeast by two-hybrid analysis, confirming approximately half of these. Significantly, many of the predicted functions and interactions would not have been identified from sequence similarity alone, demonstrating that network comparisons provide essential biological information beyond what is gleaned from the genome.

With the increased interest in understanding biological networks, such as protein-protein interaction networks and gene regulatory networks, methods for representing and communicating such networks in both human- and machine-readable form have become increasingly important. Although there has been significant progress in machine-readable representation of networks, as exemplified by the Systems Biology Mark-up Language (SBML) (http://www.sbml.org) issues in human-readable representation have been largely ignored. This article discusses human-readable diagrammatic representations and proposes a set of notations that enhances the formality and richness of the information represented. The process diagram is a fully state transition−based diagram that can be translated into machine-readable forms such as SBML in a straightforward way. It is supported by CellDesigner, a diagrammatic network editing software (http://www.celldesigner.org/), and has been used to represent a variety of networks of various sizes (from only a few components to several hundred components).

Plasmodium falciparum causes the most severe form of malaria and kills up to 2.7 million people annually1. Despite the global importance of P. falciparum, the vast majority of its proteins have not been characterized experimentally. Here we identify P. falciparum protein–protein interactions using a high-throughput version of the yeast two-hybrid assay that circumvents the difficulties in expressing P. falciparum proteins in Saccharomyces cerevisiae. From more than 32,000 yeast two-hybrid screens with P. falciparum protein fragments, we identified 2,846 unique interactions, most of which include at least one previously uncharacterized protein. Informatic analyses of network connectivity, coexpression of the genes encoding interacting fragments, and enrichment of specific protein domains or Gene Ontology annotations2 were used to identify groups of interacting proteins, including one implicated in chromatin modification, transcription, messenger RNA stability and ubiquitination, and another implicated in the invasion of host cells. These data constitute the first extensive description of the protein interaction network for this important human pathogen.

Oligonucleotide and complementary DNA microarrays are being used to subclassify histologically similar tumours, monitor disease progress, and individualize treatment regimens. However, extracting new biological insight from high-throughput genomic studies of human diseases is a challenge, limited by difficulties in recognizing and evaluating relevant biological processes from huge quantities of experimental data. Here we present a structured network knowledge-base approach to analyse genome-wide transcriptional responses in the context of known functional interrelationships among proteins, small molecules and phenotypes. This approach was used to analyse changes in blood leukocyte gene expression patterns in human subjects receiving an inflammatory stimulus (bacterial endotoxin). We explore the known genome-wide interaction network to identify significant functional modules perturbed in response to this stimulus. Our analysis reveals that the human blood leukocyte response to acute systemic inflammation includes the transient dysregulation of leukocyte bioenergetics and modulation of translational machinery. These findings provide insight into the regulation of global leukocyte activities as they relate to innate immune system tolerance and increased susceptibility to infection in humans.

A billion nonredundant molecular interactions lie buried in the biomedical literature. A text-mining approach could help scientists better exploit this knowledge.

Protein-protein interactions may impose constraints on both structural and regulatory evolution. Here we show that protein-protein interactions are negatively associated with evolutionary variation in gene expression. Moreover, interacting proteins have similar levels of variation in expression, and their expression levels are positively correlated across strains. Our results suggest that interacting proteins undergo similar evolutionary dynamics, and that their expression levels are evolutionarily coupled. These patterns hold for organisms as diverse as budding yeast and fruit flies.

Extensive protein interaction maps are being constructed for yeast, worm, and fly to ask how the proteins organize into pathways and systems, but no such genome-wide interaction map yet exists for the set of human proteins. To prepare for studies in humans, we wished to establish tests for the accuracy of future interaction assays and to consolidate the known interactions among human proteins.