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The primary objective of the Human Genome Project was to produce high-quality sequences not just for the human genome but also for those of the chief model organisms: Escherichia coli, yeast (Saccharomyces cerevisiae), worm (Caenorhabditis elegans), fly (Drosophila melanogaster) and mouse (Mus musculus). Free access to the resultant data has prompted much biological research, including development of a map of common human genetic variants (the International HapMap Project)1, expression profiling of healthy and diseased cells2 and in-depth studies of many individual genes. These genome sequences have enabled researchers to carry out genetic and functional genomic studies not previously possible, revealing new biological insights with broad relevance across the animal kingdom 3, 4.

A key challenge of functional genomics today is to generate well-annotated data sets that can be interpreted across different platforms and technologies. Large-scale functional genomics data often fail to connect to standard experimental approaches of gene characterization in individual laboratories. Furthermore, a lack of universal annotation standards for phenotypic data sets makes it difficult to compare different screening approaches. Here we address this problem in a screen designed to identify all genes required for the first two rounds of cell division in the Caenorhabditis elegans embryo. We used RNA-mediated interference to target 98% of all genes predicted in the C. elegans genome in combination with differential interference contrast time-lapse microscopy. Through systematic annotation of the resulting movies, we developed a phenotypic profiling system, which shows high correlation with cellular processes and biochemical pathways, thus enabling us to predict new functions for previously uncharacterized genes.

The early stages of embryogenesis in the nematode Caenorhabditis elegans are an ideal system in which to test the potential of ‘system-level’ approaches to understanding biological processes, as opposed to the conventional reductionist approach that focuses on individual enzymes. By integrating information on how proteins interact with each other, how genes are expressed and the effect of ‘knocking-down’ hundreds of genes, Gunsalus et al. have generated a string of models of molecular machines involved in driving early embryogenesis, and are able to propose functional links between these machines. Ten previously uncharacterized proteins were identified as significant in these pathways, showing how the systems can feed information back into single-reaction experiments. Although numerous fundamental aspects of development have been uncovered through the study of individual genes and proteins, system-level models are still missing for most developmental processes. The first two cell divisions of Caenorhabditis elegans embryogenesis constitute an ideal test bed for a system-level approach. Early embryogenesis, including processes such as cell division and establishment of cellular polarity, is readily amenable to large-scale functional analysis. A first step toward a system-level understanding is to provide ‘first-draft’ models both of the molecular assemblies involved1 and of the functional connections between them. Here we show that such models can be derived from an integrated gene/protein network generated from three different types of functional relationship2: protein interaction3, expression profiling similarity4 and phenotypic profiling similarity5, as estimated from detailed early embryonic RNA interference phenotypes systematically recorded for hundreds of early embryogenesis genes6. The topology of the integrated network suggests that C. elegans early embryogenesis is achieved through coordination of a limited set of molecular machines. We assessed the overall predictive value of such molecular machine models by dynamic localization of ten previously uncharacterized proteins within the living embryo.