688839C2d01 - Predictive models of molecular machines...

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Unformatted text preview: Predictive models of molecular machines involved in Caenorhabditis elegans early embryogenesis Kristin C. Gunsalus 1 * , Hui Ge 2 * , Aaron J. Schetter 1 * , Debra S. Goldberg 3 * , Jing-Dong J. Han 2 , Tong Hao 2 , Gabriel F. Berriz 3 , Nicolas Bertin 2 , Jerry Huang 1 , Ling-Shiang Chuang 1 , Ning Li 2 , Ramamurthy Mani 3 , Anthony A. Hyman 4 , Birte So ¨nnichsen 5 , Christophe J. Echeverri 5 , Frederick P. Roth 3 , Marc Vidal 2 & Fabio Piano 1 Although numerous fundamental aspects of development have been uncovered through the study of individual genes and pro- teins, system-level models are still missing for most developmen- tal 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 involved 1 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 relationship 2 : protein interaction 3 , expression profiling similarity 4 and phenotypic profiling simi- larity 5 , as estimated from detailed early embryonic RNA inter- ference phenotypes systematically recorded for hundreds of early embryogenesis genes 6 . 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. Global correlations between transcriptome profiling and interac- tome data sets have been used to derive network graphs that combine similarity relationships from transcription profiling with physical interactions between proteins 3,7–13 . Suggestive correlations between interactome or transcriptome data and phenotypic data sets 5,10,14,15 support the notion that these three types of data might complement one another in predicting functional relationships. To model C. elegans early embryogenesis globally, we generated network graphs in which each node represents an early embryogen- esis gene 6 and its product(s), and each edge represents a potential functional connection based on one of three data sets (Fig. 1a): (1) 6,572 binary physical interactions between 3,848 C. elegans proteins (WI7 data set; Supplementary Methods and Supplementary Table S1) 3 ; (2) expression profiling similarity above a given threshold (transcriptional Pearson correlation coefficients (transcriptional PCCs) from a compendium of C. elegans microarray profiles 4 ); and (3) phenotypic similarity above another threshold (described below)....
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688839C2d01 - Predictive models of molecular machines...

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