Background: Cell growth underlies many key cellular and developmental processes, yet a limited number of studies have been carried out on cell-growth regulation. Comprehensive studies at the transcriptional, proteomic and metabolic levels under defined controlled conditions are currently lacking.

The comparison of pairs of gene duplications generated by small-scale duplications with those created by large-scale duplications shows that they differ in quantifiable ways. It is suggested that this is directly due to biases on the paths to gene retention rather than association with different functional categories.

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Studies of interacting proteins have found correlated evolution of the sequences of binding partners, apparently as a result of compensating mutations to maintain specificity (i.e., molecular coevolution). Here, we analyze the coevolution of interacting proteins in yeast and demonstrate correlated evolution of binding partners in eukaryotes. Detailed investigation of this apparent coevolution, focusing on the proteins' surface and binding interface, surprisingly leads to no improvement in the correlation. We conclude that true coevolution, as characterized by compensatory mutations between binding partners, is unlikely to be chiefly responsible for the apparent correlated evolution. We postulate that the correlation between sequence alignments is simply due to interacting proteins being subject to similar constraints on their evolutionary rate. Because gene expression has a strong influence on evolutionary rate, and interacting proteins will tend to have similar levels of expression, we investigated this particular constraint. We found that the absolute expression level outperformed correlated evolution for predicting interacting protein partners. A correlation between sequence alignments could also be identified not only between pairs of proteins that physically interact but also between those that are merely functionally related (i.e., within the same protein complex). This indicates that the observed correlated evolution of interacting proteins is due to similar constraints on evolutionary rate and not coevolution.evolutionary constraints ͉ gene expression ͉ binding interfaces

Researchers working on environmentally relevant organisms, populations, and communities are increasingly turning to the application of OMICS technologies to answer fundamental questions about the natural world, how it changes over time, and how it is influenced by anthropogenic factors. In doing so, the need to capture meta-data that accurately describes the biological "source" material used in such experiments is growing in importance. Here, we provide an overview of the formation of the "Env" community of environmental OMICS researchers and its efforts at considering the meta-data capture needs of those working in environmental OMICS. Specifically, we discuss the development to date of the Env specification, an informal specification including descriptors related to geographic location, environment, organism relationship, and phenotype. We then describe its application to the description of environmental transcriptomic experiments and how we have used it to extend the Minimum Information About a Microarray Experiment (MIAME) data standard to create a domain-specific extension that we have termed MIAME/Env. Finally, we make an open call to the community for participation in the Env Community and its future activities.