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Bioinformatics Journal
SIGSIM: Systems Biology of E.coli

Keynote Speaker - John Mattick Institute for Molecular Bioscience, University of Queensland

Programming of the autopoietic development of complex organisms: the hidden layer of noncoding RNA
Perhaps the most striking feature of the higher organisms is the massive amount of transcription of non-protein-coding RNAs, which comprise about 98% of all genomic output in mammals. At least half of the mammalian genome is transcribed and at least half of mammalian genes do not encode proteins, rather astounding facts that are generally unappreciated. Where they have been studied these RNAs have been found to be developmentally regulated and to have function. Many of these RNAs are processed to form smaller trans-acting species, termed microRNAs. In addition, complex genetic phenomena such as RNA interference, co-suppression, transgene silencing, imprinting, and DNA methylation all involve intersecting pathways based on or connected to RNA signaling. Taken together the evidence suggests that intronic and other noncoding RNAs comprise a second tier of gene expression in the higher eukaryotes which enables the integration of complex suites of gene activity. Thus, while proteins are the fundamental components and effectors of cellular structure and function, the programming of eukaryotic complexity and phenotypic variation may be primarily embedded in an endogenous network of trans-acting RNAs that relay state information required for the coordination and modulation of gene expression via RNA-DNA, RNA-RNA and RNA-protein interactions, acting to control chromatin architecture and epigenetic memory, transcription, alternative splicing, RNA turnover, translation and other cellular processes. This regulatory network has similarities to those underpinning advanced information processing in other domains such as computers and the brain (termed "the hidden layer" in neural networks), and may have been the essential prerequisite for the appearance of developmentally complex multicellular organisms. The hypothesis makes many predictions amenable to bioinformatic and experimental testing, and provides a platform for computational modeling of the regulatory architecture underpinning the evolution and ontogeny of higher eukaryotes.