http://precedings.nature.com/tags/networks Recently posted documents tagged with 'networks' Nature Publishing Group en Nature Precedings http://precedings.nature.com/images/header_logo.gif http://precedings.nature.com http://precedings.nature.com/documents/3069/version/1 The brain is a complex network of interconnecting neurons that combines regional specificity with distributed processing. Recent advances in the field of network theory have facilitated ground-breaking analyses demonstrating that brain connectivity exhibits small-world properties similar to other self-organized networks such as the internet, the genome, or even social organizations. Brain connectivity supports local and global processing through high clustering and short connectivity paths, respectively. While these comprehensive network indices highlight the global organization of the network, the regional specificity is related to the interconnectivity of local neighborhoods within the global system. The work presented here evaluated the community structure of resting human brain networks to identify the local neighborhoods and map those interconnected areas back to the brain. The study identified predictable clustering in unisensory cortices. However, the unexpected community structure in the default-mode network (DMN) revealed three separate modules and included the lateral frontal cortices in addition to traditional DMN regions. These results are the first to map modularity across the entire brain without restricting analyses to predefined anatomical structures. Such analyses provide an unbiased view of network communities and promise to provide new insights into organization of the brain. Evaluation of modular brain structure across states, during demanding tasks, or in disease populations will reveal dynamic connectivity changes in whole-brain networks. http://precedings.nature.com/documents/3069/version/1 Fri, 17 Apr 2009 20:57:44 UTC Modularity maps reveal community structure in the resting human brain hdl:10101/npre.2009.3069.1 2009-04-17 Paul Laurienti Nature Precedings 2009-04-17T20:57:44Z Manuscript Neuroscience Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://precedings.nature.com/documents/2041/version/1 A compilation of experimentally verified interactions between HIV-1 and human proteins allows insights into the intricate interplay between viral and host proteins on a large scale.We find that HIV-1 predominantly targets rich-clubs, human proteins that are not only well connected but also strongly intertwined among each other. These assemblies of proteins putatively serve as an infection gateway, allowing the virus to take control of the human host by reaching protein pathways and diversified cellular functions in a pronounced and focused way. In particular, HIV-1 utilizes its small number of proteins in a combinatorial manner, exerting a significant influence on pathways that deal with transcriptional, translational and degradation processes. Surprisingly, the small repertoire of HIV proteins also interferes loosely with many signaling and regulation pathways, suggesting that a widespread involvement in such pathways secures the control of the host cell. Such insights offer novel perspectives to investigate the progression of HIV infection and potentially can contribute to our abilities to fight this virus. http://precedings.nature.com/documents/2041/version/1 Wed, 09 Jul 2008 17:18:11 UTC Viral organization of human proteins hdl:10101/npre.2008.2041.1 2008-07-09 Stefan Wuchty Nature Precedings 2008-07-09T17:18:11Z Manuscript Microbiology Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2007.1370.1 A grant proposal submitted for support to fund a research network focussed on identifying and dealing with the practical issues of enabling open practise in research. The text of the proposal was written by a large number of people and coordinated by Cameron Neylon. http://dx.doi.org/10.1038/npre.2007.1370.1 Mon, 10 Dec 2007 16:51:06 UTC The Open Practises E-Science Network (OPEN) doi:10.1038/npre.2007.1370.1 2007-12-10 Cameron Neylon Nature Precedings 2007-12-10T16:51:06Z Manuscript Biotechnology Chemistry Genetics & Genomics Molecular Cell Biology Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://precedings.nature.com/documents/378/version/1 The construction of complex statial simulation models such as those used in network epidemiology, is a daunting task due to the large amount of data involved in their parameterization. Such data, which frequently resides on large geo-referenced databases, has to be processed and assigned to the various components of the model. All this just to construct the model, then it still has to be simulated and analyzed under different epidemiological scenarios. This workflow can only be achieved efficiently by computational tools that can automate most if not all these time-consuming tasks. In this paper, we present a simulation software, Epigrass, aimed to help designing and simulating network-epidemic models with any kind of node behavior.A Network epidemiological model representing the spread of a directly transmitted disease through a bus-transportation network connecting mid-size cities in Brazil. Results show that the topological context of the starting point of the epidemic is of great importance from both control and preventive perspectives.Epigrass is shown to facilitate greatly the construction, simulation and analysis of complex network models. The output of model results in standard GIS file formats facilitate the post-processing and analysis of results by means of sophisticated GIS software. http://precedings.nature.com/documents/378/version/1 Fri, 06 Jul 2007 15:56:10 UTC Epigrass: a tool to study disease spread in complex networks. hdl:10101/npre.2007.378.1 2007-07-06 Flávio Codeço Coelho Nature Precedings 2007-07-06T15:56:10Z Manuscript Bioinformatics http://creativecommons.org/licenses/by/2.5/ http://dx.doi.org/10.1038/npre.2007.26.1 Progress in uncovering the protein interaction networks of several species has led to questions of what underlying principles might govern their organization. Few studies have tried to determine the impact of protein interaction network evolution on the observed physiological differences between species. Using comparative genomics and structural information, we show here that eukaryotic species have rewired their interactomes at a fast rate of approximately 10?5 interactions changed per protein pair, per million years of divergence. For Homo sapiens this corresponds to 103 interactions changed per million years. Additionally we find that the specificity of binding strongly determines the interaction turnover and that different biological processes show significantly different link dynamics. In particular, human proteins involved in immune response, transport, and establishment of localization show signs of positive selection for change of interactions. Our analysis suggests that a small degree of molecular divergence can give rise to important changes at the network level. We propose that the power law distribution observed in protein interaction networks could be partly explained by the cell’s requirement for different degrees of protein binding specificity. http://dx.doi.org/10.1038/npre.2007.26.1 Fri, 02 Mar 2007 11:42:42 UTC Specificity and Evolvability in Eukaryotic Protein Interaction Networks doi:10.1038/npre.2007.26.1 2009-03-23 Pedro Beltrao Nature Precedings 2007-03-02T11:42:42Z Presentation Bioinformatics Evolutionary Biology http://creativecommons.org/licenses/by/2.5/ http://dx.doi.org/10.1038/npre.2007.22.1 It has been previously shown [1] that S. cerevisiae proteins preferentially interact with proteins of the same estimated likely time of origin. To study this observation further, the protein interaction networks of S. cerevisiae and H. sapiens were analyzed taking into account an estimate for the age of the proteins in these species. These estimates were obtained by studying the presence and absence of putative orthologs in other eukaryotic species. In this work preliminary results are described that point to a dependence of the likelihood of protein interaction on the proteins’ age. The probability of two proteins interactions was found to be linearly dependent on the time the proteins have co-existed in the species. http://dx.doi.org/10.1038/npre.2007.22.1 Mon, 22 Jan 2007 16:50:37 UTC The likelihood that two proteins interact might depend on the proteins’ age doi:10.1038/npre.2007.22.1 2009-03-04 Pedro Beltrao Nature Precedings 2007-01-22T16:50:37Z Manuscript Bioinformatics Evolutionary Biology http://creativecommons.org/licenses/by/2.5/