http://precedings.nature.com/tags/Systems Recently posted documents tagged with 'Systems' Nature Publishing Group en Nature Precedings http://precedings.nature.com/images/header_logo.gif http://precedings.nature.com http://precedings.nature.com/documents/3312/version/1 Transgenerational spermatogenic inheritance of adult male acquired CNS gene expression characteristics has recently been discovered using a Drosophila systems model. In this novel mode of inheritance, transcriptomic alteration induced by the neuroactive drug pentylenetetrazole (PTZ) has been found to leak to future generations. Here, the available microarray gene expression data pertaining to CNS and/or testis of exposed F0 and the resulting F1 and F2 generations has been pooled and analyzed in an unbiased manner at four levels, namely, biological processes and pathways, protein interactome networks, miRNA-targets, and microarray expression profile similarities. Enrichment for processes related to translation, energy metabolism, cell proliferation, cell differentiation, secretion, central nervous system development, germ cell development, gamete generation, wing development, nutrition etc. was observed. Also, ribosome, oxidative phosphorylation and, to a lesser extent, wingless signaling pathway showed overrepresentation. In the proteomic interactome map, the cell cycle gene Ras85D exhibited overinteraction. In miRNA-target network, the fly transgenerational genes showed overrepresentation of mir-315 targets. Transcriptomic matching revealed overlap of transgenerational set with genes related to epigenetic drug treatment, stem cells, Myc targets and miRNA targets. Many of the findings were consistent with the existing epigenetic evidence in complex mammalian traits. Converging evidence suggests that ribosomal RNA and proteins may serve as candidate biomarkers of transgenerational environmental effect. A compelling systems biology frame-work integrative of transgenerational epigenetic inheritance is suggested. Nutrient, circulating peptide hormone, Myc, Wnt, and stem cell signaling pathways constitute the frame-work. The analysis has implications in explaining missing heritability in complex traits including common human disorders. The fly model offers an excellent opportunity to understand somatic and germline communication, and epigenetic memory formation and its retention across generations in molecular details. http://precedings.nature.com/documents/3312/version/1 Fri, 05 Jun 2009 20:16:28 UTC Systems level analysis of transgenerational spermatogenic inheritance predicts biomarkers and underlying pathways hdl:10101/npre.2009.3312.1 2009-06-05 Abhay Sharma Nature Precedings 2009-06-05T20:16:28Z Manuscript Genetics & Genomics http://creativecommons.org/licenses/by/3.0/ http://precedings.nature.com/documents/3204/version/1 In recent years, magnetic resonance imaging (MRI) has allowed researchers to individuate an earlier morphological development of the right hemisphere compared to the left hemisphere before birth. Anatomical asymmetry, however, does not necessarily mean functional asymmetry and whether the anatomical differences between hemispheres at this early age are paralleled by functional specializations is still unknown. Here we show electrophysiological evidence of an early functional right lateralization for pitch processing arising by 30 gestational weeks, not before, in preterm newborns. http://precedings.nature.com/documents/3204/version/1 Fri, 01 May 2009 10:35:54 UTC When does right functional hemispheric lateralization arise? Evidence from preterm infants hdl:10101/npre.2009.3204.1 2009-05-01 Giovanni Mento Nature Precedings 2009-05-01T10:35:54Z Manuscript Neuroscience http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2008.2715.1 With the rise of Systems Biology as a new paradigm for understanding biological processes, the development of quantitative models is no longer restricted to a small circle of theoreticians. The dramatic increase in the number of these models precipitates the need to exchange and reuse both existing and newly created models. The Systems Biology Markup Language (SBML) is a free, open, XML-based format for representing quantitative models of biological interest that advocates the consistent specification of such models and thus facilitates both software development and model exchange.Principally oriented towards describing systems of biochemical reactions, such as cell signalling pathways, metabolic networks and gene regulation etc., SBML can also be used to encode any kinetic model. SBML offers mechanisms to describe biological components by means of compartments and reacting species, as well as their dynamic behaviour, using reactions, events and arbitrary mathematical rules. SBML also offers all the housekeeping structures needed to ensure an unambiguous understanding of quantitative descriptions.This is Release 1 of the specification for SBML Level 2 Version 4, describing the structures of the language and the rules used to build a valid model. SBML XML Schema and other related documents and software are also available from the SBML project web site, http://sbml.org/. http://dx.doi.org/10.1038/npre.2008.2715.1 Wed, 24 Dec 2008 15:41:15 UTC Systems Biology Markup Language (SBML) Level 2: Structures and Facilities for Model Definitions doi:10.1038/npre.2008.2715.1 2008-12-24 Nicolas Le Novère Nature Precedings 2008-12-24T15:41:15Z Manuscript Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://precedings.nature.com/documents/1768/version/1 Multivariate experiments and genomics studies applied to mammalian cells often produce lists of genes or proteins altered under treatment/disease vs. control/normal conditions. Such lists can be identified in known protein-protein interaction networks to produce subnetworks that “connect” the genes or proteins from the lists. Such subnetworks are valuable for biologists since they can suggest regulatory mechanisms that are altered under different conditions. Often such subnetworks are overloaded with links and nodes resulting in connectivity diagrams that are illegible due to edge overlap. In this study, we attempt to address this problem by implementing an approximation to the Steiner Tree problem to connect seed lists of mammalian proteins/genes using literature-based protein-protein interaction networks. To avoid over-representation of hubs in the resultant Steiner Trees we assign a cost to Steiner Vertices based on their connectivity degree. We applied the algorithm to lists of genes commonly mutated in colorectal cancer to demonstrate the usefulness of this approach. http://precedings.nature.com/documents/1768/version/1 Mon, 07 Apr 2008 16:22:36 UTC Connecting Seed Lists of Mammalian Proteins Using Steiner Trees hdl:10101/npre.2008.1768.1 2008-04-07 Avi Ma'ayan Nature Precedings 2008-04-07T16:22:36Z Manuscript Biotechnology Cancer Molecular Cell Biology Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://precedings.nature.com/documents/35/version/1 In recent years, in-silico literature-based mammalian protein-protein interaction network datasets have been developed. These datasets contain binary interactions extracted manually from legacy experimental biomedical research literature. Placing lists of genes or proteins identified as significantly changing in multivariate experiments, in the context of background knowledge about binary interactions, can be used to place these genes or proteins in the context of pathways and protein complexes.Genes2Networks is a software system that integrates the content of ten mammalian literature-based interaction network datasets. Filtering to prune low-confidence interactions was implemented. Genes2Networks is delivered as a web-based service using AJAX. The system can be used to extract relevant subnetworks created from “seed” lists of human Entrez gene names. The output includes a dynamic linkable three color web-based network map, with a statistical analysis report that identifies significant intermediate nodes used to connect the seed list. Genes2Networks is available at http://actin.pharm.mssm.edu/genes2networks.Genes2Network is a powerful web-based software application tool that can help experimental biologists to interpret high-throughput experimental results used in genomics and proteomics studies where the output of these experiments is a list of significantly changing genes or proteins. The system can be used to find relationships between nodes from the seed list, and predict novel nodes that play a key role in a common function. http://precedings.nature.com/documents/35/version/1 Thu, 07 Jun 2007 11:14:46 UTC Genes2Networks: Connecting Lists of Proteins by Using Background Literature-based Mammalian Networks hdl:10101/npre.2007.35.1 2007-06-11 Avi Ma'ayan Nature Precedings 2007-06-07T11:14:46Z Manuscript Biotechnology Molecular Cell Biology Bioinformatics http://creativecommons.org/licenses/by/2.5/