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      Predicting the impact of diet and enzymopathies on human small intestinal epithelial cells

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      1 , 1 , 2 , *
      Human Molecular Genetics
      Oxford University Press

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          Abstract

          Small intestinal epithelial cells (sIECs) have a significant share in whole body metabolism as they perform enzymatic digestion and absorption of nutrients. Furthermore, the diet plays a key role in a number of complex diseases including obesity and diabetes. The impact of diet and altered genetic backgrounds on human metabolism may be studied by using computational modeling. A metabolic reconstruction of human sIECs was manually assembled using the literature. The resulting sIEC model was subjected to two different diets to obtain condition-specific metabolic models. Fifty defined metabolic tasks evaluated the functionalities of these models, along with the respective secretion profiles, which distinguished between impacts of different dietary regimes. Under the average American diet, the sIEC model resulted in higher secretion flux for metabolites implicated in metabolic syndrome. In addition, enzymopathies were analyzed in the context of the sIEC metabolism. Computed results were compared with reported gastrointestinal (GI) pathologies and biochemical defects as well as with biomarker patterns used in their diagnosis. Based on our simulations, we propose that (i) sIEC metabolism is perturbed by numerous enzymopathies, which can be used to study cellular adaptive mechanisms specific for such disorders, and in the identification of novel co-morbidities, (ii) porphyrias are associated with both heme synthesis and degradation and (iii) disturbed intestinal gamma-aminobutyric acid synthesis may be linked to neurological manifestations of various enzymopathies. Taken together, the sIEC model represents a comprehensive, biochemically accurate platform for studying the function of sIEC and their role in whole body metabolism.

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          Genome sequence of the nematode C. elegans: a platform for investigating biology.

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          The 97-megabase genomic sequence of the nematode Caenorhabditis elegans reveals over 19,000 genes. More than 40 percent of the predicted protein products find significant matches in other organisms. There is a variety of repeated sequences, both local and dispersed. The distinctive distribution of some repeats and highly conserved genes provides evidence for a regional organization of the chromosomes.
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            Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0.

            Over the past decade, a growing community of researchers has emerged around the use of constraint-based reconstruction and analysis (COBRA) methods to simulate, analyze and predict a variety of metabolic phenotypes using genome-scale models. The COBRA Toolbox, a MATLAB package for implementing COBRA methods, was presented earlier. Here we present a substantial update of this in silico toolbox. Version 2.0 of the COBRA Toolbox expands the scope of computations by including in silico analysis methods developed since its original release. New functions include (i) network gap filling, (ii) (13)C analysis, (iii) metabolic engineering, (iv) omics-guided analysis and (v) visualization. As with the first version, the COBRA Toolbox reads and writes systems biology markup language-formatted models. In version 2.0, we improved performance, usability and the level of documentation. A suite of test scripts can now be used to learn the core functionality of the toolbox and validate results. This toolbox lowers the barrier of entry to use powerful COBRA methods.
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              Reorganizing the protein space at the Universal Protein Resource (UniProt)

              The mission of UniProt is to support biological research by providing a freely accessible, stable, comprehensive, fully classified, richly and accurately annotated protein sequence knowledgebase, with extensive cross-references and querying interfaces. UniProt is comprised of four major components, each optimized for different uses: the UniProt Archive, the UniProt Knowledgebase, the UniProt Reference Clusters and the UniProt Metagenomic and Environmental Sequence Database. A key development at UniProt is the provision of complete, reference and representative proteomes. UniProt is updated and distributed every 4 weeks and can be accessed online for searches or download at http://www.uniprot.org.
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                Author and article information

                Journal
                Hum Mol Genet
                Hum. Mol. Genet
                hmg
                hmg
                Human Molecular Genetics
                Oxford University Press
                0964-6906
                1460-2083
                1 July 2013
                13 March 2013
                13 March 2013
                : 22
                : 13
                : 2705-2722
                Affiliations
                [1 ]Center for Systems Biology and
                [2 ]Faculty of Industrial Engineering, Mechanical Engineering & Computer Science, University of Iceland , 101 Reykjavik, Iceland
                Author notes
                [* ]To whom correspondence should be addressed at: Center for Systems Biology, University of Iceland, Sturlugata 8, 101 Reykjavik, Iceland. Email: ines.thiele@ 123456gmail.com .
                Article
                ddt119
                10.1093/hmg/ddt119
                3674809
                23492669
                8b9ff111-371b-4792-ae6f-28c765de38ed
                © The Author 2013. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by-nc/3.0/), which permits non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permission@ 123456oup.com

                History
                : 9 January 2013
                : 7 March 2013
                Categories
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                Genetics
                Genetics

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