Computational Approaches: A Worm Case Study

Mahdavi, Mahmood A.; Lin, Yen-Han; Mahdavi, Mahmood A.; Lin, Yen-Han

ISBN 10: 383832630X ISBN 13: 9783838326306
Editorial: Lap Lambert Academic Publishing, 2010
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140 pages. 8.66x5.91x0.32 inches. In Stock. N° de ref. del artículo 383832630X

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Systems biology seeks to integrate high-throughput biological studies to understand how biological systems function, by studying the relationships and interactions between various parts of a biological system. The metabolic networks, as a systemic cellular organization, can be reconstructed based on genomic information. The center- piece of this information is protein-protein interaction map of the studied organism. Signature content of proteins was utilized to reveal protein interactions upon a notion that when two proteins share signature in their primary structures, the two proteins are more likely to interact. Protein interaction methods often predict true positive interactions along with numerous false positive interactions. A global algorithm was also proposed to reduce the number of false positives. The predicted interactions were incorporated into the metabolic network of C. elegans, resulting in new interactions among metabolic pathways. This case study of integrating genomic information with biochemical knowledge helps biologists to improve the extent of their understanding of biological networks using huge genomic information

Acerca de los autores: "Mahmood A. Mahdavi, Assistant professor, Department of Chemical Engineering, Ferdowsi University of Mashhad, Mashhad, 9177948974, Iran."

"Mahmood A. Mahdavi, Assistant professor, Department of Chemical Engineering, Ferdowsi University of Mashhad, Mashhad, 9177948974, Iran."

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Título: Computational Approaches: A Worm Case Study
Editorial: Lap Lambert Academic Publishing
Año de publicación: 2010
Encuadernación: Paperback
Condición: Brand New

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Mahmood A. Mahdavi|Yen-Han Lin
Publicado por LAP LAMBERT Academic Publishing, 2010
ISBN 10: 383832630X ISBN 13: 9783838326306
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Mahdavi Mahmood A. Mahmood A. Mahdavi, Assistant professor, Department of Chemical Engineering, Ferdowsi University of Mashhad, Mashhad, 9177948974, Iran. Autor/Autorin: Lin Yen-Han Mahmood A. Mahdavi, Assistant profes. Nº de ref. del artículo: 5413265

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Mahmood A. Mahdavi (u. a.)
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Taschenbuch. Condición: Neu. Computational Approaches | A worm case study | Mahmood A. Mahdavi (u. a.) | Taschenbuch | 140 S. | Englisch | 2012 | LAP LAMBERT Academic Publishing | EAN 9783838326306 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu. Nº de ref. del artículo: 101429771

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Mahmood A. Mahdavi
ISBN 10: 383832630X ISBN 13: 9783838326306
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Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Systems biology seeks to integrate high-throughput biological studies to understand how biological systems function, by studying the relationships and interactions between various parts of a biological system. The metabolic networks, as a systemic cellular organization, can be reconstructed based on genomic information. The center- piece of this information is protein-protein interaction map of the studied organism. Signature content of proteins was utilized to reveal protein interactions upon a notion that when two proteins share signature in their primary structures, the two proteins are more likely to interact. Protein interaction methods often predict true positive interactions along with numerous false positive interactions. A global algorithm was also proposed to reduce the number of false positives. The predicted interactions were incorporated into the metabolic network of C. elegans, resulting in new interactions among metabolic pathways. This case study of integrating genomic information with biochemical knowledge helps biologists to improve the extent of their understanding of biological networks using huge genomic informationVDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 140 pp. Englisch. Nº de ref. del artículo: 9783838326306

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Mahmood A. Mahdavi
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Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Systems biology seeks to integrate high-throughput biological studies to understand how biological systems function, by studying the relationships and interactions between various parts of a biological system. The metabolic networks, as a systemic cellular organization, can be reconstructed based on genomic information. The center- piece of this information is protein-protein interaction map of the studied organism. Signature content of proteins was utilized to reveal protein interactions upon a notion that when two proteins share signature in their primary structures, the two proteins are more likely to interact. Protein interaction methods often predict true positive interactions along with numerous false positive interactions. A global algorithm was also proposed to reduce the number of false positives. The predicted interactions were incorporated into the metabolic network of C. elegans, resulting in new interactions among metabolic pathways. This case study of integrating genomic information with biochemical knowledge helps biologists to improve the extent of their understanding of biological networks using huge genomic information. Nº de ref. del artículo: 9783838326306

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Mahmood A. Mahdavi
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Systems biology seeks to integrate high-throughput biological studies to understand how biological systems function, by studying the relationships and interactions between various parts of a biological system. The metabolic networks, as a systemic cellular organization, can be reconstructed based on genomic information. The center- piece of this information is protein-protein interaction map of the studied organism. Signature content of proteins was utilized to reveal protein interactions upon a notion that when two proteins share signature in their primary structures, the two proteins are more likely to interact. Protein interaction methods often predict true positive interactions along with numerous false positive interactions. A global algorithm was also proposed to reduce the number of false positives. The predicted interactions were incorporated into the metabolic network of C. elegans, resulting in new interactions among metabolic pathways. This case study of integrating genomic information with biochemical knowledge helps biologists to improve the extent of their understanding of biological networks using huge genomic information 140 pp. Englisch. Nº de ref. del artículo: 9783838326306

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