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Taschenbuch. Zustand: Neu. Computational Systems Biology | Jason McDermott (u. a.) | Taschenbuch | xviii | Englisch | 2016 | Humana | EAN 9781493956449 | Verantwortliche Person für die EU: Humana Press in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

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Hardcover. Zustand: new. Hardcover. The recent confluence of high throughput methodology for biological data gathering, genome-scale sequencing, and computational processing power has driven a reinvention and expansion of the way we identify, infer, model, and store relationships between molecules, pathways, and cells in living organisms. In Computational Systems Biology, expert investigators contribute chapters which bring together biological data and computational and/or mathematical models of the data to aid researchers striving to create a system that provides both predictive and mechanistic information for a model organism. The volume is organized into five major sections involving network components, network inference, network dynamics, function and evolutionary system biology, and computational infrastructure for systems biology. As a volume of the highly successful Methods in Molecular Biology series, this work provides the kind of detailed description and implementation advice that is crucial for getting optimal results. Comprehensive and up-to-date, Computational Systems Biology serves to motivate and inspire all those who wish to develop a complete description of a biological system. Computational systems biology is the term that we use to describe computational methods to identify, infer, model, and store relationships between the molecules, pathways, and cells (systems) involved in a living organism. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. …

Zustand: New. This volume sees expert investigators contribute chapters which bring together biological data and computational/mathematical models of the data to aid researchers create a system that provides both predictive and mechanistic information for a model organism. Series: Methods in Molecular Biology. Num Pages: 592 pages, 47 black & white tables, biography. BIC Classification: PS; UY. Category: (P) Professional & Vocational. Dimension: 260 x 193 x 33. Weight in Grams: 1361. . 2009. Hardback. . . . .…

Zustand: New. Presents a broad range of topics related to computational systems biology, including mechanistic modeling, high-throughput data analysis, biological network analysis and data representation and managementProvides a number of clearly described, det.

Hardcover. Zustand: Brand New. 1st edition. 587 pages. 10.00x7.50x1.25 inches. In Stock.

Zustand: New. This volume sees expert investigators contribute chapters which bring together biological data and computational/mathematical models of the data to aid researchers create a system that provides both predictive and mechanistic information for a model organism. Series: Methods in Molecular Biology. Num Pages: 592 pages, 47 black & white tables, biography. BIC Classification: PS; UY. Category: (P) Professional & Vocational. Dimension: 260 x 193 x 33. Weight in Grams: 1361. . 2009. Hardback. . . . . Books ship from the US and Ireland.…

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Zustand: New. pp. 587 Index 2009th edition.

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Buch. Zustand: Neu. Neuware - Computational systems biology is the term that we use to describe computational methods to identify, infer, model, and store relationships between the molecules, pathways, and cells (''systems'') involved in a living organism. Based on this definition, the field of computational systems biology has been in existence for some time. However, the recent confluence of high-throughput methodology for biological data gathering,genome-scalesequencing,andcomputationalprocessingpowerhasdrivena reinvention and expansion of this field. The expansions include not only modeling of small metabolic (1-3) and signaling systems (2, 4) but also modeling of the relati- ships between biological components in very large systems, including whole cells and organisms (5-15). Generally, these models provide a general overview of one or more aspects of these systems and leave the determination of details to experimentalists focused on smaller subsystems. The promise of such approaches is that they will elucidate patterns, relationships, and general features, which are not evident from examining specific components or subsystems. These predictions are either interesting in and of themselves (e. g. , the identification of an evolutionary pattern) or interesting andvaluabletoresearchersworkingonaparticularproblem(e. g. ,highlightapreviously unknown functional pathway). Two events have occurred to bring the field of computational systems biology to theforefront. Oneistheadventofhigh-throughputmethodsthathavegeneratedlarge amounts of information about particular systems in the form of genetic studies, gene and protein expression analyses and metabolomics. With such tools, research to c- sidersystemsasawholearebeingconceived,planned,andimplementedexperimentally on an ever more frequent andwider scale.…

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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Computational systems biology is the term that we use to describe computational methods to identify, infer, model, and store relationships between the molecules, pathways, and cells (''systems'') involved in a living organism. Based on this definition, the field of computational systems biology has been in existence for some time. However, the recent confluence of high-throughput methodology for biological data gathering,genome-scalesequencing,andcomputationalprocessingpowerhasdrivena reinvention and expansion of this field. The expansions include not only modeling of small metabolic (1-3) and signaling systems (2, 4) but also modeling of the relati- ships between biological components in very large systems, including whole cells and organisms (5-15). Generally, these models provide a general overview of one or more aspects of these systems and leave the determination of details to experimentalists focused on smaller subsystems. The promise of such approaches is that they will elucidate patterns, relationships, and general features, which are not evident from examining specific components or subsystems. These predictions are either interesting in and of themselves (e. g. , the identification of an evolutionary pattern) or interesting andvaluabletoresearchersworkingonaparticularproblem(e. g. ,highlightapreviously unknown functional pathway). Two events have occurred to bring the field of computational systems biology to theforefront. Oneistheadventofhigh-throughputmethodsthathavegeneratedlarge amounts of information about particular systems in the form of genetic studies, gene and protein expression analyses and metabolomics. With such tools, research to c- sidersystemsasawholearebeingconceived,planned,andimplementedexperimentally on an ever more frequent and wider scale. 612 pp. Englisch.…

Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents a broad range of topics related to computational systems biology, including mechanistic modeling, high-throughput data analysis, biological network analysis and data representation and managementProvides a number of clearly described, det.…

Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Computational systems biology is the term that we use to describe computational methods to identify, infer, model, and store relationships between the molecules, pathways, and cells (¿¿systems¿¿) involved in a living organism. Based on this definition, the field of computational systems biology has been in existence for some time. However, the recent confluence of high-throughput methodology for biological data gathering,genome-scalesequencing,andcomputationalprocessingpowerhasdrivena reinvention and expansion of this field. The expansions include not only modeling of small metabolic (1¿3) and signaling systems (2, 4) but also modeling of the relati- ships between biological components in very large systems, including whole cells and organisms (5¿15). Generally, these models provide a general overview of one or more aspects of these systems and leave the determination of details to experimentalists focused on smaller subsystems. The promise of such approaches is that they will elucidate patterns, relationships, and general features, which are not evident from examining specific components or subsystems. These predictions are either interesting in and of themselves (e. g. , the identification of an evolutionary pattern) or interesting andvaluabletoresearchersworkingonaparticularproblem(e. g. ,highlightapreviously unknown functional pathway). Two events have occurred to bring the field of computational systems biology to theforefront. Oneistheadventofhigh-throughputmethodsthathavegeneratedlarge amounts of information about particular systems in the form of genetic studies, gene and protein expression analyses and metabolomics. With such tools, research to c- sidersystemsasawholearebeingconceived,planned,andimplementedexperimentally on an ever more frequent andwider scale.Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 612 pp. Englisch.…

Zustand: New. Print on Demand pp. 610.

Zustand: New. PRINT ON DEMAND pp. 610.

Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Computational systems biology is the term that we use to describe computational methods to identify, infer, model, and store relationships between the molecules, pathways, and cells (''systems'') involved in a living organism. Based on this definition, the field of computational systems biology has been in existence for some time. However, the recent confluence of high-throughput methodology for biological data gathering,genome-scalesequencing,andcomputationalprocessingpowerhasdrivena reinvention and expansion of this field. The expansions include not only modeling of small metabolic (1-3) and signaling systems (2, 4) but also modeling of the relati- ships between biological components in very large systems, including whole cells and organisms (5-15). Generally, these models provide a general overview of one or more aspects of these systems and leave the determination of details to experimentalists focused on smaller subsystems. The promise of such approaches is that they will elucidate patterns, relationships, and general features, which are not evident from examining specific components or subsystems. These predictions are either interesting in and of themselves (e. g. , the identification of an evolutionary pattern) or interesting andvaluabletoresearchersworkingonaparticularproblem(e. g. ,highlightapreviously unknown functional pathway). Two events have occurred to bring the field of computational systems biology to theforefront. Oneistheadventofhigh-throughputmethodsthathavegeneratedlarge amounts of information about particular systems in the form of genetic studies, gene and protein expression analyses and metabolomics. With such tools, research to c- sidersystemsasawholearebeingconceived,planned,andimplementedexperimentally on an ever more frequent andwider scale.…