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The emerging field of systems biology involves the application of experimental, theoretical, and modeling techniques to the study of biological organisms at all levels, from the molecular, through the cellular, to the behavioral. Its aim is to understand biological processes as whole systems instead of as isolated parts. Developments in the field have been made possible by advances in molecular biology--in particular, new technologies for determining DNA sequence, gene expression profiles, protein-protein interactions, and so on. Foundations of Systems Biology provides an overview of the state of the art of the field. The book covers the central topics of systems biology: comprehensive and automated measurements, reverse engineering of genes and metabolic networks from experimental data, software issues, modeling and simulation, and system-level analysis.
Achat du livre
Foundations of Systems Biology, Hiroaki Kitano
- Langue
- Année de publication
- 2001
- product-detail.submit-box.info.binding
- (rigide),
- État du livre
- Très bon
- Prix
- 7,99 €
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- Titre
- Foundations of Systems Biology
- Langue
- Anglais
- Auteurs
- Hiroaki Kitano
- Éditeur
- MIT Press
- Publié
- 2001
- Format
- rigide
- Pages
- 297
- ISBN10
- 0262112663
- ISBN13
- 9780262112666
- Séries
- Description
- The emerging field of systems biology involves the application of experimental, theoretical, and modeling techniques to the study of biological organisms at all levels, from the molecular, through the cellular, to the behavioral. Its aim is to understand biological processes as whole systems instead of as isolated parts. Developments in the field have been made possible by advances in molecular biology--in particular, new technologies for determining DNA sequence, gene expression profiles, protein-protein interactions, and so on. Foundations of Systems Biology provides an overview of the state of the art of the field. The book covers the central topics of systems biology: comprehensive and automated measurements, reverse engineering of genes and metabolic networks from experimental data, software issues, modeling and simulation, and system-level analysis.