Buch, Englisch, 884 Seiten, Format (B × H): 222 mm x 282 mm, Gewicht: 2619 g
Buch, Englisch, 884 Seiten, Format (B × H): 222 mm x 282 mm, Gewicht: 2619 g
ISBN: 978-0-12-410411-2
Verlag: Elsevier Science
Dynamic Systems Biology Modeling and Simuation consolidates and unifies classical and contemporary multiscale methodologies for mathematical modeling and computer simulation of dynamic biological systems - from molecular/cellular, organ-system, on up to population levels. The book pedagogy is developed as a well-annotated, systematic tutorial - with clearly spelled-out and unified nomenclature - derived from the author's own modeling efforts, publications and teaching over half a century. Ambiguities in some concepts and tools are clarified and others are rendered more accessible and practical. The latter include novel qualitative theory and methodologies for recognizing dynamical signatures in data using structural (multicompartmental and network) models and graph theory; and analyzing structural and measurement (data) models for quantification feasibility. The level is basic-to-intermediate, with much emphasis on biomodeling from real biodata, for use in real applications.
Zielgruppe
upper-division undergraduate, graduate level, and research level students systems biology, computational biology, biomathematics, biomedical engineering (bioengineering), pharmacology and areas using contemporary dynamical biosystem modeling and simulation methodology.
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
1. Biosystem Modeling and Simulation: Nomenclature and Philosophy2. Math Models of Systems: Biomodeling 1013. Computer Simulation Methods4. Structural Biomodeling from Theory & Data: Compartmentalizations5. Structural Biomodeling from Theory & Data: Sizing, Distinguishing & Simplifying Multicompartmental Models6. Nonlinear Mass Action & Biochemical Kinetic Interaction Modeling7. Cellular Systems Biology Modeling: Deterministic & Stochastic8. Physiologically Based, Whole-Organism Kinetics & Noncompartmental Modeling9. Biosystem Stability & Oscillations10. Structural Identifiability11. Parameter Sensitivity Methods12. Parameter Estimation & Numerical Identifiability13. Parameter Estimation Methods II: Facilitating, Simplifying & Working With Data14. Biocontrol System Modeling, Simulation, and Analysis15. Data-Driven Modeling and Alternative Hypothesis Testing16. Experiment Design and Optimization17. Model Reduction and Network Inference in Dynamic Systems Biology
