Biosystem modeling and simulation : nomenclature and philosophy -- Math models of systems : biomodeling 101 -- Computer simulation methods -- Structural biomodeling from theory and data : compartmentalizations -- Structural biomodeling from theory and data : sizing, distinguishing and simplifying multicompartmental models -- Nonlinear mass action and biochemical kinetic interaction modeling -- Cellular systems biology modeling : deterministic and stochastic -- Physiologically based, whole-organism kinetics and noncompartmental modeling -- Biosystem stability and oscillations -- Structural identifiability -- Parameter sensitivity methods -- Parameter estimation and numerical identifiability -- Parameter estimation methods II : facilitating, simplifying and working with data -- Biocontrol system modeling, simulation, and analysis -- Data-driven modeling and alternative hypothesis testing -- Experiment design and optimization -- Model reduction and network inference in dynamic systems biology.

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.