Dynamic Systems Biology Modeling and SimulationAcademic Press, 10.01.2015 - 884 Seiten 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.
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Inhalt
39 | |
85 | |
Compartmentalizations | 143 |
Sizing Distinguishing Simplifying Multicompartmental Models | 205 |
6 Nonlinear Mass Action Biochemical Kinetic Interaction Modeling | 253 |
Deterministic Stochastic | 307 |
8 Physiologically Based WholeOrganism Kinetics Noncompartmental Modeling | 345 |
9 Biosystem Stability Oscillations | 403 |
14 Biocontrol System Modeling Simulation and Analysis | 595 |
15 DataDriven Modeling and Alternative Hypothesis Testing | 633 |
16 Experiment Design and Optimization | 671 |
17 Model Reduction and Network Inference in Dynamic Systems Biology | 705 |
A Short Course in Laplace Transform Representations ODE Solutions | 725 |
Linear Algebra for Biosystem Modeling | 739 |
InputOutput State Variable Biosystem Modeling Going Deeper | 759 |
Controllability Observability Reachability | 787 |
10 Structural Identifiability | 435 |
11 Parameter Sensitivity Methods | 489 |
12 Parameter Estimation Numerical Identifiability | 521 |
Facilitating Simplifying Working With Data | 559 |
Decomposition Equivalence Minimal Canonical State Variable Models | 809 |
More on Simulation Algorithms Model Information Criteria | 833 |
845 | |
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algebraic algorithm analysis applications approximation biochemical biomodeling biosystem blood cell Chapter coefficients compartment compartmental models complex components computed concentration constraints developed diagram differential discrete-time DiStefano DiStefano III drug dynamic system models eigenvalues enzyme equations equivalent error example experiment design exponential feedback flux Gillespie algorithm graph hormone IÀO illustrated impulse impulse response initial conditions input inputÀoutput interactions kinetic Laplace transform limit cycle linear MÀM mass math mathematical Matlab matrix Mdm2 MdmX measured metabolic methods model parameters model structure modes molecules multicompartmental multiexponential NL models nonlinear nullclines ODE models optimization oscillations output parameter estimation pathways phase plane physiological plasma pool problem properties protein QSSA quantifying rate constants reaction represent response sampling sensitivity signal simulation Simulink solution solve stability steady stochastic submodel substrate systems biology Taylor series thyroid time-invariant tion tissue tQSSA transfer function transformation uniquely variables vector VisSim zero