Dynamic Systems Biology Modeling and Simulation

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Academic 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.
  • Introductory coverage of core mathematical concepts such as linear and nonlinear differential and difference equations, Laplace transforms, linear algebra, probability, statistics and stochastics topics
  • The pertinent biology, biochemistry, biophysics or pharmacology for modeling are provided, to support understanding the amalgam of “math modeling” with life sciences
  • Strong emphasis on quantifying as well as building and analyzing biomodels: includes methodology and computational tools for parameter identifiability and sensitivity analysis; parameter estimation from real data; model distinguishability and simplification; and practical bioexperiment design and optimization
  • Companion website provides solutions and program code for examples and exercises using Matlab, Simulink, VisSim, SimBiology, SAAMII, AMIGO, Copasi and SBML-coded models
  • A full set of PowerPoint slides are available from the author for teaching from his textbook. He uses them to teach a 10 week quarter upper division course at UCLA, which meets twice a week, so there are 20 lectures. They can easily be augmented or stretched for a 15 week semester course
  • Importantly, the slides are editable, so they can be readily adapted to a lecturer’s personal style and course content needs. The lectures are based on excerpts from 12 of the first 13 chapters of DSBMS. They are designed to highlight the key course material, as a study guide and structure for students following the full text content
  • The complete PowerPoint slide package (~25 MB) can be obtained by instructors (or prospective instructors) by emailing the author directly, at: joed@cs.ucla.edu
 

Inhalt

Biomodeling 101
39
3 Computer Simulation Methods
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
Index
845
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