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Systems Biology: A Textbook 2nd edition [Mīkstie vāki]

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(MPI for Molecular Genetics, Berlin, Germany), (MPI for Molecular Genetics, Berlin, Germany), (MPI for Molecular Genetics, Berlin, Germany), (MPI for Molecular Genetics, Berlin, Germany)
  • Formāts: Paperback / softback, 504 pages, height x width x depth: 274x206x25 mm, weight: 1452 g
  • Izdošanas datums: 04-May-2016
  • Izdevniecība: Blackwell Verlag GmbH
  • ISBN-10: 3527336362
  • ISBN-13: 9783527336364
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Systems Biology: A Textbook 2nd editionPalielināt
  • Formāts: Paperback / softback, 504 pages, height x width x depth: 274x206x25 mm, weight: 1452 g
  • Izdošanas datums: 04-May-2016
  • Izdevniecība: Blackwell Verlag GmbH
  • ISBN-10: 3527336362
  • ISBN-13: 9783527336364
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9783527336364.html
Keywords:

This advanced textbook is tailored for an introductory course in Systems Biology and is well-suited for biologists as well as engineers and computer scientists.

It comes with student-friendly reading lists and a companion website featuring a short exam prep version of the book and educational modeling programs. The text is written in an easily accessible style and includes numerous worked examples and study questions in each chapter. For this edition, a section on medical systems biology has been included.

Preface xi
Guide to Different Topics of the Book xiii
About the Authors xv
Part One Introduction to Systems Biology 1(330)
1 Introduction
3(12)
1.1 Biology in Time and Space
3(1)
1.2 Models and Modeling
4(2)
1.2.1 What Is a Model?
4(1)
1.2.2 Purpose and Adequateness of Models
5(1)
1.2.3 Advantages of Computational Modeling
5(1)
1.3 Basic Notions for Computational Models
6(2)
1.3.1 Model Scope
6(1)
1.3.2 Model Statements
6(1)
1.3.3 System State
6(1)
1.3.4 Variables, Parameters, and Constants
6(1)
1.3.5 Model Behavior
7(1)
1.3.6 Model Classification
7(1)
1.3.7 Steady States
7(1)
1.3.8 Model Assignment Is Not Unique
7(1)
1.4 Networks
8(1)
1.5 Data Integration
8(1)
1.6 Standards
9(1)
1.7 Model Organisms
9(3)
1.7.1 Escherichia coli
9(2)
1.7.2 Saccharomyces cerevisiae
11(1)
1.7.3 Caenorhabditis elegans
11(1)
1.7.4 Drosophila melanogaster
11(1)
1.7.5 Mus musculus
12(1)
References
12(2)
Further Reading
14(1)
2 Modeling of Biochemical Systems
15(8)
2.1 Overview of Common Modeling Approaches for Biochemical Systems
15(2)
2.2 ODE Systems for Biochemical Networks
17(4)
2.2.1 Basic Components of ODE Models
18(1)
2.2.2 Illustrative Examples of ODE Models
18(3)
References
21(1)
Further Reading
21(2)
3 Structural Modeling and Analysis of Biochemical Networks
23(16)
3.1 Structural Analysis of Biochemical Systems
24(6)
3.1.1 System Equations
24(1)
3.1.2 Information Encoded in the Stoichiometric Matrix N
25(2)
3.1.3 The Flux Cone
27(1)
3.1.4 Elementary Flux Modes and Extreme Pathways
27(2)
3.1.5 Conservation Relations - Null Space of NT
29(1)
3.2 Constraint-Based Flux Optimization
30(5)
3.2.1 Flux Balance Analysis
31(1)
3.2.2 Geometric Interpretation of Flux Balance Analysis
31(1)
3.2.3 Thermodynamic Constraints
31(1)
3.2.4 Applications and Tests of the Flux Optimization Paradigm
32(1)
3.2.5 Extensions of Flux Balance Analysis
33(2)
Exercises
35(1)
References
36(1)
Further Reading
37(2)
4 Kinetic Models of Biochemical Networks: Introduction
39(24)
4.1 Reaction Kinetics and Thermodynamics
39(11)
4.1.1 Kinetic Modeling of Enzymatic Reactions
39(1)
4.1.2 The Law of Mass Action
40(1)
4.1.3 Reaction Thermodynamics
40(2)
4.1.4 Michaelis-Menten Kinetics
42(2)
4.1.5 Regulation of Enzyme Activity by Effectors
44(4)
4.1.6 Generalized Mass Action Kinetics
48(1)
4.1.7 Approximate Kinetic Formats
48(1)
4.1.8 Convenience Kinetics and Modular Rate Laws
49(1)
4.2 Metabolic Control Analysis
50(11)
4.2.1 The Coefficients of Control Analysis
51(2)
4.2.2 The Theorems of Metabolic Control Theory
53(2)
4.2.3 Matrix Expressions for Control Coefficients
55(3)
4.2.4 Upper Glycolysis as Realistic Model Example
58(1)
4.2.5 Time-Dependent Response Coefficients
59(2)
Exercises
61(1)
References
61(1)
Further Reading
62(1)
5 Data Formats, Simulation Techniques, and Modeling Tools
63(24)
5.1 Simulation Techniques and Tools
63(9)
5.1.1 Differential Equations
63(1)
5.1.2 Stochastic Simulations
64(1)
5.1.3 Simulation Tools
65(7)
5.2 Standards and Formats for Systems Biology
72(3)
5.2.1 Systems Biology Markup Language
72(2)
5.2.2 BioPAX
74(1)
5.2.3 Systems Biology Graphical Notation
74(1)
5.3 Data Resources for Modeling of Cellular Reaction Systems
75(3)
5.3.1 General-Purpose Databases
75(1)
5.3.2 Pathway Databases
76(1)
5.3.3 Model Databases
77(1)
5.4 Sustainable Modeling and Model Semantics
78(5)
5.4.1 Standards for Systems Biology Models
78(1)
5.4.2 Model Semantics and Model Comparison
78(2)
5.4.3 Model Combination
80(2)
5.4.4 Model Validity
82(1)
References
83(2)
Further Reading
85(2)
6 Model Fitting, Reduction, and Coupling
87(34)
6.1 Parameter Estimation
88(11)
6.1.1 Regression, Estimators, and Maximal Likelihood
88(2)
6.1.2 Parameter Identifiability
90(1)
6.1.3 Bootstrapping
91(1)
6.1.4 Bayesian Parameter Estimation
92(2)
6.1.5 Probability Distributions for Rate Constants
94(3)
6.1.6 Optimization Methods
97(2)
6.2 Model Selection
99(5)
6.2.1 What Is a Good Model?
99(1)
6.2.2 The Problem of Model Selection
100(2)
6.2.3 Likelihood Ratio Test
102(1)
6.2.4 Selection Criteria
102(1)
6.2.5 Bayesian Model Selection
103(1)
6.3 Model Reduction
104(6)
6.3.1 Model Simplification
104(1)
6.3.2 Reduction of Fast Processes
105(2)
6.3.3 Quasi-Equilibrium and Quasi-Steady State
107(1)
6.3.4 Global Model Reduction
108(2)
6.4 Coupled Systems and Emergent Behavior
110(6)
6.4.1 Modeling of Coupled Systems
111(2)
6.4.2 Combining Rate Laws into Models
113(1)
6.4.3 Modular Response Analysis
113(1)
6.4.4 Emergent Behavior in Coupled Systems
114(1)
6.4.5 Causal Interactions and Global Behavior
115(1)
Exercises
116(1)
References
117(2)
Further Reading
119(2)
7 Discrete, Stochastic, and Spatial Models
121(24)
7.1 Discrete Models
122(5)
7.1.1 Boolean Networks
122(2)
7.1.2 Petri Nets
124(3)
7.2 Stochastic Modeling of Biochemical Reactions
127(6)
7.2.1 Chance in Biochemical Reaction Systems
127(2)
7.2.2 The Chemical Master Equation
129(1)
7.2.3 Stochastic Simulation
129(1)
7.2.4 Chemical Langevin Equation and Chemical Noise
130(2)
7.2.5 Dynamic Fluctuations
132(1)
7.2.6 From Stochastic to Deterministic Modeling
133(1)
7.3 Spatial Models
133(9)
7.3.1 Types of Spatial Models
134(1)
7.3.2 Compartment Models
135(1)
7.3.3 Reaction-Diffusion Systems
136(2)
7.3.4 Robust Pattern Formation in Embryonic Development
138(1)
7.3.5 Spontaneous Pattern Formation
139(1)
7.3.6 Linear Stability Analysis of the Activator-Inhibitor Model
140(2)
Exercises
142(1)
References
143(1)
Further Reading
144(1)
8 Network Structure, Dynamics, and Function
145(26)
8.1 Structure of Biochemical Networks
146(6)
8.1.1 Random Graphs
147(1)
8.1.2 Scale-Free Networks
148(1)
8.1.3 Connectivity and Node Distances
149(1)
8.1.4 Network Motifs and Significance Tests
150(1)
8.1.5 Explanations for Network Structures
151(1)
8.2 Regulation Networks and Network Motifs
152(8)
8.2.1 Structure of Transcription Networks
153(3)
8.2.2 Regulation Edges and Their Steady-State Response
156(1)
8.2.3 Negative Feedback
156(1)
8.2.4 Adaptation Motif
157(1)
8.2.5 Feed-Forward Loops
158(2)
8.3 Modularity and Gene Functions
160(6)
8.3.1 Cell Functions Are Reflected in Structure, Dynamics, Regulation, and Genetics
160(2)
8.3.2 Metabolics Pathways and Elementary Modes
162(1)
8.3.3 Epistasis Can Indicate Functional Modules
163(1)
8.3.4 Evolution of Function and Modules
163(2)
8.3.5 Independent Systems as a Tacit Model Assumption
165(1)
8.3.6 Modularity and Biological Function Are Conceptual Abstractions
165(1)
Exercises
166(1)
References
167(2)
Further Reading
169(2)
9 Gene Expression Models
171(38)
9.1 Mechanisms of Gene Expression Regulation
171(9)
9.1.1 Transcription Factor-Initiated Gene Regulation
171(2)
9.1.2 General Promoter Structure
173(1)
9.1.3 Prediction and Analysis of Promoter Elements
174(2)
9.1.4 Posttranscriptional Regulation through microRNAs
176(4)
9.2 Dynamic Models of Gene Regulation
180(7)
9.2.1 A Basic Model of Gene Expression and Regulation
180(3)
9.2.2 Natural and Synthetic Gene Regulatory Networks
183(3)
9.2.3 Gene Expression Modeling with Stochastic Equations
186(1)
9.3 Gene Regulation Functions
187(9)
9.3.1 The Lac Operon in E. coli
187(1)
9.3.2 Gene Regulation Functions Derived from Equilibrium Binding
188(1)
9.3.3 Thermodynamic Models of Promoter Occupancy
189(2)
9.3.4 Gene Regulation Function of the Lac Promoter
191(1)
9.3.5 Inferring Transcription Factor Activities from Transcription Data
192(2)
9.3.6 Network Component Analysis
194(2)
9.3.7 Correspondences between mRNA and Protein Levels
196(1)
9.4 Fluctuations in Gene Expression
196(7)
9.4.1 Stochastic Model of Transcription and Translation
197(3)
9.4.2 Intrinsic and Extrinsic Variability
200(2)
9.4.3 Temporal Fluctuations in Gene Cascades
202(1)
Exercises
203(2)
References
205(2)
Further Reading
207(2)
10 Variability, Robustness, and Information
209(32)
10.1 Variability and Biochemical Models
210(7)
10.1.1 Variability and Uncertainty Analysis
210(2)
10.1.2 Flux Sampling
212(1)
10.1.3 Elasticity Sampling
213(1)
10.1.4 Propagation of Parameter Variability in Kinetic Models
214(2)
10.1.5 Models with Parameter Fluctuations
216(1)
10.2 Robustness Mechanisms and Scaling Laws
217(12)
10.2.1 Robustness in Biochemical Systems
218(1)
10.2.2 Robustness by Backup Elements
219(1)
10.2.3 Feedback Control
219(3)
10.2.4 Perfect Robustness by Structure
222(2)
10.2.5 Scaling Laws
224(3)
10.2.6 Time Scaling, Summation Laws, and Robustness
227(1)
10.2.7 The Role of Robustness in Evolution and Modeling
228(1)
10.3 Adaptation and Exploration Strategies
229(7)
10.3.1 Information Transmission in Signaling Pathways
230(1)
10.3.2 Adaptation and Fold-Change Detection
230(1)
10.3.3 Two Adaptation Mechanisms: Sensing and Random Switching
231(1)
10.3.4 Shannon Information and the Value of Information
232(1)
10.3.5 Metabolic Shifts and Anticipation
233(1)
10.3.6 Exploration Strategies
234(2)
Exercises
236(1)
References
237(2)
Further Reading
239(2)
11 Optimality and Evolution
241(44)
11.1 Optimality in Systems Biology Models
243(12)
11.1.1 Mathematical Concepts for Optimality and Compromise
245(3)
11.1.2 Metabolism Is Shaped by Optimality
248(2)
11.1.3 Optimality Approaches in Metabolic Modeling
250(2)
11.1.4 Metabolic Strategies
252(1)
11.1.5 Optimal Metabolic Adaptation
253(2)
11.2 Optimal Enzyme Concentrations
255(6)
11.2.1 Optimization of Catalytic Properties of Single Enzymes
255(2)
11.2.2 Optimal Distribution of Enzyme Concentrations in a Metabolic Pathway
257(2)
11.2.3 Temporal Transcription Programs
259(2)
11.3 Evolution and Self-Organization
261(10)
11.3.1 Introduction
261(2)
11.3.2 Selection Equations for Biological Macromolecules
263(2)
11.3.3 The Quasispecies Model: Self-Replication with Mutations
265(2)
11.3.4 The Hypercycle
267(2)
11.3.5 Other Mathematical Models of Evolution: Spin Glass Model
269(1)
11.3.6 The Neutral Theory of Molecular Evolution
270(1)
11.4 Evolutionary Game Theory
271(8)
11.4.1 Social Interactions
272(1)
11.4.2 Game Theory
273(1)
11.4.3 Evolutionary Game Theory
274(1)
11.4.4 Replicator Equation for Population Dynamics
274(1)
11.4.5 Evolutionarily Stable Strategies
275(1)
11.4.6 Dynamical Behavior in the Rock-Scissors-Paper Game
276(1)
11.4.7 Evolution of Cooperative Behavior
276(2)
11.4.8 Compromises between Metabolic Yield and Efficiency
278(1)
Exercises
279(1)
References
280(3)
Further Reading
283(2)
12 Models of Biochemical Systems
285(46)
12.1 Metabolic Systems
285(6)
12.1.1 Basic Elements of Metabolic Modeling
286(1)
12.1.2 Toy Model of Upper Glycolysis
286(3)
12.1.3 Threonine Synthesis Pathway Model
289(2)
12.2 Signaling Pathways
291(16)
12.2.1 Function and Structure of Intra- and Intercellular Communication
292(1)
12.2.2 Receptor-Ligand Interactions
293(2)
12.2.3 Structural Components of Signaling Pathways
295(9)
12.2.4 Analysis of Dynamic and Regulatory Features of Signaling Pathways
304(3)
12.3 The Cell Cycle
307(7)
12.3.1 Steps in the Cycle
309(1)
12.3.2 Minimal Cascade Model of a Mitotic Oscillator
310(1)
12.3.3 Models of Budding Yeast Cell Cycle
311(3)
12.4 The Aging Process
314(13)
12.4.1 Evolution of the Aging Process
316(2)
12.4.2 Using Stochastic Simulations to Study Mitochondrial Damage
318(5)
12.4.3 Using Delay Differential Equations to Study Mitochondrial Damage
323(4)
Exercises
327(1)
References
327(4)
Part Two Reference Section 331(144)
13 Cell Biology
333(24)
13.1 The Origin of Life
334(2)
13.2 Molecular Biology of the Cell
336(9)
13.2.1 Chemical Bonds and Forces Important in Biological Molecules
336(2)
13.2.2 Functional Groups in Biological Molecules
338(1)
13.2.3 Major Classes of Biological Molecules
338(7)
13.3 Structural Cell Biology
345(6)
13.3.1 Structure and Function of Biological Membranes
347(2)
13.3.2 Nucleus
349(1)
13.3.3 Cytosol
349(1)
13.3.4 Mitochondria
350(1)
13.3.5 Endoplasmic Reticulum and Golgi Complex
350(1)
13.3.6 Other Organelles
351(1)
13.4 Expression of Genes
351(5)
13.4.1 Transcription
351(2)
13.4.2 Processing of the mRNA
353(1)
13.4.3 Translation
353(2)
13.4.4 Protein Sorting and Posttranslational Modifications
355(1)
13.4.5 Regulation of Gene Expression
355(1)
Exercises
356(1)
References
356(1)
Further Reading
356(1)
14 Experimental Techniques
357(24)
14.1 Restriction Enzymes and Gel Electrophoresis
358(1)
14.2 Cloning Vectors and DNA Libraries
359(2)
14.3 1D and 2D Protein Gels
361(1)
14.4 Hybridization and Blotting Techniques
362(2)
14.4.1 Southern Blotting
363(1)
14.4.2 Northern Blotting
363(1)
14.4.3 Western Blotting
363(1)
14.4.4 In Situ Hybridization
364(1)
14.5 Further Protein Separation Techniques
364(1)
14.5.1 Centrifugation
364(1)
14.5.2 Column Chromatography
364(1)
14.6 Polymerase Chain Reaction
365(1)
14.7 Next-Generation Sequencing
366(1)
14.8 DNA and Protein Chips
367(1)
14.8.1 DNA Chips
367(1)
14.8.2 Protein Chips
367(1)
14.9 RNA-Seq
368(1)
14.10 Yeast Two-Hybrid System
368(1)
14.11 Mass Spectrometry
369(1)
14.12 Transgenic Animals
370(1)
14.12.1 Microinjection and ES Cells
370(1)
14.12.2 Genome Editing Using ZFN, TALENs, and CRISPR
370(1)
14.13 RNA Interference
371(1)
14.14 ChIP-on-Chip and ChIP-PET
372(2)
14.15 Green Fluorescent Protein
374(1)
14.16 Single-Cell Experiments
375(1)
14.17 Surface Plasmon Resonance
376(1)
Exercises
377(1)
References
377(4)
15 Mathematical and Physical Concepts
381(64)
15.1 Linear Algebra
381(5)
15.1.1 Linear Equations
381(3)
15.1.2 Matrices
384(2)
15.2 Dynamic Systems
386(5)
15.2.1 Describing Dynamics with Ordinary Differential Equations
386(2)
15.2.2 Linearization of Autonomous Systems
388(1)
15.2.3 Solution of Linear ODE Systems
388(1)
15.2.4 Stability of Steady States
388(2)
15.2.5 Global Stability of Steady States
390(1)
15.2.6 Limit Cycles
390(1)
15.3 Statistics
391(14)
15.3.1 Basic Concepts of Probability Theory
391(5)
15.3.2 Descriptive Statistics
396(3)
15.3.3 Testing Statistical Hypotheses
399(2)
15.3.4 Linear Models
401(3)
15.3.5 Principal Component Analysis
404(1)
15.4 Stochastic Processes
405(7)
15.4.1 Chance in Physical Theories
405(1)
15.4.2 Mathematical Random Processes
406(1)
15.4.3 Brownian Motion as a Random Process
407(2)
15.4.4 Markov Processes
409(1)
15.4.5 Markov Chains
410(1)
15.4.6 Jump Processes in Continuous Time
410(1)
15.4.7 Continuous Random Processes
411(1)
15.4.8 Moment-Generating Functions
412(1)
15.5 Control of Linear Dynamical Systems
412(5)
15.5.1 Linear Dynamical Systems
413(1)
15.5.2 System Response and Linear Filters
414(1)
15.5.3 Random Fluctuations and Spectral Density
415(1)
15.5.4 The Gramian Matrices
415(1)
15.5.5 Model Reduction
416(1)
15.5.6 Optimal Control
416(1)
15.6 Biological Thermodynamics
417(9)
15.6.1 Microstate and Statistical Ensemble
417(1)
15.6.2 Boltzmann Distribution and Free Energy
418(1)
15.6.3 Entropy
419(2)
15.6.4 Equilibrium Constant and Energies
421(1)
15.6.5 Chemical Reaction Systems
422(2)
15.6.6 Nonequilibrium Reactions
424(1)
15.6.7 The Role of Thermodynamics in Systems Biology
425(1)
15.7 Multivariate Statistics
426(15)
15.7.1 Planning and Designing Experiments for Case-Control Studies
426(1)
15.7.2 Tests for Differential Expression
427(1)
15.7.3 Multiple Testing
428(1)
15.7.4 ROC Curve Analysis
429(1)
15.7.5 Clustering Algorithms
430(5)
15.7.6 Cluster Validation
435(1)
15.7.7 Overrepresentation and Enrichment Analyses
436(2)
15.7.8 Classification Methods
438(3)
Exercises
441(2)
References
443(2)
16 Databases
445(12)
16.1 General-Purpose Data Resources
445(1)
16.1.1 PathGuide
445(1)
16.1.2 BioNumbers
446(1)
16.2 Nucleotide Sequence Databases
446(2)
16.2.1 Data Repositories of the National Center for Biotechnology Information
446(1)
16.2.2 GenBank/RefSeq/UniGene
446(1)
16.2.3 Entrez
447(1)
16.2.4 EMBL Nucleotide Sequence Database
447(1)
16.2.5 European Nucleotide Archive
447(1)
16.2.6 Ensembl
447(1)
16.3 Protein Databases
448(1)
16.3.1 UniProt/Swiss-Prot/TrEMBL
448(1)
16.3.2 Protein Data Bank
448(1)
16.3.3 PANTHER
448(1)
16.3.4 InterPro
448(1)
16.3.5 iHOP
449(1)
16.4 Ontology Databases
449(1)
16.4.1 Gene Ontology
449(1)
16.5 Pathway Databases
449(2)
16.5.1 KEGG
450(1)
16.5.2 Reactome
450(1)
16.5.3 ConsensusPathDB
451(1)
16.5.4 WikiPathways
451(1)
16.6 Enzyme Reaction Kinetics Databases
451(1)
16.6.1 BRENDA
451(1)
16.6.2 SABIO-RK
452(1)
16.7 Model Collections
452(1)
16.7.1 BioModels
452(1)
16.7.2 JWS Online
452(1)
16.8 Compound and Drug Databases
452(1)
16.8.1 ChEBI
453(1)
16.8.2 Guide To Pharmacology
453(1)
16.9 Transcription Factor Databases
453(1)
16.9.1 JASPAR
453(1)
16.9.2 TRED
453(1)
16.9.3 Transcription Factor Encyclopedia
454(1)
16.10 Microarray and Sequencing Databases
454(1)
16.10.1 Gene Expression Omnibus
454(1)
16.10.2 ArrayExpress
454(1)
References
455(2)
17 Software Tools for Modeling
457(18)
17.1 13C-Flux2
458(1)
17.2 Antimony
458(1)
17.3 Berkeley Madonna
459(1)
17.4 BIOCHAM
459(1)
17.5 BioNetGen
459(1)
17.6 Biopython
459(1)
17.7 BioTapestry
460(1)
17.8 BioUML
460(1)
17.9 CellDesigner
460(1)
17.10 CeliNetAnalyzer
460(1)
17.11 Copasi
461(1)
17.12 CPN Tools
461(1)
17.13 Cytoscape
461(1)
17.14 E-Cell
461(1)
17.15 EvA2
461(1)
17.16 FEniCS Project
462(1)
17.17 Genetic Network Analyzer (GNA)
462(1)
17.18 Jarnac
462(1)
17.19 JDesigner
463(1)
17.20 JSim
463(1)
17.21 KNIME
463(1)
17.22 libSBML
464(1)
17.23 MASON
464(1)
17.24 Mathematica
464(1)
17.25 MathSBML
465(1)
17.26 Matlab
465(1)
17.27 MesoRD
465(1)
17.28 Octave
465(1)
17.29 Omix Visualization
466(1)
17.30 OpenCOR
466(1)
17.31 Oscill8
466(1)
17.32 PhysioDesigner
466(1)
17.33 PottersWheel
467(1)
17.34 PyBioS
467(1)
17.35 PySCeS
467(1)
17.36 R
468(1)
17.37 SAAM II
468(1)
17.38 SBMLeditor
468(1)
17.39 SemanticSBML
468(1)
17.40 SBML-PET-MPI
469(1)
17.41 SBMLsimulator
469(1)
17.42 SBMLsqueezer
469(1)
17.43 SBML Toolbox
470(1)
17.44 SBtoolbox2
470(1)
17.45 SBML Validator
470(1)
17.46 SensA
470(1)
17.47 SmartCell
471(1)
17.48 STELLA
471(1)
17.49 STEPS
471(1)
17.50 StochKit2
471(1)
17.51 SystemModeler
472(1)
17.52 Systems Biology Workbench
472(1)
17.53 Taverna
472(1)
17.54 VANTED
473(1)
17.55 Virtual Cell (VCell)
473(1)
17.56 xCellerator
473(1)
17.57 XPPAUT
473(1)
Exercises
474(1)
References
474(1)
Index 475
Edda Klipp (born 1965) studied theoretical biophysics at the Humboldt University Berlin. A member of the Yeast Systems Biology Network, her research interests include mathematical modeling of cellular systems, signal transduction, systems biology, and text mining.

Wolfram Liebermeister (born 1972) studied physics in Tubingen and Hamburg and obtained a PhD of theoretical biophysics at the Humboldt University of Berlin. In his works on complex biological systems, he points out functional aspects like variability, information, and optimality.

Christoph Wierling (born 1973) studied biology at the University of Munster and recently obtained a PhD degree on the modeling and simulation of biological systems.

Axel Kowald (born 1963) holds a PhD in mathematical biology from the National Institute for Medical Research, London. His current research interests focus on the mathematical modeling of processes involved in the biology of aging and systems biology.