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Guide to Simulation and Modeling for Biosciences 2nd ed. 2015 [Kõva köide]

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  • Formaat: Hardback, 339 pages, kõrgus x laius: 235x155 mm, kaal: 6506 g, 80 Illustrations, black and white; XII, 339 p. 80 illus., 1 Hardback
  • Sari: Simulation Foundations, Methods and Applications
  • Ilmumisaeg: 11-Sep-2015
  • Kirjastus: Springer London Ltd
  • ISBN-10: 1447167619
  • ISBN-13: 9781447167617
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Guide to Simulation and Modeling for Biosciences 2nd ed. 2015Suurem pilt
  • Formaat: Hardback, 339 pages, kõrgus x laius: 235x155 mm, kaal: 6506 g, 80 Illustrations, black and white; XII, 339 p. 80 illus., 1 Hardback
  • Sari: Simulation Foundations, Methods and Applications
  • Ilmumisaeg: 11-Sep-2015
  • Kirjastus: Springer London Ltd
  • ISBN-10: 1447167619
  • ISBN-13: 9781447167617
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781447167617.html
Märksõnad:
This accessible text presents a detailed introduction to the use of a wide range of software tools and modeling environments for use in the biosciences, as well as the fundamental mathematical background. The practical constraints presented by each modeling technique are described in detail, enabling the researcher to determine which software package would be most useful for a particular problem. Features: introduces a basic array of techniques to formulate models of biological systems, and to solve them; discusses agent-based models, stochastic modeling techniques, differential equations, spatial simulations, and Gillespie’s stochastic simulation algorithm; provides exercises; describes such useful tools as the Maxima algebra system, the PRISM model checker, and the modeling environments Repast Simphony and Smoldyn; contains appendices on rules of differentiation and integration, Maxima and PRISM notation, and some additional mathematical concepts; offers supplementary material at an associated website.
1 Foundations of Modeling 1(14)
1.1 Simulation Versus Analytic Results
3(2)
1.2 Stochastic Versus Deterministic Models
5(1)
1.3 Fundamentals of Modeling
6(5)
1.4 Validity and Purpose of Models
11(3)
References
14(1)
2 Agent-Based Modeling 15(64)
2.1 Mathematical and Computational Modeling
16(5)
2.1.1 Limits to Modeling
17(4)
2.2 Agent-Based Models
21(10)
2.2.1 The Structure of ABMs
23(2)
2.2.2 Algorithms
25(2)
2.2.3 Time-Driven Algorithms
27(1)
2.2.4 Event-Driven Models
28(3)
2.3 Game of Life
31(4)
2.4 Malaria
35(11)
2.4.1 A Digression
38(2)
2.4.2 Stochastic Systems
40(3)
2.4.3 Immobile Agents
43(3)
2.5 General Considerations When Analyzing a Model
46(3)
2.5.1 Testing ABMs
47(2)
2.6 Case Study: The Evolution of Fimbriation
49(29)
2.6.1 Group Selection
49(3)
2.6.2 A Model of Martian Mice
52(26)
References
78(1)
3 ABMs Using Repast Simphony 79(42)
3.1 Mapping Agent Concepts to Object-Oriented Languages
80(4)
3.1.1 Hand-Coding an Agent in Java
81(3)
3.2 The Repast Suite
84(1)
3.2.1 Repast Simphony and ReLogo
84(1)
3.3 The Game of Life Using ReLogo and Groovy
85(11)
3.3.1 Creating a New Model
86(1)
3.3.2 The UserPatch Class
87(2)
3.3.3 The UserObserver Class
89(1)
3.3.4 Visualizing the Model
90(2)
3.3.5 Implementing Cell State
92(4)
3.4 Malaria Model in Repast Using ReLogo
96(22)
3.4.1 The Malaria Model
97(1)
3.4.2 Creating the Malaria Model
97(1)
3.4.3 A Basic Human Turtle Agent
97(3)
3.4.4 Model Parameterization
100(1)
3.4.5 The UserGlobalsAndPanelFactory Class
101(1)
3.4.6 The @ScheduledMethod Annotation
102(2)
3.4.7 The Huan Agent
104(3)
3.4.8 parameters.xml and the SimBuilder Class
107(2)
3.4.9 The Mosquito Agent
109(2)
3.4.10 Runtime Memory Configuration for Large Models
111(1)
3.4.11 Recognizing the Common Elements of the Agents
112(1)
3.4.12 Data Sets and Chart
113(1)
3.4.13 Outputting Data with Text Sinks
114(2)
3.4.14 Batch Runs with Varying Parameter Values
116(2)
3.4.15 Summary of Concepts Relating to the Malaria Model
118(1)
3.4.16 Going Further with Repast Simphony
118(1)
References
118(3)
4 Differential Equations 121(54)
4.1 Differentiation
122(9)
4.1.1 A Mathematical Example
126(3)
4.1.2 A Small Digression
129(2)
4.2 Integration
131(3)
4.3 Differential Equations
134(12)
4.3.1 Limits to Growth
138(3)
4.3.2 Steady State
141(2)
4.3.3 Bacterial Growth Revisited
143(3)
4.4 Case Study: Malaria
146(11)
4.4.1 A Brief Note on Stability
152(5)
4.5 Chemical Reactions
157(11)
4.5.1 Michaelis—Menten and Hill Kinetics
159(6)
4.5.2 Modeling Gene Expression
165(3)
4.6 Case Study: Cherry and Adler's Bistable Switch
168(5)
4.7 Summary
173(1)
References
174(1)
5 Mathematical Tools 175(32)
5.1 A Word of Warning: Pitfalls of CAS
175(2)
5.2 Existing Tools and Types of System
177(1)
5.3 Maxima: Preliminaries
178(2)
5.4 Maxima: Simple Sample Sessions
180(6)
5.4.1 The Basics
180(5)
5.4.2 Saving and Recalling Sessions
185(1)
5.5 Maxima: Beyond Preliminaries
186(14)
5.5.1 Solving Equations
187(2)
5.5.2 Matrices and Eigenvalues
189(2)
5.5.3 Graphics and Plotting
191(6)
5.5.4 Integrating and Differentiating
197(3)
5.6 Maxima: Case Studies
200(5)
5.6.1 Gene Expression
201(1)
5.6.2 Malaria
202(1)
5.6.3 Cherry and Adler's Bistable Switch
203(2)
5.7 Summary
205(1)
References
206(1)
6 Other Stochastic Methods and Prism 207(58)
6.1 The Master Equation
209(8)
6.2 Partition Functions
217(10)
6.2.1 Weighted Configurations
219(4)
6.2.2 Binding to DNA
223(3)
6.2.3 Codon Bias in Proteins
226(1)
6.3 Markov Chains
227(10)
6.3.1 Absorbing Markov Chains
232(1)
6.3.2 Continuous-Time Markov Chains
233(2)
6.3.3 An Example from Gene Activation
235(2)
6.4 Analyzing Markov Chains: Sample Paths
237(2)
6.5 Analyzing Markov Chains: Using PRISM
239(17)
6.5.1 The PRISM Modeling Language
240(3)
6.5.2 Running PRISM
243(5)
6.5.3 Rewards
248(4)
6.5.4 Simulation in PRISM
252(2)
6.5.5 The PRISM GUI
254(2)
6.6 Examples
256(8)
6.6.1 Fim Switching
256(4)
6.6.2 Stochastic Versions of a Differential Equation
260(3)
6.6.3 Tricks for PRISM Models
263(1)
References
264(1)
7 Simulating Biochemical Systems 265(36)
7.1 The Gillespie Algorithms
265(10)
7.1.1 Gillespie's Direct Method
267(1)
7.1.2 Gillespie's First Reaction Method
267(1)
7.1.3 Java Implementation of the Direct Method
268(2)
7.1.4 Example Reactions
270(5)
7.2 The Gibson—Bruck Algorithm
275(6)
7.2.1 The Dependency Graph
276(1)
7.2.2 The Indexed Priority Queue
277(2)
7.2.3 Updating the τ Values
279(1)
7.2.4 Analysis
279(2)
7.3 A Constant Time Method
281(4)
7.3.1 Selection Procedure
281(2)
7.3.2 Reaction Selection
283(2)
7.4 Practical Implementation Considerations
285(3)
7.4.1 Data Structures—the Dependency Tree
285(1)
7.4.2 Programming Techniques—Tree Updating
286(2)
7.4.3 Runtime Environment
288(1)
7.5 Reaction Equation Systems for Biological Models
288(2)
7.6 The Tau-Leap Method
290(1)
7.7 Delayed Stochastic Models
290(3)
7.8 Dizzy
293(3)
7.9 The Stochastic Genetic Networks Simulator
296(2)
7.10 Summary
298(1)
References
298(3)
8 Biochemical Models Beyond the Perfect Mixing Assumption 301(24)
8.1 Conceptual Differences Between Perfectly Mixed and Spatial Model Systems
303(1)
8.2 Spatial Modeling with Smoldyn
304(1)
8.3 Basic Concepts of Spatial Modeling
305(7)
8.4 Case Study: Change Detector I
312(6)
8.4.1 Simulations of the System
316(2)
8.5 Free-Change Detector II
318(5)
8.6 Alternatives to Smoldyn
323(1)
References
324(1)
Appendix A: Reference Material 325(10)
Index 335
David J. Barnes is a senior lecturer in computer science at the University of Kent, UK, with a strong background in the teaching of programming and the implementation of computational models of biological systems.

Dominique Chu is a senior lecturer in computer science at the University of Kent, UK. He is an expert in mathematical and computational modeling of biological systems, with years of experience in these fields.