Muutke küpsiste eelistusi

Simulation Algorithms for Computational Systems Biology 1st ed. 2017 [Kõva köide]

, ,
  • Formaat: Hardback, 238 pages, kõrgus x laius: 235x155 mm, kaal: 5029 g, 23 Illustrations, color; 29 Illustrations, black and white; XI, 238 p. 52 illus., 23 illus. in color., 1 Hardback
  • Sari: Texts in Theoretical Computer Science. An EATCS Series
  • Ilmumisaeg: 09-Oct-2017
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 331963111X
  • ISBN-13: 9783319631110
Teised raamatud teemal:
  • Kõva köide
  • Hind: 55,18 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Tavahind: 64,92 €
  • Säästad 15%
  • Raamatu kohalejõudmiseks kirjastusest kulub orienteeruvalt 2-4 nädalat
  • Kogus:
  • Lisa ostukorvi
  • Tasuta tarne
  • Tellimisaeg 2-4 nädalat
  • Lisa soovinimekirja
Simulation Algorithms for Computational Systems Biology 1st ed. 2017Suurem pilt
  • Formaat: Hardback, 238 pages, kõrgus x laius: 235x155 mm, kaal: 5029 g, 23 Illustrations, color; 29 Illustrations, black and white; XI, 238 p. 52 illus., 23 illus. in color., 1 Hardback
  • Sari: Texts in Theoretical Computer Science. An EATCS Series
  • Ilmumisaeg: 09-Oct-2017
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 331963111X
  • ISBN-13: 9783319631110
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783319631110.html

This book explains the state-of-the-art algorithms used to simulate biological dynamics. Each technique is theoretically introduced and applied to a set of modeling cases. Starting from basic simulation algorithms, the book also introduces more advanced techniques that support delays, diffusion in space, or that are based on hybrid simulation strategies.

This is a valuable self-contained resource for graduate students and practitioners in computer science, biology and bioinformatics. An appendix covers the mathematical background, and the authors include further reading sections in each chapter.

Arvustused

I will not hesitate to recommend this book, both as an introductory explanation as well as later on when they are deep in a modeling exercise and need to understand the many subtle yet important variations of stochastic simulation techniques applicable to biological systems. (Sara Kalvala, Computing Reviews, March, 2018)

1 Introduction
1(6)
1.1 Simulation Approaches for Biochemical Reactions
3(2)
1.2 Further Reading
5(2)
2 Stochastic Simulation of Biochemical Reaction Systems
7(22)
2.1 Stochastic Chemical Kinetics
7(9)
2.1.1 Biochemical Reactions
7(4)
2.1.2 Reaction Propensity
11(2)
2.1.3 Chemical Master Equation
13(3)
2.2 Stochastic Simulation
16(4)
2.3 Simulation Output Analysis
20(7)
2.3.1 Confidence Interval Estimation
20(2)
2.3.2 Probability Distribution Estimation
22(1)
2.3.3 Illustrative Examples
22(5)
2.4 Summary
27(1)
2.5 Further Reading
27(2)
3 Implementations of the Stochastic Simulation Algorithm
29(84)
3.1 Direct Method
31(6)
3.1.1 Enhanced Direct Method
34(3)
3.2 Improvements for Direct Method
37(21)
3.2.1 Direct Method with Sorted Reactions
38(5)
3.2.2 Direct Method with Multi-level Search
43(2)
3.2.3 Direct Method with Tree-Based Search
45(10)
3.2.4 Direct Method with Composition-Rejection Search
55(3)
3.3 Partial-Propensity Direct Method
58(10)
3.3.1 PDM with Composition-Rejection Search
66(2)
3.4 Benchmark of DM and Its Derived Algorithms
68(3)
3.5 First Reaction Method
71(4)
3.5.1 First Family Method
74(1)
3.6 Next Reaction Method
75(10)
3.6.1 Modified Next Reaction Method
81(4)
3.7 Benchmark of FRM and Its Derived Algorithms
85(2)
3.8 Rejection-Based SSA
87(9)
3.8.1 Simultaneous RSSA
95(1)
3.9 Improvements for RSSA
96(8)
3.9.1 RSSA with Tree-Based Search
96(3)
3.9.2 RSSA with Composition-Rejection Search
99(2)
3.9.3 RSSA with Table-Lookup Search
101(3)
3.10 Benchmark of RSSA and Its Derived Algorithms
104(4)
3.11 Summary
108(3)
3.12 Further Reading
111(2)
4 Approximate Simulation of Biochemical Reaction Systems
113(68)
4.1 Probability-Weighted Dynamic Monte Carlo Method
115(3)
4.2 Bounded Acceptance Probability RSSA
118(4)
4.3 T-Leaping Method
122(11)
4.3.1 Leap Selection
125(6)
4.3.2 Avoiding the Negative Population Problem
131(1)
4.3.3 Switching to Exact Simulation
131(1)
4.3.4 The T-Leaping Algorithm
132(1)
4.4 Improvements for T-Leaping
133(7)
4.4.1 Modified T-Leaping
133(4)
4.4.2 Binomial T-Leaping
137(1)
4.4.3 Implicit T-Leaping
138(2)
4.5 kα-Leaping Method
140(4)
4.5.1 K-Leaping Method
142(2)
4.6 Benchmark of Approximate Stochastic Algorithms
144(3)
4.7 Chemical Langevin Method
147(1)
4.8 Deterministic Simulation
148(30)
4.8.1 From Biochemical Reactions to ODEs
150(5)
4.8.2 Numerical Solution of ODEs
155(3)
4.8.3 Improving the Accuracy of Numerical Methods
158(6)
4.8.4 Multistep Methods
164(4)
4.8.5 Adaptive Methods
168(6)
4.8.6 Issues of Deterministic Simulation
174(4)
4.9 Summary
178(1)
4.10 Further Reading
178(3)
5 Hybrid Simulation Algorithms
181(26)
5.1 Motivation
181(3)
5.2 Reaction-Based System Partitioning
184(3)
5.3 Synchronization of Exact and Approximate Simulations
187(4)
5.4 Hybrid Rejection-Based SSA (HRSSA)
191(6)
5.4.1 Correctness of the Simulation of Slow Reactions
195(2)
5.5 Hybrid Simulation with Stiffness
197(7)
5.5.1 Formulation of Reactions with Stiffness
198(4)
5.5.2 Slow-Scale Stochastic Simulation Algorithm
202(2)
5.5.3 Nested Stochastic Simulation Algorithm
204(1)
5.6 Summary
204(1)
5.7 Further Reading
205(2)
A Benchmark Models
207(10)
A.1 Birth Process Model
207(1)
A.2 Fast Isomerization Model
207(1)
A.3 Oscillator Model
208(1)
A.4 Schlogl Model
208(1)
A.5 Oregonator Model
209(1)
A.6 Gene Expression Model
210(1)
A.7 Folate Cycle Model
211(1)
A.8 MAPK Cascade Model
212(1)
A.9 FcεRI Pathway Model
213(1)
A.10 B Cell Antigen Receptor Signaling Model
214(1)
A.11 Linear Chain Model
214(3)
B Random Number Generation
217(8)
B.1 Uniform Random Number Generator
217(1)
B.2 Non-uniform Random Number Generator
218(7)
B.2.1 General Techniques
218(2)
B.2.2 Exponential Distribution
220(1)
B.2.3 Erlang Distribution
220(1)
B.2.4 Normal Distribution
221(1)
B.2.5 Discrete Distribution with Given Probability Vector
222(1)
B.2.6 Poisson Distribution
222(1)
B.2.7 Binomial Distribution
223(1)
B.2.8 Multinomial Distribution
224(1)
References 225
Luca Marchetti is the head of the computational biology team at COSBI, the Centre for Computational and Systems Biology, a bioinformatics company jointly owned by Microsoft Research and the University of Trento. He is also Contract Professor at the University of Verona, and an Associate Editor of the journal Optimization, Frontiers in Applied Mathematics and Statistics. He is in charge of several research projects in collaboration with important universities and pharmaceutical companies, and he is the author of scientific papers in international journals, books and conference proceedings.

Corrado Priami has been a professor of computer science at the University of Trento since 2001. The results of his PhD thesis on stochastic pi-calculus were the basis for the foundation of COSBI. He has published over 190 scientific papers, given more than 90 invited talks and lectures, and regularly serves in related advisory, scientific, and reviewing boards. He is a visiting professor atStanford University.

Vo Hong Thanh is a researcher at COSBI. His research interests include computational biology, chemical physics, and computational physics.