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E-raamat: Analysis Of Biological Systems

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(Univ Of Trento, Italy), (Univ Of Trento, Italy)
  • Formaat: 432 pages
  • Ilmumisaeg: 29-Jan-2015
  • Kirjastus: Imperial College Press
  • Keel: eng
  • ISBN-13: 9781783266890
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Analysis Of Biological SystemsSuurem pilt
  • Formaat: 432 pages
  • Ilmumisaeg: 29-Jan-2015
  • Kirjastus: Imperial College Press
  • Keel: eng
  • ISBN-13: 9781783266890
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Modeling is fast becoming fundamental to understanding the processes that define biological systems. High-throughput technologies are producing increasing quantities of data that require an ever-expanding toolset for their effective analysis and interpretation. Analysis of high-throughput data in the context of a molecular interaction network is particularly informative as it has the potential to reveal the most relevant network modules with respect to a phenotype or biological process of interest.Analysis of Biological Systems collects classical material on analysis, modeling and simulation, thereby acting as a unique point of reference. The joint application of statistical techniques to extract knowledge from big data and map it into mechanistic models is a current challenge of the field, and the reader will learn how to build and use models even if they have no computing or math background. An in-depth analysis of the currently available technologies, and a comparison between them, is also included. Unlike other reference books, this in-depth analysis is extended even to the field of language-based modeling. The overall result is an indispensable, self-contained and systematic approach to a rapidly expanding field of science.
Preface vii
Acknowledgments ix
1 Algorithmic systems biology
1(8)
1.1 Converging sciences
1(3)
1.2 The approach
4(2)
1.3 Structure of the book
6(1)
1.4 Summary
7(1)
1.5 Further reading
7(2)
2 Setting the context
9(40)
2.1 The structure of the cell
9(4)
2.2 DNA, RNA and genes
13(7)
2.2.1 DNA replication
17(1)
2.2.2 DNA repair
18(1)
2.2.3 DNA recombination
19(1)
2.2.4 Transcription
20(1)
2.3 Proteins
20(4)
2.3.1 Translation
22(2)
2.3.2 Protein folding
24(1)
2.4 Metabolites
24(5)
2.5 Cellular processes
29(6)
2.5.1 Metabolism
29(1)
2.5.2 Cellular signaling
30(3)
2.5.3 Trafficking and translocation
33(2)
2.6 Experimental methods
35(11)
2.6.1 Microarray technology
35(2)
2.6.2 DNA sequencing
37(6)
2.6.3 Mass spectrometry
43(1)
2.6.4 Nuclear magnetic resonance spectroscopy
44(2)
2.6.5 Environmental data
46(1)
2.7 Summary
46(1)
2.8 Further reading
47(2)
3 Systems and models
49(42)
3.1 Systems
49(25)
3.1.1 Classification of systems
50(7)
3.1.2 Properties of systems
57(12)
3.1.3 Complexity
69(3)
3.1.4 Hierarchical systems
72(2)
3.2 Model
74(14)
3.2.1 Classification of models
77(3)
3.2.2 Properties of models
80(6)
3.2.3 Complexity
86(1)
3.2.4 Hierarchical models
87(1)
3.3 Summary
88(1)
3.4 Further reading
89(2)
4 Static modeling technologies
91(40)
4.1 Preliminary assessment
91(3)
4.2 Linear regression
94(8)
4.2.1 Penalized regression
99(3)
4.3 Dimensionality reduction methods
102(8)
4.3.1 Principal component analysis
103(3)
4.3.2 Linear discriminant analysis
106(1)
4.3.3 Canonical correlation analysis
107(3)
4.4 Clustering
110(5)
4.4.1 Hierarchical clustering
112(1)
4.4.2 K-means clustering
112(3)
4.5 Gene set analysis
115(2)
4.6 Analysis of biological networks
117(12)
4.6.1 Network topology
120(2)
4.6.2 Identification of active subnetworks
122(7)
4.7 Summary
129(1)
4.8 Further reading
130(1)
5 Dynamic modeling technologies
131(56)
5.1 Equation-based approaches
131(18)
5.1.1 Differential equations
131(17)
5.1.2 Difference equations
148(1)
5.2 Rewriting systems
149(14)
5.2.1 Chemical reactions
152(9)
5.2.2 P-systems and membrane computing
161(2)
5.3 Network-based approaches
163(10)
5.3.1 Boolean networks
163(3)
5.3.2 Petri nets
166(7)
5.4 Automata-based approaches
173(3)
5.4.1 Cellular automata
174(1)
5.4.2 Hybrid automata
175(1)
5.5 Relationship between continuous and stochastic models
176(1)
5.6 Diagrammatic modeling
177(7)
5.6.1 Elements
179(2)
5.6.2 Reactions
181(3)
5.7 Summary
184(1)
5.8 Further reading
184(3)
6 Language-based modeling
187(94)
6.1 Process calculi
187(18)
6.1.1 First generation
192(5)
6.1.2 Second generation of calculi for biology
197(8)
6.2 Third generation: from calculi to modeling languages
205(17)
6.2.1 BlenX
207(15)
6.3 Self-assembly
222(27)
6.3.1 Filaments
226(1)
6.3.2 Trees
227(2)
6.3.3 Introducing controls
229(12)
6.3.4 Rings
241(8)
6.4 An evolutionary framework
249(12)
6.4.1 Mutations
251(5)
6.4.2 A case study: MAPK
256(5)
6.5 Domain-specific languages
261(18)
6.5.1 l design
262(3)
6.5.2 l intuition
265(3)
6.5.3 l definition
268(7)
6.5.4 Relationships with other formalisms
275(4)
6.6 Summary
279(1)
6.7 Further reading
279(2)
7 Dynamic modeling process
281(20)
7.1 Setting the objectives and the acceptance criteria
282(2)
7.2 Building the knowledge base
284(6)
7.3 From the knowledge base to a model schema
290(7)
7.4 From the model schema to a concrete model
297(2)
7.5 Model calibration, evaluation and refinement
299(1)
7.6 Summary
299(1)
7.7 Further reading
300(1)
8 Simulation
301(28)
8.1 Model execution
302(4)
8.2 Random number generation
306(5)
8.2.1 Uniform random number generators
307(3)
8.2.2 General random number generators
310(1)
8.3 Stochastic simulation algorithms
311(16)
8.3.1 Direct method
311(4)
8.3.2 Some SSA variants
315(7)
8.3.3 SSA-based reaction-diffusion
322(1)
8.3.4 The T-leaping approximation
323(1)
8.3.5 Language-based simulation
324(3)
8.4 Summary
327(1)
8.5 Further reading
327(2)
9 Perspectives and conclusions
329(4)
Appendix A Basic math
333(12)
A.1 Sets, relations and functions
333(3)
A.2 Logics
336(4)
A.3 Algebra
340(5)
Appendix B Probability and statistics
345(18)
B.1 Probability
345(1)
B.2 Random variables
346(9)
B.2.1 Useful random variables
347(4)
B.2.2 Joint random variables
351(1)
B.2.3 Some important results
352(2)
B.2.4 Some useful integer random variables
354(1)
B.3 Statistics
355(8)
B.3.1 Sample measures
355(1)
B.3.2 Basic data visualization
356(2)
B.3.3 Hypothesis testing
358(5)
Appendix C Semantics of modeling languages
363(30)
C.1 Languages and grammars
363(2)
C.2 Structural operational semantics
365(6)
C.2.1 Transition systems
365(3)
C.2.2 Structural operational semantic definitions
368(3)
C.3 The π-calculus and its stochastic extension
371(3)
C.4 β-binders
374(4)
C.5 BlenX
378(6)
C.6 l
384(9)
C.6.1 Basic notions
384(1)
C.6.2 Semantics of commands and expressions
385(2)
C.6.3 Syntactic desugaring
387(2)
C.6.4 Semantics of rules
389(4)
Bibliography 393(14)
Index 407
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