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Handbook of Statistical Systems Biology [Kietas viršelis]

(Theoretical Systems Biology, Imperial College London, UK), (Statistical Genetics in the Institute of Genetics, University College London, UK), (Department of Computing Science and the Department of Statistics, University College Londo)
  • Formatas: Hardback, 530 pages, aukštis x plotis x storis: 254x196x26 mm, weight: 1297 g
  • Išleidimo metai: 21-Oct-2011
  • Leidėjas: John Wiley & Sons Inc
  • ISBN-10: 0470710861
  • ISBN-13: 9780470710869
Kitos knygos pagal šią temą:
Handbook of Statistical Systems BiologyDidesnis paveikslėlis
  • Formatas: Hardback, 530 pages, aukštis x plotis x storis: 254x196x26 mm, weight: 1297 g
  • Išleidimo metai: 21-Oct-2011
  • Leidėjas: John Wiley & Sons Inc
  • ISBN-10: 0470710861
  • ISBN-13: 9780470710869
Kitos knygos pagal šią temą:
Pastovi nuoroda: https://www.kriso.lt/db/9780470710869.html
Raktažodžiai:
Systems Biology is now entering a mature phase in which the key issues are characterising uncertainty and stochastic effects in mathematical models of biological systems. The area is moving towards a full statistical analysis and probabilistic reasoning over the inferences that can be made from mathematical models. This handbook presents a comprehensive guide to the discipline for practitioners and educators, in providing a full and detailed treatment of these important and emerging subjects. Leading experts in systems biology and statistics have come together to provide insight in to the major ideas in the field, and in particular methods of specifying and fitting models, and estimating the unknown parameters. This book:





Provides a comprehensive account of inference techniques in systems biology. Introduces classical and Bayesian statistical methods for complex systems. Explores networks and graphical modeling as well as a wide range of statistical models for dynamical systems. Discusses various applications for statistical systems biology, such as gene regulation and signal transduction. Features statistical data analysis on numerous technologies, including metabolic and transcriptomic technologies. Presents an in-depth presentation of reverse engineering approaches. Provides colour illustrations to explain key concepts.

This handbook will be a key resource for researchers practising systems biology, and those requiring a comprehensive overview of this important field.

Recenzijos

A very remarkable collection of essays. Strongly recommended to workers in this area.  (International Statistical Review, 1 October 2013)

I would highly recommend this book as a useful guide for the students and practitioners of systems biology.  (Science Progress, 1 September 2012)

This handbook will be a key resource for researchers practising systems biology, and those requiring a comprehensive overview of this important field. (Zentralblatt MATH, 2012)

Preface xvii
Contributors xix
A METHODOLOGICAL CHAPTERS
1(132)
1 Two Challenges of Systems Biology
3(12)
William S. Hlavacek
1.1 Introduction
3(1)
1.2 Cell signaling systems
4(1)
1.3 The challenge of many moving parts
5(2)
1.4 The challenge of parts with parts
7(2)
1.5 Closing remarks
9(1)
References
10(5)
2 Introduction to Statistical Methods for Complex Systems
15(24)
Tristan Mary-Huard
Stephane Robin
2.1 Introduction
15(1)
2.2 Class comparison
16(6)
2.2.1 Models for dependent data
16(3)
2.2.2 Multiple testing
19(3)
2.3 Class prediction
22(9)
2.3.1 Building a classifier
22(3)
2.3.2 Aggregation
25(2)
2.3.3 Regularization
27(2)
2.3.4 Performance assessment
29(2)
2.4 Class discovery
31(5)
2.4.1 Geometric methods
31(1)
2.4.2 (Discrete) latent variable models
32(1)
2.4.3 Inference
33(3)
References
36(3)
3 Bayesian Inference and Computation
39(27)
Christian P. Robert
Jean-Michel Marin
Judith Rousseau
3.1 Introduction
39(1)
3.2 The Bayesian argument
39(9)
3.2.1 Bases
39(2)
3.2.2 Bayesian analysis in action
41(1)
3.2.3 Prior distributions
42(4)
3.2.4 Confidence intervals
46(2)
3.3 Testing hypotheses
48(7)
3.3.1 Decisions
48(1)
3.3.2 The Bayes factor
48(1)
3.3.3 Point null hypotheses
49(1)
3.3.4 The ban on improper priors
50(2)
3.3.5 The case of nuisance parameters
52(2)
3.3.6 Bayesian multiple testing
54(1)
3.4 Extensions
55(2)
3.4.1 Prediction
55(1)
3.4.2 Outliers
56(1)
3.4.3 Model choice
56(1)
3.5 Computational issues
57(7)
3.5.1 Computational challenges
57(2)
3.5.2 Monte Carlo methods
59(2)
3.5.3 MCMC methods
61(2)
3.5.4 Approximate Bayesian computation techniques
63(1)
Acknowledgements
64(1)
References
64(2)
4 Data Integration: Towards Understanding Biological Complexity
66(17)
David Gomez-Cabrero
Jesper Tegner
4.1 Storing knowledge: Experimental data, knowledge databases, ontologies and annotation
66(8)
4.1.1 Data repositories
67(1)
4.1.2 Knowledge Databases
68(2)
4.1.3 Ontologies
70(1)
4.1.4 Annotation
71(3)
4.2 Data integration in biological studies
74(3)
4.2.1 Integration of experimental data
74(3)
4.2.2 Ontologies and experimental data
77(1)
4.2.3 Networks and visualization software as integrative tools
77(1)
4.3 Concluding remarks
77(1)
References
78(5)
5 Control Engineering Approaches to Reverse Engineering Biomolecular Networks
83(31)
Francesco Montefusco
Carlo Cosentino
Declan G. Bates
5.1 Dynamical models for network inference
83(6)
5.1.1 Linear models
84(1)
5.1.2 Nonlinear models
85(4)
5.2 Reconstruction methods based on linear models
89(15)
5.2.1 Least squares
89(1)
5.2.2 Methods based on least squares
90(1)
5.2.3 Dealing with noise: Ctls
91(6)
5.2.4 Convex optimization methods
97(3)
5.2.5 Sparsity pattern of the discrete-time model
100(1)
5.2.6 Application examples
101(3)
5.3 Reconstruction methods based on nonlinear models
104(7)
5.3.1 Approaches based on polynomial and rational models
105(2)
5.3.2 Approaches based on S-systems
107(2)
5.3.3 A case-study
109(2)
References
111(3)
6 Algebraic Statistics and Methods in Systems Biology
114(19)
Carsten Wiuf
6.1 Introduction
114(1)
6.2 Overview of chapter
115(1)
6.3 Computational algebra
116(2)
6.4 Algebraic statistical models
118(4)
6.4.1 Definitions
118(1)
6.4.2 Further examples
119(3)
6.5 Parameter inference
122(2)
6.6 Model invariants
124(2)
6.7 Log-linear models
126(3)
6.8 Reverse engineering of networks
129(1)
6.9 Concluding remarks
130(1)
References
130(3)
B TECHNOLOGY-BASED CHAPTERS
133(102)
7 Transcriptomic Technologies and Statistical Data Analysis
135(28)
Elizabeth Purdom
Sach Mukherjee
7.1 Biological background
135(1)
7.2 Technologies for genome-wide profiling of transcription
136(4)
7.2.1 Microarray technology
136(1)
7.2.2 Mrna Expression Estimates From Microarrays
137(1)
7.2.3 High throughput sequencing (HTS)
137(2)
7.2.4 Mrna Expression Estimates From Hts
139(1)
7.3 Evaluating the significance of individual genes
140(7)
7.3.1 Common approaches for significance testing
140(1)
7.3.2 Moderated statistics
141(1)
7.3.3 Statistics for HTS
142(1)
7.3.4 Multiple testing corrections
143(2)
7.3.5 Filtering genes
145(2)
7.4 Grouping genes to find biological patterns
147(6)
7.4.1 Gene-set analysis
147(2)
7.4.2 Dimensionality reduction
149(1)
7.4.3 Clustering
150(3)
7.5 Prediction of a biological response
153(4)
7.5.1 Variable selection
153(3)
7.5.2 Estimating the performance of a model
156(1)
References
157(6)
8 Statistical Data Analysis in Metabolomics
163(18)
Timothy M. D. Ebbels
Maria De Iorio
8.1 Introduction
163(1)
8.2 Analytical technologies and data characteristics
164(5)
8.2.1 Analytical technologies
164(2)
8.2.2 Preprocessing
166(3)
8.3 Statistical analysis
169(9)
8.3.1 Unsupervised methods
169(2)
8.3.2 Supervised methods
171(1)
8.3.3 Metabolome-wide association studies
172(1)
8.3.4 Metabolic correlation networks
173(3)
8.3.5 Simulation of metabolic profile data
176(2)
8.4 Conclusions
178(1)
Acknowledgements
178(1)
References
178(3)
9 Imaging and Single-Cell Measurement Technologies
181(19)
Yu-ichi Ozaki
Shinya Kuroda
9.1 Introduction
181(1)
9.1.1 Intracellular signal transduction
181(1)
9.1.2 Lysate-based assay and single-cell assay
182(1)
9.1.3 Live cell and fixed cell
182(1)
9.2 Measurement techniques
182(12)
9.2.1 Western blot analysis
183(1)
9.2.2 Immunocytochemistry
183(1)
9.2.3 Flow cytometry
184(1)
9.2.4 Fluorescent microscope
185(2)
9.2.5 Live cell imaging
187(1)
9.2.6 Fluorescent probes for live cell imaging
188(2)
9.2.7 Image cytometry
190(1)
9.2.8 Image processing
191(3)
9.3 Analysis of signal cell measurement data
194(3)
9.3.1 Time series (mean, variation, correlation, localization
194(3)
9.3.2 Bayesian network modeling with single-cell data
197(1)
9.3.3 Quantifying sources of cell-to-cell variation
197(1)
9.4 Summary
197(2)
Acknowledgements
199(1)
References
199(1)
10 Protein Interaction Networks and Their Statistical Analysis
200(35)
Waqar Ali
Charlotte Deane
Gesine Reinert
10.1 Introduction
200(1)
10.2 Proteins and their interactions
201(4)
10.2.1 Protein structure and function
201(1)
10.2.2 Protein-protein interactions
202(1)
10.2.3 Experimental techniques for interaction detection
202(1)
10.2.4 Computationally predicted data-sets
203(1)
10.2.5 Protein interaction databases
204(1)
10.2.6 Error in PPI data
204(1)
10.2.7 The interactome concept and protein interaction networks
205(1)
10.3 Network analysis
205(6)
10.3.1 Graphs
205(1)
10.3.2 Network summary statistics
206(1)
10.3.3 Network motifs
207(1)
10.3.4 Models of random networks
207(2)
10.3.5 Parameter estimation for network models
209(1)
10.3.6 Approximate Bayesian Computation
209(1)
10.3.7 Threshold behaviour in graphs
210(1)
10.4 Comparison of protein interaction networks
211(6)
10.4.1 Network comparison based on subgraph counts
211(2)
10.4.2 Network alignment
213(2)
10.4.3 Using functional annotation for network alignment
215(2)
10.5 Evolution and the protein interaction network
217(1)
10.5.1 How evolutionary models affect network alignment
217(1)
10.6 Community detection in PPI networks
218(3)
10.6.1 Community detection methods
219(1)
10.6.2 Evaluation of results
220(1)
10.7 Predicting function using PPI networks
221(2)
10.8 Predicting interactions using PPI networks
223(3)
10.8.1 Tendency to form triangles
224(1)
10.8.2 Using triangles for predicting interactions
224(2)
10.9 Current trends and future directions
226(2)
10.9.1 Dynamics
226(1)
10.9.2 Integration with other networks
227(1)
10.9.3 Limitations of models, prediction and alignment methods
227(1)
10.9.4 Biases, error and weighting
228(1)
10.9.5 New experimental sources of PPI data
228(1)
References
228(7)
C NETWORKS AND GRAPHICAL MODELS
235(96)
11 Introduction to Graphical Modelling
237(18)
Marco Scutari
Korbinian Strimmer
11.1 Graphical structures and random variables
237(4)
11.2 Learning graphical models
241(5)
11.2.1 Structure learning
242(4)
11.2.2 Parameter learning
246(1)
11.3 Inference on graphical models
246(1)
11.4 Application of graphical models in systems biology
247(4)
11.4.1 Correlation networks
247(1)
11.4.2 Covariance selection networks
248(2)
11.4.3 Bayesian networks
250(1)
11.4.4 Dynamic Bayesian networks
250(1)
11.4.5 Other graphical models
250(1)
References
251(4)
12 Recovering Genetic Network from Continuous Data with Dynamic Bayesian Networks
255(15)
Gaelle Lelandais
Sophie Lebre
12.1 Introduction
255(1)
12.1.1 Regulatory networks in biology
255(1)
12.1.2 Objectives and challenges
256(1)
12.2 Reverse engineering time-homogeneous DBNs
256(5)
12.2.1 Genetic network modelling with DBNs
256(3)
12.2.2 DBN for linear interactions and inference procedures
259(2)
12.3 Go forward: How to recover the structure changes with time
261(6)
12.3.1 ARTIVA network model
262(1)
12.3.2 ARTIVA inference procedure and performance evaluation
263(4)
12.4 Discussion and Conclusion
267(1)
References
268(2)
13 Advanced Applications of Bayesian Networks in Systems Biology
270(20)
Dirk Husmeier
Adriano V. Werhli
Marco Grzegorczyk
13.1 Introduction
270(3)
13.1.1 Basic concepts
270(2)
13.1.2 Dynamic Bayesian networks
272(1)
13.1.3 Bayesian learning of Bayesian networks
273(1)
13.2 Inclusion of biological prior knowledge
273(8)
13.2.1 The `energy' of a network
274(1)
13.2.2 Prior distribution over network structures
275(1)
13.2.3 MCMC sampling scheme
276(1)
13.2.4 Practical implementation
277(1)
13.2.5 Empirical evaluation on the Raf signalling pathway
277(4)
13.3 Heterogeneous DBNs
281(6)
13.3.1 Motivation: Inferring spurious feedback loops with DBNs
281(1)
13.3.2 A nonlinear/nonhomogeneous DBN
282(2)
13.3.3 MCMC inference
284(1)
13.3.4 Simulation results
284(1)
13.3.5 Results on Arabidopsis gene expression time series
285(2)
13.4 Discussion
287(1)
Acknowledgements
288(1)
References
288(2)
14 Random Graph Models and Their Application to Protein-Protein Interaction Networks
290(19)
Desmond J. Higham
Natasa Przulj
14.1 Background and motivation
290(3)
14.2 What do we want from a PPI network?
293(1)
14.3 PPI network models
294(7)
14.3.1 Lock and key
294(3)
14.3.2 Geometric networks
297(4)
14.4 Range-dependent graphs
301(4)
14.5 Summary
305(1)
References
306(3)
15 Modelling Biological Networks via Tailored Random Graphs
309(22)
Anthony C. C. Coolen
Franca Fraternali
Alessia Annibale
Luis Fernandes
Jens Kleinjung
15.1 Introduction
309(1)
15.2 Quantitative characterization of network topologies
310(2)
15.2.1 Local network features and their statistics
310(1)
15.2.2 Examples
311(1)
15.3 Network families and random graphs
312(3)
15.3.1 Network families, hypothesis testing and null models
312(1)
15.3.2 Tailored random graph ensembles
313(2)
15.4 Information-theoretic deliverables of tailored random graphs
315(2)
15.4.1 Network complexity
315(1)
15.4.2 Information-theoretic dissimilarity
316(1)
15.5 Applications to PPINs
317(6)
15.5.1 PPIN assortativity and wiring complexity
320(1)
15.5.2 Mapping PPIN data biases
320(3)
15.6 Numerical generation of tailored random graphs
323(2)
15.6.1 Generating random graphs via Markov chains
323(1)
15.6.2 Degree-constrained graph dynamics based on edge swaps
324(1)
15.6.3 Numerical examples
325(1)
15.7 Discussion
325(2)
References
327(4)
D DYNAMICAL SYSTEMS
331(86)
16 Nonlinear Dynamics: A Brief Introduction
333(6)
Alessandro Moura
Celso Grebogi
16.1 Introduction
333(1)
16.2 Sensitivity to initial conditions and the Lyapunov exponent
334(1)
16.3 The natural measure
334(1)
16.4 The Kolmogorov-Sinai entropy
335(1)
16.5 Symbolic dynamics
336(2)
16.6 Chaos in biology
338(1)
References
338(1)
17 Qualitative Inference in Dynamical Systems
339(20)
Fatihcan M. Atay
Jurgen Jost
17.1 Introduction
339(4)
17.2 Basic solution types
343(3)
17.3 Qualitative behaviour
346(1)
17.4 Stability and bifurcations
347(6)
17.5 Ergodicity
353(1)
17.6 Timescales
354(2)
17.7 Time series analysis
356(1)
References
357(2)
18 Stochastic Dynamical Systems
359(17)
Darren J. Wilkinson
18.1 Introduction
359(1)
18.2 Origins of stochasticity
359(1)
18.2.1 Low copy number
359(1)
18.2.2 Other sources of noise and heterogeneity
360(1)
18.3 Stochastic chemical kinetics
360(6)
18.3.1 Reaction networks
360(1)
18.3.2 Markov jump process
360(4)
18.3.3 Diffusion approximation
364(1)
18.3.4 Reaction rate equations
365(1)
18.3.5 Modelling extrinsic noise
365(1)
18.4 Inference for Markov process models
366(6)
18.4.1 Likelihood-based inference
366(1)
18.4.2 Partial observation and data augmentation
367(1)
18.4.3 Data augmentation MCMC approaches
368(1)
18.4.4 Likelihood-free approaches
369(1)
18.4.5 Approximate Bayesian computation
370(1)
18.4.6 Particle MCMC
371(1)
18.4.7 Iterative filtering
371(1)
18.4.8 Stochastic model emulation
371(1)
18.4.9 Inference for stochastic differential equation models
372(1)
18.5 Conclusions
372(1)
Acknowledgements
373(1)
References
373(3)
19 Gaussian Process Inference for Differential Equation Models of Transcriptional Regulation
376(19)
Neil Lawrence
Magnus Rattray
Antti Honkela
Michalis Titsias
19.1 Introduction
376(3)
19.1.1 A simple systems biology model
377(2)
19.2 Generalized linear model
379(8)
19.2.1 Fitting basis function models
380(3)
19.2.2 An infinite basis
383(2)
19.2.3 Gaussian processes
385(2)
19.2.4 Sampling approximations
387(1)
19.3 Model based target ranking
387(4)
19.4 Multiple tanscription factors
391(2)
19.5 Conclusion
393(1)
References
394(1)
20 Model Identification by Utilizing Likelihood-Based Methods
395(22)
Andreas Raue
Jens Timmer
20.1 ODE models for reaction networks
396(2)
20.1.1 Rate equations
397(1)
20.2 Parameter estimation
398(5)
20.2.1 Sensitivity equations
399(1)
20.2.2 Testing hypothesis
400(1)
20.2.3 Confidence intervals
401(2)
20.3 Identifiability
403(2)
20.3.1 Structural nonidentifiability
403(1)
20.3.2 Practical nonidentifiability
404(1)
20.3.3 Connection of identifiability and observability
405(1)
20.4 The profile likelihood approach
405(8)
20.4.1 Experimental design
406(1)
20.4.2 Model reduction
407(1)
20.4.3 Observability and confidence intervals of trajectories
407(1)
20.4.4 Application
408(5)
20.5 Summary
413(1)
Acknowledgements
414(1)
References
415(2)
E APPLICATION AREAS
417(78)
21 Inference of Signalling Pathway Models
419(21)
Tina Toni
Juliane Liepe
Michael P. H. Stumpf
21.1 Introduction
419(1)
21.2 Overview of inference techniques
420(2)
21.3 Parameter inference and model selection for dynamical systems
422(3)
21.3.1 Model selection
424(1)
21.4 Approximate Bayesian computation
425(1)
21.5 Application: Akt signalling pathway
426(9)
21.5.1 Exploring different distance functions
428(2)
21.5.2 Posteriors
430(1)
21.5.3 Parameter sensitivity through marginal posterior distributions
430(1)
21.5.4 Sensitivity analysis by principal component analysis (PCA)
430(5)
21.6 Conclusion
435(1)
References
436(4)
22 Modelling Transcription Factor Activity
440(11)
Martino Barenco
Daniel Brewer
Robin Callard
Michael Hubank
22.1 Integrating an ODE with a differential operator
441(2)
22.2 Computation of the entries of the differential operator
443(4)
22.2.1 Taking into account the nature of the biological system being modelled
443(2)
22.2.2 Bounds choice for polynomial interpolation
445(2)
22.3 Applications
447(2)
22.4 Estimating intermediate points
449(1)
Acknowledgements
450(1)
References
450(1)
23 Host-Pathogen Systems Biology
451(16)
John W. Pinney
23.1 Introduction
451(2)
23.2 Pathogen genomics
453(1)
23.3 Metabolic models
453(2)
23.4 Protein-protein interactions
455(2)
23.5 Response to environment
457(1)
23.6 Immune system interactions
458(1)
23.7 Manipulation of other host systems
459(1)
23.8 Evolution of the host-pathogen system
460(2)
23.9 Towards systems medicine for infectious diseases
462(1)
23.10 Concluding remarks
462(1)
Acknowledgements
463(1)
References
463(4)
24 Bayesian Approaches for Mass Spectrometry-Based Metabolomics
467(10)
Simon Rogers
Richard A. Scheltema
Michael Barrett
Rainer Breitling
24.1 Introduction
467(1)
24.2 The challenge of metabolite identification
468(1)
24.3 Bayesian analysis of metabolite mass spectra
469(2)
24.4 Incorporating additional information
471(1)
24.5 Probabilistic peak detection
472(1)
24.6 Statistical inference
473(1)
24.7 Software development for metabolomics
474(1)
24.8 Conclusion
475(1)
References
475(2)
25 Systems Biology of microRNAs
477(18)
Doron Betel
Raya Khanin
25.1 Introduction
477(1)
25.2 Current approaches in microRNA Systems Biology
477(1)
25.3 Experimental findings and data that guide the developments of computational tools
478(1)
25.4 Approaches to microRNA target predictions
479(3)
25.5 Analysis of mRNA and microRNA expression data
482(3)
25.5.1 Identifying microRNA activity from mRNA expression
482(2)
25.5.2 Modeling combinatorial microRNA regulation from joint microRNA and mRNA expression data
484(1)
25.6 Network approach for studying microRNA-mediated regulation
485(1)
25.7 Kinetic modeling of microRNA regulation
486(4)
25.7.1 A basic model of microRNA-mediated regulation
487(1)
25.7.2 Estimating fold-changes of mRNA and proteins in microRNA transfection experiments
488(1)
25.7.3 The influence of protein and mRNA stability on microRNA function
489(1)
25.7.4 Microrna Efficacy Depends On Target Abundance
489(1)
25.7.5 Reconstructing microRNA kinetics
489(1)
25.8 Discussion
490(1)
References
491(4)
Index 495
Michael Stumpf, Theoretical Systems Biology at Imperial College London

David Balding, Statistical Genetics in the Institute of Genetics at University College London

Mark Girolami, Department of Computing Science and the Department of Statistics