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Principles of Computational Cell Biology: From Protein Complexes to Cellular Networks 2nd edition [Minkštas viršelis]

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(Universität des Saarlandes, Saarbrücke)
  • Formatas: Paperback / softback, 464 pages, aukštis x plotis x storis: 241x168x23 mm, weight: 839 g
  • Išleidimo metai: 13-Feb-2019
  • Leidėjas: Blackwell Verlag GmbH
  • ISBN-10: 3527333584
  • ISBN-13: 9783527333585
Kitos knygos pagal šią temą:
Principles of Computational Cell Biology: From Protein Complexes to Cellular Networks 2nd editionDidesnis paveikslėlis
  • Formatas: Paperback / softback, 464 pages, aukštis x plotis x storis: 241x168x23 mm, weight: 839 g
  • Išleidimo metai: 13-Feb-2019
  • Leidėjas: Blackwell Verlag GmbH
  • ISBN-10: 3527333584
  • ISBN-13: 9783527333585
Kitos knygos pagal šią temą:
Pastovi nuoroda: https://www.kriso.lt/db/9783527333585.html
Raktažodžiai:
Computational cell biology courses are increasingly obligatory for biology students around the world but of course also a must for mathematics and informatics students specializing in bioinformatics. This book, now in its second edition is geared towards both audiences. The author, Volkhard Helms, has, in addition to extensive teaching experience, a strong background in biology and informatics and knows exactly what the key points are in making the book accessible for students while still conveying in depth knowledge of the subject.About 50% of new content has been added for the new edition. Much more room is now given to statistical methods, and several new chapters address protein-DNA interactions, epigenetic modifications, and microRNAs.
Preface of the First Edition xv
Preface of the Second Edition xvii
1 Networks in Biological Cells 1(20)
1.1 Some Basics About Networks
1(3)
1.1.1 Random Networks
2(1)
1.1.2 Small-World Phenomenon
2(1)
1.1.3 Scale-Free Networks
3(1)
1.2 Biological Background
4(4)
1.2.1 Transcriptional Regulation
5(1)
1.2.2 Cellular Components
5(2)
1.2.3 Spatial Organization of Eukaryotic Cells into Compartments
7(1)
1.2.4 Considered Organisms
8(1)
1.3 Cellular Pathways
8(4)
1.3.1 Biochemical Pathways
8(3)
1.3.2 Enzymatic Reactions
11(1)
1.3.3 Signal Transduction
11(1)
1.3.4 Cell Cycle
12(1)
1.4 Ontologies and Databases
12(5)
1.4.1 Ontologies
12(1)
1.4.2 Gene Ontology
13(1)
1.4.3 Kyoto Encyclopedia of Genes and Genomes
13(1)
1.4.4 Reactome
13(1)
1.4.5 Brenda
14(1)
1.4.6 DAVID
14(1)
1.4.7 Protein Data Bank
15(1)
1.4.8 Systems Biology Markup Language
15(2)
1.5 Methods for Cellular Modeling
17(1)
1.6 Summary
17(1)
1.7 Problems
17(1)
Bibliography
18(3)
2 Structures of Protein Complexes and Subcellular Structures 21(42)
2.1 Examples of Protein Complexes
22(6)
2.1.1 Principles of Protein-Protein Interactions
24(3)
2.1.2 Categories of Protein Complexes
27(1)
2.2 Complexome: The Ensemble of Protein Complexes
28(3)
2.2.1 Complexome of Saccharomyces cerevisiae
28(2)
2.2.2 Bacterial Protein Complexomes
30(1)
2.2.3 Complexome of Human
31(1)
2.3 Experimental Determination of Three-Dimensional Structures of Protein Complexes
31(7)
2.3.1 X-ray Crystallography
32(2)
2.3.2 NMR
34(1)
2.3.3 Electron Crystallography/Electron Microscopy
34(1)
2.3.4 Cryo-EM
34(1)
2.3.5 Immunoelectron Microscopy
35(1)
2.3.6 Fluorescence Resonance Energy Transfer
35(1)
2.3.7 Mass Spectroscopy
36(2)
2.4 Density Fitting
38(2)
2.4.1 Correlation-Based Density Fitting
38(2)
2.5 Fourier Transformation
40(4)
2.5.1 Fourier Series
40(1)
2.5.2 Continuous Fourier Transform
41(1)
2.5.3 Discrete Fourier Transform
41(1)
2.5.4 Convolution Theorem
41(1)
2.5.5 Fast Fourier Transformation
42(2)
2.6 Advanced Density Fitting
44(2)
2.6.1 Laplacian Filter
45(1)
2.7 FFT Protein-Protein Docking
46(2)
2.8 Protein-Protein Docking Using Geometric Hashing
48(1)
2.9 Prediction of Assemblies from Pairwise Docking
49(4)
2.9.1 CombDock
49(3)
2.9.2 Multi-LZerD
52(1)
2.9.3 3D-MOSAIC
52(1)
2.10 Electron Tomography
53(3)
2.10.1 Reconstruction of Phantom Cell
55(1)
2.10.2 Protein Complexes in Mycoplasma pneumoniae
55(1)
2.11 Summary
56(1)
2.12 Problems
57(3)
2.12.1 Mapping of Crystal Structures into EM Maps
57(3)
Bibliography
60(3)
3 Analysis of Protein-Protein Binding 63(26)
3.1 Modeling by Homology
63(3)
3.2 Properties of Protein-Protein Interfaces
66(9)
3.2.1 Size and Shape
66(2)
3.2.2 Composition of Binding Interfaces
68(1)
3.2.3 Hot Spots
69(2)
3.2.4 Physicochemical Properties of Protein Interfaces
71(1)
3.2.5 Predicting Binding Affinities of Protein-Protein Complexes
72(1)
3.2.6 Forces Important for Biomolecular Association
73(2)
3.3 Predicting Protein-Protein Interactions
75(11)
3.3.1 Pairing Propensities
75(3)
3.3.2 Statistical Potentials for Amino Acid Pairs
78(1)
3.3.3 Conservation at Protein Interfaces
79(4)
3.3.4 Correlated Mutations at Protein Interfaces
83(3)
3.4 Summary
86(1)
3.5 Problems
86(1)
Bibliography
86(3)
4 Algorithms on Mathematical Graphs 89(22)
4.1 Primer on Mathematical Graphs
89(1)
4.2 A Few Words About Algorithms and Computer Programs
90(3)
4.2.1 Implementation of Algorithms
91(1)
4.2.2 Classes of Algorithms
92(1)
4.3 Data Structures for Graphs
93(2)
4.4 Dijkstra's Algorithm
95(6)
4.4.1 Description of the Algorithm
96(4)
4.4.2 Pseudocode
100(1)
4.4.3 Running Time
101(1)
4.5 Minimum Spanning Tree
101(1)
4.5.1 Kruskal's Algorithm
102(1)
4.6 Graph Drawing
102(2)
4.7 Summary
104(1)
4.8 Problems
105(5)
4.8.1 Force Directed Layout of Graphs
107(3)
Bibliography
110(1)
5 Protein-Protein Interaction Networks - Pairwise Connectivity 111(30)
5.1 Experimental High-Throughput Methods for Detecting Protein-Protein Interactions
111(9)
5.1.1 Gel Electrophoresis
112(1)
5.1.2 Two-Dimensional Gel Electrophoresis
112(1)
5.1.3 Affinity Chromatography
113(1)
5.1.4 Yeast Two-hybrid Screening
114(1)
5.1.5 Synthetic Lethality
115(1)
5.1.6 Gene Coexpression
116(1)
5.1.7 Databases for Interaction Networks
116(1)
5.1.8 Overlap of Interactions
116(2)
5.1.9 Criteria to Judge the Reliability of Interaction Data
118(2)
5.2 Bioinformatic Prediction of Protein-Protein Interactions
120(4)
5.2.1 Analysis of Gene Order
121(1)
5.2.2 Phylogenetic Profiling/Coevolutionary Profiling
121(3)
5.2.2.1 Coevolution
122(2)
5.3 Bayesian Networks for Judging the Accuracy of Interactions
124(7)
5.3.1 Bayes' Theorem
125(1)
5.3.2 Bayesian Network
125(1)
5.3.3 Application of Bayesian Networks to Protein-Protein Interaction Data
126(5)
5.3.3.1 Measurement of Reliability "Likelihood Ratio"
127(1)
5.3.3.2 Prior and Posterior Odds
127(1)
5.3.3.3 A Worked Example: Parameters of the Naive Bayesian Network for Essentiality
128(1)
5.3.3.4 Fully Connected Experimental Network
129(2)
5.4 Protein Interaction Networks
131(1)
5.4.1 Protein Interaction Network of Saccharomyces cerevisiae
131(1)
5.4.2 Protein Interaction Network of Escherichia coli
131(1)
5.4.3 Protein Interaction Network of Human
132(1)
5.5 Protein Domain Networks
132(3)
5.6 Summary
135(1)
5.7 Problems
136(2)
5.7.1 Bayesian Analysis of (Fake) Protein Complexes
136(2)
Bibliography
138(3)
6 Protein-Protein Interaction Networks - Structural Hierarchies 141(40)
6.1 Protein Interaction Graph Networks
141(4)
6.1.1 Degree Distribution
141(2)
6.1.2 Clustering Coefficient
143(2)
6.2 Finding Cliques
145(1)
6.3 Random Graphs
146(1)
6.4 Scale-Free Graphs
147(2)
6.5 Detecting Communities in Networks
149(6)
6.5.1 Divisive Algorithms for Mapping onto Tree
153(2)
6.6 Modular Decomposition
155(6)
6.6.1 Modular Decomposition of Graphs
157(4)
6.7 Identification of Protein Complexes
161(4)
6.7.1 MCODE
161(1)
6.7.2 ClusterONE
162(1)
6.7.3 DACO
163(1)
6.7.4 Analysis of Target Gene Coexpression
164(1)
6.8 Network Growth Mechanisms
165(4)
6.9 Summary
169(1)
6.10 Problems
169(9)
Bibliography
178(3)
7 Protein-DNA Interactions 181(16)
7.1 Transcription Factors
181(2)
7.2 Transcription Factor-Binding Sites
183(1)
7.3 Experimental Detection of TFBS
183(4)
7.3.1 Electrophoretic Mobility Shift Assay
183(1)
7.3.2 DNAse Footprinting
184(1)
7.3.3 Protein-Binding Microarrays
185(2)
7.3.4 Chromatin Immunoprecipitation Assays
187(1)
7.4 Position-Specific Scoring Matrices
187(2)
7.5 Binding Free Energy Models
189(2)
7.6 Cis-Regulatory Motifs
191(1)
7.6.1 DACO Algorithm
192(1)
7.7 Relating Gene Expression to Binding of Transcription Factors
192(2)
7.8 Summary
194(1)
7.9 Problems
194(1)
Bibliography
195(2)
8 Gene Expression and Protein Synthesis 197(30)
8.1 Regulation of Gene Transcription at Promoters
197(1)
8.2 Experimental Analysis of Gene Expression
198(3)
8.2.1 Real-time Polymerase Chain Reaction
199(1)
8.2.2 Microarray Analysis
199(2)
8.2.3 RNA-seq
201(1)
8.3 Statistics Primer
201(6)
8.3.1 t-Test
203(1)
8.3.2 z-Score
203(1)
8.3.3 Fisher's Exact Test
203(2)
8.3.4 Mann-Whitney-Wilcoxon Rank Sum Tests
205(1)
8.3.5 Kolmogorov-Smirnov Test
206(1)
8.3.6 Hypergeometric Test
206(1)
8.3.7 Multiple Testing Correction
207(1)
8.4 Preprocessing of Data
207(2)
8.4.1 Removal of Outlier Genes
207(1)
8.4.2 Quantile Normalization
208(1)
8.4.3 Log Transformation
208(1)
8.5 Differential Expression Analysis
209(5)
8.5.1 Volcano Plot
210(1)
8.5.2 SAM Analysis of Microarray Data
210(2)
8.5.3 Differential Expression Analysis of RNA-seq Data
212(2)
8.5.3.1 Negative Binomial Distribution
213(1)
8.5.3.2 DESeq
213(1)
8.6 Gene Ontology
214(3)
8.6.1 Functional Enrichment
216(1)
8.7 Similarity of GO Terms
217(1)
8.8 Translation of Proteins
217(2)
8.8.1 Transcription and Translation Dynamics
218(1)
8.9 Summary
219(1)
8.10 Problems
220(4)
Bibliography
224(3)
9 Gene Regulatory Networks 227(30)
9.1 Gene Regulatory Networks (GRNs)
228(3)
9.1.1 Gene Regulatory Network of E. coli
228(3)
9.1.2 Gene Regulatory Network of S. cerevisiae
231(1)
9.2 Graph Theoretical Models
231(3)
9.2.1 Coexpression Networks
232(1)
9.2.2 Bayesian Networks
233(1)
9.3 Dynamic Models
234(4)
9.3.1 Boolean Networks
234(1)
9.3.2 Reverse Engineering Boolean Networks
235(1)
9.3.3 Differential Equations Models
236(2)
9.4 DREAM: Dialogue on Reverse Engineering Assessment and Methods
238(6)
9.4.1 Input Function
239(1)
9.4.2 YAYG Approach in DREAM3 Contest
240(4)
9.5 Regulatory Motifs
244(3)
9.5.1 Feed-forward Loop (FFL)
245(1)
9.5.2 SIM
245(1)
9.5.3 Densely Overlapping Region (DOR)
246(1)
9.6 Algorithms on Gene Regulatory Networks
247(3)
9.6.1 Key-pathway Miner Algorithm
247(1)
9.6.2 Identifying Sets of Dominating Nodes
248(1)
9.6.3 Minimum Dominating Set
249(1)
9.6.4 Minimum Connected Dominating Set
249(1)
9.7 Summary
250(1)
9.8 Problems
251(3)
Bibliography
254(3)
10 Regulatory Noncoding RNA 257(16)
10.1 Introduction to RNAs
257(2)
10.2 Elements of RNA Interference: siRNAs and miRNAs
259(2)
10.3 miRNA Targets
261(3)
10.4 Predicting miRNA Targets
264(1)
10.5 Role of TFs and miRNAs in Gene-Regulatory Networks
264(2)
10.6 Constructing TF/miRNA Coregulatory Networks
266(4)
10.6.1 TFmiR Web Service
267(6)
10.6.1.1 Construction of Candidate TF-miRNA-Gene FFLs
268(1)
10.6.1.2 Case Study
269(1)
10.7 Summary
270(1)
Bibliography
270(3)
11 Computational Epigenetics 273(30)
11.1 Epigenetic Modifications
273(8)
11.1.1 DNA Methylation
273(4)
11.1.1.1 CpG Islands
276(1)
11.1.2 Histone Marks
277(1)
11.1.3 Chromatin-Regulating Enzymes
278(1)
11.1.4 Measuring DNA Methylation Levels and Histone Marks Experimentally
279(2)
11.2 Working with Epigenetic Data
281(5)
11.2.1 Processing of DNA Methylation Data
281(1)
11.2.1.1 Imputation of Missing Values
281(1)
11.2.1.2 Smoothing of DNA Methylation Data
281(1)
11.2.2 Differential Methylation Analysis
282(1)
11.2.3 Comethylation Analysis
283(2)
11.2.4 Working with Data on Histone Marks
285(1)
11.3 Chromatin States
286(6)
11.3.1 Measuring Chromatin States
286(1)
11.3.2 Connecting Epigenetic Marks and Gene Expression by Linear Models
287(1)
11.3.3 Markov Models and Hidden Markov Models
288(2)
11.3.4 Architecture of a Hidden Markov Model
290(1)
11.3.5 Elements of an HMM
291(1)
11.4 The Role of Epigenetics in Cellular Differentiation and Reprogramming
292(3)
11.4.1 Short History of Stem Cell Research
293(1)
11.4.2 Developmental Gene Regulatory Networks
293(2)
11.5 The Role of Epigenetics in Cancer and Complex Diseases
295(1)
11.6 Summary
296(1)
11.7 Problems
296(5)
Bibliography
301(2)
12 Metabolic Networks 303(46)
12.1 Introduction
303(3)
12.2 Resources on Metabolic Network Representations
306(2)
12.3 Stoichiometric Matrix
308(1)
12.4 Linear Algebra Primer
309(5)
12.4.1 Matrices: Definitions and Notations
309(1)
12.4.2 Adding, Subtracting, and Multiplying Matrices
310(1)
12.4.3 Linear Transformations, Ranks, and Transpose
311(1)
12.4.4 Square Matrices and Matrix Inversion
311(1)
12.4.5 Eigenvalues of Matrices
312(1)
12.4.6 Systems of Linear Equations
313(1)
12.5 Flux Balance Analysis
314(5)
12.5.1 Gene Knockouts: MOMA Algorithm
316(2)
12.5.2 OptKnock Algorithm
318(1)
12.6 Double Description Method
319(5)
12.7 Extreme Pathways and Elementary Modes
324(8)
12.7.1 Steps of the Extreme Pathway Algorithm
324(4)
12.7.2 Analysis of Extreme Pathways
328(1)
12.7.3 Elementary Flux Modes
329(2)
12.7.4 Pruning Metabolic Networks: NetworkReducer
331(1)
12.8 Minimal Cut Sets
332(7)
12.8.1 Applications of Minimal Cut Sets
337(2)
12.9 High-Flux Backbone
339(2)
12.10 Summary
341(1)
12.11 Problems
341(5)
12.11.1 Static Network Properties: Pathways
341(5)
Bibliography
346(3)
13 Kinetic Modeling of Cellular Processes 349(26)
13.1 Biological Oscillators
349(1)
13.2 Circadian Clocks
350(3)
13.2.1 Role of Post-transcriptional Modifications
352(1)
13.3 Ordinary Differential Equation Models
353(3)
13.3.1 Examples for ODEs
354(2)
13.4 Modeling Cellular Feedback Loops by ODEs
356(10)
13.4.1 Protein Synthesis and Degradation: Linear Response
356(1)
13.4.2 Phosphorylation/Dephosphorylation - Hyperbolic Response
357(2)
13.4.3 Phosphorylation/Dephosphorylation - Buzzer
359(1)
13.4.4 Perfect Adaptation - Sniffer
360(1)
13.4.5 Positive Feedback - One-Way Switch
361(1)
13.4.6 Mutual Inhibition - Toggle Switch
362(1)
13.4.7 Negative Feedback - Homeostasis
362(2)
13.4.8 Negative Feedback: Oscillatory Response
364(1)
13.4.9 Cell Cycle Control System
365(1)
13.5 Partial Differential Equations
366(3)
13.5.1 Spatial Gradients of Signaling Activities
368(1)
13.5.2 Reaction-Diffusion Systems
368(1)
13.6 Dynamic Phosphorylation of Proteins
369(1)
13.7 Summary
370(2)
13.8 Problems
372(1)
Bibliography
373(2)
14 Stochastic Processes in Biological Cells 375(34)
14.1 Stochastic Processes
375(3)
14.1.1 Binomial Distribution
376(1)
14.1.2 Poisson Process
377(1)
14.1.3 Master Equation
377(1)
14.2 Dynamic Monte Carlo (Gillespie Algorithm)
378(2)
14.2.1 Basic Outline of the Gillespie Method
379(1)
14.3 Stochastic Effects in Gene Transcription
380(5)
14.3.1 Expression of a Single Gene
380(1)
14.3.2 Toggle Switch
381(4)
14.4 Stochastic Modeling of a Small Molecular Network
385(7)
14.4.1 Model System: Bacterial Photosynthesis
385(1)
14.4.2 Pools-and-Proteins Model
386(1)
14.4.3 Evaluating the Binding and Unbinding Kinetics
387(2)
14.4.4 Pools of the Chromatophore Vesicle
389(1)
14.4.5 Steady-State Regimes of the Vesicle
389(3)
14.5 Parameter Optimization with Genetic Algorithm
392(3)
14.6 Protein-Protein Association
395(1)
14.7 Brownian Dynamics Simulations
396(2)
14.8 Summary
398(2)
14.9 Problems
400(7)
14.9.1 Dynamic Simulations of Networks
400(7)
Bibliography
407(2)
15 Integrated Cellular Networks 409(18)
15.1 Response of Gene Regulatory Network to Outside Stimuli
410(2)
15.2 Whole-Cell Model of Mycoplasma genitalium
412(4)
15.3 Architecture of the Nuclear Pore Complex
416(1)
15.4 Integrative Differential Gene Regulatory Network for Breast Cancer Identified Putative Cancer Driver Genes
416(5)
15.5 Particle Simulations
421(2)
15.6 Summary
423(1)
Bibliography
424(3)
16 Outlook 427(2)
Index 429
Volkhard Helms, PhD is a full professor of bioinformatics at Saarland University. He has authored more than 100 scientific publications and received the EMBO Young Investigator Award in 2001.