Introduction to biological networks:
"Preface In the 1940s and 1950s, biology was transformed by physicists and physical chemists, who employed simple yet powerful concepts and engaged the powers of genetics to infer mechanisms of biological processes. The biological sciences borrowed from the physical sciences the notion of build...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boca Raton [u.a.]
CRC Press
2013
|
| Schriftenreihe: | Chapman & Hall/CRC mathematical and computational biology series
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Zusammenfassung: | "Preface In the 1940s and 1950s, biology was transformed by physicists and physical chemists, who employed simple yet powerful concepts and engaged the powers of genetics to infer mechanisms of biological processes. The biological sciences borrowed from the physical sciences the notion of building intuitive, testable, and physically realistic models by reducing the complexity of biological systems to the components essential for studying the problem at hand. Molecular biology was born. A similar migration of physical scientists and of methods of physical sciences into biology has been occurring in the decade following the complete sequencing of the human genome, whose discrete character and similarity to natural language has additionally facilitated the application of the techniques of modern computer science. Furthermore, the vast amount of genomic data spawned by the sequencing projects has led to the development and application of statistical methods for making sense of this data. The sheer amount of data at the genome scale that is available to us today begs for descriptions that go beyond simple models of the function of a single gene to embrace a systemlevel understanding of large sets of genes functioning in unison. It is no longer sufficient to understand how a single gene mutation causes a change in its product's biochemical function, although this is in many cases still an important problem. It is now possible to address how the consequences of a mutation might reverberate through the interconnected system of genes and their products within the cell"-- |
| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | XIII, 321 S. Ill., graph. Darst. |
| ISBN: | 9781584884637 |
Internformat
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| 100 | 1 | |a Raval, Alpan |e Verfasser |0 (DE-588)1017103607 |4 aut | |
| 245 | 1 | 0 | |a Introduction to biological networks |c Alpan Raval ; Animesh Ray |
| 264 | 1 | |a Boca Raton [u.a.] |b CRC Press |c 2013 | |
| 300 | |a XIII, 321 S. |b Ill., graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Chapman & Hall/CRC mathematical and computational biology series | |
| 500 | |a Includes bibliographical references and index | ||
| 520 | 1 | |a "Preface In the 1940s and 1950s, biology was transformed by physicists and physical chemists, who employed simple yet powerful concepts and engaged the powers of genetics to infer mechanisms of biological processes. The biological sciences borrowed from the physical sciences the notion of building intuitive, testable, and physically realistic models by reducing the complexity of biological systems to the components essential for studying the problem at hand. Molecular biology was born. A similar migration of physical scientists and of methods of physical sciences into biology has been occurring in the decade following the complete sequencing of the human genome, whose discrete character and similarity to natural language has additionally facilitated the application of the techniques of modern computer science. Furthermore, the vast amount of genomic data spawned by the sequencing projects has led to the development and application of statistical methods for making sense of this data. The sheer amount of data at the genome scale that is available to us today begs for descriptions that go beyond simple models of the function of a single gene to embrace a systemlevel understanding of large sets of genes functioning in unison. It is no longer sufficient to understand how a single gene mutation causes a change in its product's biochemical function, although this is in many cases still an important problem. It is now possible to address how the consequences of a mutation might reverberate through the interconnected system of genes and their products within the cell"-- | |
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| 999 | |a oai:aleph.bib-bvb.de:BVB01-026040789 | ||
Datensatz im Suchindex
| _version_ | 1804150429270933504 |
|---|---|
| adam_text | Contents
Preface
xi
1
The Living Interactome
1
1.1
Biological Function Depends on Context
........ 9
1.2
The Nature of Interactions
................ 12
1.2.1
Protein Protein Interactions
........... 12
1.2.2
Protein
DNA
Interactions
............. 14
1.2.3
Genetic Interactions
................ 14
1.3
Network as a Metaphor in Biology
............ 20
1.4
Metabolic Networks
.................... 21
1.5
Robustness at the Network Level
............. 28
2
Experimental Inference of Interactions
33
2.1
Direct versus Indirect Inference of Interactions
..... 33
2.2
Physical versus Genetic Interactions
........... 36
2.3
The Two-Hybrid Interaction
............... 40
2.4
Affinity Co-purification
.................. 45
2.5
DNA-Protein Interactions
................. 49
2.6
Classification and Interpretation of Genetic Interactions
51
2.7
Inference of Genetic Interactions
............. 56
2.7.1
Finding Genetic Interactions with Inhibitory
RNA
......................... 58
2.7.2
Genetic Interactions in Organisms with Multiple
Cell Types
..................... 61
3
Prediction of Physical Interactions
65
3.1
Entropy and Information
................. 66
3.1.1
Interpretations of Mutual Information
...... 70
3.2
Boolean Networks REVEAL Regulatory Interactions
. 72
3.2.1
The REVEAL Algorithm
............. 73
3.3
Variants of the REVEAL Method
............ 76
3.4
Dynamic Bayesian Networks
............... 80
3.4.1
Scoring and Network Inference with DBNs
... 81
3.4.2
Extensions, Variants, and Applications of DBNs
84
3.5
Predicting Protein-Protein Interactions
......... 89
3.5.1
Structural Features
................. 90
3.5.2
Gene Neighborhood Analysis
........... 93
3.5.3
Phylogenetic Profiles
................ 94
3.5.4
Gene Fusion
..................... 96
VII
Vlil
3.5.5
History
of
Correlatoci
Mutations
......... 97
3.5.6
Existence of Interologs
............. 98
3.G Data Integration to Improve Prediction Performance
. . 99
3.G.1 Bayosian Networks and Feature Integration
. . . 101
Metabolic Networks and Genetic Interactions
107
4.1
Optimal Metabolic States and Flux Balance Analysis
. 109
4.1.1
Flux Balance Analysis: Basic Assumptions
. ...
Ill
4.1.2
Flux Balance Analysis: The Objective Function
. 115
4.1.3
Optimization Problem in Flux Balance1 Analysis
Ilo
4.1.4
FBA Enables the Genotype Phenotype Connec¬
tion
.........................117
4.2
Minimization of Metabolic Adjustment and
Geno
Dele¬
tions
............................119
4.3
Predicting Genetic Interactions among Metabolic Genes
120
4.3.1
Computational Simulation of Mutation Effects
. 124
4.4
General Methods for Predicting Genetic Interactions
. . 127
4.4.1
^hop Properties and Genetic Interactions
. . . 131
4.4.2
Predicting Genetic Interactions from Protein In¬
teraction Networks
.................133
4.5
Approaches for Integrated Prediction of Networks
... 133
Testing Inferred Networks
139
5.1
Co-expression as a Test of Physical Interaction
..... 139
5.2
An Experimental Test of Predicted Protein Protein In¬
teractions
.......................... 141
5.3
Combining Protein Interactions Discovered by High-
Throughput Experiments
................. 145
5.3.1
Polya s Proofreaders
................ 146
5.3.2
Integration of Independent High-Throughput
Datasets
....................... 147
5.4
From Curated Protein-Protein Interactions to Protein
Complexes
......................... 151
5.4.1
Complex Identification Using Markov Clustering
152
5.4.2
Complex Identification Using Supervised Clus¬
tering
........................ 156
5.5
Testing DNA-Protein Interactions
............ 159
5.6
Congruence of Biological Function among Interacting
Partners
.......................... 161
5.7
Testing Genetic Interactions
............... 165
5.8
Structure-Based Tests of Interaction
........... 168
Small Model Networks
171
6.1
Life Cycle of the Lambda Phage
.............172
6.2
Interaction Network in the Lysis Lysogeny Decision
. . 175
6.2.1
Regulatory Loops in the Lysis-Lysogeny Deci¬
sion and Maintenance
...............175
їх
6.2.2
Tlie
Lysis Lysogeny
Switch
............ 178
6.2.3
Stability
of
t
lio
Propliage
............. 179
6.2.4
Induction
of the Lytic Cycle
............ 181
6.3
Recruitment of Proteins as a Theme in Regulatory Net¬
works
............................ 182
6.3.1
Regulatory Mechanisms Other than Passive Re¬
cruitment
...................... 182
6.4
The Network of Cell Cycle Regulation
.......... 183
Tractable Models of Large Networks
193
7.1
Models of Regular Networks
............... 195
7.1.1
Path Length in a Regular Network
........ 197
7.1.2
Clustering Coefficient of a Regular Network
. . . 197
7.2
Random Network Models
................. 199
7.2.1
Random Networks with Specified Degree Distri¬
butions
....................... 201
7.2.2
Clustering Coefficient of a General Random Net¬
work
......................... 202
7.2.3
Path Length in a General Random Network
. . . 204
7.2.4
The Configuration Model and Its Modifications
. 206
7.2.5
Erdős Renyi
Random Networks
.......... 209
7.2.6
Small-World Networks
............... 209
7.3
Evolving Networks by Preferential Attachment
..... 211
7.4
Evolving Networks Based on Gene Duplication
..... 216
7.4.1
Network Growth via Node Duplication Alone
. . 217
7.4.2
A Duplication-Mutation-Complementation Model
of Network Growth
................. 219
7.4.3
A Generalized Model of Duplication Followed by
Divergence
..................... 222
7.5
Reconstruction of Ancient Networks from Modern Ones
226
7.5.1
Network Reconstruction Based on the DMC
Model
........................ 227
7.6
Large-Scale Biophysics-Inspired Models for Protein In¬
teraction Networks
..................... 229
7.6.1
The MpK Model
.................. 229
7.6.2
A Configuration-Like Model Based on Protein
Stickiness
..................... 234
7.6.3
Growing Crystals on Evolutionary Timescales
. . 235
Network Modularity and Robustness
237
8.1
Modularity Implies Robustness in the
Drosophila
Seg¬
ment Polarity Network
.................. 238
8.2
Topological and Functional Aspects of Network Modu¬
larity
............................ 246
8.2.1
Newman-Girvan Modularity
........... 248
8.2.2
Functional Modules from Expression Data
.... 253
χ
8.3
Robustness Implications of Topology of Biological Net¬
works
............................ 255
8.4
Robustness and Network Dynamics
........... 257
8.5
Network Robustness by Rewiring
............ 261
9
Networks and Disease
265
9.1
Disease Loci Identification and Mapping
........ 267
9.2
Network as a Paradigm for Linking Diseases and Disease
Genes
............................ 271
9.3
Network-Based Prediction of Disease Genes
....... 276
9.3.1
Predicting Disease Genes from Known Disease
Genes
........................ 276
9.3.2
Predicting Disease Genes
ab initio........ 281
9.4
Network Analysis of Disease Mechanisms and Disease
Response
.......................... 283
9.5
Network-Based Prediction of Disease Comorbidity
. . . 288
9.6
Cancer and Synthetic Lethality
.............. 291
References
295
Index
313
|
| any_adam_object | 1 |
| author | Raval, Alpan Ray, Animesh C. 1936- |
| author_GND | (DE-588)1017103607 (DE-588)171298802 |
| author_facet | Raval, Alpan Ray, Animesh C. 1936- |
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| bvnumber | BV041063726 |
| classification_rvk | ST 690 WC 7700 |
| ctrlnum | (OCoLC)854721356 (DE-599)GBV736479554 |
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| format | Book |
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| id | DE-604.BV041063726 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T00:38:47Z |
| institution | BVB |
| isbn | 9781584884637 |
| language | English |
| lccn | 2013003654 |
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| spelling | Raval, Alpan Verfasser (DE-588)1017103607 aut Introduction to biological networks Alpan Raval ; Animesh Ray Boca Raton [u.a.] CRC Press 2013 XIII, 321 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Chapman & Hall/CRC mathematical and computational biology series Includes bibliographical references and index "Preface In the 1940s and 1950s, biology was transformed by physicists and physical chemists, who employed simple yet powerful concepts and engaged the powers of genetics to infer mechanisms of biological processes. The biological sciences borrowed from the physical sciences the notion of building intuitive, testable, and physically realistic models by reducing the complexity of biological systems to the components essential for studying the problem at hand. Molecular biology was born. A similar migration of physical scientists and of methods of physical sciences into biology has been occurring in the decade following the complete sequencing of the human genome, whose discrete character and similarity to natural language has additionally facilitated the application of the techniques of modern computer science. Furthermore, the vast amount of genomic data spawned by the sequencing projects has led to the development and application of statistical methods for making sense of this data. The sheer amount of data at the genome scale that is available to us today begs for descriptions that go beyond simple models of the function of a single gene to embrace a systemlevel understanding of large sets of genes functioning in unison. It is no longer sufficient to understand how a single gene mutation causes a change in its product's biochemical function, although this is in many cases still an important problem. It is now possible to address how the consequences of a mutation might reverberate through the interconnected system of genes and their products within the cell"-- Bioinformatik (DE-588)4611085-9 gnd rswk-swf Netzwerk (DE-588)4171529-9 gnd rswk-swf Bioinformatik (DE-588)4611085-9 s Netzwerk (DE-588)4171529-9 s DE-604 Ray, Animesh C. 1936- Verfasser (DE-588)171298802 aut Digitalisierung UB Bamberg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026040789&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Raval, Alpan Ray, Animesh C. 1936- Introduction to biological networks Bioinformatik (DE-588)4611085-9 gnd Netzwerk (DE-588)4171529-9 gnd |
| subject_GND | (DE-588)4611085-9 (DE-588)4171529-9 |
| title | Introduction to biological networks |
| title_auth | Introduction to biological networks |
| title_exact_search | Introduction to biological networks |
| title_full | Introduction to biological networks Alpan Raval ; Animesh Ray |
| title_fullStr | Introduction to biological networks Alpan Raval ; Animesh Ray |
| title_full_unstemmed | Introduction to biological networks Alpan Raval ; Animesh Ray |
| title_short | Introduction to biological networks |
| title_sort | introduction to biological networks |
| topic | Bioinformatik (DE-588)4611085-9 gnd Netzwerk (DE-588)4171529-9 gnd |
| topic_facet | Bioinformatik Netzwerk |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026040789&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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