Principles of computational cell biology: from protein complexes to cellular networks
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Weinheim
WILEY-VCH
2008
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| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Beschreibung für Leser Inhaltsverzeichnis |
| Beschreibung: | XII, 277 S. Ill., graph. Darst. 240 mm x 170 mm |
| ISBN: | 9783527315550 3527315551 |
Internformat
MARC
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| 100 | 1 | |a Helms, Volkhard |e Verfasser |0 (DE-588)135851246 |4 aut | |
| 245 | 1 | 0 | |a Principles of computational cell biology |b from protein complexes to cellular networks |c Volkhard Helms |
| 264 | 1 | |a Weinheim |b WILEY-VCH |c 2008 | |
| 300 | |a XII, 277 S. |b Ill., graph. Darst. |c 240 mm x 170 mm | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 650 | 4 | |a Datenverarbeitung | |
| 650 | 4 | |a Computational Biology | |
| 650 | 4 | |a Cytology |x Computer simulation | |
| 650 | 4 | |a Cytology |x Data processing | |
| 650 | 4 | |a Models, Biological | |
| 650 | 0 | 7 | |a Cytologie |0 (DE-588)4070177-3 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Mathematisches Modell |0 (DE-588)4114528-8 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Bioinformatik |0 (DE-588)4611085-9 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Cytologie |0 (DE-588)4070177-3 |D s |
| 689 | 0 | 1 | |a Bioinformatik |0 (DE-588)4611085-9 |D s |
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| 856 | 4 | |u http://deposit.dnb.de/cgi-bin/dokserv?id=3036965&prov=M&dok_var=1&dok_ext=htm |3 Beschreibung für Leser | |
| 856 | 4 | 2 | |m Digitalisierung UB Regensburg |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016404871&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-016404871 | |
Datensatz im Suchindex
| _version_ | 1805090169820282880 |
|---|---|
| adam_text |
Contents
Prefece
Xi
1 Networks
¡η
Biological Cells
2
1.1
Some Basics about Networks
1
1.1.1
Random Networks
2
1.1.2
Small-World Phenomenon
2
1.1.3
Scale-Free Network Model
3
1.2
Biological Background
4
1.2.1
Cellular Components
б
1.2.2
Spatial Organization ofEukaryotic Cells-Compartments
7
1.2.3
Cellular Organisms
7
1.3
Cellular Pathways
7
1.3.1
Biochemical Pathways
7
1.3.2
Enzymatic Reactions
8
1.3.3
Signal Txansduction
11
1.3.4
Cell Cycle
11
1.4
Ontologies and Databases
12
1.4.1
Ontologies
12
1.4.2
Systems Biology Markup Language
12
1.4.3
KEGG
13
1.4.4
Brenda
13
1.5
Methods in
CeîMar
Modeling
14
2
Algorithms on Mathematical Graphs
17
2.1
Primer on Mathematical Graphs
17
2.2
A Few Words about Algorithms and Computer Programs
18
2.2.1
Implementation of Algorithms
19
2.2.2
Classes of
Algoriäuns 20
2.3
Data Structures for Graphs
21
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Ш
Bïöfcgy
-
їда» Ргвйй»
Сетфхсѕ
te
СЛЉг
Jv'elusîfe.
Volkhard
Hetes
Gopjright
©
2O08
WIŁEWCH
Verlag
GmbH
&
Co. KGaA, Wdnhdm
ISBN:
978-3-517-31555-0
VI
Contents
2.4
Dijkstra's
Algorithm
23
2
АЛ.
Description of the Algorithm
25
2.4.2
Pseudocode
27
2.4.3
Running Time
29
2.5
Minimum Spanning Tree
29
2.5.1
Kruskal's Algorithm
31
2.6
Graph Drawing
31
3
Protein-Protein Interaction Networks
-
Pairwise Connectivity
39
3.1
Principles of Protein-Protein Interactions
39
3.2
Experimental High-Throughput Methods for Detecting
Protein-Protein Interactions
40
3.2.1
Gel Electrophoresis
41
3.2.2
Two-Dimensional Gel Electrophoresis
41
3.2.3
Affinity Chromatography
42
3.2.4
Yeast Two-Hybrid Screening
42
3.2.5
Synthetic Lethality
44
3.2.6
Gene Coexpression
44
3.2.7
Mass Spectroscopy
44
3.2.8
Databases for Interaction Networks
44
3.2.9
Overlap of Interactions
45
3.2.10
Criteria to Judge the Reliability of Interaction Data
47
3.2.11
How Many Protein-Protein Interactions can be Expected
in Yeast?
48
3.3
Bioinformaüc
Prediction of Protein-Protein Interactions
49
3.3.1
Analysis of Gene Order
49
3.3.2
Phylogenetic Profiling/Coevolutionary Profiling
50
3.3.3
Coevolution
51
3.4
Bayesian Networks for Judging the Accuracy of Interactions
52
3.4.1
Bayes'
Theorem
53
3.4.2
Bayesian Network
54
3.4.3
Application of Bayesian Networks to Protein-Protein
Interaction Data
55
3.4.3.1
Measurement of reliability "likelihood ratio"
55
3.4.3.2
Prior and posterior odds
56
3.4.3.3
A worked example: parameters of the
naïve
Bayesian network
for essentiality
57
3.4.3.4
Fully connected experimental network
57
3.5
Protein Domain Networks
59
4
Protein-Protein Interaction Networks
-
Structural Hierarchies
67
4.1
Protein Interaction Graph Networks
67
4.1.1
Degree Distribution
68
4.1.2
Clustering Coefficient
69
4.2
Finding Cliques
71
Contents
VII
4.3
Random Graphs
72
4.4
Scale-Free Graphs
73
4.5
Detecting Communities in Networks
75
4.5.1
Divisive Algorithms for Mapping onto Tree
78
4.6
Modular Decomposition
82
4.6.1
Modular Decomposition of Graphs
82
4.7
Network Growth Mechanisms
86
5
Gene Regulatory Networks
99
5.1
Regulation of Gene Transcription at Promoters
100
5.2
Gene Regulatory Networks
101
5.2.1
Gene Regulatory Network of
E. coli
101
5.3
Graph Theoretical Models
305
5.3.1
Coexpression Networks
105
5.3.2
Bayesian Networks
106
5.4
Dynamic Models
106
5.4.1
Boolean Networks
106
5.4.2
Reverse Engineering Boolean Networks
107
5.4.3
Differential Equations Models
110
5.5
Motifs 111
5.5.1
Feed-Forward Loop (FFL)
112
5.5.2
SIM Motif
112
5.5.3
Densely Overlapping Region (DOR)
112
6
Metabolic Networks
115
6.1
Introduction
115
6.2
Stoichiometric Matrix
118
6.3
Linear Algebra Primer
121
6.3.1
Matrices: Definitions and Notations
121
6.3.2
Adding, Subtracting and Multiplying Matrices
121
6.3.3
Linear Transformations, Ranks and Transpose
122
6.3.4
Square Matrices and Matrix Inversion
123
6.3.5
Eigenvalues of Matrices
124
6.3.6
System of Linear Equations
124
6.4
Flux Balance Analysis
125
6.5
Double Description Method
128
6.6
Extreme Pathways and Elementary Modes
133
6.6.1
Analysis of Eextreme Pathways
137
6.6.2
Elementary Flux Modes
139
6.7
Minimal Cut Sets
140
6.7.1
Applications of Minimal Cut Sets
344
6.8
High-Flux Backbone
146
7
Kinetic Modeling of Cellular Processes
155
7.1
Ordinary Differential Equation Models
355
VIH
Contents
7.1.1
Examples for ODEs
156
7.2
Modeling Cellular Feedback Loops by ODEs
158
7.2.1
Protein Synthesis and Degradation: Linear Response
159
7.2.2
Phosphorylation/Dephosphorylation
-
Hyperbolic Response
360
7.2.3
Phosphorylation/Dephosphorylation
-
Buzzer
162
7.2.4
Perfect Adaptation
-
Sniffer
163
7.2.5
Positive Feedback
-
One-Way Switch
164
7.2.6
Mutual Inhibition
-
Toggle Switch
165
7.2.7
Negative Feedback
-
Homeostasis
166
7.2.8
Negative Feedback: Oscillatory Response
166
7.2.9
Cell Cycle Control System
167
7.3
Partial Differential Equations
169
7.3.1
Spatial Gradients of Signaling Activities
170
7.4
Dynamic Monte Carlo (Gfflespie Algorithm)
172
7.4.1
Basic Outline of the Gillespie Method
173
7.5
Stochastic Modeling of a Small Molecular Network
173
7.5.1
Model System: Bacterial Photosynthesis
174
7.5.2
Pools-and-Proteins Model
176
7.5.3
Evaluating the Binding and Unbinding Kinetics
177
7.5.4
Pools of the Chromatophore Vesicle
178
7.5.5
Results for the Steady-State Regimes of the Vesicle
179
7.6
Parameter Optimization with Genetic Algorithms
182
8
Structures of Protein Complexes and Subcellular Structures
193
8.1
Examples of Protein Complexes
193
8.2
Complexeome
of S. cerevisiae
197
8.3
Experimental Determination of Three-dimensional Structures
of Protein Complexes
199
8.3.1
X-ray Crystallography
199
8.3.2
NMR
200
8.3.3
Electron Crystallography/Electron Microscopy
201
8.3.4
Immuno-electron Microscopy
201
8.3.5
Fluorescence Resonance Energy Transfer
202
8.4
Density Fitting
204
8.4.1
Correlation-based Fitting
204
8.5
Fourier Transformation
206
8.5.1
Fourier Series
206
8.5.2
Continuous Fourier Transform
207
8.5.3
Discrete Fourier Transform
207
8.5.4
Convolution Theorem
208
8.5.5
Fast Fourier Transformation
208
8.6
Advanced Density Fitting
220
8.6.1
Laplacian
Füter 211
8.6.2
Fitting Using Core Downweighting
212
8.6.3
Core-weighted Correlation Function
214
Contents
8.6.4
Surface
Overlap
Maximization
(SOM)
215
8.7
FFT
Protein-Protein
Docking
216
8.8
Prediction of Assemblies from Pairwise Docking
218
8.9
Electron Tomography
221
8.9.1
Reconstruction of a Phantom Cell
222
9
Biomolecular Association and Binding
231
9.1
Modeling by Homology
231
9.2
Structural Properties of Protein-Protein Interfaces
233
9.2.1
Size and Shape
233
9.2.2
Hot Spots
235
9.2.3
An Experimental Model System: Human Growth Hormone
and its Receptor
236
9.3
Bioinformatic Prediction of Protein-Protein Interfaces
239
9.3.1
Amino
acid Composition of Protein Interfaces
239
9.3.2
Pairing Propensities
240
9.3.3
Interface Statistical Potentials
240
9.3.4
Conservation at Protein Interfaces
241
9.3.5
Correlated Mutations at Protein Interfaces
243
9.3.6
Classification of Protein Interfaces
245
9.4
Forces Important for Biomolecular Association
246
9.5
Protein-Protein Association
249
9.5.1
Brownian Dynamics Simulations
250
9.6
Assembly of Macromolecular Complexes: the Ribosome
254
10
Integrated Networks
261
10.1
Correlating Interactome and Gene Regulation
261
10.2
Response of Gene Regulatory Network to Outside Stimuli
263
10.3
Integrated Analysis of Metabolic and Regulatory Networks
266
Π
Outlook
271
Index
273 |
| adam_txt |
Contents
Prefece
Xi
1 Networks
¡η
Biological Cells
2
1.1
Some Basics about Networks
1
1.1.1
Random Networks
2
1.1.2
Small-World Phenomenon
2
1.1.3
Scale-Free Network Model
3
1.2
Biological Background
4
1.2.1
Cellular Components
б
1.2.2
Spatial Organization ofEukaryotic Cells-Compartments
7
1.2.3
Cellular Organisms
7
1.3
Cellular Pathways
7
1.3.1
Biochemical Pathways
7
1.3.2
Enzymatic Reactions
8
1.3.3
Signal Txansduction
11
1.3.4
Cell Cycle
11
1.4
Ontologies and Databases
12
1.4.1
Ontologies
12
1.4.2
Systems Biology Markup Language
12
1.4.3
KEGG
13
1.4.4
Brenda
13
1.5
Methods in
CeîMar
Modeling
14
2
Algorithms on Mathematical Graphs
17
2.1
Primer on Mathematical Graphs
17
2.2
A Few Words about Algorithms and Computer Programs
18
2.2.1
Implementation of Algorithms
19
2.2.2
Classes of
Algoriäuns 20
2.3
Data Structures for Graphs
21
ftf&iffe of
€ό>Μ]»ί«κϋ»ϊ
Ш
Bïöfcgy
-
їда» Ргвйй»
Сетфхсѕ
te
СЛЉг
Jv'elusîfe.
Volkhard
Hetes
Gopjright
©
2O08
WIŁEWCH
Verlag
GmbH
&
Co. KGaA, Wdnhdm
ISBN:
978-3-517-31555-0
VI
Contents
2.4
Dijkstra's
Algorithm
23
2
АЛ.
Description of the Algorithm
25
2.4.2
Pseudocode
27
2.4.3
Running Time
29
2.5
Minimum Spanning Tree
29
2.5.1
Kruskal's Algorithm
31
2.6
Graph Drawing
31
3
Protein-Protein Interaction Networks
-
Pairwise Connectivity
39
3.1
Principles of Protein-Protein Interactions
39
3.2
Experimental High-Throughput Methods for Detecting
Protein-Protein Interactions
40
3.2.1
Gel Electrophoresis
41
3.2.2
Two-Dimensional Gel Electrophoresis
41
3.2.3
Affinity Chromatography
42
3.2.4
Yeast Two-Hybrid Screening
42
3.2.5
Synthetic Lethality
44
3.2.6
Gene Coexpression
44
3.2.7
Mass Spectroscopy
44
3.2.8
Databases for Interaction Networks
44
3.2.9
Overlap of Interactions
45
3.2.10
Criteria to Judge the Reliability of Interaction Data
47
3.2.11
How Many Protein-Protein Interactions can be Expected
in Yeast?
48
3.3
Bioinformaüc
Prediction of Protein-Protein Interactions
49
3.3.1
Analysis of Gene Order
49
3.3.2
Phylogenetic Profiling/Coevolutionary Profiling
50
3.3.3
Coevolution
51
3.4
Bayesian Networks for Judging the Accuracy of Interactions
52
3.4.1
Bayes'
Theorem
53
3.4.2
Bayesian Network
54
3.4.3
Application of Bayesian Networks to Protein-Protein
Interaction Data
55
3.4.3.1
Measurement of reliability "likelihood ratio"
55
3.4.3.2
Prior and posterior odds
56
3.4.3.3
A worked example: parameters of the
naïve
Bayesian network
for essentiality
57
3.4.3.4
Fully connected experimental network
57
3.5
Protein Domain Networks
59
4
Protein-Protein Interaction Networks
-
Structural Hierarchies
67
4.1
Protein Interaction Graph Networks
67
4.1.1
Degree Distribution
68
4.1.2
Clustering Coefficient
69
4.2
Finding Cliques
71
Contents
VII
4.3
Random Graphs
72
4.4
Scale-Free Graphs
73
4.5
Detecting Communities in Networks
75
4.5.1
Divisive Algorithms for Mapping onto Tree
78
4.6
Modular Decomposition
82
4.6.1
Modular Decomposition of Graphs
82
4.7
Network Growth Mechanisms
86
5
Gene Regulatory Networks
99
5.1
Regulation of Gene Transcription at Promoters
100
5.2
Gene Regulatory Networks
101
5.2.1
Gene Regulatory Network of
E. coli
101
5.3
Graph Theoretical Models
305
5.3.1
Coexpression Networks
105
5.3.2
Bayesian Networks
106
5.4
Dynamic Models
106
5.4.1
Boolean Networks
106
5.4.2
Reverse Engineering Boolean Networks
107
5.4.3
Differential Equations Models
110
5.5
Motifs 111
5.5.1
Feed-Forward Loop (FFL)
112
5.5.2
SIM Motif
112
5.5.3
Densely Overlapping Region (DOR)
112
6
Metabolic Networks
115
6.1
Introduction
115
6.2
Stoichiometric Matrix
118
6.3
Linear Algebra Primer
121
6.3.1
Matrices: Definitions and Notations
121
6.3.2
Adding, Subtracting and Multiplying Matrices
121
6.3.3
Linear Transformations, Ranks and Transpose
122
6.3.4
Square Matrices and Matrix Inversion
123
6.3.5
Eigenvalues of Matrices
124
6.3.6
System of Linear Equations
124
6.4
Flux Balance Analysis
125
6.5
Double Description Method
128
6.6
Extreme Pathways and Elementary Modes
133
6.6.1
Analysis of Eextreme Pathways
137
6.6.2
Elementary Flux Modes
139
6.7
Minimal Cut Sets
140
6.7.1
Applications of Minimal Cut Sets
344
6.8
High-Flux Backbone
146
7
Kinetic Modeling of Cellular Processes
155
7.1
Ordinary Differential Equation Models
355
VIH
Contents
7.1.1
Examples for ODEs
156
7.2
Modeling Cellular Feedback Loops by ODEs
158
7.2.1
Protein Synthesis and Degradation: Linear Response
159
7.2.2
Phosphorylation/Dephosphorylation
-
Hyperbolic Response
360
7.2.3
Phosphorylation/Dephosphorylation
-
Buzzer
162
7.2.4
Perfect Adaptation
-
Sniffer
163
7.2.5
Positive Feedback
-
One-Way Switch
164
7.2.6
Mutual Inhibition
-
Toggle Switch
165
7.2.7
Negative Feedback
-
Homeostasis
166
7.2.8
Negative Feedback: Oscillatory Response
166
7.2.9
Cell Cycle Control System
167
7.3
Partial Differential Equations
169
7.3.1
Spatial Gradients of Signaling Activities
170
7.4
Dynamic Monte Carlo (Gfflespie Algorithm)
172
7.4.1
Basic Outline of the Gillespie Method
173
7.5
Stochastic Modeling of a Small Molecular Network
173
7.5.1
Model System: Bacterial Photosynthesis
174
7.5.2
Pools-and-Proteins Model
176
7.5.3
Evaluating the Binding and Unbinding Kinetics
177
7.5.4
Pools of the Chromatophore Vesicle
178
7.5.5
Results for the Steady-State Regimes of the Vesicle
179
7.6
Parameter Optimization with Genetic Algorithms
182
8
Structures of Protein Complexes and Subcellular Structures
193
8.1
Examples of Protein Complexes
193
8.2
Complexeome
of S. cerevisiae
197
8.3
Experimental Determination of Three-dimensional Structures
of Protein Complexes
199
8.3.1
X-ray Crystallography
199
8.3.2
NMR
200
8.3.3
Electron Crystallography/Electron Microscopy
201
8.3.4
Immuno-electron Microscopy
201
8.3.5
Fluorescence Resonance Energy Transfer
202
8.4
Density Fitting
204
8.4.1
Correlation-based Fitting
204
8.5
Fourier Transformation
206
8.5.1
Fourier Series
206
8.5.2
Continuous Fourier Transform
207
8.5.3
Discrete Fourier Transform
207
8.5.4
Convolution Theorem
208
8.5.5
Fast Fourier Transformation
208
8.6
Advanced Density Fitting
220
8.6.1
Laplacian
Füter 211
8.6.2
Fitting Using Core Downweighting
212
8.6.3
Core-weighted Correlation Function
214
Contents
8.6.4
Surface
Overlap
Maximization
(SOM)
215
8.7
FFT
Protein-Protein
Docking
216
8.8
Prediction of Assemblies from Pairwise Docking
218
8.9
Electron Tomography
221
8.9.1
Reconstruction of a Phantom Cell
222
9
Biomolecular Association and Binding
231
9.1
Modeling by Homology
231
9.2
Structural Properties of Protein-Protein Interfaces
233
9.2.1
Size and Shape
233
9.2.2
Hot Spots
235
9.2.3
An Experimental Model System: Human Growth Hormone
and its Receptor
236
9.3
Bioinformatic Prediction of Protein-Protein Interfaces
239
9.3.1
Amino
acid Composition of Protein Interfaces
239
9.3.2
Pairing Propensities
240
9.3.3
Interface Statistical Potentials
240
9.3.4
Conservation at Protein Interfaces
241
9.3.5
Correlated Mutations at Protein Interfaces
243
9.3.6
Classification of Protein Interfaces
245
9.4
Forces Important for Biomolecular Association
246
9.5
Protein-Protein Association
249
9.5.1
Brownian Dynamics Simulations
250
9.6
Assembly of Macromolecular Complexes: the Ribosome
254
10
Integrated Networks
261
10.1
Correlating Interactome and Gene Regulation
261
10.2
Response of Gene Regulatory Network to Outside Stimuli
263
10.3
Integrated Analysis of Metabolic and Regulatory Networks
266
Π
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| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Helms, Volkhard |
| author_GND | (DE-588)135851246 |
| author_facet | Helms, Volkhard |
| author_role | aut |
| author_sort | Helms, Volkhard |
| author_variant | v h vh |
| building | Verbundindex |
| bvnumber | BV023218932 |
| callnumber-first | Q - Science |
| callnumber-label | QH585 |
| callnumber-raw | QH585.5.D38 |
| callnumber-search | QH585.5.D38 |
| callnumber-sort | QH 3585.5 D38 |
| callnumber-subject | QH - Natural History and Biology |
| classification_rvk | WC 5100 WC 7000 WC 7700 |
| classification_tum | BIO 200f BIO 110f BIO 105f BIO 220f |
| ctrlnum | (OCoLC)233007549 (DE-599)DNB986648655 |
| dewey-full | 571.60113 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 571 - Physiology & related subjects |
| dewey-raw | 571.60113 |
| dewey-search | 571.60113 |
| dewey-sort | 3571.60113 |
| dewey-tens | 570 - Biology |
| discipline | Biologie Informatik |
| discipline_str_mv | Biologie Informatik |
| format | Book |
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| id | DE-604.BV023218932 |
| illustrated | Illustrated |
| index_date | 2024-07-02T20:15:09Z |
| indexdate | 2024-07-20T09:35:32Z |
| institution | BVB |
| isbn | 9783527315550 3527315551 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016404871 |
| oclc_num | 233007549 |
| open_access_boolean | |
| owner | DE-703 DE-M49 DE-BY-TUM DE-20 DE-355 DE-BY-UBR DE-29T DE-19 DE-BY-UBM DE-634 DE-898 DE-BY-UBR DE-11 DE-526 DE-188 DE-578 |
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| physical | XII, 277 S. Ill., graph. Darst. 240 mm x 170 mm |
| publishDate | 2008 |
| publishDateSearch | 2008 |
| publishDateSort | 2008 |
| publisher | WILEY-VCH |
| record_format | marc |
| spelling | Helms, Volkhard Verfasser (DE-588)135851246 aut Principles of computational cell biology from protein complexes to cellular networks Volkhard Helms Weinheim WILEY-VCH 2008 XII, 277 S. Ill., graph. Darst. 240 mm x 170 mm txt rdacontent n rdamedia nc rdacarrier Datenverarbeitung Computational Biology Cytology Computer simulation Cytology Data processing Models, Biological Cytologie (DE-588)4070177-3 gnd rswk-swf Mathematisches Modell (DE-588)4114528-8 gnd rswk-swf Bioinformatik (DE-588)4611085-9 gnd rswk-swf Cytologie (DE-588)4070177-3 s Bioinformatik (DE-588)4611085-9 s DE-604 Mathematisches Modell (DE-588)4114528-8 s DE-188 http://d-nb.info/986648655/04 Inhaltsverzeichnis http://deposit.dnb.de/cgi-bin/dokserv?id=3036965&prov=M&dok_var=1&dok_ext=htm Beschreibung für Leser Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016404871&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Helms, Volkhard Principles of computational cell biology from protein complexes to cellular networks Datenverarbeitung Computational Biology Cytology Computer simulation Cytology Data processing Models, Biological Cytologie (DE-588)4070177-3 gnd Mathematisches Modell (DE-588)4114528-8 gnd Bioinformatik (DE-588)4611085-9 gnd |
| subject_GND | (DE-588)4070177-3 (DE-588)4114528-8 (DE-588)4611085-9 |
| title | Principles of computational cell biology from protein complexes to cellular networks |
| title_auth | Principles of computational cell biology from protein complexes to cellular networks |
| title_exact_search | Principles of computational cell biology from protein complexes to cellular networks |
| title_exact_search_txtP | Principles of computational cell biology from protein complexes to cellular networks |
| title_full | Principles of computational cell biology from protein complexes to cellular networks Volkhard Helms |
| title_fullStr | Principles of computational cell biology from protein complexes to cellular networks Volkhard Helms |
| title_full_unstemmed | Principles of computational cell biology from protein complexes to cellular networks Volkhard Helms |
| title_short | Principles of computational cell biology |
| title_sort | principles of computational cell biology from protein complexes to cellular networks |
| title_sub | from protein complexes to cellular networks |
| topic | Datenverarbeitung Computational Biology Cytology Computer simulation Cytology Data processing Models, Biological Cytologie (DE-588)4070177-3 gnd Mathematisches Modell (DE-588)4114528-8 gnd Bioinformatik (DE-588)4611085-9 gnd |
| topic_facet | Datenverarbeitung Computational Biology Cytology Computer simulation Cytology Data processing Models, Biological Cytologie Mathematisches Modell Bioinformatik |
| url | http://d-nb.info/986648655/04 http://deposit.dnb.de/cgi-bin/dokserv?id=3036965&prov=M&dok_var=1&dok_ext=htm http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016404871&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT helmsvolkhard principlesofcomputationalcellbiologyfromproteincomplexestocellularnetworks |
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