Principles of Computational Cell Biology : From Protein Complexes to Cellular Networks
, by Helms, VolkhardNote: Supplemental materials are not guaranteed with Rental or Used book purchases.
- ISBN: 9783527315550 | 3527315551
- Cover: Paperback
- Copyright: 7/21/2008
"This volume contains succinct, yet clear, descriptions of each of these topics and is best suited for readers who are delving into these concepts for the first time." -The Quarterly Review of Biology, 2009This textbook provides an ideal introduction to computational cell biology for students of biology and bioinformatics. In particular the text focuses on a network-based approach to the study of cellular systems. Almost 30 carefully designed study exercises offer excellent support for those preparing for exams in these subjects, and help introduce the more technical aspects of the topic while keeping maths to a minimum.
Volkhard Helms has been Professor of Bioinformatics in the Center of Bioinformatics, Saarland University since 2003, where he heads the bioinformatics department. He worked for his PhD in the European Molecular Biology Laboratory in Heidelberg and carried out post-doctoral work at UC San Diego. He then went on to lead a research group in theoretical biophysics at the Max Plank Institute of Biophysics in Frankfurt. In 2001 he was selected as an EMBO Young Investigator, and since 2000 he has been a member of the "Faculty of 1000". His work focuses on experimental and computational approaches to the study of protein-protein interactions.
Preface | p. XI |
Networks in Biological Cells | p. 1 |
Some Basics about Networks | p. 1 |
Random Networks | p. 2 |
Small-World Phenomenon | p. 2 |
Scale-Free Network Model | p. 3 |
Biological Background | p. 4 |
Cellular Components | p. 6 |
Spatial Organization of Eukaryotic Cells - Compartments | p. 7 |
Cellular Organisms | p. 7 |
Cellular Pathways | p. 7 |
Biochemical Pathways | p. 7 |
Enzymatic Reactions | p. 8 |
Signal Transduction | p. 11 |
Cell Cycle | p. 11 |
Ontologies and Databases | p. 12 |
Ontologies | p. 12 |
Systems Biology Markup Language | p. 12 |
KEGG | p. 13 |
Brenda | p. 13 |
Methods in Cellular Modeling | p. 14 |
Algorithms on Mathematical Graphs | p. 17 |
Primer on Mathematical Graphs | p. 17 |
A Few Words about Algorithms and Computer Programs | p. 18 |
Implementation of Algorithms | p. 19 |
Classes of Algorithms | p. 20 |
Data Structures for Graphs | p. 21 |
Dijkstra's Algorithm | p. 23 |
Description of the Algorithm | p. 25 |
Pseudocode | p. 27 |
Running Time | p. 29 |
Minimum Spanning Tree | p. 29 |
Kruskal's Algorithm | p. 31 |
Graph Drawing | p. 31 |
Protein-Protein Interaction Networks - Pairwise Connectivity | p. 39 |
Principles of Protein-Protein Interactions | p. 39 |
Experimental High-Throughput Methods for Detecting Protein-Protein Interactions | p. 40 |
Gel Electrophoresis | p. 41 |
Two-Dimensional Gel Electrophoresis | p. 41 |
Affinity Chromatography | p. 42 |
Yeast Two-Hybrid Screening | p. 42 |
Synthetic Lethality | p. 44 |
Gene Coexpression | p. 44 |
Mass Spectroscopy | p. 44 |
Databases for Interaction Networks | p. 44 |
Overlap of Interactions | p. 45 |
Criteria to Judge the Reliability of Interaction Data | p. 47 |
How Many Protein-Protein Interactions can be Expected in Yeast? | p. 48 |
Bioinformatic Prediction of Protein-Protein Interactions | p. 49 |
Analysis of Gene Order | p. 49 |
Phylogenetic Profiling/Coevolutionary Profiling | p. 50 |
Coevolution | p. 51 |
Bayesian Networks for Judging the Accuracy of Interactions | p. 52 |
Bayes' Theorem | p. 53 |
Bayesian Network | p. 54 |
Application of Bayesian Networks to Protein-Protein Interaction Data | p. 55 |
Measurement of reliability "likelihood ratio" | p. 55 |
Prior and posterior odds | p. 56 |
A worked example: parameters of the naive Bayesian network for essentiality | p. 57 |
Fully connected experimental network | p. 57 |
Protein Domain Networks | p. 59 |
Protein-Protein Interaction Networks - Structural Hierarchies | p. 67 |
Protein Interaction Graph Networks | p. 67 |
Degree Distribution | p. 68 |
Clustering Coefficient | p. 69 |
Finding Cliques | p. 71 |
Random Graphs | p. 72 |
Scale-Free Graphs | p. 73 |
Detecting Communities in Networks | p. 75 |
Divisive Algorithms for Mapping onto Tree | p. 78 |
Modular Decomposition | p. 82 |
Modular Decomposition of Graphs | p. 82 |
Network Growth Mechanisms | p. 86 |
Gene Regulatory Networks | p. 99 |
Regulation of Gene Transcription at Promoters | p. 100 |
Gene Regulatory Networks | p. 101 |
Gene Regulatory Network of E. coli | p. 101 |
Graph Theoretical Models | p. 105 |
Coexpression Networks | p. 105 |
Bayesian Networks | p. 106 |
Dynamic Models | p. 106 |
Boolean Networks | p. 106 |
Reverse Engineering Boolean Networks | p. 107 |
Differential Equations Models | p. 110 |
Motifs | p. 111 |
Feed-Forward Loop (FFL) | p. 112 |
SIM Motif | p. 112 |
Densely Overlapping Region (DOR) | p. 112 |
Metabolic Networks | p. 115 |
Introduction | p. 115 |
Stoichiometric Matrix | p. 118 |
Linear Algebra Primer | p. 121 |
Matrices: Definitions and Notations | p. 121 |
Adding, Subtracting and Multiplying Matrices | p. 121 |
Linear Transformations, Ranks and Transpose | p. 122 |
Square Matrices and Matrix Inversion | p. 123 |
Eigenvalues of Matrices | p. 124 |
System of Linear Equations | p. 124 |
Flux Balance Analysis | p. 125 |
Double Description Method | p. 128 |
Extreme Pathways and Elementary Modes | p. 133 |
Analysis of Eextreme Pathways | p. 137 |
Elementary Flux Modes | p. 139 |
Minimal Cut Sets | p. 140 |
Applications of Minimal Cut Sets | p. 144 |
High-Flux Backbone | p. 146 |
Kinetic Modeling of Cellular Processes | p. 155 |
Ordinary Differential Equation Models | p. 155 |
Examples for ODEs | p. 156 |
Modeling Cellular Feedback Loops by ODEs | p. 158 |
Protein Synthesis and Degradation: Linear Response | p. 159 |
Phosphorylation/Dephosphorylation - Hyperbolic Response | p. 160 |
Phosphorylation/Dephosphorylation - Buzzer | p. 162 |
Perfect Adaptation - Sniffer | p. 163 |
Positive Feedback - One-Way Switch | p. 164 |
Mutual Inhibition - Toggle Switch | p. 165 |
Negative Feedback - Homeostasis | p. 166 |
Negative Feedback: Oscillatory Response | p. 166 |
Cell Cycle Control System | p. 167 |
Partial Differential Equations | p. 169 |
Spatial Gradients of Signaling Activities | p. 170 |
Dynamic Monte Carlo (Gillespie Algorithm) | p. 172 |
Basic Outline of the Gillespie Method | p. 173 |
Stochastic Modeling of a Small Molecular Network | p. 173 |
Model System: Bacterial Photosynthesis | p. 174 |
Pools-and-Proteins Model | p. 176 |
Evaluating the Binding and Unbinding Kinetics | p. 177 |
Pools of the Chromatophore Vesicle | p. 178 |
Results for the Steady-State Regimes of the Vesicle | p. 179 |
Parameter Optimization with Genetic Algorithms | p. 182 |
Structures of Protein Complexes and Subcellular Structures | p. 193 |
Examples of Protein Complexes | p. 193 |
Complexeome of S. cerevisiae | p. 197 |
Experimental Determination of Three-dimensional Structures of Protein Complexes | p. 199 |
X-ray Crystallography | p. 199 |
NMR | p. 200 |
Electron Crystallography/Electron Microscopy | p. 201 |
Immuno-electron Microscopy | p. 201 |
Fluorescence Resonance Energy Transfer | p. 202 |
Density Fitting | p. 204 |
Correlation-based Fitting | p. 204 |
Fourier Transformation | p. 206 |
Fourier Series | p. 206 |
Continuous Fourier Transform | p. 207 |
Discrete Fourier Transform | p. 207 |
Convolution Theorem | p. 208 |
Fast Fourier Transformation | p. 208 |
Advanced Density Fitting | p. 210 |
Laplacian Filter | p. 211 |
Fitting Using Core Downweighting | p. 212 |
Core-weighted Correlation Function | p. 214 |
Surface Overlap Maximization (SOM) | p. 215 |
FFT Protein-Protein Docking | p. 216 |
Prediction of Assemblies from Pairwise Docking | p. 218 |
Electron Tomography | p. 221 |
Reconstruction of a Phantom Cell | p. 222 |
Biomolecular Association and Binding | p. 231 |
Modeling by Homology | p. 231 |
Structural Properties of Protein-Protein Interfaces | p. 233 |
Size and Shape | p. 233 |
Hot Spots | p. 235 |
An Experimental Model System: Human Growth Hormone and its Receptor | p. 236 |
Bioinformatic Prediction of Protein-Protein Interfaces | p. 239 |
Amino acid Composition of Protein Interfaces | p. 239 |
Pairing Propensities | p. 240 |
Interface Statistical Potentials | p. 240 |
Conservation at Protein Interfaces | p. 241 |
Correlated Mutations at Protein Interfaces | p. 243 |
Classification of Protein Interfaces | p. 245 |
Forces Important for Biomolecular Association | p. 246 |
Protein-Protein Association | p. 249 |
Brownian Dynamics Simulations | p. 250 |
Assembly of Macromolecular Complexes: the Ribosome | p. 254 |
Integrated Networks | p. 261 |
Correlating Interactome and Gene Regulation | p. 261 |
Response of Gene Regulatory Network to Outside Stimuli | p. 263 |
Integrated Analysis of Metabolic and Regulatory Networks | p. 266 |
Outlook | p. 271 |
Index | p. 273 |
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