Practical Systems Biology: Volume 61

Practical Systems Biology: Volume 61

by Alistair Hetherington, Claire Grierson

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Product Details

ISBN-13: 9781134133031
Publisher: CRC Press
Publication date: 11/19/2008
Series: Society for Experimental Biology
Sold by: Barnes & Noble
Format: NOOK Book
Pages: 300
File size: 2 MB

Table of Contents

Contributors     ix
Preface     xiii
Bioinformatic approaches to biological systems   David R. Westhead   Iain W. Manfield   Christopher J. Needham     1
Introduction     1
Getting systems information from public data sources     2
Using informatics methods to drive data generation     11
Conclusion     15
References     15
Cell sampling and global nucleic acid amplification   Vigdis Nygaard   Eivind Hovig     17
Introduction     17
Cellular characterization and assaying gene expression     17
Global analysis of the transcriptome - gene expression analysis at the RNA level     18
Cell sampling considerations: Effect of sample properties on the transcriptome/transcript distribution to be assayed     20
Approaches to reduce RNA requirements in microarray experiments     22
Global mRNA amplification     23
Concluding remarks     30
Linear RNA amplification     31
References     34
Methods of proteome analysis: Challenges and opportunities   Sarah R. Hart   Simon J. Gaskell     37
Proteomics and the quantitative challenge     37
Qualitative proteome analyses     38
Common analytical workflows     38
Protein recognition using conventional mass spectrometric data     39
Protein recognition using tandem MS data     40
Characterization of post-translational modifications     46
Quantitative proteome analyses     48
Relative quantification strategies     48
Absolute quantification     52
The kinetics of the proteome     53
Protein turnover     53
Kinetics of phosphorylation     53
Concluding remarks and future challenges     54
References     54
Vertical systems biology: From DNA to flux and back   Annamaria Bevilacqua   Stephen J. Wilkinson   Richard Dimelow   Ettore Murabito   Samrina Rehman   Maria Nardelli   Karen van Eunen   Sergio Rossell   Frank J. Bruggeman   Nils Bluthgen   Dirk de Vos   Jildau Bouwman   Barbara M. Bakker   Hans V. Westerhoff     65
Hierarchies in control: Living the dogma of molecular biology     65
Hierarchies in regulation: Hierarchical Regulation Analysis     70
Hierarchical Regulation Analysis: A practical approach      78
An exemplary experimental analysis: Alcohol production by yeast as a function of time after nitrogen starvation     80
Concluding remarks     88
References     90
Using mathematical models to probe dynamic expression data   Nick Monk     93
Introduction     93
The structure of interaction networks     94
Mathematical models of interaction networks     95
Linking state evolution models to expression data     95
Differential equation models of cellular interaction networks     96
Extracting information from expression data     98
Example 1: Inference of a transcription factor expression profile, given time-course mRNA expression data for its target genes     99
Example 2: Inference of the regulatory structure of a transcription network using protein expression data     101
Using mathematical models to explore partial networks     103
Concluding remarks     108
References     108
Gene regulatory network models: A dynamic and integrative approach to development   Elena R. Alvarez-Buylla   Enrique Balleza   Mariana Benitez   Carlos Espinosa-Soto   Pablo Padilla-Longoria     113
Gene regulatory network models: Are they useful for understanding development?      114
Mathematical tools for integrating biological processes at different time-space scales: Key for understanding pattern formation     114
Dynamic gene regulatory network models: The Boolean case     118
Epistasis and robustness: Two sides of the same coin for understanding developmental constraints?     121
Dynamic GRN models for animal and plant modules     123
Cell patterning     123
Body plan development and evolution     126
Genome level GRN models from microarray data: A challenge still ahead     127
Gene network inference methods: From raw microarray data to a GRN model     128
References     135
Spatio-temporal dynamics of protein modification cascades   Boris N. Kholodenko   Herbert M. Sauro     141
Introduction     141
Computational modelling of growth factor signalling     142
Temporal dynamics of protein modification cascades     144
The role of feedback     146
Spatial gradients of protein activities within a cell     150
Waves of protein phosphorylation     151
Concluding remarks     152
References     153
Intracellular signalling during bacterial chemotaxis   Marcus J. Tindall   Philip K. Maini    Judy P. Armitage   Colin Singleton   Amy Mason     161
Bacterial chemotaxis     161
Intracellular signalling within bacterial chemotaxis     162
Mathematical modelling and bacterial chemotaxis     163
Developing a model of intracellular signalling     166
Non-dimensionalization     170
Parameterizing the model     171
Model solutions and results     171
Summary and future work     172
References     172
Modelling the mammalian heart   Richard Clayton   Martyn Nash     175
Introduction     175
Physiological background     176
Structure and function of the heart     176
Cardiac cells     176
The action potential     176
Cardiac tissue and action potential propagation     177
Ca[superscript 2+]-mediated coupling of electrical and mechanical activity     177
Tissue mechanics     177
Mechano-electrical feedback     178
Role of modelling     178
Cell models     179
Electrical models     179
Mechanical models     180
Cardiac tissue models      182
Electrical models     182
Mechanical models     183
Whole-organ models     184
Anatomical models     184
Whole-organ electrical models     185
Whole-organ mechanics models     185
Coupled electromechanical models     185
Numerical and computational issues     186
Two success stories     187
Ion channel phenotypes of gene polymorphisms     187
Models of fibrillation     188
Concluding remarks     189
References     189
Modelling root growth and development   Eric M. Kramer   Xavier Draye   Malcolm J. Bennett     195
Introduction     195
Background to modelling root development     196
Modelling hormone regulated root development     198
A primer on computer models for auxin transport     198
Model parameters     201
Employing ISB to probe developmental processes in roots     202
Towards a virtual root model     203
Lessons from above     203
The need to integrate many more signals     206
References     207
Index     213

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