Handbook of Statistical Systems BiologySystems Biology is now entering a mature phase in which the key issues are characterising uncertainty and stochastic effects in mathematical models of biological systems. The area is moving towards a full statistical analysis and probabilistic reasoning over the inferences that can be made from mathematical models. This handbook presents a comprehensive guide to the discipline for practitioners and educators, in providing a full and detailed treatment of these important and emerging subjects. Leading experts in systems biology and statistics have come together to provide insight in to the major ideas in the field, and in particular methods of specifying and fitting models, and estimating the unknown parameters. This book:
This handbook will be a key resource for researchers practising systems biology, and those requiring a comprehensive overview of this important field. |
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Inhalt
Bayesian Inference and Computation | |
Data Integration Towards Understanding | |
Control Engineering Approaches toReverse Engineering Biomolecular Networks | |
Transcriptomic Technologies and Statistical Data | |
StatisticalData Analysis inMetabolomics 8 1 Introduction | |
ImagingandSingleCell Measurement | |
Chapter | |
References | |
Stochastic Dynamical Systems | |
Gaussian Process Inferencefor Differential Equation Models of Transcriptional Regulation | |
Model Identification by Utilizing Likelihood | |
1ODE Models forReaction Networks 20 2 Parameter Estimation | |
Acknowledgements | |
Inferenceand ModelSelectionforDynamical | |
References | |
Introduction to Graphical Modelling | |
Recovering Genetic Network from Continuous | |
Advanced Applications of Bayesian Networks | |
RandomGraphModels | |
3PPI network models 14 4Rangedependent graphs | |
Chapter | |
Chapter | |
HostPathogen Systems Biology | |
Bayesian Approaches for Based Metabolomics Mass Spectrometry | |
Systems Biologyof microRNAs 25 1 Introduction | |
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Handbook of Statistical Systems Biology Michael Stumpf,David J. Balding,Mark Girolami Keine Leseprobe verfügbar - 2011 |
Häufige Begriffe und Wortgruppen
algorithm alignment analysis approach approximation Bayes Bayesian networks biochemical Bioinformatics canbe cell cellular changepoint classifier clustering coefficients complex components computational concentration conditional confidence intervals consider correlation corresponding covariance database datasets defined degree distribution depend dynamical systems edges etal example experimental data Figure function Gaussian gene expression gene regulatory networks genetic Genome graphical models Gröbner basis identified inference integration interactome inthe isthe kinetic likelihood linear Lyapunov exponent Markov mathematical matrix MCMC measurements metabolic metabolites metabolomics methods microarray microRNA molecular mRNA multiple network structure nodes nonidentifiability nonlinear numberof observed obtained ofthe onthe optimization pathogen pathway polynomial posterior distribution posterior probability PPI networks prediction prior distribution probability problem procedure profiles protein protein–protein interactions random graphs reaction regression regulation regulatory networks sample sequence signaling simulation specific stochastic systems biology target techniques tothe trajectories transcription factor values variables vector yeast
Bibliografische Informationen
| Titel | Handbook of Statistical Systems Biology |
| Autoren | Michael Stumpf, David J. Balding, Mark Girolami |
| Verlag | John Wiley & Sons, 2011 |
| ISBN | 1119952042, 9781119952046 |
| Länge | 530 Seiten |
| Zitat exportieren | BiBTeX EndNote RefMan |