Stumpf M., Balding D.J., Girolami M. (eds.) Handbook of Statistical Systems Biology

New York: Wiley, 2011. - 532 p.Systems 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:Provides a comprehensive account of inference techniques in systems biology.Introduces classical and Bayesian statistical methods for complex systems.Explores networks and graphical modeling as well as a wide range of statistical models for dynamical systems.Discusses various applications for statistical systems biology, such as gene regulation and signal transduction.Features statistical data analysis on numerous technologies, including metabolic and transcriptomic technologies.Presents an in-depth presentation of reverse engineering approaches.Provides colour illustrations to explain key concepts.This handbook will be a key resource for researchers practicing systems biology, and those requiring a comprehensive overview of this important field.Methodological ChaptersCell signaling systems
The challenge of many moving parts
The challenge of parts with parts
Closing remarksModels for dependent data
Multiple testing
Building a classifier
Aggregation
Regularization
Performance assessment
Geometric methods
(Discrete) latent variable models
InferenceBases
Bayesian analysis in action
Prior distributions
Confidence intervals
The Bayes factor
Point null hypotheses
The ban on improper priors
The case of nuisance parameters
Bayesian multiple testing
Prediction
Model choice
Computational challenges
Monte Carlo methods
MCMC methods
Approximate Bayesian computation techniquesStoring knowledge: Experimental data, knowledge databases, ontologies and annotation
Data repositories
Knowledge Databases
Ontologies
Annotation
Integration of experimental data
Concluding remarksDynamical models for network inference
Linear models
Nonlinear models
Least squares
Methods based on least squares
Dealing with noise: CTLS
Convex optimization methods
Sparsity pattern of the discrete-time model
Application examples
Reconstruction methods based on nonlinear models
Approaches based on polynomial and rational models
Approaches based on S-systems
A case-studyOverview of chapter
Computational algebra
Definitions
Further examples
Parameter inference
Model invariants
Log-linear models
Reverse engineering of networksTechnology-based Chapters
Biological background
Microarray technology
High throughput sequencing (HTS)
mRNA expression estimates from HTS
Common approaches for significance testing
Moderated statistics
Statistics for HTS
Multiple testing corrections
Filtering genes
Gene-set analysis
Dimensionality reduction
Clustering
Variable selection
Estimating the performance of a modelAnalytical technologies
Preprocessing
Unsupervised methods
Supervised methods
Metabolome-wide association studies
Metabolic correlation networks
Simulation of metabolic profile dataIntracellular signal transduction
Measurement techniques
Immunocytochemistry
Flow cytometry
Fluorescent microscope
Live cell imaging
Fluorescent probes for live cell imaging
Image cytometry
Image processing
Time series (mean, variation, correlation, localizationProtein structure and function
Experimental techniques for interaction detection
Computationally predicted data-sets
Error in PPI data
Graphs
Network summary statistics
Models of random networks
Approximate Bayesian Computation
Threshold behaviour in graphs
Network comparison based on subgraph counts
Network alignment
Using functional annotation for network alignment
How evolutionary models affect network alignment
Community detection in PPI networks
Community detection methods
Evaluation of results
Predicting function using PPI networks
Predicting interactions using PPI networks
Using triangles for predicting interactions
Dynamics
Limitations of models, prediction and alignment methodsNetworks and Graphical Models
Graphical structures and random variables
Learning graphical models
Structure learning
Inference on graphical models
Correlation networks
Covariance selection networks
Other graphical modelsRegulatory networks in biology
Genetic network modeling with DBNs
DBN for linear interactions and inference procedures
Go forward: how to recover the structure changes with time
ARTIVA network model
ARTIVA inference procedure and performance evaluation
Discussion and ConclusionBasic concepts
Dynamic Bayesian networks
Inclusion of biological prior knowledge
The ‘energy’ of a network
Prior distribution over network structures
MCMC sampling scheme
Empirical evaluation on the Raf signalling pathway
Motivation: Inferring spurious feedback loops with DBNs
A nonlinear/nonhomogeneous DBN
Simulation results
Results on Arabidopsis gene expression time series
DiscussionBackground and motivation
What do we want from a PPI network?
Lock and key
Geometric networks
Range-dependent graphsLocal network features and their statistics
Examples
Network families, hypothesis testing and null models
Tailored random graph ensembles
Network complexity
Information-theoretic dissimilarity
Applications to PPINs
Mapping PPIN data biases
Generating random graphs via Markov chains
Degree-constrained graph dynamics based on edge swaps
DiscussionDynamical SystemsThe natural measure
The Kolmogorov–Sinai entropy
Symbolic dynamicsBasic solution types
Qualitative behaviour
Stability and bifurcations
Ergodicity
Timescales
Time series analysisLow copy number
Markov jump process
Diffusion approximation
Modeling extrinsic noise
Likelihood-based inference
Partial observation and data augmentation
Data augmentation MCMC approaches
Likelihood-free approaches
Approximate Bayesian computation
Stochastic model emulationA simple systems biology model
Generalized linear model
Fitting basis function models
An infinite basis
Gaussian processes
Model based target ranking
Multiple tanscription factorsModel Identification by Utilizing Likelihood-Based Methods
ODE models for reaction networks
Rate equations
Parameter estimation
Sensitivity equations
Testing hypothesis
Confidence intervals
Structural nonidentifiability
Practical nonidentifiability
The profile likelihood approach
Experimental design
Observability and confidence intervals of trajectories
ApplicationApplication AreasOverview of inference techniques
Parameter inference and model selection for dynamical systems
Model selection
Approximate Bayesian computation
Application: Akt signalling pathway
Exploring different distance functions
Sensitivity analysis by principal component analysis (PCA)Modeling Transcription Factor Activity
Integrating an ODE with a differential operator
Taking into account the nature of the biological system being modelled
Bounds choice for polynomial interpolation
Applications
Estimating intermediate pointsMetabolic models
Protein–protein interactions
Response to environment
Immune system interactions
Manipulation of other host systems
Evolution of the host–pathogen system
Concluding remarksThe challenge of metabolite identification
Bayesian analysis of metabolite mass spectra
Incorporating additional information
Probabilistic peak detection
Statistical inference
Software development for metabolomicsCurrent approaches in microRNA Systems Biology
Experimental findings and data that guide the developments of computational tools
Approaches to microRNA target predictions
Identifying microRNA activity from mRNA expression
Modeling combinatorial microRNA regulation from joint microRNA and mRNA expression data
Network approach for studying microRNA-mediated regulation
Kinetic modeling of microRNA regulation
A basic model of microRNA-mediated regulation
Estimating fold-changes of mRNA and proteins in microRNA transfection experiments
Reconstructing microRNA kinetics
Discussion