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Table of contents
1 Introduction
1.1 Basics of Genetics: DNA and RNA
1.2 Measuring Gene Expression
1.2.1 Microarrays
1.2.2 Serial Analysis of Gene Expression
1.2.3 Next-Generation Sequencing
1.2.4 Time-Course Gene Expression
1.3 Gene Regulatory Networks
2 Methods for Inferring Gene Regulatory Networks
2.1 Current State of Network Inference Methods
2.1.1 Regression Techniques
2.1.2 Integrative Bioinformatics Approaches
2.1.3 Statistical Methods
2.1.4 Optimization Methods
2.2 Dynamic Bayesian Networks
2.2.1 State Space Models
2.2.2 Auto-Regressive Models
2.3 Approximate Bayesian Methods for Reverse Engineering Gene Regulatory Networks
3 Approximate Bayesian Methodology
3.1 Empirical Bayes Methods
3.2 Approximate Bayesian Computation
3.2.1 Approximate Sampling from the Posterior
3.2.2 Post-Adjustment Techniques
3.2.3 Monte Carlo Techniques
3.3 Summary
4 The Empirical Bayes Dynamic Bayesian Network Algorithm
4.1 Model Selection
4.2 Estimation of Hidden States
4.3 Calculation of Posterior Distributions
4.3.1 Implementation of the EM Algorithm
4.3.2 Initial Values
4.4 EBDBN Algorithm Iterations
4.5 R package: ebdbNet
4.6 Unequally Spaced Observations and Missing Data
4.7 Simulations
4.7.1 Methods for Comparison
4.7.2 Model-Based Simulations
4.7.3 Data-Based Simulations
4.8 Summary
5 ABC-MCMC Network Algorithm
5.1 Simulating Data for Gene Regulatory Networks
5.2 Distance Function
5.3 Network Proposal Distributions
5.4 Network Prior Distributions
5.5 ABC-Net Implementation
5.5.1 Burn-In Period
5.5.2 Chain Convergence
5.6 Extension to RNA Sequencing Data
5.7 Simulations
5.7.1 Choice of and
5.7.2 Sensitivity to prior distribution bounds
5.7.3 Suitability of VAR(1) simulator
5.7.4 Effect of noise in observed data
5.7.5 Including Prior Biological Information
5.8 Summary
6 Application to Real Data
6.1 T-Cell Activation Network in Humans
6.2 S.O.S. DNA Repair System in Escherichia coli
6.3 Neurotrophin Signaling Pathway in Mouse
7 Summary and Future Work
7.1 Future Work
7.2 Conclusions
LIST OF REFERENCES
