Epigenetics and Systems Biology

Section I: Introduction

• References

Section II: Where Am I? Genomic Features and DNA Sequence Principles Defining Sites of Epigenetic Regulation: Machine Learning

Chapter 1. Computational Identification of Polycomb/Trithorax Response Elements

• Abstract
• Introduction to Polycomb/Trithorax Response Elements
• 2003 Ad Hoc Approach to PRE Prediction, Together with its Particular Motivations
• Evaluating Classification Performance
• Results of 2003 PRE Prediction
• New Motifs Discovered
• Recasting PRE Prediction as a Machine Learning Problem
• Misclassification Costs and the Trade-off Dimension
• Evolutionary Analysis and Search-Space Reduction
• Today: Genome-Wide Profiling Data
• How Good is Our Method When Evaluated Under These Data?
• Sensitivity and Specificity of Genome-Wide Profiling
• Conclusion
• References
• Glossary
• List of Acronyms and Abbreviations

Chapter 2. Modeling Chromatin States

• Abstract
• The Purpose of Modeling Chromatin States
• The Common Approach
• What Have We Learned From those Models?
• Static Versus Dynamical Models
• Chromatin States are Attractors
• References
• Glossary
• List of Acronyms and Abbreviations

Chapter 3. Crossing Borders: Modeling Approaches to Understand Chromatin Domains and Their Boundaries

• Abstract
• Introduction
• The Expanding Universe of Structural Domains
• Experimental Techniques
• Modeling Higher Order Chromatin Structure
• Formalizing Compartment Calling and Boundary Prediction
• Outlook
• References
• List of Acronyms and Abbreviations

Chapter 4. Inferring Chromatin Signaling From Genome-Wide ChIP-seq Data

• Abstract
• Introduction
• Experimental Techniques
• Modeling
• Perturbing the System
• Outlook
• References
• Glossary
• List of Acronyms and Abbreviations

Section III: Everything’s Moving: In Vivo Dynamics of Epigenetic Regulators: Kinetic Models Based on Ordinary Differential Equations

Chapter 5. “In Vivo Biochemistry”: Absolute Quantification and Kinetic Modeling Applied to Polycomb and Trithorax Regulation

• Abstract
• Introduction
• Absolute Quantification In Vivo: A Technical Challenge
• Everything’s Moving: Methods for Measuring Kinetic Parameters In Vivo
• In Vivo Biochemistry of an Epigenetic System: What Did We Learn?
• Mathematical Modeling of the System: Defining What We Don’t Know
• Back to the Bench: Testing the Model by Perturbing the System
• Outlook
• Acknowledgments
• References
• Glossary
• List of Acronyms and Abbreviations

Chapter 6. Modeling Distributive Histone Modification by Dot1 Methyltransferases: From Mechanism to Biological Insights

• Abstract
• Introduction
• Modeling Histone Modification by Dot1
• Acknowledgments
• References
• Glossary
• List of Acronyms and Abbreviations
• Appendix: Parameter Estimation and Formal Mathematical Description of the Models

Section IV: Reconciling Randomness and Precision: Bistable Epigenetic Memory and Switching: Stochastic Models

Chapter 7. Modeling Bistable Chromatin States

• Abstract
• Cell Memory by Chromatin-Based Epigenetics
• Modeling Approach and Philosophy
• Nucleosome-Mediated Epigenetics
• DNA Methylation-Mediated Epigenetics
• Outlook
• References
• Glossary
• List of Acronyms and Abbreviations

Chapter 8. Quantitative Environmentally Triggered Switching Between Stable Epigenetic States

• Abstract
• Introduction
• Memory of the Cold is Digital and is Stored Locally in the Chromatin
• Cold Registration is Digital
• Model Validation
• Outlook
• References
• Glossary
• List of Acronyms and Abbreviations

Section V: The Third and Fourth Dimensions: Chromosomal Long Range Interactions: Polymer Models

Chapter 9. On the Nature of Chromatin 3D Organization: Lessons From Modeling

• Abstract
• Introduction
• Polymer Models of Chromatin
• Models to Reconstruct 3D-Conformation From Contact Data
• Conclusions
• References
• Glossary

Chapter 10. From Chromosome Conformation Capture to Polymer Physics and Back: Investigating the Three-Dimensional Structure of Chromatin Within Topological Associating Domains

• Abstract
• Introduction
• From 5C/Hi-C Maps to Three-Dimensional Structures
• Description of the Data-Driven Polymer Model
• What can be Learned From Data-Driven, Polymer-Based Reconstructions of Chromosome Structure?
• What are the Molecular Mechanisms Behind Interaction Energies?
• Conclusions and Perspectives
• References
• Glossary
• List of Acronyms and Abbreviations

Chapter 11. A Combination Approach Based on Live-Cell Imaging and Computational Modeling to Further Our Understanding of Chromatin, Epigenetics, and the Genome

• Abstract
• Introduction: Genomic DNA, Nucleosomes, and Chromatin
• Experimental Techniques: Single-Nucleosome Imaging in Living Cells Using Super-Resolution Microscopy
• Modeling: Monte Carlo Simulation of Chromatin Dynamics
• Another Result From the Model: Physical Size of Transcription Complexes
• Perspective
• References
• Glossary
• List of Acronyms and Abbreviations

Chapter 12. Capturing Chromosome Structural Properties From Their Spatial and Temporal Fluctuations

• Abstract
• Introduction
• Chromosome Labeling Technologies
• Spatial Dynamics: Real-Time Imaging and Tracking of Single Chromosome Loci
• Mapping Chromosome Conformational Dynamics by Single/Multi Loci Positioning
• Outlook—Future Developments
• References
• Glossary