Biological Networks

The following sections are included:

• PREFACE

• CONTENTS

• CONTRIBUTORS

The following sections are included:

• Introduction

• Characterizing Network Topology
  • Degree Distribution
  • Clustering Coefficient
  • Subgraphs and Motifs

• Network Models
  • Random Network Model
  • Scale-Free Network Model
  • Hierarchical Network Model
  • Bose-Einstein Condensation and Networks

• Network Utilization
  • Flux Utilization
  • Gene Interactions

• Conclusion

• References

The following sections are included:

• Introduction

• Topological Overlap

• Modular Networks: The Role of Tinkering

• Conclusions

• Acknowledgments

• References

The following sections are included:

• Introduction
  • Feasibility of Inference
  • Overview of Methods

• The Inference of Gene Regulatory Networks as a Machine Learning Problem
  • Gene Regulatory Networks
  • Machine Learning: A Short Definition
  • A Methodology for the Conception of a Learning Algorithm

• Representation Issues
  • Prerequisites
  • Questions When Accounting for Dynamics
    • Encoding the Data
    • Identifiability, Learnability and Sample Complexity
    • Time-Scale, Sampling Frequency and Irregular Sampling
    • Continuous versus Discretized Encoding
  • Deterministic Models of Dynamics
    • Temporal Boolean Network Models
    • Linear Networks
    • Artificial Recurrent Neural Networks
  • Probabilistic Models of Dynamics
    • Linear Models and Linear State-Space Models
    • Dynamical Bayesian Networks Using non Parametric Regression for Conditional Probability Distributions(CPD)
    • Models of Biochemical Processes
  • Static Models of Causal Dependencies
    • Bayesian Networks
    • Probabilistic Relational Models
    • Module Networks
    • Factor Graph Networks (FGN)

• Learning and Optimization
  • Exact Learning and Best-Fit Approaches
  • Statistical Learning
    • Mean Squared Error and Weight Decay for Neural Networks
    • Maximum A Posteriori Approaches for Learning Parameters of Bayesian Networks
    • Structure Learning

• Validation
  • Introduction to Validation
  • Statistical Validation of Network Inference
    • Model Selection via Sampling and Re-sampling Methods
    • Prediction on Unseen Data
    • Performance Evaluation on Known Networks (Simulated or Real)
  • Biological Validation

• Conclusion and Perspectives

• References

The following sections are included:

• Introduction

• Interacting Partners
  • Genes and DNA Regulatory Regions
  • Regulatory Proteins or Dedicated Transcription Factors

• Mode of Interaction Between Transcription Factors and DNA Regulatory Regions
  • Genetic Interactions
  • Genetic Interaction Map

• Methodology
  • Complementary DNA Microarrays
  • Oligonucleotide Chips
  • Normalization of cDNA Microarray Experiments
  • Reverse Transcription–Polymerase Chain Reaction (RT-PCR)
  • Serial Analysis of Gene Expression (SAGE)
  • Chromatin Immunoprecipitation
  • Bioinformatics
  • Combining Several Approaches

• Computational Modeling
  • Graphs and Their Derivatives
  • Boolean Modeling
  • Generalized Logical Modeling
  • Petri Nets
  • Bayesian Networks
  • Ordinary Differential Equations
  • Partial Differential Equations
  • Stochastic Equations
  • Modeling Strategy

• Global Topology of Transcriptional Networks
  • Introduction to Topological Analysis
  • Analysis of the Global Topology
  • A Case Study and Its Biological Interpretation

• Local Topology
  • Analysis of the Local Topology
  • A Case Study of a Microorganism
  • A Case Study of a Multicellular Organism
  • Combinatorial Transcription Logic

• Dynamics and Modularity in Macromolecular Networks
  • Modularity
    • Interest of Modularity
    • Implementing Modularity
  • A Taxonomy of Modules Involved in Regulatory Networks
    • Feedback Circuits
    • Regulatory Triangles (“Feedforward Loops”)
    • Cascades
    • Combination of Cascades and Positive Circuits
    • Fans
  • Community Structure

• Spatial Aspects

• Conclusion and Perspectives

• References

The following sections are included:

• Introduction

• Methodologies to Obtain Protein Interaction Data
  • Experimental Technologies to Identify Protein-Protein Interactions
  • Computational Approaches to Predict Protein-Protein Interactions

• Computational Modeling of Protein Networks
  • Visualization of Protein Interaction Networks
  • Topological Properties of Protein Interaction Networks
  • Integrating Protein Interaction Networks with Complementary Data
  • Network Alignment and Comparison

• Robustness of Protein Interaction Networks

• Evolution of Protein Interaction Networks

• Perspectives

• References

The following sections are included:

• Introduction

• Interacting Partners

• Methodology
  • Mathematical Representation
  • Defining the Biological System

• Computational Modelling
  • Sources of Data
  • Algorithms and Analyses
    • Graph Analysis
    • Flux Balance Analyses
  • Are Paths in a Graphs Metabolic Pathways?

• Global Topology of the Network
  • Introduction
  • Small World Characteristics
  • Short Path Lengths
  • Power Law Connectivity
  • Modular Structure
  • Evolution of the Structure
  • Robustness and Damage

• Dynamics
  • Distribution of Fluxes

• Conclusion and Perspective

• References

The following sections are included:

• Introduction

• Heterogeneous Networks: Definitions and Scope
  • Types of Component Networks
  • Types of Composite Networks

• Methodology: Initial Reconstruction of Heterogeneous Networks
  • Obtaining the Component Networks
  • Reconstructing the Composite Network

• Computational Modeling
  • Topological Properties of Composite Networks can be Assessed Using Graph-based Models
  • Structural Properties of Steady-state Dynamics can be Assessed Using Stoichiometric Models of Metabolism

• Logical Functions as a Unifying Framework for Steady State Dynamics of Regulation and Metabolism

• Topology of Composite Networks
  • Correlation Between Topological Properties in Pairs of Coupled Networks
    • Metabolism and Coexpression
    • Protein-Protein Interactions and Coexpression
    • Genetic Interactions and Physical Interactions
  • Topological Features of the Composite Network
    • Layered Structure of the Protein Interaction Network
    • Modules Defined over Composite Networks

• Dynamics
  • Interplay Between the Dynamics of Component Networks
    • Correlation Between Steady State Fluxes and mRNA Expression Levels Assessed Using Stiochiometric Models of Metabolism
    • Synchronization Between Complex Formation and Cell-Cycle Regulation
  • Investigating the Dynamics of Heterogeneous Networks

• Interaction Prediction and Network Refinement
  • Filling Gaps in Metabolic Networks
  • Predicting Genetic Interactions
  • Predicting Protein-Protein Interactions
  • Refining the Structure and Logic of Transcriptional Regulation Network

• Conclusions and Perspectives

• Acknowledgements

• References

The following sections are included:

• Introduction

• Definitions

• Evolution at the Molecular Level
  • Transcription Factors: DNA-Binding Domains
  • Transcription Factors: Dimerisation Domains
  • Transcription Factors: Effector Domains
  • Transcription Factors: Cofactors
  • Evolution of Promoters
  • Co-evolution and Decoupled Evolution

• Motifs to Modules to Networks

• Models of Network Evolution and Simulation Studies
  • Simulation Principles
  • Duplication–Mutation Models
  • Mutation-Only Models
  • Using Subgraphs Count
  • Models Motivated by Pattern Formation
  • Can Biological Network Evolution Be Modeled?

• Conclusions

• References

The following sections are included:

• Introduction
  • Interacting Partners

• Modes of Interaction
  • Synaptic Transmission
  • Synaptic Dynamics and Plasticity
  • Interdependency Between Intrinsic Excitability and Extrinsic Synaptic Factors

• Computational Modelling
  • Modelling Cells and Synaptic Interactions
  • Modelling Networks

• Complexity in Structural and Functional Network Topology
  • Diversity of Structural Network Topology
  • Complexity of Structural Network Topology
  • Structural Network Topology and the ‘Small-World’ Analogy
  • Functional Network Topology and the ‘Scale-Free’ Analogy

• Complexity in Network Dynamics
  • A Possible Role of ‘Noise’ in the Functional Dynamics of Cortical Networks
  • Self-Organisation and Adaptive Properties in Network Dynamics

• Conclusion and Perspectives: Complexity as a Computational Principle?

• Acknowledgements

• References

The following sections are included:

• Introduction

• Outline of the Biology of the Immune Response

• The Components of the Immune System

• The Architecture of the Immune Network
  • Gene Networks
  • Intracellular Signalling Networks
  • Intercellular Signalling Networks
  • Networks of Microenvironments

• Integration Between Different Levels

• Modelling Immune Networks

• References

The following sections are included:

• Introduction

• The Pioneers

• Energy Based Approaches

• Complexity and Stability

• The Topological Structure of Food Webs

• Indirect Effects

• Networking with Non Trophic Interactions

• Future Avenues of Research

• Acknowledgements

• References

The following sections are included:

• Introduction

• Models
  • Structural Models and Food-Web Topology
  • Bioenergetic Model of Nonlinear Food-Web Dynamics

• Topology and Dynamics
  • Effects of Structure on Dynamics
  • Effects of Dynamics on Structure
  • Omnivory

• Conclusion

• Acknowledgements

• References

The following sections are included:

• Introduction

• Inference Robustness Assessment
  • Transitions within Mass Action Models from Continuous Population Deterministic to Discrete Individual Stochastic Models
  • Stochastic Compartmental Mass Action — Network Transitions
  • Dynamics Network Models
  • ODE Network Models of Corelation in Infection Status
  • Mathematical Analysis of Network Models with other Structures
  • Transiting from Undirected to Directed Graphs
  • Models Involving Contact Processes That Generate Networks
  • Statistical Analysis of Network Structure
  • Designing Network Models with Robustness Assessment in Mind

• Nucleotide Sequence Traces through Contact Networks

• Risk Factors for Transmissibility

• Overview of Infection Transmission Network Models
  • Infection Processes and Infectious Agent Characteristics
  • Modes of Transmission
  • Interacting Partners in Infection Transmission Network Models
  • Methodology
    • Micro-network Interview Data
    • Macro-network Interview Data
    • Environmental Contamination Data
    • Nucleotide Sequence Data
  • Computational Modeling
  • Macro Topology of the Network
  • Micro Topology of the Network
  • Spatial Aspects
  • Dynamics and Control

• Conclusion and Perspective

• References

The following sections are included:

• INDEX