Computational systems biology : inference and modelling / Paola Lecca ... [et al]. -- Waltham, MA : Elsevier, c2016. – (58.17115 /L457)

Contents

About the Authors

Preface

Acknowledgments

1.  Overview of Biological Network Inference and Modeling of Dynamics

1.1 Introduction to Inference of Topologies, Causalities, and Dynamic Models

1.2 The Data

2.  Network Inference From Steady-State Data

2.1 Median-Corrected Z Scores

2.2 Multiple Regression Method

2.3 Bayesian Variable Selection Method

3.  Network Inference From Time-Course Data

3.1 Time-Lagged-Correlation-Based Network Inference

3.2 Bayesian Approaches

3.3 The Method of Variational Bayesian Inference

4.  Network-Based Conceptualization of Observational Data

4.1 Biological Network Data, Sampling, and Predictability

4.2 Characteristics of Biological Networks

4.3 Module Discovery Approaches

4.4 Categorization of Network Inference Methods

4.5 Performance of Network Inference Methods

4.6 Comparison of Network Inference Methods

4.7 Applications of Network-Based Data Integration

5.  Deterministic Differential Equations

5.1 The Rationale of Deterministic Modeling

5.2 Modeling Elemental and Abstract Biological Phenomena

5.3 Analysis of Deterministic Differential Models

5.4 Case Studies

6.  Stochastic Differential Equations

6.1 Reaction Kinetics: The Molecular Approach to Kinetics

6.2 Stochastic Differential Equations

7.  From Network Inference to the Study of Human Diseases

7.1 Introduction to Network Medicine

7.2 Databases and Tools for Network Medicine

7.3 A Case Study of Neurodegenerative Diseases

7.4 Conclusion and Perspectives

8.  Conclusions

8.1 Network Inference, Modeling, and Simulation in the Era of Big Data and High-Throughput Experiments

Bibliography

Index