Computational Systems Biology: Inference and Modelling

Computational Systems Biology: Inference and Modelling provides an introduction to, and overview of, network analysis inference approaches which form the backbone of the model of the complex behavior of biological systems.

This book addresses the challenge to integrate highly diverse quantitative approaches into a unified framework by highlighting the relationships existing among network analysis, inference, and modeling.

The chapters are light in jargon and technical detail so as to make them accessible to the non-specialist reader. The book is addressed at the heterogeneous public of modelers, biologists, and computer scientists.

Provides a unified presentation of network inference, analysis, and modeling Explores the connection between math and systems biology, providing a framework to learn to analyze, infer, simulate, and modulate the behavior of complex biological systemsIncludes chapters in modular format for learning the basics quickly and in the context of questions posed by systems biology Offers a direct style and flexible formalism all through the exposition of mathematical concepts and biological applications

Chapter 1: Overview of Biological Network Inference and Modeling of Dynamics

Chapter 2: Network Inference From Steady-State Data

Chapter 3: Network Inference From Time-Course Data

Chapter 4: Network-Based Conceptualization of Observational Data

Chapter 5: Deterministic Differential Equations

Chapter 6: Stochastic Differential Equations

Chapter 7: From Network Inference to the Study of Human Diseases

Chapter 8: Conclusions