Description
As the first comprehensive title on network biology, this book covers a wide range of subjects including scientific fundamentals (graphs, networks, etc) of network biology, construction and analysis of biological networks, methods for identifying crucial nodes in biological networks, link prediction, flow analysis, network dynamics, evolution, simulation and control, ecological networks, social networks, molecular and cellular networks, network pharmacology and network toxicology, big data analytics, and more.
Across 12 parts and 26 chapters, with Matlab codes provided for most models and algorithms, this self-contained title provides an in-depth and complete insight on network biology. It is a valuable read for high-level undergraduates and postgraduates in the areas of biology, ecology, environmental sciences, medical science, computational science, applied mathematics, and social science.
Sample Chapter(s)
1. Fundamentals of Graph Theory
Contents:
Mathematical Fundamentals:
Fundamentals of Graph Theory
Graph Algorithms
Fundamentals of Network Theory
Other Fundamentals
Crucial Nodes/Subnetworks/Modules, Network Types, and Structural Comparison:
Identification of Crucial Nodes and Subnetworks/Modules
Detection of Network Types
Comparison of Network Structure
Network Dynamics, Evolution, Simulation and Control:
Network Dynamics
Network Robustness and Sensitivity Analysis
Network Control
Network Evolution
Cellular Automata
Self-Organization
Agent-based Modeling
Flow Analysis:
Flow/Flux Analysis
Link and Node Prediction:
Link Prediction: Sampling-based Methods
Link Prediction: Structure- and Perturbation-based Methods
Link Prediction: Node-Similarity-based Methods
Node Prediction
Network Construction:
Construction of Biological Networks
Pharmacological and Toxicological Networks:
Network Pharmacology and Toxicology
Ecological Networks:
Food Webs
Microscopic Networks:
Molecular and Cellular Networks
Social Networks:
Social Network Analysis
Software:
Software for Network Analysis
Big Data Analytics:
Big Data Analytics for Network Biology
Readership: Advanced undergraduates and graduate students and researchers in biology, ecology, pharmacology, applied mathematics, computational science, etc.