By providing expositions to modeling principles, theories, computational solutions, and open problems, this reference presents a full scope on relevant biological phenomena, modeling frameworks, technical challenges, and algorithms.

Models and Algorithms for Biomolecules and Molecular Networks begins by covering areas of structural and geometric models of biomolecules and their shape characterization. The first topic discussed is protein geometry, including voids and pockets and how to effectively use them to infer and characterize biological functions of proteins. Scoring functions for predicting structures and binding proteins, using both statistical analysis and optimization techniques are then discussed, followed by the development of sampling techniques for conformation of biomolecules and protein evolution.

The second part of the book covers interaction networks of biomolecules. Topics include stochastic models for networks with small copy numbers of molecular species, signal transduction networks, and biological models in the framework of dynamic systems. In the final chapter, dynamical and topological properties of specific biological models are discussed, as well as their salient features, which make them attractive in their applications.

Special features of this book include:

• Up-to-date developments of structures of biomolecules, systems biology, advanced models, and algorithms
• Sampling techniques for estimating evolutionary rates and generating molecular structures
• Accurate computation of probability landscape of stochastic networks, solving discrete chemical master equations
• End-of-Chapter exercises

This book will equip readers with the necessary background knowledge and skillset to solve practical problems and carry out research for future advancements in the field.