Bacterial Molecular Networks provides authoritative descriptions of various experimental and computational methods that enable the characterization and analysis of molecular interaction networks.

An understanding of the nervous system at virtually any level of analysis requires an understanding of its basic building block, the neuron. From Molecules to Networks provides the solid foundation of the morphologic, biochemical, and biophysical properties of nerve cells. All chapters have been thoroughly revised for this second edition to reflect the significant advances of the past 5 years. The new edition expands on the network aspects of cellular neurobiology by adding a new chapter, Information Processing in Neural Networks, and on the relation of cell biological processes to various neurological diseases. The new concluding chapter illustrates how the great strides in understanding the biochemical and biophysical properties of nerve cells have led to fundamental insights into important aspects of neurodegenerative disease. - Written and edited by leading experts in the field, the second edition completely and comprehensively updates all chapters of this unique textbook - Discusses emerging new understanding of non-classical molecules that affect neuronal signaling - Full colour, professional graphics throughout - Includes two new chapters: Information Processing in Neural Networks - describes the principles of operation of neural networks and the key circuit motifs that are common to many networks in the nervous system. Molecular and Cellular Mechanisms of Neurodegenerative Disease - introduces the progress made in the last 20 years in elucidating the cellular and molecular mechanisms underlying brain disorders, including Amyotrophic Lateral Sclerosis (ALS), Parkinson disease, and Alzheimer's disease

Inferring gene regulatory networks is a difficult problem to solve due to the relative scarcity of data compared to the potential size of the networks. While researchers have developed techniques to find some of the underlying network structure, there is still no one-size-fits-all algorithm for every data set. Network Inference in Molecular Biology examines the current techniques used by researchers, and provides key insights into which algorithms best fit a collection of data. Through a series of in-depth examples, the book also outlines how to mix-and-match algorithms, in order to create one tailored to a specific data situation. Network Inference in Molecular Biology is intended for advanced-level students and researchers as a reference guide. Practitioners and professionals working in a related field will also find this book valuable.

This book addresses a number of questions from the perspective of complex systems: How can we quantitatively understand the life phenomena? How can we model life systems as complex bio-molecular networks? Are there any methods to clarify the relationships among the structures, dynamics and functions of bio-molecular networks? How can we statistically analyse large-scale bio-molecular networks? Focusing on the modeling and analysis of bio-molecular networks, the book presents various sophisticated mathematical and statistical approaches. The life system can be described using various levels of bio-molecular networks, including gene regulatory networks, and protein-protein interaction networks. It first provides an overview of approaches to reconstruct various bio-molecular networks, and then discusses the modeling and dynamical analysis of simple genetic circuits, coupled genetic circuits, middle-sized and large-scale biological networks, clarifying the relationships between the structures, dynamics and functions of the networks covered. In the context of large-scale bio-molecular networks, it introduces a number of statistical methods for exploring important bioinformatics applications, including the identification of significant bio-molecules for network medicine and genetic engineering. Lastly, the book describes various state-of-art statistical methods for analysing omics data generated by high-throughput sequencing. This book is a valuable resource for readers interested in applying systems biology, dynamical systems or complex networks to explore the truth of nature.

Molecular networks provide descriptions of the organization of various biological processes, including cellular signaling, metabolism, and genetic regulation. Knowledge on molecular networks is commonly used for systems level analysis of biological function; research and method development in this area has grown tremendously in the past few years. This book will provide a detailed review of existing knowledge on the functional characterization of biological networks. In 15 chapters authored by an international group of prolific systems biology and bioinformatics researchers, it will organize, conceptualize, and summarize the existing core of research results and computational methods on understanding biological function from a network perspective.

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. 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