Key introductory text for graduate students and researchers in physics, biology and biochemistry.

System Biology encompasses the knowledge from diverse fields such as Molecular Biology, Immunology, Genetics, Computational Biology, Mathematical Biology, etc. not only to address key questions that are not answerable by individual fields alone, but also to help in our understanding of the complexities of biological systems. Whole genome expression studies have provided us the means of studying the expression of thousands of genes under a particular condition and this technique had been widely used to find out the role of key macromolecules that are involved in biological signaling pathways. However, making sense of the underlying complexity is only possible if we interconnect various signaling pathways into human and computer readable network maps. These maps can then be used to classify and study individual components involved in a particular phenomenon. Apart from transcriptomics, several individual gene studies have resulted in adding to our knowledge of key components that are involved in a signaling pathway. It therefore becomes imperative to take into account of these studies also, while constructing our network maps to highlight the interconnectedness of the entire signaling pathways and the role of that particular individual protein in the pathway. This collection of articles will contain a collection of pioneering work done by scientists working in regulatory signaling networks and the use of large scale gene expression and omics data. The distinctive features of this book would be: Act a single source of information to understand the various components of different signaling network (roadmap of biochemical pathways, the nature of a molecule of interest in a particular pathway, etc.), Serve as a platform to highlight the key findings in this highly volatile and evolving field, and Provide answers to various techniques both related to microarray and cell signaling to the readers.

How new modeling techniques can be used to explore functionally relevant molecular and cellular relationships.

Biological systems are extremely complex and have emergent properties that cannot be explained or even predicted by studying their individual parts in isolation. The reductionist approach, although successful in the early days of molecular biology, underestimates this complexity. As the amount of available data grows, so it will become increasingly important to be able to analyse and integrate these large data sets. This book introduces novel approaches and solutions to the Big Data problem in biomedicine, and presents new techniques in the field of graph theory for handling and processing multi-type large data sets. By discussing cutting-edge problems and techniques, researchers from a wide range of fields will be able to gain insights for exploiting big heterogonous data in the life sciences through the concept of 'network of networks'.

Current Topics in Developmental Biology, Volume 143, the latest release in this acclaimed series, highlights new advances in the field, with this new volume presenting new sections on Building a ciliated epithelium: Transcriptional regulation and radial intercalation of multiciliated cells, Biomechanics of Amphibian Morphogenesis, Planar cell polarity during neural tube closure, Left-right asymmetry research in Xenopus: Questions solved and new frontiers, Xenopus neural crest and its relevance to human disease, Endoderm organogenesis, From egg to embryo in marsupial frogs, Evo-devo lessons from the analysis of Xenopus genomes, Transcriptional regulation during zygotic genome activation, and much more. Provides the authority and expertise of leading contributors from an international board of authors Presents the latest release in the Current Topics in Developmental Biology series

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. Contents: Mathematical Fundamentals: Fundamentals of Graph TheoryGraph AlgorithmsFundamentals of Network TheoryOther FundamentalsCrucial Nodes/Subnetworks/Modules, Network Types, and Structural Comparison: Identification of Crucial Nodes and Subnetworks/ModulesDetection of Network TypesComparison of Network StructureNetwork Dynamics, Evolution, Simulation and Control: Network DynamicsNetwork Robustness and Sensitivity AnalysisNetwork ControlNetwork EvolutionCellular AutomataSelf-OrganizationAgent-based ModelingFlow Analysis: Flow/Flux AnalysisLink and Node Prediction: Link Prediction: Sampling-based MethodsLink Prediction: Structure- and Perturbation-based MethodsLink Prediction: Node-Similarity-based MethodsNode PredictionNetwork Construction: Construction of Biological NetworksPharmacological and Toxicological Networks: Network Pharmacology and ToxicologyEcological Networks: Food WebsMicroscopic Networks: Molecular and Cellular NetworksSocial Networks: Social Network AnalysisSoftware: Software for Network AnalysisBig 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. Keywords: Network Biology;Network Analysis;Food Webs;Molecular Networks;Social Networks;Network Pharmacology;Link Prediction;Network Dynamics;Big Data Analytics;Software;Models;Algorithms;Nodes;LinksReview:0

This volume presents a timely and comprehensive overview of biological networks at all organization levels in the spirit of the complex systems approach. It discusses the transversal issues and fundamental principles as well as the overall structure, dynamics, and modeling of a wide array of biological networks at the molecular, cellular, and population levels. Anchored in both empirical data and a strong theoretical background, the book therefore lends valuable credence to the complex systems approach. Sample Chapter(s). Chapter 1: Scale-Free Networks in Biology (821 KB). Contents: Scale-Free Networks in Biology (E Almaas et al.); Modularity in Biological Networks (R V Sol(r) et al.); Inference of Biological Regulatory Networks: Machine Learning Approaches (F d''Alch(r)-Buc); Transcriptional Networks (F K(r)p s); Protein Interaction Networks (K Tan & T Ideker); Metabolic Networks (D A Fell); Heterogeneous Molecular Networks (V Schnchter); Evolution of Regulatory Networks (A Veron et al.); Complexity in Neuronal Networks (Y Fr(r)gnac et al.); Networks of the Immune System (R E Callard & J Stark); A History of the Study of Ecological Networks (L-F Bersier); Dynamic Network Models of Ecological Diversity, Complexity, and Nonlinear Persistence (R J Williams & N D Martinez); Infection Transmission through Networks (J S Koopman). Readership: Graduate students and industry experts in systems biology and complex systems; biologists; chemists; physicists; mathematicians; computer scientists