Cancer Systems Biology Bioinformatics And Medicine

This teaching monograph on systems approaches to cancer research and clinical applications provides a unique synthesis, by world-class scientists and doctors, of laboratory, computational, and clinical methods, thereby establishing the foundations for major advances not possible with current methods. Specifically, the book: 1) Sets the stage by describing the basis of systems biology and bioinformatics approaches, and the clinical background of cancer in a systems context; 2) Summarizes the laboratory, clinical, data systems analysis and bioinformatics tools, along with infrastructure and resources required; 3) Demonstrates the application of these tools to cancer research; 4) Extends these tools and methods to clinical diagnosis, drug development and treatment applications; and 5) Finishes by exploring longer term perspectives and providing conclusions. This book reviews the state-of-the-art, and goes beyond into new applications. It is written and highly referenced as a textbook and practical guide aimed at students, academics, doctors, clinicians, industrialists and managers in cancer research and therapeutic applications. Ideally, it will set the stage for integration of available knowledge to optimize communication between basic and clinical researchers involved in the ultimate fight against cancer, whatever the field of specific interest, whatever the area of activity within translational research.

Systems Biology in Cancer Research and Drug Discovery provides a unique collection of chapters, by world-class researchers, describing the use of integrated systems biology and network modeling in the cancer field where traditional tools have failed to deliver expected promise. This book touches four applications/aspects of systems biology (i) in understanding aberrant signaling in cancer (ii) in identifying biomarkers and prognostic markers especially focused on angiogenesis pathways (iii) in unwinding microRNAs complexity and (iv) in anticancer drug discovery and in clinical trial design. This book reviews the state-of-the-art knowledge and touches upon cutting edge newer and improved applications especially in the area of network modeling. It is aimed at an audience ranging from students, academics, basic researcher and clinicians in cancer research. This book is expected to benefit the field of translational cancer medicine by bridging the gap between basic researchers, computational biologists and clinicians who have one ultimate goal and that is to defeat cancer.

This teaching monograph on systems approaches to cancer research and clinical applications provides a unique synthesis, by world-class scientists and doctors, of laboratory, computational, and clinical methods, thereby establishing the foundations for major advances not possible with current methods. Specifically, the book: 1) Sets the stage by describing the basis of systems biology and bioinformatics approaches, and the clinical background of cancer in a systems context; 2) Summarizes the laboratory, clinical, data systems analysis and bioinformatics tools, along with infrastructure and resources required; 3) Demonstrates the application of these tools to cancer research; 4) Extends these tools and methods to clinical diagnosis, drug development and treatment applications; and 5) Finishes by exploring longer term perspectives and providing conclusions. This book reviews the state-of-the-art, and goes beyond into new applications. It is written and highly referenced as a textbook and practical guide aimed at students, academics, doctors, clinicians, industrialists and managers in cancer research and therapeutic applications. Ideally, it will set the stage for integration of available knowledge to optimize communication between basic and clinical researchers involved in the ultimate fight against cancer, whatever the field of specific interest, whatever the area of activity within translational research.

This book comprises protocols describing systems biology methodologies and computational tools, offering a variety of ways to analyze different types of high-throughput cancer data. Chapters give an overview over data types available in large-scale data repositories and state-of-the-art methods used in the field of cancer systems biology. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and practical, Cancer Systems Biology : Methods and Protocols aims to ensure successful results in the further study of this vital field. The chapters "Identifying Genetic Dependencies in Cancer by Analyzing siRNA Screens in Tumor Cell Line Panels", "Perseus: A Bioinformatics Platform for Integrative Analysis of Proteomics Data in Cancer Research" and "Phosphoproteomics-based Profiling of Kinase Activities in Cancer Cells" are available open access under a CC BY 4.0 license via link.springer.com.

Praise for Computational Systems BiologyApproaches in Cancer Research: "Complex concepts are written clearly and with informative illustrations and useful links. The book is enjoyable to read yet provides sufficient depth to serve as a valuable resource for both students and faculty." — Trey Ideker, Professor of Medicine, UC Xan Diego, School of Medicine "This volume is attractive because it addresses important and timely topics for research and teaching on computational methods in cancer research. It covers a broad variety of approaches, exposes recent innovations in computational methods, and provides acces to source code and to dedicated interactive web sites." — Yves Moreau, Department of Electrical Engineering, SysBioSys Centre for Computational Systems Biology, University of Leuven With the availability of massive amounts of data in biology, the need for advanced computational tools and techniques is becoming increasingly important and key in understanding biology in disease and healthy states. This book focuses on computational systems biology approaches, with a particular lens on tackling one of the most challenging diseases - cancer. The book provides an important reference and teaching material in the field of computational biology in general and cancer systems biology in particular. The book presents a list of modern approaches in systems biology with application to cancer research and beyond. It is structured in a didactic form such that the idea of each approach can easily be grasped from the short text and self-explanatory figures. The coverage of topics is diverse: from pathway resources, through methods for data analysis and single data analysis to drug response predictors, classifiers and image analysis using machine learning and artificial intelligence approaches. Features Up to date using a wide range of approaches Applicationexample in each chapter Online resources with useful applications’

Systems biology combines computational and experimental approaches to analyze complex biological systems and focuses on understanding functional activities from a systems-wide perspective. It provides an iterative process of experimental measurements, data analysis, and computational simulation to model biological behavior. This book provides explained protocols for high-throughput experiments and computational analysis procedures central to cancer systems biology research and education. Readers will learn how to generate and analyze high-throughput data, therapeutic target protein structure modeling and docking simulation for drug discovery. This is the first practical guide for students and scientists who wish to become systems biologists or utilize the approach for cancer research. Contents: Introduction to Cancer Systems Biology (Hsueh-Fen Juan and Hsuan-Cheng Huang)Transcriptome Analysis: Library Construction (Hsin-Yi Chang and Hsueh-Fen Juan)Quantitative Proteome: The Isobaric Tags for Relative and Absolute Quantitation (iTRAQ) (Yi-Hsuan Wu and Hsueh-Fen Juan)Phosphoproteome: Sample Preparation (Chia-Wei Hu and Hsueh-Fen Juan)Transcriptomic Data Analysis: RNA-Seq Analysis Using Galaxy (Chia-Lang Hsu and Chantal Hoi Yin Cheung)Proteomic Data Analysis: Functional Enrichment (Hsin-Yi Chang and Hsueh-Fen Juan)Phosphorylation Data Analysis (Chia-Lang Hsu and Wei-Hsuan Wang)Pathway and Network Analysis (Chen-Tsung Huang and Hsueh-Fen Juan)Dynamic Modeling (Yu-Chao Wang)Protein Structure Modeling (Chia-Hsien Lee and Hsueh-Fen Juan)Docking Simulation (Chia-Hsien Lee and Hsueh-Fen Juan) Readership: Graduate students and researchers entering the cancer systems biology field. Keywords: Systems Biology;Transcriptomics;Proteomics;Network Biology;Dynamic Modeling;Protein Structure Modeling;Docking Simulation;BioinformaticsReview: Key Features: Written by two active researchers in the fieldCovers both experimental and computational areas in cancer systems biologyStep-by-step instructions help beginners who are interested in creating biological data and analyzing the data by themselvesReaders will gain the skills to generate and analyze omics data and discover potential therapeutic targets and drug candidates

The future of cancer research and the development of new therapeutic strategies rely on our ability to convert biological and clinical questions into mathematical models-integrating our knowledge of tumour progression mechanisms with the tsunami of information brought by high-throughput technologies such as microarrays and next-generation sequencin

This volume covers techniques in computational biology and their applications in oncology. It details advanced statistical methods, heuristic algorithms, cluster analysis, data modeling, and image and pattern analysis applied to cancer research.