Systems Biology In Practice

Presenting the main concepts, this book leads students as well as advanced researchers from different disciplines to an understanding of current ideas in the complex field of comprehensive experimental investigation of biological objects, analysis of data, development of models, simulation, and hypothesis generation. It provides readers with guidance on how a specific complex biological question may be tackled: - How to formulate questions that can be answered - Which experiments to perform - Where to find information in databases and on the Internet - What kinds of models are appropriate - How to use simulation tools - What can be learned from the comparison of experimental data and modeling results - How to make testable predictions. The authors demonstrate how mathematical concepts can illuminate the principles underlying biology at a genetic, molecular, cellular and even organism level, and how to use mathematical tools for analysis and prediction.

This advanced textbook is tailored to the needs of introductory course in Systems Biology. It has a compagnion website (www.wiley-vch.de/home/systemsbiology)with solutions to questions in the book and several additional extensive working models. The book is related to the very successful previous title 'Systems Biology in Practice' and has incorporated the feedback and suggestions from many lecturers worldwide. The book addresses biologists as well as engineers and computer scientists. The interdisciplinary team of acclaimed authors worked closely together to ensure a comprehensive coverage with no overlaps in a homogenous and compelling style.

This advanced textbook is tailored for an introductory course in Systems Biology and is well-suited for biologists as well as engineers and computer scientists. It comes with student-friendly reading lists and a companion website featuring a short exam prep version of the book and educational modeling programs. The text is written in an easily accessible style and includes numerous worked examples and study questions in each chapter. For this edition, a section on medical systems biology has been included.

Are we satisfied with the rate of drug development? Are we happy with the drugs that come to market? Are we getting our money s worth in spending for basic biomedical research? In Translational Systems Biology, Drs. Yoram Vodovotz and Gary An address these questions by providing a foundational description the barriers facing biomedical research today and the immediate future, and how these barriers could be overcome through the adoption of a robust and scalable approach that will form the underpinning of biomedical research for the future. By using a combination of essays providing the intellectual basis of the Translational Dilemma and reports of examples in the study of inflammation, the content of Translational Systems Biology will remain relevant as technology and knowledge advances bring broad translational applicability to other diseases. Translational systems biology is an integrated, multi-scale, evidence-based approach that combines laboratory, clinical and computational methods with an explicit goal of developing effective means of control of biological processes for improving human health and rapid clinical application. This comprehensive approach to date has been utilized for in silico studies of sepsis, trauma, hemorrhage, and traumatic brain injury, acute liver failure, wound healing, and inflammation. Provides an explicit, reasoned, and systematic approach to dealing with the challenges of translational science across disciplines Establishes the case for including computational modeling at all stages of biomedical research and healthcare delivery, from early pre-clinical studies to long-term care, by clearly delineating efficiency and costs saving important to business investment Guides readers on how to communicate across domains and disciplines, particularly between biologists and computational researchers, to effectively develop multi- and trans-disciplinary research teams "

Throughout most of the twentieth century, the biomedical model dominated healthcare. However, the biomedical model had its critics, who proposed alternative models to replace it. Eventually, biomedicine became fragmented at its foundations with a variety of approaches to its nature and practice. Medicine's current response to this fragmentation is to combine these disparate approaches into a single system--systems medicine. In the present book, I examine the shift, during the postgenomics era, from the biomedical model to systems medicine vis-à-vis systems biology, as well as the challenges facing systems medicine's implementation in the twenty-first century. The main goal of the present book is to provide a disciplinary framework for examining the rise of systems medicine, especially in terms of the incorporation of systems biology into the biomedical model. To realize that goal, the following questions are addressed. What is a disciplinary framework? And, why is this framework important for understanding systems biology and medicine? Briefly, a disciplinary framework represents the relational structure among disparate disciplines that support and ground a discipline and its corpus. For traditional biology and medicine, that framework consists of various disciplines within the biological and biomedical sciences, including physiology, neuroscience, pathology, and epidemiology--to name a few. For the present purpose, systems biology within the last several decades is reshaping the disciplinary framework of the biological and biomedical sciences, which is also responsible for the emergence of systems medicine. In addition, the challenges facing systems medicine, especially its operationalization and implementation with respect to medical education and practice, as well as research, are also explored.

Translational Bioinformatics and Systems Biology Methods for Personalized Medicine introduces integrative approaches in translational bioinformatics and systems biology to support the practice of personalized, precision, predictive, preventive, and participatory medicine. Through the description of important cutting-edge technologies in bioinformatics and systems biology, readers may gain an essential understanding of state-of-the-art methodologies. The book discusses topics such as the challenges and tasks in translational bioinformatics; pharmacogenomics, systems biology, and personalized medicine; and the applicability of translational bioinformatics for biomarker discovery, epigenomics, and molecular dynamics. It also discusses data integration and mining, immunoinformatics, and neuroinformatics. With broad coverage of both basic scientific and clinical applications, this book is suitable for a wide range of readers who may not be scientists but who are also interested in the practice of personalized medicine. Introduces integrative approaches in translational bioinformatics and systems biology to support the practice of personalized, precision, predictive, preventive, and participatory medicine Presents a problem-solving oriented methodology to deal with practical problems in various applications Covers both basic scientific and clinical applications in order to enhance the collaboration between researchers and clinicians Brings integrative and multidisciplinary approaches to bridge the gaps among various knowledge domains in the field

Drawing on the latest research in the field, Systems Biology: Mathematical Modeling and Model Analysis presents many methods for modeling and analyzing biological systems, in particular cellular systems. It shows how to use predictive mathematical models to acquire and analyze knowledge about cellular systems. It also explores how the models are systematically applied in biotechnology. The first part of the book introduces biological basics, such as metabolism, signaling, gene expression, and control as well as mathematical modeling fundamentals, including deterministic models and thermodynamics. The text also discusses linear regression methods, explains the differences between linear and nonlinear regression, and illustrates how to determine input variables to improve estimation accuracy during experimental design. The second part covers intracellular processes, including enzymatic reactions, polymerization processes, and signal transduction. The author highlights the process–function–behavior sequence in cells and shows how modeling and analysis of signal transduction units play a mediating role between process and function. The third part presents theoretical methods that address the dynamics of subsystems and the behavior near a steady state. It covers techniques for determining different time scales, sensitivity analysis, structural kinetic modeling, and theoretical control engineering aspects, including a method for robust control. It also explores frequent patterns (motifs) in biochemical networks, such as the feed-forward loop in the transcriptional network of E. coli. Moving on to models that describe a large number of individual reactions, the last part looks at how these cellular models are used in biotechnology. The book also explains how graphs can illustrate the link between two components in large networks with several interactions.

Overview of ecological principles underlying natural and synthetic microbial communities Techniques to infer relationships in microbial communities from metagenomic sequences Detailed account of constraint-based methods to decipher metabolic interactions in microbial communities Case studies to illustrate applications of various community modelling approaches Brief outline of experimental methods to understand and characterise microbial communities