Olaf Wolkenhauer – författare

Systems biology refers to the quantitative analysis of the dynamic interactions among several components of a biological system and aims to understand the behavior of the system as a whole. Systems biology involves the development and application of systems theory concepts for the study of complex biological systems through iteration over mathematical modeling, computational simulation and biological experimentation. Systems biology could be viewed as a tool to increase our understanding of biological systems, to develop more directed experiments, and to allow accurate predictions.

 The Encyclopedia of Systems Biology is conceived as a comprehensive reference work covering all aspects of systems biology, in particular the investigation of living matter involving a tight coupling of biological experimentation, mathematical modeling and computational analysis and simulation. The main goal of the Encyclopedia is to provide a complete reference of established knowledge in systems biology – a ‘one-stop shop’ for someone seeking information on key concepts of systems biology. As a result, the Encyclopedia comprises a broad range of topics relevant in the context of systems biology. The audience targeted by the Encyclopedia includes researchers, developers, teachers, students and practitioners who are interested or working in the field of systems biology. Keeping in mind the varying needs of the potential readership, we have structured and presented the content in a way that is accessible to readers from wide range of backgrounds. In contrast to encyclopedic online resources, which often rely on the general public to author their content, a key consideration in the development of the Encyclopedia of Systems Biology was to have subject matter experts define the concepts and subjects of systems biology.

This textbook focuses on stochastic analysis in  systems biology  containing both the theory and application. While the authors provide a review of probability and random variables, subsequent notions of biochemical reaction systems and the relevant concepts of probability theory are introduced side by side. This leads to an intuitive and easy-to-follow presentation of stochastic framework for modeling subcellular biochemical systems. In particular, the authors make an effort to show how the notion of propensity, the chemical master equation and the stochastic simulation algorithm arise as consequences of the Markov property.

The text contains many  illustrations, examples and exercises to illustrate the ideas and methods that are introduced. Matlab code is also provided where appropriate. Additionally, the cell cycle is  introduced as a more complex case study.

Senior undergraduate and graduate students in mathematics and physics as well as researchers  working in the area of systems biology, bioinformatics and related areas will find this text useful.

This volume guides readers through the field of systems medicine by defining the terminology, and describing how established computational methods form bioinformatics and systems biology can be taken forward to an integrative systems medicine approach. Chapters provide an outlook on the role that systems medicine may or should play in various medical fields, and describe different facets of the systems medicine approach in action. Ultimately it introduces tools, resources and methodologies from bioinformatics and systems biology, and how to apply these in a systems medicine project. Written for the Methods in Molecular Biology series, chapters include introductions to their respective topics, and discuss experimental and computational approaches, methods, and tools that should be considered for a successful systems medicine project. Systems Medicine aims to motivate and provide guidance for collaborations across disciplines to tackle today''s challenges related to human healthand well-being.

This book provides an update on the latest development in the field of microRNAs in cancer research with an emphasis on translational research.

Since the early 2000s, microRNAs have been recognized as important and ubiquitous regulators of gene expression. Soon it became evident that their deregulation can cause human diseases including cancer. This book focuses on the emerging opportunities for the application of microRNA research in clinical practice. In this context, computer models are presented that can help to identify novel biomarkers, e.g. in circulating microRNAs, and tools that can help to design microRNA-based therapeutic interventions. Other chapters evaluate the role of microRNAs in immunotherapy, immune responses and drug resistance.

Covering key topics on microRNAs in cancer research this book is a valuable resource for both emerging and established microRNA researchers who want to explore the potential of microRNAs as therapeutic targets or co-adjuvants in cancer therapies.