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.
Professor Werner Dubitzky holds a Chair of Bioinformatics at the School of Biomedical Sciences, Faculty of Life and Health Science, at the University of Ulster, Northern Ireland
Applications in drug discovery.- Aritificial life.- Artificial intelligence and machine learning.- Cancer.- Cell cycle.- Computational microRNA biology.- Functional imaging.- Gene regulatory networks: modeling, reconstruction and analysis.- Large-scale and high-performance computing.- Mathematical theory and methods.- Metabolic networks.- Model databases.- Philosophical and foundational issues.- Quantitative experiment design.- Resources.- Single cell experiments.- Standards, exchange formats, guidelines and ontologies.- Statistical theory and methods.- Structural network analysis.- Systems approaches to translational biomedical research: focus on diagnostic and prognostic applications.- Systems immunology.- Text mining.- Toponomics.- Transcriptional regulation.- Translational control and systems biology.