{"product_id":"handbook-of-statistical-systems-biology-isbn-9780470710869","title":"Handbook of Statistical Systems Biology","description":"Systems Biology is now entering a mature phase in which the key issues are characterising uncertainty and stochastic effects in mathematical models of biological systems. The area is moving towards a full statistical analysis and probabilistic reasoning over the inferences that can be made from mathematical models. This handbook presents a comprehensive guide to the discipline for practitioners and educators, in providing a full and detailed treatment of these important and emerging subjects. Leading experts in systems biology and statistics have come together to provide insight in to the major ideas in the field, and in particular methods of specifying and fitting models, and estimating the unknown parameters.  \u003cp\u003eThis book:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eProvides a comprehensive account of inference techniques in systems biology.\u003c\/li\u003e \u003cli\u003eIntroduces classical and Bayesian statistical methods for complex systems.\u003c\/li\u003e \u003cli\u003eExplores networks and graphical modeling as well as a wide range of statistical models for dynamical systems.\u003c\/li\u003e \u003cli\u003eDiscusses various applications for statistical systems biology, such as gene regulation and signal transduction.\u003c\/li\u003e \u003cli\u003eFeatures statistical data analysis on numerous technologies, including metabolic and transcriptomic technologies.\u003c\/li\u003e \u003cli\u003ePresents an in-depth presentation of reverse engineering approaches.\u003c\/li\u003e \u003cli\u003eProvides colour illustrations to explain key concepts.\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eThis handbook will be a key resource for researchers practising systems biology, and those requiring a comprehensive overview of this important field.\u003c\/p\u003e  Chapter 1 Two challenges of systems biology.  \u003cp\u003eChapter 2 Introduction to Statistical Methods for Complex Systems.\u003c\/p\u003e \u003cp\u003eChapter 3 Bayesian Inference and Computation.\u003c\/p\u003e \u003cp\u003eChapter 4 Data Integration: Towards Understanding Biological Complexity.\u003c\/p\u003e \u003cp\u003eChapter 5 Control Engineering Approaches to Reverse Engineering Biomolecular Approaches.\u003c\/p\u003e \u003cp\u003eChapter 6 Algebraic Statistics and Methods in Systems Biology.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eB. Technology-based Chapters.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eChapter 7 Transcriptomic Technologies and Statistical Data Analysis.\u003c\/p\u003e \u003cp\u003eChapter 8 Statistical Data Analysis in Metabolomics.\u003c\/p\u003e \u003cp\u003eChaper 9 Imaging and Single-Cell Measurement Technologies.\u003c\/p\u003e \u003cp\u003eChapter 10 Protein Interaction Networks and Their Statistical Analysis.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eC. Networks and Graphical Models.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eChapter 11 Introduction to Graphical Modelling.\u003c\/p\u003e \u003cp\u003eChapter 12 Recovering Genetic Network from Continuous Data with Dynamic Bayesian Networks.\u003c\/p\u003e \u003cp\u003eChapter 13 Advanced Applications of Bayesian Networks in Systems Biology.\u003c\/p\u003e \u003cp\u003eChapter 14 Random Graph Models and Their Application to Protein-Protein Interaction Networks.\u003c\/p\u003e \u003cp\u003eChapter 15 Modelling Biological Networks Via Tailored Random Graphs.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eD. Dynamical Systems.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eChapter 16 Nonlinear Dynamics: a Brief Introduction.\u003c\/p\u003e \u003cp\u003eChapter 17 Qualitative Inference for Dynamical Systems.\u003c\/p\u003e \u003cp\u003eChapter 18 Stochastic Dynamical Systems.\u003c\/p\u003e \u003cp\u003eChapter 19 State-Space models.\u003c\/p\u003e \u003cp\u003eChapter 20 Model Identification by Utilizing Likelihood-Based Methods.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eE. Application Areas.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eChapter 21 Inference of Signalling Pathway Models.\u003c\/p\u003e \u003cp\u003eChapter 22 Modelling Transcription Factor Activity.\u003c\/p\u003e \u003cp\u003eChapter 23 Host-Pathogen Systems Biology.\u003c\/p\u003e \u003cp\u003eChapter 24 Statistical Metabolomics: Bayesian Challenges in the Analysis of Metabolomic Data.\u003c\/p\u003e \u003cp\u003eChapter 25 Systems Biology of microRNA.\u003c\/p\u003e  \u003cp\u003e“A very remarkable collection of essays. Strongly recommended to workers in this area.”  (\u003ci\u003eInternational Statistical Review\u003c\/i\u003e, 1 October 2013)\u003c\/p\u003e \u003cp\u003e“I would highly recommend this book as a useful guide for the students and practitioners of systems biology.”  (\u003ci\u003eScience Progress\u003c\/i\u003e, 1 September 2012)\u003c\/p\u003e \u003cp\u003e“This handbook will be a key resource for researchers practising systems biology, and those requiring a comprehensive overview of this important field.” (\u003ci\u003eZentralblatt MATH\u003c\/i\u003e, 2012)\u003c\/p\u003e \u003cp\u003e\u003cb\u003eMichael Stumpf\u003c\/b\u003e, Theoretical Systems Biology at Imperial College London\u003c\/p\u003e \u003cp\u003e\u003cb\u003eDavid Balding\u003c\/b\u003e, Statistical Genetics in the Institute of Genetics at University College London\u003c\/p\u003e \u003cp\u003e\u003cb\u003eMark Girolami\u003c\/b\u003e, Department of Computing Science and the Department of Statistics\u003c\/p\u003e  Systems Biology is now entering a mature phase in which the key issues are characterising uncertainty and stochastic effects in mathematical models of biological systems. The area is moving towards a full statistical analysis and probabilistic reasoning over the inferences that can be made from mathematical models. This handbook presents a comprehensive guide to the discipline for practitioners and educators, in providing a full and detailed treatment of these important and emerging subjects. Leading experts in systems biology and statistics have come together to provide insight in to the major ideas in the field, and in particular methods of specifying and fitting models, and estimating the unknown parameters.  \u003cp\u003eThis book:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eProvides a comprehensive account of inference techniques in systems biology.\u003c\/li\u003e \u003cli\u003eIntroduces classical and Bayesian statistical methods for complex systems.\u003c\/li\u003e \u003cli\u003eExplores networks and graphical modeling as well as a wide range of statistical models for dynamical systems.\u003c\/li\u003e \u003cli\u003eDiscusses various applications for statistical systems biology, such as gene regulation and signal transduction.\u003c\/li\u003e \u003cli\u003eFeatures statistical data analysis on numerous technologies, including metabolic and transcriptomic technologies.\u003c\/li\u003e \u003cli\u003ePresents an in-depth presentation of reverse engineering approaches.\u003c\/li\u003e \u003cli\u003eProvides colour illustrations to explain key concepts.\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eThis handbook will be a key resource for researchers practising systems biology, and those requiring a comprehensive overview of this important field.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989342830821,"sku":"NP9780470710869","price":244.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780470710869.jpg?v=1761783742","url":"https:\/\/k12savings.com\/products\/handbook-of-statistical-systems-biology-isbn-9780470710869","provider":"K12savings","version":"1.0","type":"link"}