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E-raamat: Elements of Computational Systems Biology

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Series edited by (University of Western Australia), Edited by (Imperial College London), Edited by (Imperial College London), Series edited by (Department of Computer Science, Georgia State University)
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Groundbreaking, long-ranging research in this emergent field that enables solutions to complex biological problems

Computational systems biology is an emerging discipline that is evolving quickly due to recent advances in biology such as genome sequencing, high-throughput technologies, and the recent development of sophisticated computational methodologies. Elements of Computational Systems Biology is a comprehensive reference covering the computational frameworks and techniques needed to help research scientists and professionals in computer science, biology, chemistry, pharmaceutical science, and physics solve complex biological problems. Written by leading experts in the field, this practical resource gives detailed descriptions of core subjects, including biological network modeling, analysis, and inference; presents a measured introduction to foundational topics like genomics; and describes state-of-the-art software tools for systems biology.





Offers a coordinated integrated systems view of defining and applying computational and mathematical tools and methods to solving problems in systems biology



Chapters provide a multidisciplinary approach and range from analysis, modeling, prediction, reasoning, inference, and exploration of biological systems to the implications of computational systems biology on drug design and medicine



Helps reduce the gap between mathematics and biology by presenting chapters on mathematical models of biological systems



Establishes solutions in computer science, biology, chemistry, and physics by presenting an in-depth description of computational methodologies for systems biology





Elements of Computational Systems Biology is intended for academic/industry researchers and scientists in computer science, biology, mathematics, chemistry, physics, biotechnology, and pharmaceutical science. It is also accessible to undergraduate and graduate students in machine learning, data mining, bioinformatics, computational biology, and systems biology courses.

Arvustused

The book should serve well as a resource for anyone interested in learning about computational systems biology.  (The Quarterly Review of Biology, 1 March 2012)

 

Part 1: Overview.
Chapter 1: Advances in Computational Methodologies for Systems Biology (Huma M. Lodhi, Imperial College London, UK).
Part 2: Biological Network Modelling in System Biology.
Chapter 2: Simulating Filament Dynamics in Cellular Systems (Wilbur E. Channels and Pablo A. Iglesias, The Johns Hopkins University, USA).
Chapter 3: From DNA Motifs to Gene Networks: A Review of the Physical Interaction Models (Panayiotis V. Benos and Alain B. Tchagang, University of Pittsburgh, USA).
Chapter 4: Rule-based Modelling and Model Refinement (Vincent Danos and Elaine Murphy, University of Edinburgh,UK).
Chapter 5: In Silico Analysis of Combined Therapeutic Strategy for Heart Failure (Sung-Young Shin, Korea Advanced Institute of Science and Technology, Korea, Sang-Mok Choo, University of Ulsan, Korea, Tae-Hwan Kim and Kwang-Hyun Cho, Korea Advanced Institute of Science and Technology, Korea).
Chapter 6: Models in Systems Biology: The Parameter Problem and the Meanings of Robustness (Jeremy Gunawardena, Harvard Medical School, USA).
Part 3: Biological Network Inference and Analysis in System Biology.
Chapter 7: Reconstruction of Biological Networks by Supervised Machine Learning Approaches, (Jean-Philippe Vert, Mines ParisTech - Institut Curie, France).
Chapter 8: Metabolic Network Inference from the Integration of Genomic Data and Chemical information (Yoshihiro Yamanishi, Mines ParisTech - Institut Curie, France).
Chapter 9: Analysis and Control of Deterministic and Probabilistic Boolean Networks (Tatsuya Akutsu, Kyoto University and Wai-Ki Ching, University of Hong Kong).
Chapter 10: Integrating Abduction and Induction in Biological Inference using CF-Induction (Katsumi Inoue, National Institute of Informatics, Japan, Yoshitaka Yamamoto, The Graduate University for Advanced Studies, Japan and Andrei Doncescu, LAAS CNRS, France).
Part 4: Genomics and Systems Biology.
Chapter 11: The Impact of Whole Genome In Silico Screening for Nuclear Receptor Binding Sites in Systems Biology (Carsten Carlberg and Merja Heinäniemi, Université du Luxembourg, Luxembourg).
Chapter 12: Probabilistic methods and rate heterogeneity (Tal Pupko and Itay Mayrose, Tel Aviv University, Israel).
Chapter 13: Environmental and Physiological Insights from Microbial Genome Sequences (Alessandra Carbone and Anthony Mathelier, Université Pierre et Marie Curie-Paris 6, France).
Part 5: System Biology Tools and Experimental Validation.
Chapter 14: Ali Baba: A Text Mining Tool for Complex Biological Systems (Jörg Hakenberg, Arizona State University USA, Conrad Plake, Technische University Dresden, Germany and Ulf Leser, Humboldt-University zu Berlin, Germany).
Chapter 15: Systems Biology: A (Natural) Computing Point of View (Matteo Cavaliere, Microsoft Research, Itlay).
Chapter 16: Validation Issues in Regulatory Module Discovery Networks (Alok Mishra and Duncan Gillies, Imperial College London, UK).
Chapter 17: Computational Imaging and Modeling for System Biology (Ling-Yun Wu, Xiaobo Zhou and Stephen TC Wong, The Methodist Hospita, Weill Cornell Medical College,USA).
HUMA M. LODHI, PhD, MBCS, is a researcher with the Department of Computing, Imperial College London. She has studied at Royal Holloway, University of London and has previously worked as a researcher with the Department of Computer Science, University of Sheffield. STEPHEN H. MUGGLETON, PhD, FAAAI, is a Professor of Machine Learning, Department of Computing, Imperial College London, and is the Director of Modeling, BBSRC Centre for Integrative Systems Biology, Imperial College London. He is a Fellow of the American Association for Artificial Intelligence and was a professor of machine learning, Department of Computing, University of York.

Both editors have published in leading international conferences and journals.
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