Elements of Computational Systems Biology
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.
PART I: OVERVIEW.
1 Advances in Computational Systems Biology (Huma M. Lodhi).
PART II: BIOLOGICAL NETWORK MODELING.
2 Models in Systems Biology: The Parameter Problem and the Meanings of Robustness (Jeremy Gunawardena).
3 In Silico Analysis of Combined Therapeutics Strategy for Hearth Failure (Sung-Young Shin, Tae-Hwan Kim, Kwang-Hyun Cho, and Sang-Mok Choo).
4 Rule-Based Modeling and Model Refinement (Elaine Murphy, Vincent Danos, Jerome Feret, Jean Krivine, and Russell Harmer).
5 A (Natural) Computing Perspective on Cellular Processes (Mateo Cavaliere and Tommaso Mazza).
6 Simulating Filament Dynamics in Cellular Systems (Wilbur E. Channels and Pablo A. Iglesias).
PART III: BIOLOGICAL NETWORK INFERENCE.
7 Reconstruction of Biological Networks by Supervised Machine Learning Approaches (Jean-Philippe Vert).
8 Supervised Inference of Metabolic Networks from the Integration of Genomic Data and Chemical Information (Yoshihiro Yamanishi).
9 Integrating Abduction and Induction in Biological Inference Using CF-Induciton (Yoshitaka Yamamoto, Katsumi Inoue, and Andrei Doncescu).
10 Analysis and Control of Deterministic and Probabilistic Boolean Networks (Tatsuya Akutsu and Wai-Ki Ching).
11 Probabilistic Methods and Rate Heterogeneity (Tal Pupko and Itay Mayrose).
PART IV: GENOMICS AND COMPUTATIONAL SYSTEMS BIOLOGY.
12 From DNA Motifs to Gene Networks: A Review of Physical Interaction Models (Panayiotis V. Benos and Alain B. Tchagang).
13 The Impact of Whole Genome In Silico Screening for Nuclear Receptor-Binding Sites in Systems Biology (Carsten Carlberg and Merja Heinaniemi).
14 Environmental and Physiological Insights from Microbial Genome Sequences (Alessandra Carbone and Anthony Mathelier).
PART V: SOFTWARE TOOLS FOR SYSTEMS BIOLOGY.
15 Ali Baba: A Text Mining Tool for Systems Biology (Jorg Hakenberg, Conrad Plake, and Ulf Leser).
16 Validation Issues in Regulatory Module Discovery (Alok Mishra and Duncan Gillies).
17 Computational Imaging and Modeling for Systems Biology (Ling-Yun Wu, Xiaobo Zhou, and Stephen T.C. Wong).
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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