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.
Inhoudsopgave
Preface.
Contributors.
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).
Index.
Series Information.
Over de auteur
HUMA M. LODHI, Ph D, 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, Ph D, 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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