Elements of Computational Systems Biology

by Lodhi, Huma M.; Muggleton, Stephen H.

• ISBN13: 9780470180938
• ISBN10: 0470180935
• Edition: 1st
• Format: Hardcover
• Copyright: 2010-02-08
• Publisher: Wiley

Summary

This comprehensive reference presents cutting-edge and long-ranging research in computational systems biology. It is written by leading experts and covers a range of topics from modeling and learning biological systems to the impact of computational systems biology on drug design and medicine. Chapters provide a multidisciplinary approach and range from analysis, modeling, prediction, reasoning, inference, and exploration of biological systems to the applications of computational systems biology. These chapters target topics and solutions in computer science, mathematics, physics, biology, and the pharmaceutical and biomedical areas.

Author Biography

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.

Table of Contents

Overview

Advances in Computational Methodologies for Systems Biology

Biological Network Modelling in System Biology

Simulating Filament Dynamics in Cellular Systems

From DNA Motifs to Gene Networks: A Review of the Physical Interaction Models

Rule-based Modelling and Model Refinement

In Silico Analysis of Combined Therapeutic Strategy for Heart Failure

Models in Systems Biology: The Parameter Problem and the Meanings of Robustness

Biological Network Inference and Analysis in System Biology

Reconstruction of Biological Networks by Supervised Machine Learning Approaches

Metabolic Network Inference from the Integration of Genomic Data and Chemical information

Analysis and Control of Deterministic and Probabilistic Boolean Networks

Integrating Abduction and Induction in Biological Inference using CF-Induction

Genomics and Systems Biology

The Impact of Whole Genome In Silico Screening for Nuclear Receptor Binding Sites in Systems Biology

Probabilistic methods and rate heterogeneity

Environmental and Physiological Insights from Microbial Genome Sequences

System Biology Tools and Experimental Validation

Ali Baba: A Text Mining Tool for Complex Biological Systems

Systems Biology: A (Natural) Computing Point of View

Validation Issues in Regulatory Module Discovery Networks

Computational Imaging and Modeling for System Biology

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