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    978-1-43-983144-1
    August 25th 2012

Description

The future of cancer research and the development of new therapeutic strategies rely on our ability to convert biological and clinical questions into mathematical models—integrating our knowledge of tumour progression mechanisms with the tsunami of information brought by high-throughput technologies such as microarrays and next-generation sequencing. Offering promising insights on how to defeat cancer, the emerging field of systems biology captures the complexity of biological phenomena using mathematical and computational tools.

Novel Approaches to Fighting Cancer

Drawn from the authors’ decade-long work in the cancer computational systems biology laboratory at Institut Curie (Paris, France), Computational Systems Biology of Cancer explains how to apply computational systems biology approaches to cancer research. The authors provide proven techniques and tools for cancer bioinformatics and systems biology research.

Effectively Use Algorithmic Methods and Bioinformatics Tools in Real Biological Applications

Suitable for readers in both the computational and life sciences, this self-contained guide assumes very limited background in biology, mathematics, and computer science. It explores how computational systems biology can help fight cancer in three essential aspects:

  1. Categorising tumours
  2. Finding new targets
  3. Designing improved and tailored therapeutic strategies

Each chapter introduces a problem, presents applicable concepts and state-of-the-art methods, describes existing tools, illustrates applications using real cases, lists publically available data and software, and includes references to further reading. Some chapters also contain exercises. Figures from the text and scripts/data for reproducing a breast cancer data analysis are available at www.cancer-systems-biology.net.

Reviews

"There is a tremendous amount of biological and biochemical detail in this book, and yet (gratifyingly and perhaps surprisingly) considerable attention is paid to mathematical definitions …"

—John Adam, Mathematical Reviews, August 2013

"An up-to-date, comprehensive and very readable overview, this book has plenty for everyone interested in computational systems biology of cancer. Almost all important topics are introduced and explained, and pointers are given to further work. The bibliography is outstanding. Think of this as your guide book to the field, as well as a way to get started in it."

—Terry Speed, Professor of Statistics, University of California, Berkeley, USA, and Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia

"This book deals with an important and very timely topic: The ongoing struggle against cancer can benefit greatly from the novel high-throughput technologies that are rapidly becoming more accessible. However, in order to make effective use of the data that these technologies produce, sophisticated computational methods that address the cancer disease on the system level are needed. The authors have made substantial and useful effort to describe the state of the art of these computational methods in an accessible and clear way. The book is a much-needed contribution to modern cancer analysis and to the emerging discipline of systems biology."

—Ron Shamir, Professor of Bioinformatics, Tel Aviv University, Israel

"This is the first book specifically focused on computational systems biology of cancer with coherent and proper vision on how to tackle this formidable challenge. I would like to congratulate the authors for their visions and dedications."

—Hiroaki Kitano, President, The Systems Biology Institute; President and Chief Operating Officer, Sony Computer Science Laboratories, Inc.; and Professor, Okinawa Institute of Science and Technology, Japan

Contents

Introduction: Why Systems Biology of Cancer?

Cancer is a major health issue

From genome to genes to network

Cancer research as a big science

Cancer is a heterogeneous disease

Cancer requires personalised medicine

What is systems biology?

About this book

Basic Principles of the Molecular Biology of Cancer

Progressive accumulation of mutations

Cancer-critical genes

Evolution of tumour cell populations

Alterations of gene regulation and signal transduction mechanisms

Cancer is a network disease

Tumour microenvironment

Hallmarks of cancer

Chromosome aberrations in cancer

Conclusion

Experimental High-Throughput Technologies for Cancer Research

Microarrays

Emerging sequencing technologies

Chromosome conformation capture

Large-scale proteomics

Cellular phenotyping

Conclusion

Bioinformatics Tools and Standards for Systems Biology

Experimental design

Normalisation

Quality control

Quality management and reproducibility in computational systems biology workflow

Data annotations and ontologies

Data management and integration

Public repositories for high-throughput data

Informatics architecture and data processing

Knowledge extraction and network visualization

Exploring the Diversity of Cancers

Traditional classification of cancer

Towards a molecular classification of cancers

Clustering for class discovery

Discovering latent processes with matrix factorization

Interpreting cancer diversity in terms of biological processes

Integrative analysis of heterogeneous data

Heterogeneity within the tumour

Conclusion

Prognosis and Prediction: Towards Individualised Treatments

Traditional prognostic and predictive factors

Predictive modelling by supervised statistical inference

Biomarker discovery and molecular signatures

Functional interpretation with group-level analysis

Network-level analysis

Integrative data analysis

Conclusion

Mathematical Modelling Applied to Cancer Cell Biology

Mathematical modelling

Mathematical modelling flowchart

Mathematical modelling of a generic cell cycle

Decomposition of the generic cell cycle into motifs

Conclusion

Mathematical Modelling of Cancer Hallmarks

Modelling the hallmarks of cancer

Discussion

Cancer Robustness: Facts and Hypotheses

Biological systems are robust

Neutral space and neutral evolution

Robustness, redundancy and degeneracy

Mechanisms of robustness in the structure of biological networks

Robustness, evolution and evolvability

Cancer cells are robust and fragile at the same time

Cancer resistance, relapse and robustness

Experimental approaches to study biological robustness

Conclusion

Cancer Robustness: Mathematical Foundations

Mathematical definition of biological robustness

Simple examples of robust functions

Forest fire model: A simple example of a evolving robust system

Robustness/fragility trade-offs

Robustness and stability of dynamical systems

Dynamical robustness and low-dimensional dynamics

Dynamical robustness and limitation in complex networks

A possible generalised view on robustness

Conclusion

Finding New Cancer Targets

Finding targets from a gene list

Prediction of drug targets from simple network analysis

Drug targets as fragile points in molecular mechanisms

Predicting drug target combinations

Conclusion

Cancer systems biology and medicine: Other paths

Forthcoming challenges

Will cancer systems biology translate into cancer systems medicine?

Holy Grail of systems biology

Appendices

Glossary

Bibliography

Index

Author Bio

Emmanuel Barillot, Laurence Calzone, Philippe Hupe, Jean-Philippe Vert, and Andrei Zinovyev are all with the Institut Curie in Paris, France.

Related Subjects

  1. Biophysics
  2. Bioinformatics

Name: Computational Systems Biology of Cancer (Hardback)CRC Press 
Description: By Emmanuel Barillot, Laurence Calzone, Philippe Hupe, Jean-Philippe Vert, Andrei Zinovyev. The future of cancer research and the development of new therapeutic strategies rely on our ability to convert biological and clinical questions into mathematical models—integrating our knowledge of tumour progression mechanisms with the...
Categories: Biophysics, Bioinformatics