461 pages | 25 Color Illus. | 87 B/W Illus.
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:
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.
"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
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
Evolution of tumour cell populations
Alterations of gene regulation and signal transduction mechanisms
Cancer is a network disease
Hallmarks of cancer
Chromosome aberrations in cancer
Experimental High-Throughput Technologies for Cancer Research
Emerging sequencing technologies
Chromosome conformation capture
Bioinformatics Tools and Standards for Systems Biology
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
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
Integrative data analysis
Mathematical Modelling Applied to Cancer Cell Biology
Mathematical modelling flowchart
Mathematical modelling of a generic cell cycle
Decomposition of the generic cell cycle into motifs
Mathematical Modelling of Cancer Hallmarks
Modelling the hallmarks of cancer
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
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 and stability of dynamical systems
Dynamical robustness and low-dimensional dynamics
Dynamical robustness and limitation in complex networks
A possible generalised view on robustness
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
Cancer systems biology and medicine: Other paths
Will cancer systems biology translate into cancer systems medicine?
Holy Grail of systems biology