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