Table of 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