• Cover image: When systems biology meets the cancer constellation
  

Introduction: Why systems biology of cancer?

• Fig. 1.1: Bathsheba at Her Bath
  

• Fig. 2.1: Oncogene and tumour suppressor gene
• Fig. 2.2: From proto-oncogene to oncogene
• Fig. 2.3: Models of tumour suppression
  
• Fig. 2.4: Role of miRNA in a cancer cell
• Fig. 2.5: Hierarchical organisation of a tumour according to the CSC model
• Fig. 2.6: Epigenetic alterations in tumour progression
• Fig. 2.7: Epigenetics patterns in a normal and cancer cells
  
• Fig. 2.8: Signalling pathways in the cell
• Fig. 2.9: Simplified representation of the mechanism regulating RB1 activity
• Fig: 2.10: Tumour progression and invasion in cervix cancer (not available)
  
• Fig. 2.11: Hallmarks of cancer
• Fig. 2.12: Karyotype of the T47D breast cancer cell line
• Fig. 2.13: Schematic illustration of chromosomal aberrations
• Fig. 2.14: Chromothripsis

Experimental high-throughput technologies for cancer research

• Fig. 3.1: Omics technologies in oncology
• Fig. 3.2: Affymetrix GeneChip and Illumina BeadChip designs
• Fig. 3.3: Array-CGH protocol
• Fig. 3.4: Theoretical array-CGH quantification
• Fig. 3.5: aCGH profile of the IMR32 neuroblastoma cell line
• Fig. 3.6: Illustration of BAF values
• Fig. 3.7: Theoretical BAF and LRR values
• Fig. 3.8 (top): LRR and BAF profiles for the T47D breast cancer cell line
• Fig. 3.8 (bottom): LRR and BAF profiles for the T47D breast cancer cell line
• Fig. 3.9: ChIP-on-chip protocol
• Fig. 3.10: ChIP-on-chip profile
• Fig. 3.11: DNA methylation probe design
• Fig. 3.12: Library preparation for the SOLiD platform
• Fig. 3.13: Identification of genome rearrangements using mate-pair sequencing
• Fig. 3.15: Characterisation of DNA copy number and identification of inter-chromosomal translocations in T47D using mate-pair sequencing
  
• Fig. 3.14: From second to fourth-generation sequencing, illustration on TAGGCT template
• Fig. 3.16: Protocols for 3C-based approaches
• Fig. 3.17: Schematic representation of the 3C-based approaches
• Fig. 3.18: Protein arrays
• Fig. 3.19: Mass spectrometry protocol
  
• Fig. 3.20: MS/MS peptide nomenclature
• Fig. 3.21: Quantitative mass spectrometry
• Fig. 3.22: Principles of yeast and mammalian two-hybrid systems
• Fig. 3.23: Principle of Tandem Affinity Purification
• Fig. 3.24: Characterisation of cell growth rate using cellular phenotyping (not available)
  

Bioinformatics tools and standards for systems biology

• Fig. 4.1: Bioinformatics workflow to analyse high-throughput experiments
• Fig. 4.2: Experimental design with a two-way ANOVA model (not available)
• Fig. 4.3: Spatial bias in aCGH experiment
• Fig. 4.4: Effect of GC-content on the number of reads in NGS experiments
  
• Fig. 4.5: Comprehensive map of RB pathway

Exploring the diversity of cancers

• Fig. 5.1: Histological sections of breast cancer (not available)
  
• Fig: 5.2: Breast cancer diversity in 2 dimensions
• Fig. 5.3: Molecular classification of breast cancer from mRNA expression profiles
• Fig. 5.4: Choosing the number of groups
• Fig. 5.5: Number of groups in the Wang dataset
• Fig. 5.6A: Breast cancer diversity in 2 dimensions (PCA)
• Fig. 5.6B: Breast cancer diversity in 2 dimensions (ICA)
• Fig. 5.6C: Breast cancer diversity in 2 dimensions (NMF)
• Fig. 5.6D: Breast cancer diversity in 2 dimensions (ISOMAP)
• Fig. 5.7A: Multiple Factor Analysis on breast cancer data (all genes)
  
• Fig. 5.7B: Multiple Factor Analysis on breast cancer data (RB pathway)
• Fig. 5.7C: Multiple Factor Analysis on breast cancer data (NFkB pathway)
• Fig. 5.7D: Multiple Factor Analysis on breast cancer data (TGFbeta pathway)
• Fig. 5.8: Modular map of RB pathway
• Fig. 5.9: PCA of the modules activity

Prognosis and prediction: towards individualised treatments

• Fig. 6.1: Prognostic and predictive factors
• Fig. 6.2: Losses of large-margin classifiers
• Fig. 6.3: Breast cancer prognosis performance
• Fig. 6.4: Influence of signature size on breast cancer prognosis performance
• Fig. 6.5: Detection of differentially expressed subnetwork with the BiNoM Cytoscape plugin
• Fig. 6.6: Network-driven linear discriminative model
• Fig. 6.7: Network-driven feature selection

Mathematical modelling applied to cancer cell biology

• Fig. 7.1: Hallmarks of mathematical modelling
• Fig. 7.2: Flowchart of mathematical modelling
• Fig. 7.3: From an influence network to a reaction network
• Fig. 7.4: Network of Novak and Tyson cell cycle
• Fig. 7.5: Simulations of Novak and Tyson cell cycle model
• Fig. 7.6: Boolean cell cycle of the restriction point developed by Faure et al.
  
• Fig 7.7: ODE model of the positive feedback loop
• Fig. 7.8: Boolean model of the positive feedback loop involving CycB and CDH1
• Fig. 7.9: Continuous model of the negative feedback loop
• Fig. 7.10: Boolean model of the negative feedback loop involving CycB and CDC20
• Fig. 7.11: Continuous model of the combined positive and negative feedback loops
• Fig. 7.12: Boolean model of the combined positive and negative feedback loops

Mathematical modelling of cancer hallmarks

• Fig. 8.1:  Examples of mutation-selection diagrams used to compute probabilities of cancer cell initiation for the simplest Knudson two-hit model of tumour suppressor inactivation
• Fig. 8.2:  Pathways involved in hallmarks of cancer

Cancer robustness: facts and hypotheses

• Fig. 9.1:  Network structures responsible for robust network properties

Cancer robustness: mathematical foundations

• Fig. 10.1: Forest-fire lattice percolation model and mechanisms of SOC and HOT (not available)
  
• Fig. 10.2: Simple mathematical view on robustness/fragility trade-off
• Fig. 10.3: A schematic illustration of the geometrical representation of a dynamical system

Finding new cancer targets

• Fig. 11.1: Identification of overrepresented regulatory motifs
• Fig. 11.2: Network hubs and routers
• Fig. 11.3: Finding a minimal cut set to disrupt signalling in a toy network

Appendices

• Fig. A.1:  Hierarchical representation of a multi-cellular living organism
• Fig. A.2:  Central dogma of molecular biology
• Fig. A.3:  Role of transcription factor in gene expression regulation
• Fig. A.4:  Alternative splicing
• Fig. A.5:  Role of miRNA in a normal cell
• Fig. A.6:  Signal transduction cascade