Figures
Introduction: Why systems biology of cancer?
Introduction: Why systems biology of cancer?
Basic principles of the molecular biology of cancer
Basic principles of the molecular biology of cancer
- 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
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
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
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
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. 6.6: Network-driven linear discriminative model
- Fig. 6.7: Network-driven feature selection
Mathematical modelling applied to cancer cell biology
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
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
Cancer robustness: facts and hypotheses
Cancer robustness: mathematical foundations
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
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
Appendices