Cancer Systems Biology, Bioinformatics and Medicine (eBook)

PART I – Introduction and background.- 1. Introduction to systems approaches to cancer.- 1.1 Cancer and systems approaches.- 1.2 Laboratory, clinical, data and educational resources.- 1.3 Bioinformatics and systems biology analysis.- 1.4 Diagnosis and treatment applications.- 1.5 Perspectives and conclusions.- 1.6 References.- 2. Cancer: clinical background and key challenges.- 2.1 Introduction.- 2.2 Pathology integration in cancer biology systems.- 2.3 Technological approaches to morphology and pathology.- 2.4 Treatments.- 2.5 Major cancers, diagnosis, disease-specific supplementary classifications, and treatment implications.- 2.6 Systems biology of cancer: key challenges for the future.- 2.7 Acknowledgements.- 2.8 References.- PART II – Laboratory, clinical, data and educational resources.- 3. Global molecular and cellular measurement technologies.- 3.1.  Introduction – the need for systems biology predictive models.- 3.2.  Sample preparation.- 3.3. Analysis of the genome.- 3.4.  Proteomics.- 3.5  Functional studies.- 3.6 Overall determining factors and future outlook.- 3.7 Acknowledgements.- 3.8 References.- 3.9 Abbreviations.- 4. Cell lines, tissue samples, model organisms, biobanks.- 4.1 Introduction.- 4.2 Human cell lines.- 4.3 Model organisms.- 4.4 Patient biobanks.- 4.5 Role of interactome maps and crucial pathways.- 4.6 Integration into systems and computational approaches.- 4.7 The future: data integration to systems-level experiments.- 4.8 References.- 5. Expression and genetic variation databases for cancer research.- 5.1 Introduction.- 5.2 Genetic variation.- 5.3 Gene expression.- 5.4 Informatics coordination by international consortia.- 5.5 References.- 6. Education and Research Infrastructures.- 6.1 The challenge.- 6.2 The actors.- 6.3 Training and education of the stakeholders.- 6.4 Organization of cancer research centres and their cross-disciplinary activities.- 6.5 Conclusion.- 6.6 Acknowledgements.- 6.7 References.- PART III – Bioinformatics and systems biology analysis.- 7. Mathematical tools in cancer signalling systems biology.- 7.1 Introduction.- 7.2 The systems approach.- 7.3 Discussion.- 7.4 Acknowledgements.- 7.5 References.- 7.6 Appendix.- 8. Computational tools for systems biology.- 8.1 Introduction.- 8.2 Standards in systems biology.- 8.3 Web Resources.- 8.4 Computational Tools.- 8.5 Visualizing networks.- 8.6 Workflows.- 8.7 Discussion.- 8.8 Acknowledgements.- 8.9 References.- 9. The hallmarks of cancer revisited through systems biology and network modeling.- 9.1 Introduction.- 9.2 Genome variation and instability revisited through genetic and genomic networks.- 9.3 Transcription and protein interaction networks revealed by modular cancer biomarkers.- 9.4 Growth, proliferation and apoptosis revisited through signalling network modeling.- 9.5 Sustained angiogenesis and metastasis revisited through multiscale modeling.- 9.6 The hallmarks of cancer extended to control of stress and metabolism.- 9.7 Conclusion and perspectives.- 9.8 Acknowledgements.- 9.9 References.- 10. Systems biology analysis of cell death pathways in cancer: how collaborative and interdisciplinary research helps.- 10.1 Introduction.- 10.2 Cell death pathways.- 10.3 Dysregulation of cell death pathways in cancer.- 10.4 Mathematical modelling of cell death pathways.- 10.5 Elements for interdisciplinary approaches to cancer research.- 10.6 How to share knowledge about systems biology approaches to cancers.- 10.7 Major collaborative efforts.- 10.8 Supporting collaborative research projects.- 10.9 Conclusion.- 10.10 Acknowledgements.- 10.11 References.- 11. Systems biology, bioinformatics and medicine approaches to cancer progression outcomes.-1 1.1 Introduction: The concept of pathway signatures.- 11.2 Identification of biological motifs from gene array data.- 11.3 From biological motifs to pathway activation.- 11.4 How realistic is modelling of carcinogenesis and tumour development in virtual tissues and organs?- 11.5 References.- 11.6 Websites.- 12. System dynamics at the physiological and tumour level.- 12.1 Introduction to mathematical modelling in cancer.- 12.2 Mathematical models in cancer.- 12.3 Model development.- 12.4 Iterative modelling of tumour systems.- 12.5 Experimental studies of tumour invasion.- 12.6 Tumour modelling collaborations.- 12.7 Detailed modelling example.- 12.8 Conclusions.- 12.9 References.- PART IV – Diagnosis, clinical and treatment applications.- 13. Diagnostic and prognostic cancer biomarkers: from traditional to systems approaches.- 13.1 Introduction.- 13.2 Role of biomarkers.- 13.3 Biomarkers for prediction of response to treatment.- 13.4 Biomarkers for prognosis.- 13.5 Biomarkers for monitoring.- 13.6 Measurement and analysis of biomarkers.- 13.7 Identification, standardization and validation of effective biomarkers.- 13.8 Annotated, high quality clinical samples.- 13.9 Analyses and simulations to predict and identify biomarkers.- 13.10 Approaches to data analyses in genomic studies.- 13.13 Pharmacokinetics and pharmacodynamics.- 13.14 Integrated approaches to biomarker discovery and development.- 13.15 References.- 14. Systems biology approaches to cancer drug development.- 14.1 Introduction.- 14.2 Model building.- 14.3 Case studies of modelling cellular networks.- 14.4 Modelling at cellular scales.- 14.5 Technologies used at Physiomics.- 14.6 Conclusion.- 14.7 References.- 15. Circadian rhythms and cancer chronotherapeutics .- 15.1 Circadian rhythms in health and diseases.- 15.2 Chronopharmacology, chronotolerance and chronoefficacy of anticancer drugs.- 15.3 From standard to personalized cancer chronotherapeutics.- 15.4 Conclusions and perspectives.- 15.5 Acknowledgements .- 15.6 References.- 16. Clinical applications of systems approaches.- 16.1 Chapter introduction.- 16.2 Systems biology approaches to identifying diagnostic, prognostic, and therapeutic biomarkers for cancer.- 16.3 Systems biology approaches to the design of combinatorial targeted therapy for cancer.- 16.4 The future of clinical trials: applying systems approaches to clinical trial design.- 16.5 References.- 17. Cancer robustness and therapy strategies.- 17.1 Introduction.- 17.2 Mechanisms for robustness.- 17.3 Mechanisms for cancer robustness.- 17.4 Robustness trade-offs.- 17.5 Theoretically-motivated therapy strategies.- 17.6 A proper index of treatment efficacy.- 17.7 Long-tail drugs.- 17.8 Conclusion.- 17.9 Acknowledgements.- 17.10 References.- PART V – Perspectives and conclusions.- 18. Synthetic biology and perspectives.- 18.1 Introduction.- 18.2 Synthetic biology for cancer research and applications.- 18.3 Synthetic biology applications to cancer.- 18.4 Review articles and workshops – integrated perspectives.- 18.5 Resources needed to support systems approaches to cancer research and diagnosis.- 18.6 Conclusions.- 18.7 References.- 19. Conclusions.- 19.1 Key points.- 19.2 Overall conclusions.- Index.