Verwandte Artikel zu Cancer Systems Biology, Bioinformatics and Medicine:...
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Inhaltsangabe
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 Ref
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