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Cancer Systems Biology, Bioinformatics and Medicine: Research and Clinical Applications 2011 [Kõva köide]

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  • Formaat: Hardback, 484 pages, kõrgus x laius: 235x155 mm, kaal: 955 g, XXVIII, 484 p., 1 Hardback
  • Ilmumisaeg: 21-Aug-2011
  • Kirjastus: Springer
  • ISBN-10: 9400715668
  • ISBN-13: 9789400715660
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  • Formaat: Hardback, 484 pages, kõrgus x laius: 235x155 mm, kaal: 955 g, XXVIII, 484 p., 1 Hardback
  • Ilmumisaeg: 21-Aug-2011
  • Kirjastus: Springer
  • ISBN-10: 9400715668
  • ISBN-13: 9789400715660
Teised raamatud teemal:
This book describes a systems approaches to cancer research. Covering a full range of topics, including laboratory, clinical and medical approaches, it demonstrates how systems approaches build upon and greatly extend current research practices.

This teaching monograph on systems approaches to cancer research and clinical applications provides a unique synthesis, by world-class scientists and doctors, of laboratory, computational, and clinical methods, thereby establishing the foundations for major advances not possible with current methods.  Specifically, the book: 1) Sets the stage by describing the basis of systems biology and bioinformatics approaches, and the clinical background of cancer in a systems context; 2) Summarizes the laboratory, clinical, data systems analysis and bioinformatics tools, along with infrastructure and resources required; 3) Demonstrates the application of these tools to cancer research; 4) Extends these tools and methods to clinical diagnosis, drug development and treatment applications; and 5) Finishes by exploring longer term perspectives and providing conclusions. This book reviews the state-of-the-art, and goes beyond into new applications. It is written and highly referenced as a textbook and practical guide aimed at students, academics, doctors, clinicians, industrialists and managers in cancer research and therapeutic applications. Ideally, it will set the stage for integration of available knowledge to optimize communication between basic and clinical researchers involved in the ultimate fight against cancer, whatever the field of specific interest, whatever the area of activity within translational research.
Part I Introduction and Background
1 Introduction to Systems Approaches to Cancer
3(26)
Frederick B. Marcus
Alfredo Cesario
1.1 Cancer and Systems Approaches
3(6)
1.1.1 Nature and Causes of Cancer
3(1)
1.1.2 The Progression of Cancer
4(1)
1.1.3 Cancer: Clinical Background and Key Challenges
5(1)
1.1.4 Systems Biology Approaches to Cancer
6(1)
1.1.5 Key Books and Reviews of Systems Approaches
7(2)
1.1.6 Importance of Legal and Ethical Considerations
9(1)
1.2 Laboratory, Clinical, Data and Educational Resources
9(3)
1.2.1 Global Molecular and Cellular Measurement Technologies
9(1)
1.2.2 Cell Lines, Tissue Samples, Model Organisms, Biobanks
10(1)
1.2.3 Expression and Genetic Variation Databases for Cancer Research
11(1)
1.2.4 Education and Research Infrastructures
11(1)
1.3 Bioinformatics and Systems Biology Analysis
12(5)
1.3.1 Mathematical Tools in Cancer Signalling
12(1)
1.3.2 Computational Tools
13(1)
1.3.3 The Hallmarks of Cancer Revisited Through Modelling
14(1)
1.3.4 Analysis of Cell Death Pathways in Cancer: The Role of Collaborative and Interdisciplinary Research
14(2)
1.3.5 Approaches to Cancer Progression Outcomes
16(1)
1.3.6 Modelling at the Physiological and Tumour Level
16(1)
1.4 Diagnosis and Treatment Applications
17(4)
1.4.1 Diagnostic and Prognostic Cancer Biomarkers
17(1)
1.4.2 Cancer Drug Development
18(1)
1.4.3 Cancer Chronotherapy
19(1)
1.4.4 Clinical Applications of Systems Biology Approaches
19(1)
1.4.5 Cancer Robustness and Therapy Strategies
20(1)
1.5 Perspectives and Conclusions
21(1)
1.5.1 Perspectives
21(1)
1.5.2 Conclusion
21(1)
References
22(7)
2 Cancer: Clinical Background and Key Challenges
29(68)
Antonio Llombart-Bosch
Ulrik Ringborg
Sergio Rutella
Julio E. Celis
2.1 Introduction
29(2)
2.2 Pathology Integration in Cancer Biology Systems
31(15)
2.2.1 Definition of a Neoplasm
32(1)
2.2.2 Tumour Nomenclature
33(2)
2.2.3 Tumour Grading
35(3)
2.2.4 Growth Rate of a Tumour
38(1)
2.2.5 Dysplasia and Carcinoma in situ
39(2)
2.2.6 Metastasis
41(1)
2.2.7 Tumour Staging
42(3)
2.2.8 Cytology and Diagnosis
45(1)
2.3 Technological Approaches to Morphology and Pathology
46(3)
2.3.1 Hematoxylin-Eosin (H&E) Staining in Histological Diagnosis
46(1)
2.3.2 Immunohistochemistry
46(1)
2.3.3 Electron Microscopy
47(1)
2.3.4 Tissue Microarray (TMA)
48(1)
2.4 Treatments
49(11)
2.4.1 Surgical Treatment
50(1)
2.4.2 Radiation Therapy
50(1)
2.4.3 Systemic Treatment
51(7)
2.4.4 Treatment Strategies
58(2)
2.5 Major Cancers, Diagnosis, Disease-specific Supplementary Classifications, and Treatment Implications
60(21)
2.5.1 Colorectal Cancer
60(3)
2.5.2 Breast Carcinoma
63(6)
2.5.3 Lung Cancer
69(4)
2.5.4 Small Round Cell Tumours (SRCT)
73(4)
2.5.5 Leukaemias and Lymphomas
77(4)
2.6 Systems Biology of Cancer: Key Challenges for the Future
81(3)
Acknowledgements
84(1)
References
84(13)
Part II Laboratory, Clinical, Data and Educational Resources
3 Global Molecular and Cellular Measurement Technologies
97(30)
Bodo M. H. Lange
Michal R. Schweiger
Hans Lehrach
3.1 Introduction---The Need for Systems Biology Predictive Models
99(1)
3.2 Sample Preparation
100(2)
3.3 Analysis of the Genome
102(8)
3.3.1 DNA Microarrays
102(1)
3.3.2 Next Generation Sequencing (NGS)
102(8)
3.4 Proteomics
110(5)
3.4.1 Two-dimensional Gel Electrophoresis
110(1)
3.4.2 Mass Spectrometry
111(1)
3.4.3 Quantitative Protein Arrays
112(1)
3.4.4 Immunohistochemistry
113(1)
3.4.5 Phosphoproteome
114(1)
3.4.6 Metabolome
115(1)
3.5 Functional Studies
115(5)
3.5.1 RNA Interferences (RNAi)
116(1)
3.5.2 Model Organisms
117(1)
3.5.3 Determining Drug and Compound Action
118(1)
3.5.4 Protein-Protein and Protein-DNA Interactions
119(1)
3.6 Overall Determining Factors and Future Outlook
120(1)
Acknowledgments
121(1)
References
121(6)
4 Cell Lines, Tissue Samples, Model Organisms, and Biobanks: Infrastructure and Tools for Cancer Systems Biology
127(26)
Sandra Tomaszek
Dennis A. Wigle
4.1 Introduction
127(1)
4.2 Human Cell Lines
128(3)
4.2.1 The NCI-60 Human Cancer Cell Line Panel
129(2)
4.3 Model Organisms
131(10)
4.3.1 Transgenic Mouse Models
132(2)
4.3.2 Chemically Induced Models
134(1)
4.3.3 Human Lung Tumour Xenografts
134(3)
4.3.4 Lung Cancer Models in Cancer Drug Development
137(1)
4.3.5 Models for the Study of Lung Cancer Metastasis
138(1)
4.3.6 Model Organisms: New Systems for Modelling Cancer
139(2)
4.3.7 Restrictions on the Use of Animals in Research
141(1)
4.4 Patient Biobanks
141(3)
4.4.1 Paraffin Embedded Tissues
142(1)
4.4.2 Snap-frozen Tissues
142(1)
4.4.3 Linking Molecular and Clinical Measurements
143(1)
4.5 Role of Interactome Maps and Crucial Pathways
144(2)
4.5.1 Links to Specific Types of Cancer
144(1)
4.5.2 Synthetic Lethality as a Network-derived Treatment Success-story
145(1)
4.6 Integration into Systems and Computational Approaches
146(1)
4.7 The Future: Data Integration to Systems-level Experiments
147(1)
References
147(6)
5 Expression and Genetic Variation Databases for Cancer Research
153(12)
Johan Rung
Alvis Brazma
5.1 Introduction
153(1)
5.2 Genetic Variation
154(5)
5.2.1 SNP Databases
155(1)
5.2.2 Databases of Structural Variants
155(1)
5.2.3 Databases for Disease-causing Variants
156(1)
5.2.4 Large-scale Repositories for Experiments
157(1)
5.2.5 Reference Genomes
158(1)
5.3 Gene Expression
159(2)
5.3.1 Archives of Gene Expression Data
159(1)
5.3.2 Added-value Databases
160(1)
5.4 Informatics Coordination by International Consortia
161(2)
References
163(2)
6 Education and Research Infrastructures
165(20)
Anna Tramontano
Alfonso Valencia
6.1 The Challenge
165(2)
6.2 The Actors
167(5)
6.2.1 Molecular and Cell Biologists
167(1)
6.2.2 Chemical Biologists
168(1)
6.2.3 Clinical Oncology Researchers
169(2)
6.2.4 General Public
171(1)
6.3 Training and Education of the Stakeholders
172(5)
6.3.1 The Core Subjects in Modern Scientific Education
172(2)
6.3.2 User Training
174(2)
6.3.3 The General Public
176(1)
6.4 Organization of Cancer Research Centres and their Cross-disciplinary Activities
177(3)
6.5 Conclusions
180(1)
Acknowledgments
180(1)
References
180(5)
Part III Bioinformatics and Systems Biology Analysis
7 Mathematical Tools in Cancer Signalling Systems Biology
185(28)
Julio Vera
Olaf Wolkenhauer
7.1 Introduction
185(3)
7.2 The Systems Approach
188(18)
7.2.1 When to Employ a Systems Biology Approach
188(2)
7.2.2 Biological Hypothesis and Set-up of the Signalling System
190(2)
7.2.3 Mathematical Modelling
192(4)
7.2.4 Experimental Techniques Used for Producing Quantitative Data
196(3)
7.2.5 Model Calibration: Parameter Estimation and Model Refinement
199(1)
7.2.6 Model Analysis
200(4)
7.2.7 Data Visualization
204(2)
7.3 Discussion
206(2)
7.4 Appendix
208(1)
Acknowledgements
208(1)
References
209(4)
8 Computational Tools for Systems Biology
213(32)
Edda Klipp
Falko Krause
8.1 Introduction
213(3)
8.2 Standards in Systems Biology
216(7)
8.2.1 Standards Support Communication in Biological Research
216(1)
8.2.2 Language Formats
217(5)
8.2.3 Ontologies
222(1)
8.3 Web Resources
223(5)
8.3.1 JWS Online and BioModels Database
224(1)
8.3.2 KEGG
225(1)
8.3.3 Reactome
226(1)
8.3.4 BioCyc
226(1)
8.3.5 BRENDA
227(1)
8.3.6 SABIO-RK
227(1)
8.4 Computational Tools
228(6)
8.4.1 Tools for Model Formulation and Simulation
229(2)
8.4.2 Spatial and Temporal Simulation
231(1)
8.4.3 Boolean and Logical Models
231(1)
8.4.4 General Purpose Tools
232(2)
8.5 Visualizing Networks
234(1)
8.6 Workflows
235(2)
8.6.1 Taverna Workbench
237(1)
8.7 Discussion
237(1)
Acknowledgements
238(1)
References
238(7)
9 The Hallmarks of Cancer Revisited Through Systems Biology and Network Modelling
245(22)
Charles Auffray
Trey Ideker
David J. Galas
Leroy Hood
9.1 Introduction
245(2)
9.1.1 Hallmarks of Cancer
245(1)
9.1.2 Network Properties
246(1)
9.1.3 Advances in Hallmark Analysis and Networks
246(1)
9.2 The Potential of Systems Approaches to Disease
247(4)
9.2.1 Principles of Systems Biology
247(1)
9.2.2 Challenges in Modelling Networks in Cancer
248(1)
9.2.3 Network Inference Through Machine Learning
249(1)
9.2.4 An Illustrative Example: Systems Biology of Prion Disease
250(1)
9.3 Transcription and Protein Interaction Networks Revealed by Modular Cancer Biomarkers
251(1)
9.3.1 Networks and Biomarkers
251(1)
9.3.2 Proteomics and Pathways
251(1)
9.4 Growth, Proliferation and Apoptosis Revisited Through Signalling Network Modelling
252(2)
9.4.1 Signalling Pathways
252(1)
9.4.2 Growth Factors and Apoptosis
253(1)
9.5 Sustained Angiogenesis and Metastasis Revisited Through Multiscale Modelling
254(1)
9.5.1 Mathematical Modelling
254(1)
9.5.2 Angiogenesis
254(1)
9.5.3 Metastasis
254(1)
9.6 The Hallmarks of Cancer Extended to the Control of Metabolism and Stress
255(1)
9.6.1 Cancer as a Metabolic Disease
255(1)
9.6.2 Beyond Oncogene Addiction
256(1)
9.7 Conclusions and Perspectives
256(3)
9.7.1 Genome Variation and Instability Revisited Through Genetic and Genomic Networks
256(1)
9.7.2 Novel Avenues for Diagnosis, Therapy and Disease Network Modelling
257(1)
9.7.3 Frontier Challenges: Multiscale Integration and Cross-disciplinarity
258(1)
Acknowledgements
259(1)
References
259(8)
10 Systems Biology Analysis of Cell Death Pathways in Cancer: How Collaborative and Interdisciplinary Research Helps
267(30)
Boris Zhivotovsky
Emmanuel Barillot
10.1 Introduction
268(1)
10.2 Cell Death Pathways
269(6)
10.2.1 The Death Receptor-mediated Pathway
271(1)
10.2.2 The Mitochondria-mediated Pathway
271(1)
10.2.3 Modulators of Caspase Activity: The IAP Family of Proteins and Their Regulators
272(1)
10.2.4 Cross-talk Between Various Modes of Cell Death
273(2)
10.3 Dysregulation of Cell Death Pathways in Cancer
275(5)
10.3.1 Defects in the Apoptotic Machinery of Tumour Cells
275(4)
10.3.2 Defects in Autophagy-regulated Machinery
279(1)
10.4 Mathematical Modelling of Cell Death Pathways
280(5)
10.4.1 Different Models of Cell Death
280(2)
10.4.2 Modelling Cell-fate Decision Between Survival, Apoptosis and Necrosis
282(3)
10.5 Elements for Interdisciplinary Approaches to Cancer Research
285(3)
10.5.1 Cancers Susceptible to Integrated Systems Approaches
285(1)
10.5.2 Laboratory and Clinical Measurements and Resources
286(2)
10.6 How to Share Knowledge About Systems Biology Approaches to Cancers (See Also Chap. 6)
288(2)
10.6.1 A Common Language
288(1)
10.6.2 Visualizing Networks as a Stimulus to Reasoning and Exchanges
289(1)
10.6.3 Sharing Network Description and Models
290(1)
10.7 Major Collaborative Efforts
290(1)
10.7.1 Apo-sys: Large Scale Collaborative Research on Apoptosis
290(1)
10.7.2 Cancersys: Medium Scale Collaborative Research on Hepatocellular Carcinoma
291(1)
10.8 Supporting Collaborative Research Projects
291(1)
10.8.1 Enfin: Systems Biology Tool Development and Application to Cancer
291(1)
10.8.2 Gen2phen: Bioinformatics Analysis of Genetic Variation and Application to Colorectal Cancer
292(1)
10.9 Conclusion
292(1)
Acknowledgements
293(1)
References
293(4)
11 Systems Biology, Bioinformatics and Medicine Approaches to Cancer Progression Outcomes
297(12)
Jan G. Hengstler
Mathias Gehrmann
Stefan Hohme
Dirk Drasdo
Joanna D. Stewart
Marcus Schmidt
11.1 Introduction: The Concept of Pathway Signatures
297(2)
11.2 Identification of Biological Motifs from Gene Array Data
299(4)
11.2.1 Gene Expression Profiling
299(2)
11.2.2 Metagenes for Clusters of Co-regulated Genes
301(2)
11.3 From Biological Motifs to Pathway Activation
303(1)
11.4 How Realistic is Modelling of Carcinogenesis and Tumour Development in Virtual Tissues and Organs?
304(3)
11.4.1 Spatial-temporal Models of Tumours
304(1)
11.4.2 Tumour Modelling Perspectives
305(2)
References
307(2)
12 System Dynamics at the Physiological and Tumour Level
309(20)
Robert A. Gatenby
12.1 Introduction to Mathematical Modelling in Cancer
309(2)
12.1.1 Lessons from History
309(2)
12.1.2 Extension to Bioinformatics and Systems Biology
311(1)
12.2 Mathematical Models in Cancer
311(3)
12.2.1 The Role of Modelling in Cancer Research
311(2)
12.2.2 Aspects of Cancer Modelling
313(1)
12.3 Model Development
314(1)
12.3.1 Historical Perspective---Understanding Tumour as a Complex System
314(1)
12.3.2 Building the Tumour System---Starting with Spheroids
314(1)
12.4 Iterative Modelling of Tumour Systems
315(2)
12.5 Experimental Studies of Tumour Invasion
317(1)
12.6 Tumour Modelling Collaborations
318(3)
12.7 Detailed Modelling Example
321(4)
12.7.1 Carcinogenesis Transitions
321(2)
12.7.2 Somatic Evolution
323(2)
12.8 Conclusions
325(1)
References
325(4)
Part IV Diagnosis, Clinical and Treatment Applications
13 Diagnostic and Prognostic Cancer Biomarkers: From Traditional to Systems Approaches
329(38)
Francesca M. Buffa
Adrian L. Harris
13.1 Introduction
329(2)
13.2 Role of Biomarkers
331(1)
13.3 Biomarkers for Prediction of Response to Treatment
331(2)
13.3.1 The ErbB Family of Receptor Tyrosine Kinases: HER2 as a Predictive Marker in Breast Cancer
331(1)
13.3.2 EGFR in Head and Neck, Colorectal and Non-small-cell Lung Cancers
332(1)
13.4 Biomarkers for Prognosis
333(7)
13.4.1 Traditional Clinical Markers---Lymph Node Involvement
333(1)
13.4.2 Histological Grade and Proliferation
333(1)
13.4.3 Gene Expression Grade
334(1)
13.4.4 Proliferation Markers
334(1)
13.4.5 Hypoxia Biomarkers
334(1)
13.4.6 Global and Multi-gene Expression Profiling
335(5)
13.4.7 New Areas for Biomarker Development---microRNA
340(1)
13.4.8 Chromosome Aberration
340(1)
13.5 Biomarkers for Monitoring
340(1)
13.5.1 DNA Methylation
341(1)
13.5.2 Mutated Plasma DNA
341(1)
13.6 Measurement and Analysis of Biomarkers
341(3)
13.6.1 Key Measurement Technologies
342(1)
13.6.2 Tissue Arrays
342(1)
13.6.3 Microarrays
343(1)
13.6.4 RNA Analysis
343(1)
13.7 Identification, Standardization and Validation of Effective Biomarkers
344(3)
13.8 Annotated High-quality Clinical Samples
347(1)
13.9 Analyses and Simulations to Predict and Identify Biomarkers
348(1)
13.10 Approaches to Data Analyses in Genomic Studies
348(5)
13.10.1 Class Discovery and Class Prediction
348(2)
13.10.2 Gene and Protein Networks
350(1)
13.10.3 Knowledge-based Class Comparison
351(1)
13.10.4 Knowledge-based Class Prediction and Mining of Genomic Data
351(1)
13.10.5 Literature Data-mining and Data Repositories
352(1)
13.11 Meta-analyses of Biomarker Studies
353(1)
13.12 Quantitative Simulations of Major Pathways Leading to Biomarker Development
353(2)
13.12.1 Simulation of Cancer Pathways: The EGFR Pathway
354(1)
13.12.2 Databases and Repositories of Models
355(1)
13.13 Pharmacokinetics and Pharmacodynamics
355(1)
13.14 Integrated Approaches to Biomarker Discovery and Development
356(2)
References
358(9)
14 Systems Biology Approaches to Cancer Drug Development
367(14)
Christopher Snell
David Orrell
Eric Fernandez
Christophe Chassagnole
David Fell
14.1 Introduction
367(3)
14.1.1 The Systems View of Drug Action
367(3)
14.1.2 Introducing Systems Biology into Drug Development
370(1)
14.2 Model Building
370(2)
14.2.1 Linking Data to the Models
370(2)
14.3 Case Studies of Modelling Cellular Networks
372(3)
14.3.1 Using Cellular Networks in Drug Development
372(1)
14.3.2 Modelling the Cellular Action of Seliciclib and Other cdk2 Inhibitors
372(1)
14.3.3 Apoptosis and Signal Transduction Pathways
373(1)
14.3.4 Difficulties with Detailed Modelling
374(1)
14.4 Modelling at Cellular Scales
375(2)
14.4.1 `Virtual Tumour' Model as a Simpler Approach
375(1)
14.4.2 Modelling Schedules and Combinations
375(1)
14.4.3 Predicting Schedules in Drug Development
376(1)
14.4.4 Chronotherapy and the TEMPO Project
376(1)
14.5 Technologies Typically Used at a Biotech Company
377(1)
14.5.1 Computing Requirements
377(1)
14.5.2 Model Database and Reports
378(1)
14.5.3 One Operational Example: Delivering the Outputs with ModelPlayer™
378(1)
14.6 Conclusion
378(1)
References
379(2)
15 Circadian Rhythms and Cancer Chronotherapeutics
381(28)
Francis Levi
Atilla Altinok
Albert Goldbeter
15.1 Circadian Rhythms in Health and Diseases
381(7)
15.1.1 Biological Evidence
382(4)
15.1.2 Experimentally-based Computational Models
386(2)
15.2 Chronopharmacology, Chronotolerance and Chronoefficacy of Anticancer Drugs
388(11)
15.2.1 Experimental Evidence and Mechanisms
388(3)
15.2.2 Clinical Cancer Chronotherapeutics
391(3)
15.2.3 Probing Circadian Patterns of Anticancer Drug Delivery in silico
394(5)
15.3 From Standard to Personalized Cancer Chronotherapeutics
399(5)
15.3.1 Experimental and Clinical Data
399(3)
15.3.2 Insights from a Modelling Approach
402(2)
15.4 Conclusions and Perspectives
404(1)
Acknowledgments
405(1)
References
405(4)
16 Clinical Applications of Systems Biology Approaches
409(20)
Sergio Iadevaia
Adel B. Tabchy
Prahlad T. Ram
Gordon B. Mills
16.1
Chapter Introduction
409(4)
16.2 Systems Biology Approaches to Identifying Diagnostic, Prognostic, and Therapeutic Biomarkers for Cancer
413(4)
16.2.1 Genomic, Transcriptomic, Proteomic, and Metabolic (Omics) Analysis of Human Tumours
413(2)
16.2.2 Computational Mining of Omics Data
415(2)
16.3 Systems Biology Approaches to the Design of Combinatorial Targeted Therapy for Cancer
417(6)
16.3.1 Animal and Cell Line Models
417(2)
16.3.2 Pharmacodynamic Modelling
419(1)
16.3.3 Pharmacokinetic Modelling
420(1)
16.3.4 Combined Pharmacodynamic-Pharmacokinetic Modelling
420(1)
16.3.5 Combined Therapy Modelling
421(1)
16.3.6 Biopsy and Virtual Biopsy Approaches to Measuring Tumours and Assessing Treatment Activity
422(1)
16.4 The Future of Clinical Trials: Applying Systems Approaches to Clinical Trial Design
423(1)
References
424(5)
17 Cancer Robustness and Therapy Strategies
429(20)
Hiroaki Kitano
17.1 Introduction
429(3)
17.1.1 Cancer as a Robust System
429(1)
17.1.2 What is Robustness?
430(1)
17.1.3 Robustness and Homeostasis
431(1)
17.2 Mechanisms for Robustness
432(3)
17.2.1 System Control
432(1)
17.2.2 Fault-tolerance
432(1)
17.2.3 Modularity
433(1)
17.2.4 Decoupling
433(2)
17.3 Mechanisms for Cancer Robustness
435(1)
17.4 Robustness Trade-offs
436(1)
17.5 Theoretically-motivated Therapeutic Strategies
437(4)
17.6 An Appropriate Index of Treatment Efficacy
441(1)
17.7 Long-tail Drugs
441(2)
17.8 Conclusion
443(1)
Acknowledgements
444(1)
References
444(5)
Part V Perspectives and Conclusions
18 Synthetic Biology and Perspectives
449(22)
Toru Yao
Frederick B. Marcus
18.1 Introduction
449(1)
18.2 Synthetic Biology for Cancer Research and Applications
450(4)
18.2.1 Introduction to Synthetic Biology
450(1)
18.2.2 Manipulation at the Molecular Level
451(1)
18.2.3 Applications in Cells
452(1)
18.2.4 Synthetic Biology in Japan
453(1)
18.3 Synthetic Biology Applications to Cancer
454(4)
18.3.1 Cancer Biology
454(1)
18.3.2 Diagnostics
455(1)
18.3.3 Drug Development
455(1)
18.3.4 Gene/Protein Therapy
456(1)
18.3.5 Immunotherapy
457(1)
18.4 Review Articles and Workshops---Integrated Perspectives
458(5)
18.4.1 How Systems Biology Can Advance Cancer Research
459(1)
18.4.2 Cancer Systems Biology---2nd Workshop
460(2)
18.4.3 Systems Medicine: The Future of Medical Genomics and Healthcare
462(1)
18.5 Resources Needed to Support Systems Approaches to Cancer Research and Diagnosis
463(2)
18.5.1 Infrastructure Requirements for Systems Biology
463(1)
18.5.2 Clinical Resources
464(1)
18.5.3 Data Resources, Analysis and Cancer Modelling Tools
464(1)
18.6 Conclusions
465(1)
References
465(6)
19 Conclusions
471(8)
Alfredo Cesario
Frederick B. Marcus
19.1 Key Points
471(5)
19.1.1 Introduction and Background
471(1)
19.1.2 Laboratory, Clinical, Data and Educational Resources for Cancer Research
472(1)
19.1.3 Bioinformatics and Systems Biology Research Results
473(2)
19.1.4 Translation to Clinical Applications
475(1)
19.1.5 Perspectives
476(1)
19.2 Overall Conclusions
476(3)
Index 479