Computational Systems Biology of Cancer

ISBN: 9781439831441 出版年：2012 页码：456 Barillot, Emmanuel Calzone, Laurence Hupe, Philippe CRC Press

Introduction: Why Systems Biology of Cancer? Cancer is a major health issue From genome to genes to network Cancer research as a big science Cancer is a heterogeneous disease Cancer requires personalised medicine What is systems biology? About this book Basic Principles of the Molecular Biology of Cancer Progressive accumulation of mutations Cancer-critical genes Evolution of tumour cell populations Alterations of gene regulation and signal transduction mechanisms Cancer is a network disease Tumour microenvironment Hallmarks of cancer Chromosome aberrations in cancer Conclusion Experimental High-Throughput Technologies for Cancer Research Microarrays Emerging sequencing technologies Chromosome conformation capture Large-scale proteomics Cellular phenotyping Conclusion Bioinformatics Tools and Standards for Systems Biology Experimental design Normalisation Quality control Quality management and reproducibility in computational systems biology workflow Data annotations and ontologies Data management and integration Public repositories for high-throughput data Informatics architecture and data processing Knowledge extraction and network visualization Exploring the Diversity of Cancers Traditional classification of cancer Towards a molecular classification of cancers Clustering for class discovery Discovering latent processes with matrix factorization Interpreting cancer diversity in terms of biological processes Integrative analysis of heterogeneous data Heterogeneity within the tumour Conclusion Prognosis and Prediction: Towards Individualised Treatments Traditional prognostic and predictive factors Predictive modelling by supervised statistical inference Biomarker discovery and molecular signatures Functional interpretation with group-level analysis Network-level analysis Integrative data analysis Conclusion Mathematical Modelling Applied to Cancer Cell Biology Mathematical modelling Mathematical modelling flowchart Mathematical modelling of a generic cell cycle Decomposition of the generic cell cycle into motifs Conclusion Mathematical Modelling of Cancer Hallmarks Modelling the hallmarks of cancer Discussion Cancer Robustness: Facts and Hypotheses Biological systems are robust Neutral space and neutral evolution Robustness, redundancy and degeneracy Mechanisms of robustness in the structure of biological networks Robustness, evolution and evolvability Cancer cells are robust and fragile at the same time Cancer resistance, relapse and robustness Experimental approaches to study biological robustness Conclusion Cancer Robustness: Mathematical Foundations Mathematical definition of biological robustness Simple examples of robust functions Forest fire model: A simple example of a evolving robust system Robustness/fragility trade-offs Robustness and stability of dynamical systems Dynamical robustness and low-dimensional dynamics Dynamical robustness and limitation in complex networks A possible generalised view on robustness Conclusion Finding New Cancer Targets Finding targets from a gene list Prediction of drug targets from simple network analysis Drug targets as fragile points in molecular mechanisms Predicting drug target combinations Conclusion Cancer systems biology and medicine: Other paths Forthcoming challenges Will cancer systems biology translate into cancer systems medicine? Holy Grail of systems biology Appendices Glossary Bibliography Index