Drawn from the authors' decade-long work in the cancer computational systems biology laboratory at Institut Curie, this self-contained guide explains how to apply computational systems biology approaches to cancer research. Suitable for readers in both computational and life sciences, the book provides proven techniques and tools for cancer bioinformatics and systems biology research. It explores how computational systems biology can help fight cancer in three essential aspects: categorizing tumors, finding new targets, and designing improved and tailored therapeutic strategies.
Drawn from the authors' decade-long work in the cancer computational systems biology laboratory at Institut Curie, this self-contained guide explains how to apply computational systems biology approaches to cancer research. Suitable for readers in both computational and life sciences, the book provides proven techniques and tools for cancer bioinformatics and systems biology research. It explores how computational systems biology can help fight cancer in three essential aspects: categorizing tumors, finding new targets, and designing improved and tailored therapeutic strategies.
Emmanuel Barillot, Laurence Calzone, Philippe Hupe, Jean-Philippe Vert, and Andrei Zinovyev are all with the Institut Curie in Paris, France.
Inhaltsangabe
Introduction: Why Systems Biology of Cancer? Basic Principles of the Molecular Biology of Cancer. Experimental High-Throughput Technologies for Cancer Research. Bioinformatics Tools and Standards for Systems Biology. Exploring the Diversity of Cancers. Prognosis and Prediction: Towards Individualised Treatments. Mathematical Modelling Applied to Cancer Cell Biology. Mathematical Modelling of Cancer Hallmarks. Cancer Robustness: Facts and Hypotheses. Cancer Robustness: Mathematical Foundations. Finding New Cancer Targets. Conclusion. Appendices. Glossary. Bibliography. Index.
Introduction: Why Systems Biology of Cancer? Basic Principles of the Molecular Biology of Cancer. Experimental High-Throughput Technologies for Cancer Research. Bioinformatics Tools and Standards for Systems Biology. Exploring the Diversity of Cancers. Prognosis and Prediction: Towards Individualised Treatments. Mathematical Modelling Applied to Cancer Cell Biology. Mathematical Modelling of Cancer Hallmarks. Cancer Robustness: Facts and Hypotheses. Cancer Robustness: Mathematical Foundations. Finding New Cancer Targets. Conclusion. Appendices. Glossary. Bibliography. Index.
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