Fr. 136.00

Systems Biology of Cell Signaling - Recurring Themes and Quantitative Models

English · Paperback / Softback

Shipping usually within 3 to 5 weeks

Description

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This textbook aims to provide advanced students with the tools and insights needed to carry out studies of signal transduction drawing upon modeling, theory and experimentation. It seeks to provide quantitatively inclined biologists and biologically inclined physicists with the tools needed to apply modeling/theory to biological processes.


List of contents










Preface. Acknowledgements. 1. Introduction. 2. Receptors I: Monomeric Receptors and Ligands. 3. Receptors II: Multimeric Receptors and Cooperativity. 4. Downstream Signaling I: Stoichiometric Regulation. 5. Downstream Signaling II: Covalent Modification. 6. Downstream Signaling III: Regulated Production or Destruction. 7. Cascades and Amplification. 8. Bistability I: Systems with One-Time Dependent Variable. 9. Bistability II: Systems with Two Time-Dependent Variables. 10. Transcritical Bifurcations in Phase Separation and Infectious Disease. 11. Negative Feedback I: Stability and Speed. 12. Negative Feedback II: Adaption. 13. Adaption II: Incoherent Feed-Forward Regulation and State-Dependent Activation. 14. Negative Feedback 3: Oscillations. 15. Relaxation Oscillators. 16. Excitability. 17. Wrap-Up. Glossary. Index.


About the author










James E. Ferrell, Jr., MD, PhD is Professor of Chemical and Systems Biology and Professor of Biochemistry at Stanford. His work, which makes use of quantitative experimental approaches, modeling and theory, looks to understand the design principles of biochemical switches, timers, and oscillators, especially those that control the cell cycle.


Summary

This textbook aims to provide advanced students with the tools and insights needed to carry out studies of signal transduction drawing upon modeling, theory and experimentation. It seeks to provide quantitatively inclined biologists and biologically inclined physicists with the tools needed to apply modeling/theory to biological processes.

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