Case Studies in Systems Biology

1. Front Matter

   Pages i-viii

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2. Mitotic Cycle Regulation. I. Oscillations and Bistability

   • John J. Tyson

   Pages 1-17

3. Mitotic Cycle Regulation. II. Traveling Waves

   • John J. Tyson

   Pages 19-40

4. Cell Cycle Regulation. Bifurcation Theory

   • John J. Tyson

   Pages 41-57

5. Glycolytic Oscillations

   • Pavel Kraikivski

   Pages 59-68

6. NF-κB Spiky Oscillations

   • Pavel Kraikivski

   Pages 69-77

7. Tick, Tock, Circadian Clocks

   • Jae Kyoung Kim

   Pages 79-94

8. Spruce Budworm and the Forest

   • Lauren M. Childs

   Pages 95-108

9. Modeling cAMP Oscillations in Budding Yeast

   • Amogh Jalihal

   Pages 109-122

10. Synchronization of Oscillatory Gene Networks

    • Pavel Kraikivski

    Pages 123-136

11. The lac Operon

    • Pavel Kraikivski

    Pages 137-147

12. Fate Decisions of CD4+ T Cells

    • Andrew Willems, Tian Hong

    Pages 149-162

13. Stochastic Gene Expression

    • Jing Chen

    Pages 163-194

14. Collective Molecular Motor Transport

    • Christopher Miles, Alex Mogilner

    Pages 195-208

15. Principle of Cooperativity in Olfactory Receptor Selection

    • Jianhua Xing, Hang Zhang

    Pages 209-227

16. Applying Quantitative Systems Pharmacology (QSP) Modeling to Understand the Treatment of Pneumocystis

    • Tongli Zhang

    Pages 229-243

17. Virus Dynamics

    • Stanca M. Ciupe, Jonathan E. Forde

    Pages 245-261

18. Identifying Virus-Like Regions in Microbial Genomes Using Hidden Markov Models

    • Frank O. Aylward

    Pages 263-270

19. Computational Software

    • Pavel Kraikivski

    Pages 271-297

20. Back Matter

    Pages 299-310

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This book provides case studies that can be used in Systems Biology related classes. Each case study has the same structure which answers the following questions: What is the biological problem and why is it interesting? What are the relevant details with regard to cell physiology and molecular mechanisms? How are the details put together into a mathematical model? How is the model analyzed and simulated? What are the results of the model? How do they compare to the known facts of the cell physiology? Does the model make predictions? What can be done to extend the model? The book presents a summary of results and references to more relevant sources.

The volume contains the classic collection of topics and studies that are well established yet novel in the systems biology field. 

• Dynamic modeling
• Biological Networks
• Bio-systems
• Genetic Networks
• Synthetic biology
• Signaling Pathways

• Academy of Integrated Science, Division of Systems Biology, Virginia Polytechnic Institute and State University, Blacksburg, USA

  Pavel Kraikivski

Dr. Kraikivski is a Professor in Academy of integrated Science, Division of Systems Biology at Virginia Tech where he teaches Systems Biology and Integrated Science classes. His current research focus is on developing a generic computational model of cancer which can successfully explain high-throughput genetic, proteomic and phenotypic Big Data. He is a biophysicist and systems biologist by training with more than 25 peer-reviewed publications on cell cycle regulation, intracellular transport, anomalous diffusion and dynamics of biopolymers. He is a member of several journals’ editorial boards and he also was involved in book editing and writing chapters in biophysics, computational and systems biology fields.

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