Computational Cell Biology 1st Edition by Christopher P Fall, Eric S Marland, John M Wagner, John J Tyson ISBN 0387224599 9780387224596

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Product details:
ISBN 10: 0387224599
ISBN 13: 9780387224596
Author: Christopher P Fall, Eric S Marland, John M Wagner, John J Tyson

This text is an introduction to dynamical modeling in cell biology. It is not meant as a complete overview of modeling or of particular models in cell biology. Rather, we use selected biological examples to motivate the concepts and techniques used in computational cell biology. This is done through a progression of increasingly more complex cellular functions modeled with increasingly complex mathematical and c- putational techniques. There are other excellent sources for material on mathematical cell biology, and so the focus here truly is computer modeling. This does not mean that there are no mathematical techniques introduced, because some of them are absolutely vital, but it does mean that much of the mathematics is explained in a more intuitive fashion, while we allow the computer to do most of the work. The target audience for this text is mathematically sophisticated cell biology or neuroscience students or mathematics students who wish to learn about modeling in cell biology. The ideal class would comprise both biology and applied math students, who might be encouraged to collaborate on exercises or class projects. We assume as little mathematical and biological background as we feel we can get away with, and we proceed fairly slowly. The techniques and approaches covered in the ?rst half of the book will form a basis for some elementary modeling or as a lead in to more advanced topics covered in the second half of the book.

Computational Cell Biology 1st Edition Table of contents:

Part I: Introduction to Computational Cell Biology

1. The Role of Computation in Cell Biology
   An overview of how computational models are used to study biological systems, from cellular processes to complex networks.

2. Mathematical and Computational Tools for Cell Biology
   Introduction to the fundamental tools and techniques used in computational cell biology, including differential equations, simulations, and numerical methods.

3. Basic Concepts in Cell Biology
   A review of core cell biology concepts, such as the structure and function of cells, signaling pathways, and gene regulation, providing the biological foundation for computational modeling.

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Part II: Modeling Biological Systems

4. Modeling Chemical Reactions in Cells
   Introduction to the mathematical modeling of biochemical reactions within cells, including enzyme kinetics, reaction networks, and metabolic pathways.

5. Genetic Networks and Gene Regulation
   Computational approaches to understanding gene regulation, transcriptional networks, and the dynamics of gene expression in response to environmental and internal signals.

6. Signal Transduction and Cell Communication
   Modeling the signaling pathways that govern cellular responses to external signals, including receptor-ligand interactions, phosphorylation cascades, and feedback loops.

7. Cell Cycle Dynamics
   Computational models of the cell cycle, including regulatory networks that control cell division and the progression through different phases of the cycle.

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Part III: Advanced Computational Techniques

8. Stochastic Modeling in Cell Biology
   Introduction to stochastic models, including Monte Carlo simulations, and their application in modeling cellular processes that involve random fluctuations, such as gene expression.

9. Agent-Based Modeling
   The use of agent-based models to simulate the behavior of individual cells in a population, and how these models are applied to understand cell behavior in tissues and populations.

10. Optimization and Parameter Estimation
    Techniques for fitting computational models to experimental data, including optimization algorithms and parameter estimation methods for model calibration.

11. High-Performance Computing and Cell Biology
    Exploring the use of high-performance computing (HPC) in simulating complex biological systems, with examples from systems biology and cell biology.

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Part IV: Applications of Computational Models in Cell Biology

12. Metabolic Networks and Systems Biology
    Modeling of metabolic pathways and their regulation, and how computational models are used to study metabolic control and disease states like cancer and diabetes.

13. Cellular Decision Making: Apoptosis and Cell Fate
    Computational models of cellular decision-making processes, including apoptosis (programmed cell death), differentiation, and responses to stress.

14. Pattern Formation and Development
    Modeling of developmental processes, including pattern formation, morphogenesis, and the dynamics of cellular interactions during development.

15. Computational Immunology
    How computational models are used to study immune cell interactions, immune responses, and the modeling of immune diseases and therapies.

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Part V: Integration and Systems Biology Approaches

16. Network Models of Cellular Signaling
    The use of network theory and computational modeling to understand the complex web of signaling pathways and their regulation in the cell.

17. Omics Data and Integrative Modeling
    Incorporating genomics, proteomics, and other high-throughput data into computational models, and how these data-driven models are used to predict cellular behaviors and interactions.

18. Multi-Scale Modeling in Cell Biology
    Approaches to modeling biological systems at multiple scales, from molecular dynamics simulations to whole-cell models and tissue-level simulations.

19. Modeling the Interactions Between Cells and the Microenvironment
    How computational models are used to study the interactions between cells and their surrounding microenvironment, including the extracellular matrix and neighboring cells.

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Part VI: Future Directions and Challenges

20. Challenges in Computational Cell Biology
    A discussion of the current challenges in the field, including model complexity, data integration, and the need for interdisciplinary collaboration.

21. Emerging Technologies in Computational Biology
    Exploring emerging computational tools, machine learning, and AI techniques that are revolutionizing cell biology and systems biology.

22. The Future of Modeling in Cell Biology
    A look ahead to the future of computational cell biology, including the potential for personalized medicine, synthetic biology, and the integration of experimental and computational approaches.

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