Computational methods in cell biology / edited by Anand R. Asthagiri, Adam P. Arkin. — Waltham, Mass. : Academic Press, 2012. – (58.1574/M592/v.110)

Contents

    CONTENTS
    
    Contributors
    Preface
    1. Principles of Model Building: An Experimentation-Aided Approach to Development of Models for Signaling Networks
    I. Introduction
    II. Signaling Systems and Mathematical Models
    III. Experimentation-aided Model Development
    IV. Conclusion
    Acknowledgments
    References
    2. Integrated Inference and Analysis of Regulatory Networks from Multi-Level Measurements
    I. Introduction
    II. Overview of Model/Algorithm
    III. Biological Insights
    IV. Open Challenges
    V. Computational Methods
    References
    3. Swimming Upstream: Identifying Proteomic Signals that Drive Transcriptional Changes using the Interactome and Multiple "-Omics" Datasets
    I. Introduction
    II. Computational Methods
    III. Biological Insights
    IV Open Challenges
    Acknowledgments
    References
    4. A Framework for Modeling the Relationship between Cellular Steady-state and Stimulus-responsiveness
    I. Introduction
    II. Overview of Algorithm
    III. Biological Insights
    IV. Open Challenges
    V. Computational Methods
    Further Reading
    References
    5. Stochastic Modeling of Cellular Networks
    I. Introduction
    II. The Need for a Stochastic Modeling Framework
    III. Overview of Computational Approach
    IV. Biological Insights from Computational Approaches
    V. Computational Methods
    VI. Open Challenges
    VII. Conclusions
    References
    Further Reading
    6. Quantifying Traction Stresses in Adherent Cells
    I. Introduction
    II. Overview of Method
    III. Biological Insights from Traction Methods
    IV. Open Challenges
    V. Methods
    VI. Summary
    Acknowledgments
    References
    Further Reading
    7. CellOrganizer: Image-Derived Models of Subcellular Organization and Protein Distribution
    I. Introduction
    II. Components of a Model of Subcellular Organization and Protein Distribution
    III. Models of Subcellular Organization
    IV Protein Distributions Across Subcellular Structures
    V. Use of Models for Testing Algorithms
    VI. Conclusion
    Acknowledgments
    References
    8. Spatial Modeling of Cell Signaling Networks
    I. Introduction
    II. Overview of Spatial Modeling
    III. Building a Spatial Model
    IV. Running Spatial Simulations with VCell: Numerical Method and Simulation Parameters
    V. Application to a Specific Example: cAMP Signaling in Neuronal Cells
    Acknowledgments
    References
    Further Reading
    9. Stochastic Models of Cell Protrusion Arising from Spatiotemporal Signaling and Adhesion Dynamics
    I. Introduction
    II. Model Synthesis
    III. Model Analysis
    IV. Biological Insights from the Modeling Approach
    V. Open Challenges
    VI. Computational Methods
    Acknowledgments
    References
    10. Nonparametric Variable Selection and Modeling for Spatial and Temporal Regulatory Networks
    I. Introduction
    II. Overview of the NODE Model
    III. Biological Insights
    IV. Open Challenges
    V. Computational Methods
    VI. Building a NODE Model
    VII. Building a Comb Diagram with the NALEDE Extension
    Further Reading
    Acknowledgments
    References
    11. Quantitative Models of the Mechanisms that Control Genome-Wide Patterns of Animal Transcription Factor Binding
    I. Introduction
    II. Overview of Model/Algorithm
    III. Biological Insights
    IV. Open Challenges
    V. Computational Methods
    VI. Glossary
    Further Reading
    References
    12. Computational Analysis of Live Cell Images of the Arabidopsis thaliana Plant
    I. Introduction
    II. Overview of Systems and Methods
    III. Biological Insights
    IV Open Computational Challenges
    V. Imaging and Computational Methods
    VI. Further Reading
    Acknowledgments
    References
    13. Multi-Scale Modeling of Tissues Using CompuCell3D
    I. Introduction
    II. Glazier-Graner-Hogeweg (GGH) Modeling
    III. CompuCell3D
    IV. Building CC3D Models
    V. Conclusion
    Acknowledgments
    References
    14. Multiseale Model of Fibrin Accumulation on the Blood Clot Surface and Platelet Dynamics
    I. Introduction
    II. Biological Background
    III. Overview of the Modeling Approach
    IV. Study of the Role of the Fibrin Network
    V. Multiscale Model of Thrombus Development with Fibrin Network Formation
    VI. GPU Implementation of the Simulation of the Platelet-Blood Flow Interaction
    VII. Concluding Remarks
    Acknowledgments
    References
    Index
    Volumes in Series