Computational methods are playing an ever increasing role in cell biology. This volume of Methods in Cell Biology focuses on Computational Methods in Cell Biology and consists of two parts: (1) data extraction and analysis to distill models and mechanisms, and (2) developing and simulating models to make predictions and testable hypotheses. - Focuses on computational methods in cell biology - Split into 2 parts--data extraction and analysis to distill models and mechanisms, and developing and simulating models to make predictions and testable hypotheses - Emphasizes the intimate and necessary connection with interpreting experimental data and proposing the next hypothesis and experiment

1. Principles of model building: an experimentation-aided approach to development of models for signaling networks
   
2. Integrated Inference and Analysis of Regulatory Networks From Multi-Level Measurements
   
3. Swimming upstream: identifying proteomic signals that drive transcriptional changes using the interactome and multiple "-omics" datasets
   
4. A framework for modeling the relationship between cellular steady-state and stimulus-responsiveness
   
5. Stochastic Modeling of Cellular Networks
   
6. Quantifying Traction Stresses in Adherent Cells
   
7. CellOrganizer: Image-derived Models of Subcellular Organization and Protein Distribution
   
8. Spatial Modeling of Cell Signaling Networks
   
9. Stochastic models of cell protrusion arising from spatiotemporal signaling and adhesion dynamics
   
10. Nonparametric Variable Selection and Modeling for Spatial and Temporal Regulatory Networks
    
11. Quantitative Models of the Mechanisms that Control Genome-Wide Patterns of Animal Transcription Factor Binding
    
12. Computational Analysis of Live Cell Images of the Arabidopsis thaliana Plant
    
13. Multi-scale modeling of tissues using CompuCell3D
    
14. Multiscale Model of Fibrin Accumulation on the Blood Clot Surface and Platelet Dynamics