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Computational methods are playing an ever-increasing role in cell biology, and this volume of Methods in Cell Biology focuses on the intimate and necessary connection with interpreting experimental data and proposing the next hypothesis and experiment.

Table of Contents

Principles of model building: an experimentation-aided approach to development of models for signaling networks

Integrated Inference and Analysis of Regulatory Networks From Multi-Level Measurements

Swimming upstream: identifying proteomic signals that drive transcriptional changes using the interactome and multiple "-omics" datasets

A framework for modeling the relationship between cellular steady-state and stimulus-responsiveness

Stochastic Modeling of Cellular Networks

Quantifying Traction Stresses in Adherent Cells

CellOrganizer: Image-derived Models of Subcellular Organization and Protein Distribution

Spatial Modeling of Cell Signaling Networks

Stochastic models of cell protrusion arising from spatiotemporal signaling and adhesion dynamics

Nonparametric Variable Selection and Modeling for Spatial and Temporal Regulatory Networks

Quantitative Models of the Mechanisms that Control Genome-Wide Patterns of Animal Transcription Factor Binding

Computational Analysis of Live Cell Images of the Arabidopsis thaliana Plant

Multi-scale modeling of tissues using CompuCell3D

Multiscale Model of Fibrin Accumulation on the Blood Clot Surface and Platelet Dynamics