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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
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- 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
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