1. Overview: what is quantitative cell biology?
1.1 Modeling to bridge the gap in scales
1.2 Emergent properties and self-organization
1.3 Predictive understanding of cellular systems
1.4 How does quantitative cell biology differ from systems biology?


2. Quantifying data
2.1 Summarizing and visualizing large numbers of examples
2.2 Allowing samples to be compared using statistics
2.3 Derived quantities
2.4 Testing models
2.5 Exploratory data analysis
2.5.1 Spatial statistics
2.5.2 Time series analysis
2.6 Machine learning and "big data"
2.7 When the numbers, themselves, are directly relevant


3. Building and using models
3.1 Role of modeling in cell biology
3.2 Types of models
3.2.1 Ordinary differential equations
3.2.2 Partial differential equations
3.2.3 Network models
3.2.4 Rule-based modeling
3.2.5 Agent-based modeling
3.2.6 Stochastic modeling
3.3 What to do with models once you have them
3.3.1 Testing theories
3.3.2 Regression and parameter estimation
3.3.3 Proofs about extreme behaviors
3.3.4 Establishing equivalence with known systems
3.3.5 From models to design tools


4. Examples of quantitative cell biology
4.1 Counting molecules in the kinetochore
4.2 Modeling cytokinesis
4.3 Understanding forces in endocytosis


5. Frontiers in quantitative cell biology
5.1 New numerical methods
5.2 Multiscale modeling of cancer
5.3 Measuring and modeling developmental biology


6. How to get started in quantitative cell biology
6.1 Prerequisites
6.2 Resources for learning and teaching
6.3 Approaches to interdisciplinary research


References
Author biography.